International Journal of Intelligent Engineering and Systems

11

Authors: Widi Aribowo

The improvement of the economy country along with developments in each sector will be followed by technological progress. This has led to an increase in electricity demand in Indonesia every year. Electricity load forecasting has an important role in the energy management system. The purpose of electricity load forecasting is as an effort to balance between electricity demand and electricity supply. Power system management can be said to be good if the planning carried out will make a major contribution to the development of electric power systems. In this study, the Feed Forward Backpropagation Neural Network method (FFBNN) is optimized using the Teaching-Learning-Based Optimization Algorithm (TLBO) to predict long-term electricity load. The results were measured and validated using the Mean Absolute Percentage Error (MAPE) method. The performance of the proposed method will be compared with the actual data, feed forward backpropagation neural network (FFBNN) and cascade forward backpropagation neural network (CFBNN). It was found that the TLBO-FFBNN method had an average MAPE value of 0.00004936%. The Comparison results show the effectiveness of the proposed method. The proposed method shows that is adapted and has a good MAPE value.

33

Authors: Ahmad Alwarid, Agus Sihabuddin

Fuzzy Time Series models have been developed in various ways, one of which is determining the intervals. Several methods were applied to determine the intervals, but the performances are still not optimal. This paper proposes a new approach that uses a combination of Absolute Differences and K-means Clustering in the Fuzzy Time Series Markov Chain model. K-means Clustering made the interval more flexible and compact based on the data it clustered. In addition, Absolute Differences was used as the based method to define how many intervals to be made. This study used Taiwan Capitalization Weighted Stock Index (TAIEX) as benchmark data to evaluate the proposed method, which produced an average Mean Absolute Percentage Error (MAPE) value of 0.42, and an average Root Mean Squared Error (RMSE) value of 51.09 for the test data. The proposed method outperformed other compared researches at the end of this paper in terms of prediction accuracy.

55

Authors: Fathima Ghouse, Kavitha Paranjothi, Revathi Vaithiyanathan

Dysgraphia is a handwriting disorder and the classification of dysgraphia in children's handwritten images helps to identify the dysgraphia patient effectively and also prevents low self-esteem. Traditional methods of dysgraphia classification based on experts manually classify the dysgraphia that requires more cost and time. Few researches apply the machine learning and deep learning techniques for the classification of dysgraphia and existing models have the limitations of overfitting problems in the training process. In this research, the Non-Discrimination Regularization in Rotational Region Convolutional Neural Network (NDR-R2CNN) is proposed to improve the efficiency of dysgraphia classification. The balancing parameters are introduced in the loss function to balance the class in the training and eliminate the features to reduce the overfitting problem. The collected children's handwriting data were used to evaluate the performance of the proposed NDR-R2CNN model. The proposed NDR-R2CNN model has the advantages of effective feature analysis and non-discrimination word analysis. The NDR-R2CNN model has the accuracy of 98.2 % and the SMOTE+SVM method has 90.2 % accuracy in Dysgraphia classification. The result shows that the NDR-R2CNN model has 98.2 % accuracy and the existing CNN has the accuracy of 94.2 %.

75

Authors: Nagaratna Parameshwar Hegde, Sireesha Vikkurty, Gnyanee Kandukuri, Sriya Musunuru, Ganapatikrishna Parameshwar Hegde

The COVID-19 pandemic has essentially transformed the way of leading a life for millions of people across the world. As offices remained closed for months, employees expressed conflicting sentimental analysis on the work-from-home culture. People worldwide use social media platforms such as Twitter to talk about their daily lives made a trend in the online platform. This research study aims to gauge the public's sentiment on working from home/ remote locations during the COVID-19 pandemic by tracking their opinions on Twitter. The existing random forest model trained the data faster but failed to predict the results faster. Therefore, an ensemble model is proposed to predict an outcome using a distinct modeling algorithm. An ensemble classifier has been used for enhancing the performances using the base learning classifiers such as Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) form an Ensemble classifier. The proposed ensemble model aggregates each base model for the prediction and results for the unseen data. These tokens are then passed to the Ensemble classifier that classifies the sentiments and assigns a polarity (positive, negative, neutral) to every tweet. The proposed Ensemble method improve the average prediction performance over any contributing member in the ensemble. The results obtained by the proposed Ensemble model reached accuracy of 97.47 % when compared to the existing models such as Deep LSTM, SVM model that obtained accuracy of 83 %, 84.46 %.

95

Authors: Lukman Hakim, Huipeng Zheng, Takio Kurita

The neighboring relationship of the pixels is fundamental information in the image. Utilizing neighboring pixel information provides an advantage over using only pixel-to-pixel information. This study presents a regularization term based on the differences of neighboring pixels. We define the differences of neighboring pixels by using the Graph Theory approach. We then propose an effective graph Laplacian regularization to enforce the differences between pixels as the nodes and differences between nodes as edges. In our scheme, Graph Laplacian constructs from the output of the convolutional neural network and ground-truth image. The generated differences matrices from the output and ground-truth are combined as a Laplacian regularization term used as the deep convolutional neural network's new objective function. Experiments show that our scheme successfully captures pixel neighbor relations and improves the performance of the convolutional neural network better than the baseline without a regularization term. Qualitative and quantitative results show that our developed regularizer proved to enhance the boundary structure on image super-resolution and image segmentation tasks, which is achieved Structural Similarity Index (SSIM) of 0.9604 and 0.8263 on Cartoon set and Manga109 datasets and area under the curve (AUC) of 0.9740 and 0.9561 on DRIVE and STARE datasets, respectively.

117

Authors: Jirasak Ponchua, Suchada Sitjongsataporn

Explosive growth and usage of internet while COVID-19 pandemic demands an access technology in our lifestyle that means the Internet of Things (IoT) and Passive Optical Networks (PONs) are increasingly being used in order to support the digital lifestyle. Mobile Internet allows us firstly to exchange the content of information and multimedia while on the move. Then, IoT makes it possible to connect the smart devices with the low latency. PONs have been suggested for providing the ultra-low latency for Quality of Service (QoS) applications such as the Tactile Internet (TI) that is the next evolution to enable real-time control of IoT. A one-millisecond round-trip delay is required to support the tactile internet. As stated on the improving bandwidth allocation for PONs and Passive Optical LAN (POLAN), we propose the FiWi-Dynamic Wavelength and Bandwidth Allocation (F-DWBA) algorithm to reduce the round-trip delay at lower than one-millisecond, which the proposed F-DWBA algorithm based on the PONs and WLAN 802.11b should have a maximum performance. The lowest round-trip delay will make the Fiber-Wireless (FiWi) system can work efficiently and support TI applications. Simulation setup are tested on the latency budget of communication for the TI system starting from the Mobile Unit (MU) through the access point (AP) connected to the Optical Network Unit (ONU) including with the Optical Line Terminal (OLT) to the server. Performance analysis is focused on three periods of the latency time at the optical access network, radio interface and MU interface. Simulation results show that the proposed F-DWBA algorithm has a better round-trip delay than the Gigabit-Passive Optical Network (G-PON) standard about 40 percentage and better than the conventional Dynamic Wavelength and Bandwidth Allocation (DWBA) algorithm about 33 percentage at the full working of the tactile internet system.

141

Authors: Gemilang Santiyuda, Retantyo Wardoyo, Afiahayati

The adaptive Lévy flower pollination algorithm (ALFPA) is a recent addition to variants of flower pollination algorithm (FPA). Despite its excellent performance on single objective problem instances, it has shown inefficiency in the number of function evaluation (FE). Inspired by this, this paper proposed two algorithms extending ALFPA to solve multiple objective problem while also improving FE efficiency. The first algorithm proposed is ALFPA with non-dominated sorting denoted as MO-ALFPA alongside two variants which are proposed to improve its FE efficiency denoted as MO-ALFPAT and MO-ALFPAB. The second proposed algorithm, MOEA/D-ALFPA, uses decomposition strategy instead of non-dominated sorting on MO-ALFPA. The empirical study on two benchmark suits shows that MO-ALFPAT and MOEA/D-ALFPA performed better than other methods. Furthermore, MO-ALFPAT and MOEA/D-ALFPA produced the best results in three benchmark instances, based on inverted generational distance indicator, and two and three best results based on the hypervolume indicator, respectively.

167

Authors: Alek Nur Fatman, Tohari Ahmad

Information has been crucial in supporting daily activities. Many tasks must be accomplished by transferring not only public but also private information. This situation has brought the necessity for protecting it during transmission. Some mechanisms introduced, like data hiding, are still challenging, considering the quality of the data and the secret size can be protected. This research presents a reversible data hiding method for digital video based on prediction error and histogram shifting to approach those problems. There are six kinds of prediction errors used, three are created from two adjacent frames, and three are from a single frame. The selection of the shift location is determined by the histogram location, which has the highest peak point. The histogram is obtained by calculating the frequency of the subtraction of each value from its predicted value. The PSNR calculated from the stego and the original videos has an average of 73.45 dB. This value demonstrates that the proposed method outperforms the previous research.

188

Authors: Ravi Kumar Sanapala, Sreenivasa Rao Duggirala

Wireless Sensor Networks (WSNs) contain thousands of small and inexpensive sensor nodes distributed in the specific area for collecting the desired information from the environment. However, the sensor nodes have restricted battery energy, hence the random deployment of sensors creates issues while managing the resources for effective data delivery over the network. Therefore, this research aims to develop an energy-efficient cluster-based WSN to minimize the energy consumption of the nodes. In this paper, the Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm is used for selecting the optimal Cluster Heads (CHs) from the network. Subsequently, the Improved Spider Monkey Optimization (ISMO) is used for creating the optimal routing through the CHs to the Base Station (BS) where the multiple fitness functions such as residual energy, distance, and routing traffic are considered to optimize the ISMO. Hence, the data communication between the intermediate CHs in hierarchical cluster-based architecture helps to minimize the energy consumption of the nodes. The performance of the proposed LEACH-ISMO method is analyzed using network lifetime, total energy consumption, normalized overhead, throughput, End-to-End Delay (EED) and Packet Delivery Ratio (PDR). The existing methods namely Fuzzy C-Means (FCM) with Artificial Bee Colony (ABC), Meta-Heuristic Ant Colony Optimization based Unequal Clustering (MHACO-UC) and Optimized Quality of Service-based Clustering and Multipath Routing Protocol (OQoS-CMRP) are used to evaluate the LEACH-ISMO method. The PDR of the LEACH-ISMO method is 96.7 % for 200s, which is high when compared to the FCM-ABC, MHACO-UC and OQoS-CMRP.

214

Authors: Gouramma Halidoddi, Rubini Pandu

Wireless Sensor Networks (WSNs) have gained huge attention in different areas due to their self-configurability, easy maintenance and scalability features. WSN is configured with more nodes to transfer the data inside the network. The sensor networks are usually classified by low bandwidth, limited energy, limited power supplies and small size memory which leads to a very demanding environment to provide security. In recent years, accomplishing the network security objective with low energy utilization is a major issue in WSN. In this paper, two-level security is proposed to secure data transmission over the WSN. First, the secure clustering and routing operation are performed by using the Multi-Objective Trust-based Bat Optimization Algorithm (MOTBOA). Second, the data security of the netw¬ork is improved by the Enhanced Homomorphic Cryptosystem (EHC). This paper aims to offer secure communication in WSN. It is achieved by using the EHC method for the operation of encryption and decryption over the data during communication among the nodes. The performance of the proposed MOTBOA-EHC method is evaluated in terms of detection rate, delay, throughput, routing load and Packet Delivery Ratio (PDR). The existing methods namely Trust based Dynamic Source Routing (TDSR), Monarch-Cat Swarm Optimization-based routing protocol and Secured Quality of Service aware Energy Efficient Routing (SQEER) are used to evaluate the performances of the MOTBOA-EHC method. The PDR of the MOTBOA-EHC method is 92% at 100 rounds that are high when compared to the TDSR, MCSO and SQEER.

235

Authors: Jeny Varghese, Jagannatha Sreenivasaiah

Multiple cloud computing services are collectively managed as federated cloud. The growth of users into this federated cloud for accessing a variety of services has introduced challenges on resource utilization and load imbalance that consumes larger waiting time of users. To address these two issues, this paper proposes data center (DC) clustering, virtual machine (VM) clustering, resource mapping and task scheduling. Initially the DCs are clustered using region based fuzzy possibilistic C-means clustering (R-FPCM) algorithm. DC clustering is performed by gathering the information of data dependency, million instructions per second (MIPS), latency, storage, bandwidth and counts of VM. Depending on DC clustering, the VMs are clustered by multi-objective density-based spatial clustering based on the estimated capacity and bandwidth. In order to balance load, Markov chain is applied to predict future load and balance accordingly. An intermediate broker is employed to monitor the VM resources and map based on the user’s task requirement. Service level agreement (SLA) is satisfied for every user by the broker and then fast 1 to N resource mapping algorithm is involved for mapping resources. This is followed by entropy-based monotonic scheduling algorithm that arranges user tasks in an order that is determined from task type, task size, task arrival time and deadline. The dynamic computation of entropy enables to improve scheduling with the current status of the system. The extended simulations of this proposed system are simulated in Cloudsim and the results are evaluated in terms of execution time, latency, resource utilization and response time and compared with the performance of Capacity based VM clustering algorithm. These simulation results show that the performance of the proposed system is much better than the existing approach.

262

Authors: Pulung Nurtantio Andono, Guruh Fajar Shidik, Dwi Puji Prabowo, Dewi Pergiwati, Ricardus Anggi Pramunendar

Indonesia is rich in flora and fauna diversity, but it is experiencing an increasing number of endangered populations. The public can prevent the expanding population threatened with extinction by knowing the types of fauna, especially birds threatened with extinction. This study proposes an automatic grouping method to recognize bird sound patterns in an open environment. This experiment used bird data from a public dataset and obtained bird sound patterns by recording from a distance far enough to make sounds without feeling disturbed freely. However, the sound of birds may contain noise and need data processing using YAMME to be carried out the noise, and birds’ voices can be separated. After that, the data was extracted using the combination of Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Cepstral Coefficients (GTCC) methods also reduced the dimensions of the feature before completing the identification. The bird identification obtained provides an accuracy performance that reaches 78.11%, and these results are higher than other feature extraction methods that also apply dimensional reduction.

282

Authors: Yunifa Miftachul Arif, Hani Nurhayati, Supeno Mardi Susiki Nugroho, Mochamad Hariadi

Halal tourism is one of the tourism products that have the prospect of contributing to economic growth in Indonesia. Therefore, the government needs to increase promotions to increase tourist interest in halal tourism destinations in Indonesia. Game is one of the alternative promotional media that can also function as an educational medium for choosing halal tourism that is fun for potential tourists. This study proposes a recommendation system to support knowledge sources in the Indonesian halal tourism game. We use destinations ratings-based multi-criteria recommender system (MCRS) to generate recommendation rankings as a reference for visualizing halal travel for players as potential tourists. This method improves the ability of the conventional tourism recommendation system, which is generally based on a single criterion. In this study, we use eight destinations rating criteria as a reference for calculating the recommender system in the halal tourism game. Each of these criteria is a reference for tourists' assessment of halal tourist destinations in Indonesia. Next, we integrate the cosine-based similarity technique in MCRS to measure the level of similarity between players and previous tourist data sets. This research's testing phase uses the theme of halal tourism destinations in the Batu City area. The test results show that the number and composition of the tourism destinations item rating as input of the recommender system affect the accuracy, precision, recall, and F1 scores. Based on 40 experiments with different tourism destination item rating input configurations, it shows that the average value of accuracy = 0.60, precision = 0.67, recall = 0.64 and F1 score = 0.65.

305

Authors: Priyanka Rajanikanth, Kalli Satyanarayan Reddy

The Last decade the demand for wireless communication has increased massively. Wireless Sensor Networks (WSN) have turned out as a potential solution for various real-time applications namely environmental monitoring, health and military applications. These networks follow the random placement strategy hence, identifying the location of sensor nodes is a tedious task. Moreover, these networks suffer from various issues such as limited power resources. This work, focusses on the sensor node localization and network lifetime enhancement of Wireless Sensor Networks (WSN). In localization, security is an important issue, so a trust-based model to secure localization is investigated in this work. Further, energy aware clustering and Cluster Head (CH) selection has been undertaken to prolong the lifetime of the network. Finally, a data aggregation model which decreases the network energy depletion by discarding the redundant data is presented. The outcome of the model which is proposed has been assessed and expressed in the form of localization error, energy depletion and alive node. The comparative analysis shows that the localization error for varied anchor nodes is obtained as 1.51, 2.28, 2.52 and 1.25 using existing techniques DMA (Distance Mapping Algorithm), MDS-Map (Multidimensional Scaling), DV-Hop (Distance Vector-Hop) and Proposed Approach, respectively. Similarly, the average energy consumption is obtained as 1.02, 1.67, 1.90 and 0.82 using DMA (Distance Mapping Algorithm), MDS-Map (Multidimensional Scaling), DV-Hop (Distance Vector-Hop) and Proposed Hybrid Approach. The comparative analysis shows that proposed hybrid approach of localization, CH (Cluster Head) formation and aggregation attains better performance when compared with existing techniques in WSN(Wireless Sensor Networks).

329

Authors: Cucun Very Angkoso, Hapsari Peni Agustin Tjahyaningtijas, I Ketut Eddy Purnama, Mauridhi Hery Purnomo

One of the objectives of medical imaging research is to develop an effective and reliable clinical support tool for the early detection of various neurological conditions in patients such as Alzheimer's Disease (AD). Classification based on three-dimensional (3D) MRI (Magnetic-Resonance-Imaging) images have shown a good performance. However, solving 3D object classification as a 3D object using classical machine learning or deep learning is computationally high. 3D images from MRI can be reconstructed into three depth directions: axial, coronal, and sagittal. However, the multi-view-based method cannot explore the content of each image slice for all planes in 3D-MRI. Our study proposes a multiplane method by using three two-dimensional (2D) CNNs to capture discriminatory information on all available planes. Our method is a new fusion strategy to deal with the disadvantages of shape-based multi-view techniques. Our framework selects three slices: the three largest 3-planes to represent whole 3D-MRI objects as multi-2DCNN inputs. Extensive experiments have shown that the proposed method can outperform the 2DCNN method, which uses only one plane. More importantly, by taking full advantage of 2DCNN, we offer a new method for identifying 3D objects that is both easy and efficient. We called this new architecture Multiplane Convolutional Neural Network (Mp-CNN) since it used multiple inputs in its design. We evaluated the proposed method using T1-weighted structural MRI data consisting of 500 AD, 500 Mild Cognitive Impairment (MCI), and 500 Normal Cognition (NC) subjects collected from the MRI database of Alzheimer's Disease Neuroimaging Initiative (ADNI). From the performed experiments, the proposed method achieves 93% accuracy for multiclass AD-MCI-NC and good precision AD 93%, MCI 91%, and NC 95%, respectively. Our study also demonstrates that the proposed multiplane approach outperforms the single-plane approach.

350

Authors: Muhammad Abdillah, Ronald Cornelius Batubara, Nita Indriani Pertiwi, Herlambang Setiadi

Maximum Power Point Tracking (MPPT) is a technique developed to obtain maximum power transfer on solar panels. Various MPPT methods developed can overcome various conditions that affect the power transfer of solar panels, such as irradiation fluctuations and an increase in the solar panels surface temperature. To handle the problem fuzzy logic controller (FLC) is employed as the MPPT method. The input of FLC utilizes the error (E) and delta error (∆E) which is obtained from solar panel or photovoltaic (PV) output including voltage Vpv and active power Ppv. Then, the output of FLC is to determine the appropriate duty cycle (D) of the single-ended primary inductor converter (SEPIC) converter. This proposed scheme is to get better performance in terms of stable voltage output produced by SEPIC converter. The novelty of this paper is the combination of MPPT based on FLC or MPPT FLC that have less ripple and SEPIC that can produce stable voltage output. Hence, the ripple out the load can be reduced significantly by using this proposed method. From the simulation results, it is found that when irradiation conditions and temperatures vary, MPPT FLC can change the operating point of the solar panels close to MPP to obtain maximum power transfer. In terms of power ripple, MPPT FLC can reduce ripple (the total ripple is around 4.9 W) compared to conventional MPPT such as perturb and observer (P&O) (the total ripple is around 9.2 W). Ripple reduction occurs in both irradiation and temperature variations.

370

Authors: Shaymaa Mahmood Mahdi, Safanah Mudheher Raafat

In this paper, the design and application of robust interactive PID control for a medical robot system is presented. The aim is to solve the problem of precise position tracking required for human walking, using a simple type of control while taking into account the interactions of the systems’ input and outputs. The medical robot system can be interpreted as pendulum links to obtain a complete motion of all muscles. Two control actions are required to be applied for the two joints. The interactions of this multivariable system have been examined using the relative gain array (RGA). In this work, PID and Fuzzy PID controllers are implemented with interactions effects. In addition, two other non-conventional types of PID control structures have been considered for comparison, where the RGA cannot be applied. These are the two-degree PID (2DoFPID) and nonlinear PID (NPID) controllers. Simulation results show that in the case of not considering the interaction effects, the Fuzzy PID controller can provide an improvement of 44% as compared with conventional PID controller. While this improvement has been increased to 70% with the consideration of interaction effects. Meanwhile, NPID controller shows higher rise and settling times (almost more than 2 seconds for the settling time). While 2DoFPID controller shows difficulty in implementation due to its highly chattered control signals with higher overshoots in the transient response as compared with the results of the interactive Fuzzy PID controller. Finally, robustness against disturbances and system uncertainties has been tested with all the developed controllers. It has been concluded that the interactive Fuzzy PID controller achieves better performance in terms of less ISE, settling time, rise time, and overshoot.

396

Authors: Ahmed Aldhahab, Ibrahim Murdas

Conventional logic gates failed to process data at a high bit rate. Therefore, researchers began to study the design of these gates with optical technology, which is characterized by high bit rate data processing. The non-linear behavior is a drawback in semiconductor optical amplifier (SOA). This nonlinearity property of the SOA is used in this paper to implement the OR optical gates. Mach–Zander interferometer (MZI)-based structures are among the numerous of structures that are available to transform a modulation of phase into a modulation of intensity. These are widely used by electro-optic (EO), or thermo-optic (TO) impacts to create various applications like optical modulators, splitters, switches, etc. In this paper, the MZI is used as switches and the SOA is employed with cross-gain modulation (XGM) to implement the optical logic gate OR at a high bit rate of 250 Gbps. Several experimental results were performed on the proposed design of the OR gate until the optimum value of the injection current, which is 0.08 A, was obtained. This optimum value led to achieve the lowest BER, highest Quality factor, and lowest power consumption, which are 0, 60.7989, and 31.13×10-3, respectively. The experimental results of the proposed design outperformed those results obtained by other state-of-the-art designs.

414

Authors: Andi Kurniawan Nugroho, Terawan Agus Putranto, I Ketut Eddy Purnama, Mauridhi Hery Purnomo

Commonly, clinicans have problems for recognising brain stroke injury images. However, with the advantages of Information technology it is expected that will be a new method that can support the clinicans’ opinion for recognising the brain stroke injury for type of stroke (hemorrhagic, ischemic, and normal). Therefore, this study aim is to discovery a new model to classify hemorrhagic, ischemic and normal based on Diffusion Weighted (DW)- Magnetic Resonance (MR) images. This study argues by using Qual Convolutional Layers (QCL-CNN) which applied in CNN can classified type of stroke. For this study experiment, this research conducted two experiment to asses the performance of QCL-CNN. The first experiments partitioned the MR image dataset into 20 percent testing and 80 percent training sets. Then, the second testing performed ten-fold cross-validation on the image dataset. The result from the first experiment of the classification accuracies obtained 93.90 percent (1st dataset) and 94.96 percent (2nd dataset). As for the second experiment, the results shows that the classification accuracies obtained 95.91 percent ( 1st data set) and 97.31 percent ( 2nd data set). The data source for this study gained from Indonesian hospital and the web sources dataset public from Ischemic Stroke Lesion Segmentation (ISLES). This study also compared, the QCL-CNN model with other architecture model such as AlexNet, ResNet50, and VGG16. The result of the comparison experiment shows that QCL-CNN architectures model has excellent performance than the others model.

441

Authors: Annisa Kesy Garside, Ikhlasul Amallynda

This paper considers the permutation flow shop scheduling problem to minimize total earliness and tardiness. We initiated a metaheuristic algorithm, namely a crossbreed discrete artificial bee colony. We modifications to the essential artificial bee colony and propose two versions of the crossbreed discrete artificial bee colony. Taguchi experimental design is used to test the performance of this. Several computational experiments using Vallada’s benchmark instances have been carried out to prove the performance of the proposed algorithms. The statistical test results show that the proposed algorithms have the largest negative mean of BRE’s value, each of -0.1959 for CDABC_ver1 and -0.1954 for CDABC_ver2. It means that the proposed algorithms perform significantly better than other algorithms. The results of the Kruskal Wallis H test show that the proposed algorithm has better performance than some dispatching rules and heuristic algorithms. Furthermore, the proposed algorithms can deliver better results in less time than mathematical model solution.

464

Authors: Lubna A. Alnabi, Abbas K. Dhaher, Mohammed B. Essa

The power loss in electrical networks is considered unavoidable, because of its inherent resistance, for effective and economical operation; network loss should be reduced to maximum extent. There are two goals for this study, the first one is extracting the optimal size and location of distribution generators (DG) and the optimal reconfiguration with the aim of decreasing power loss and enhance voltage profile, while the second objective is to prove the success of the methods of Newton Raphson (NR) and guess sidle(GS) in the study and analysis of radial distribution networks. In order to reduce power loss, the genetic algorithm (GA) is used to address the network distribution problem. Software package MATPOWER-7 and MATLAB are utilized to simulate a 33-bus testing system for achieving four cases of various Distribution generator (DG) numbers. A comparison with another previous studies done and the conclusion indicated a positive impact on the efficiency of the system, this work support loss reduction and voltage improvement with the lowest Distribution generator (DG) size and prove that the increase of Distribution generator (DG) number not always can give better result though system cost increase, maintenance, along with the units’ distance for gas supplying increases. There are 4 cases with distinctive Distribution generator (DG) number applied in this paper and a compassion is done of power losses and voltage profile with references where case4 show better result with voltage profile 0.96 and power losses 59.09. As load flow method the Gauss-Seidel (GS) method used because it is a simple iterative method for solving n number of load flow equations by using iterative method. Where partial derivatives not require. And a Newton-Raphson (NR) method used where it is based on Taylor’s series and partial derivatives. The Newton-Raphson (NR) method advantages are less number of iterations needs to reach convergence, takes less computer time when computation cost is less and the convergence is definite. Also it is more accurate, and is insensitive to factors like slack bus selection, regulating transformers. and the number of iterations required in this method is almost independent of the system size. Genetic algorithm (GA) has a good ability to making a global search and explore the search space using their different kind of crossover. Also you can combine it with local search method to increase the exploitation search of the Genetic algorithm. In this paper the introduction part introduced where it is the general part of the paper then the research method proposed in section 3 with details then a Genetic algorithm proposed finally the result with conclusions are proposed.

490

Authors: Nam-Soo Kim

Recently, the non-orthogonal multiple access (NOMA) technique, which transmits multiple users in the same time-frequency block, has been focused on for future cellular networks. Conventional NOMA-based aerial cellular networks consider an unmanned aerial vehicle (UAV) as an aerial user, in which secure control information for UAV is important. In recent days, the deployment of urban air mobility (UAM) has been actively discussed for a new aerial transport service in an urban area. Since UAM conveys passengers, both the traffic and the control information for passengers and a vehicle are important. Therefore, this paper considers a NOMA-based aerial cellular network with a UAM as an aerial user and a ground user as a terrestrial user. And we derive closed-form outage probabilities of the forward and reverse links of UAM, and the Monet-Carlo simulation verifies the results. Further, the condition to maintain the balance between the forward and the reverse links for the identical quality of service (QoS) is derived. Numerical results show that the outage probability of the forward link decreases with the increase of the received signal-to-noise ratio (SNR). However, we noticed that the outage probability of the reverse link shows the error floor, which does not decrease with the received SNR and remains constant, caused by the interference from the ground user (GU). Also, it is shown that there is an average transmit power of UAM to conform the link balance regardless of strong or weak users.

509

Authors: Riyadh Rahef Nuiaa, Selvakumar Manickam, Ali Hakem Alsaeedi, Dhiah Eadan Jabor Al-Shammary

Distinguishing between network traffic activity, intrusion, and normal bahavour is very difficult and very time-consuming. An analyst has to review all the large and wide data to find the order of intrusion in the network connection. Therefore, a method that can detect network intrusion and reflect the current network traffic is required. In this paper, a new EDFC model (Evolving Dynamic Fuzzy clustering) algorithm is generated to improve and enhance the detection mechanism. The proposed model contains two parts: the cluster part and evolving part. This paper's main objective is to design and implement a novel and data density-based clustering scheme that provides high system performance and persistent grouping of data with high similarity and performance on big data for efficient machine learning. Compared to previous techniques, the suggested model's performance with several standard datasets such as the UNSW-NB15 dataset, KDD99 dataset, and NSLKDD dataset indicates a higher silhouette coefficient. In the EDFC model two metrics have been used to verify the quality of clusters, and these are the silhouette coefficient and the number of clusters. The EDFC model has achieved a high silhouette coefficient, and a low number of clusters compare to other models. Our focus is to enhance the detection mechanism for the DRDoS_DNS attacks. Therefore, The EDFC model has been implemented on the standard CICDDoS2019 dataset which contains DRDoS_DNS attacks and achieved a silhouette coefficient of 0.76 and a number of cluster 13.

530

Authors: Purba Daru Kusuma, Ratna Astuti Nugrahaeni

This work proposes a new flow-shop scheduling model that consists of several flow-shops. Every flow-shop acts as an independent entity, but there is a collaboration among them. Although the relation between flow-shops and their customers is exclusive, collaboration through production sharing is possible. This circumstance is different from most studies in flow-shop scheduling problems (FSP), for example, parallel or distributed, where all jobs come from a single point and are then distributed to the production resources. In the multiple independent flow-shops, each flow-shop has its own processing time and production cost. Through collaboration, efficiency can be achieved in the make-span and total cost aspects, which becomes the objective of this work. This model is developed by combining the first price sealed bid auction and cloud theory-based simulated annealing. The first price sealed bid auction is conducted to minimize the total production cost. Meanwhile, the cloud theory-based simulated annealing is conducted to minimize the make-span. This model is then compared with the existing non-dominated sorting genetic algorithm (NSGA II) based flow-shop scheduling models. The first existing model is a parallel flow-shop, while the second one is a collaborative flow-shop. The simulation result shows that the proposed model outperforms the existing models in the total cost aspect. The proposed model creates a 13 to 29 percent lower total cost than the NSGA II-based parallel flow-shop. Meanwhile, the proposed model creates a 16 to 28 percent higher make-span than the NSGA II-based parallel flow-shop.

549

Authors: Abdelghafour Herizi, Riyadh Rouabhi

The efficiency of a wind turbine depends primarily on the power of the wind, the power curve of the turbine and the ability of the generator to respond to fluctuations in the wind. This article then proposes a robust control strategy of the doubly-fed induction generator applied in the wind energy conversion system, the active and reactive powers, which are generated by the DFIG will be decoupled by the orientation of the stator flux. The proposed control is combined between sliding mode control and type-2 fuzzy logic, this technique is robust to the modeling uncertainties of the generator and the wind generator, and which makes it possible to optimize energy production (efficiency). Finally, the performance of the system was tested and compared by simulation in terms of follow-up of instructions and the robustness with respect to the parameter variations of the DFIG. The results obtained show the interest of such a control in this system.

575

Authors: Mala Sinnoor, Shanthi Kaliyil Janardhan

The Electrocardiogram signal (ECG) is highly susceptible to the electrical environment from where the motion artifacts were being recorded. The accurate representation of the ECG signal will necessitate removing the noise from various sources thereby resulting in a noise-free model. This present research work uses Multi scale Local Polynomial Transform (MLPT) technique that provides wavelet transform as an alternative when the MLPT model was non-equispaced. The proposed research combines two approaches such as MLPT and Ensemble Empirical Mode Decomposition (EEMD) forming a Hybrid Transform Model which is used for denoising in the research. The present research work considers white Gaussian noise and experimental results are based on the MIT-BIH Physionet Database. The results of the proposed hybrid MLPT-EEMD obtained better SNR values of 25.93 dB when compared to the existing Empirical Mode Decomposition technique (EMD) of 5.43dB, EMD with adaptive switching mean filter obtained 9.135 dB and S Transform based Time–Frequency Filtering Approach obtained 13.82 dB.

10

Authors: Farid Ali Mousa, Ibrahim Eldesouky Fattoh

The unequal representation of the various divisions existing in the data is one of the main data inconsistency issues. Data with an imbalanced distribution negatively influence the efficiency of most conventional classifiers. This paper introduces a new method for over-sampling the handling of imbalanced data sets. The method hybridize chicken swarm optimization and fuzzy logic (CSO-FL). The proposed model ensures that the synthetic samples generated reside in minority regions. The proposed hybrid CSO-FL applied on three datasets with different imbalanced ratios between 1.78 and 129.44. It demonstrated significant improvements in the efficiency of various classification techniques. During the classification process, we used KNN, DT, SVM and Naïve classifiers. The obtained results were very promising; the precision, sensitivity, and F_score values are enhanced in all classifiers. The values in one dataset improved with ratios >90 % in many classifiers because of the high imbalanced ratio in this dataset, while in the other two datasets, the measurement values enhanced with ratios from nearly 10% to 30%. The CSO-FL approach compared with three different approaches on the same datasets. The approaches are SMOTE algorithm, modified SMOTE, and TGT algorithm and proved to outperform their results.

30

Authors: Ethala Sandhya, Annapurani Kumarappan

The Internet of Things (IoT) has led to an era with the development of communication between smart devices used in various fields. Due to insufficient measures in the infrastructure of smart devices, it is prone to various attacks, which are launched by intruders during data transmission through the internet. Therefore, providing security measures for IoT systems is considered the highest priority. The present work ensures an Intrusion Detection System (IDS), which monitors malicious activities and helps in the successful functioning of IoT networks. Therefore, Spider Monkey Optimization with Random Forest (SMO-RF) algorithm is proposed to detect attacks in IoT environments. The NSL-KDD dataset is used where the input data is pre-processed and then classification is done using Random Forest (RF). The SMO will consider the features from where the decision for splitting or combining of the data taken by the female leader based on the 80-20 rule at the Global Leader Phase (GLP). Calculating the feature or variable importance with a Random Forest model shows which and all the features of the data are the most helpful for Classification is used. Thus, proposed SMO selects the best highest feature importance value for the classification of attacks using RF that overcomes the problem of over fitting. The results obtained from the proposed SMO-RF show the accuracy of 99.98 % better when compared with the existing SVM-parameter optimization and PSO-multi SVM techniques of 99.8% and 98 % respectively.

50

Authors: Halemirle Appaji Deepak, Thippeswamy Vijayakumar

In recent years, several methods have been established for the automatic detection of the characteristics of electrocardiogram (ECG) signals based on mathematical tools, among which the Fourier Transform, the Continuous Wavelet Transform (CWT), the Discrete Wavelet Transform (DWT) stand out among others. This type of procedure is important because it can be more efficiently detected if a certain patient has heart disease, such as an arrhythmia or ischemia. The primary goal of this paper is the development and implementation of a classification approach based on hybrid features i. e., Dual-Tree Complex Wavelet Transform (DTCWT), SVD-Entropy, Autoregressive modeling, and Multifractal analysis based feature extraction. To accomplish the better classification, the extracted features are further classified by Random Forest Classifier, K-Nearest Neighbors (KNN), and Bayesian Optimized-KNN classifiers utilizing the MIT-BIH database. Highest accuracy achieved in random forest classifier is 98.29 %.The results obtained show the feasibility and practical efficiency of the methodology as a tool to aid in the diagnosis of heart disease in hospital environments.

80

Authors: Kavita Avinash Patil, K. V. Mahendra Prashanth, A Ramalingaiah

The proposed work can be divided into two parts: first, remove the noise from the trabecular lumbar spine L1-L4 of the X-ray images using two-stage principal component analysis with neighbourhood pixel grouping, followed by a hybrid median filter, and the texture features are enhanced with sharpening combined with range filters. Second, detect osteoporosis using texture features. This can be done by one-dimensional discrete wavelet transform, followed by a two-dimensional edge detection filter. Finally, classify the normal or osteoporotic images according to conventional classifications. Testing is conducted using X-ray images and dual-energy X-ray absorptiometry (DXA) reports from the same person. The DXA report describes a statistical analysis of normal or osteoporotic, but the proposed work is classified as osteoporotic or normal according to the texture features. Classified results are validated with the DXA and provide an average accuracy of 99.18%. The proposed method has better diagnostic accuracy than the existing method using DXA with X-ray.

105

Authors: Tallapureddy Subba Reddy, Thimmasandra Venkataswamy Sreerama Reddy, Erappa Rajj Babu, Hosur Nanji Reddy Reddappa

Organic Rankine Cycle (ORC) fluids are applied in Shell and Tube Heat Exchanger for waste heat recovery to increase its efficiency. Various methods have been applied in the ORC in shell and tube heat exchanger, which has the limitation of low efficiency in optimization. In this research, Enhanced Teacher Learning Based Optimization (E-TLBO) method has been proposed to increase the efficiency of the ORC in shell and tube heat exchanger. The Michalewicz was applied to the optimization method to provide global optimum and escape from the local optima. The E-TLBO method improves the convergence of the optimization and provides the effective performance in finding solution. The E-TLBO method has the advantages of two parameters such as population size and the number of iterations requires to set the value for optimization. The Shell and tube heat exchanger model has been developed to evaluate the performance of the optimization. The performance of E-TLBO is evaluated in four fluids namely R245fa, R134a, R290, and R600a in ORC analysis. The proposed E-TLBO method is compared with existing methods of Bell-Delaware and Kinetic Gas Molecule Optimization (KGMO) method. This analysis showed that R245fa fluid has a higher mass flow rate compared to other fluids. The proposed E-TLBO method has an enhancement factor of 3.14 and the existing KGMO has a 1.88 enhancement factor in R245fa fluid.

125

Authors: Elmostafa Chetouani, Youssef Errami, Abdellatif Obbadi, Smail Sahnoun

This paper proposes a nonlinear optimal Backstepping method for controlling the doubly-fed induction generator used in wind energy conversion and obtainingmaximum power. The grid is connected directly to the stator. The rotor, on the other hand, is connected to the grid via two bidirectional converters. This work aims to regulate active and reactive power while maintaining a unit power factor using the proposed controller. The Lyapunov function guarantees the stability of the system. The most challenging aspect of Backstepping is determining the best positive constants, which are critical to the system performance. This process becomes more complex, especially when the generator parameters are uncertain or when the wind profile varies. As a result, optimizing the gains is an essential aspect of the controller design. The particle swarm optimization method is suggested for determining the optimum Backstepping constants. The performance and robustness of the proposed method are investigated and compared to the Conventional Backstepping and the Proportional-Integral Control strategies of a 5 MW wind power plant system under parameter variations and quickly changing wind speed profiles. Matlab/Simulink makes it possible to get results. The advised methodology ensures the tracking system's robust stability and reduces the response time to 1.8 (m s). Furthermore, it guarantees a negligible static error.

146

Authors: Sandeep Dhanraj Bawage, Manjula Shivkumar

Massive Multi-In-Multi-Out (MIMO) is a promising and popular technique in wireless communication systems, which is used to deliver high reliability, spectral efficiency to several users. The major problem arises in channel contamination and allocation in a massive-MIMO network. In this paper, we proposed a novel adaptive channel allocation scheme (NACAS) to reduce channel contamination and improve channel allocation. The process of channel assignment is randomized at each time slot where data is repetitively transmitted with successive-interference-cancellation across similar packets. Massive-MIMO has the property of successive interference cancellation and majorly depends on asymptotic in-variance power which is received from the user in less time of interval, and asymptotic orthogonality among the user channels. Therefore, the proposed method permit received signals that turn contaminated into the combination of linear data, which provides an increment in throughput compared to the conventional techniques. The numeric and graphical analysis is shown in the result section, evaluation parameters are considered such as throughput, number of antennas, fading effect, etc. where our proposed approach has shown significant improvement as compared to state of art techniques.

168

Authors: Md Shoaibuddin Madni, C. Vijaya

Hand Gestures provide a means for a series of interactive communications with human beings who are deaf and dumb. Deep learning based models have been revolutionized for achieving human level performance through computer vision that automates, classifies and detects presence of objects in an image. Various challenges were faced through this process such as variation in the hand size, color, illumination, skin tone, view point, complex natural backgrounds etc, because of the various conditions present on the images. The present research overcomes the problem of high dimensional data and improved the classification accuracy in gesture pattern recognition for the incomplete data. Then, multilevel segmentation method and morphological model are used to segment the gesture regions and furthermore, Auto Encoder (AE) algorithm is applied to reduce the dimension of the data. Additionally, Bi-Long Short Term Memory (LSTM) classifier is applied to classify the alphabets, numbers, and video symbols. In the experimental phase, the improved AE-BiLSTM model achieved effective performance in recognition of hand gesture in terms of accuracy. Compared to the existing CNN and PCA based uni-modal feature-level fusion model, the proposed AE-BiLSTM model showed a maximum value of 99.85 % in terms of accuracy for ISL dataset and 99.75% for NUS dataset.

187

Authors: Qasim Hadi Kareem, Malik Jasim Farhan

This paper proposes a dual-polarized compact eight-element antenna system with high isolation for 5 G mobile terminal applications, consisting of four elements symmetrically located on the system plane's long edges, while the other four are symmetrically placed on both sides of the substrate surface (upper and lower). The proposed antenna system can, therefore, produce a dual-polarized pattern that can operate with a wide bandwidth (3200-3900 MHz) and therefore cover Long Term Evolution (LTE) 42 (3400–3600 MHz) band and LTE 43 (3600–3800 MHz) band. The equivalent circuit model for the proposed design is analyzed using Advanced System Design (ADS) tool, and the behaviour of the circuit is compared to the Computer Simulation Technology (CST) tool results and shows a match. The designed system displays a reasonable 10-dB effective impedance bandwidth, high isolation levels above 15-dB, lower Envelope Correlation Coefficients (ECC) below 0.01, and an acceptable radiant output. The nominal Mean Effective Gain (MEG) ratio is less than 1 dB and the maximum channel capacity of 8 x 8 Multi-Input-Multi-Output (MIMO) is 42.3 bps / Hz, with the total operating band being 20-dB Signal to Noise Ratio (SNR). The calculations, analysis, and discussions of the main parameters that characterize the proposed design were verified and show the appropriate matching impedance and high isolation (high performance) between simulated and measured results for the entire operating band.

208

Authors: Yolla Faradhilla, Agus Zainal Arifin, Nanik Suciati, Eha Renwi Astuti, Rarasmaya Indraswari, Monica Widiasri

In performing dental implant surgery on the mandible, it is necessary to measure the height and width between alveolar bone and mandibular canal. A system that is able to measure the height and width automatically is required since a manual measurement takes a long time. One of the important processes in this system is mandibular canal segmentation. The main problem in this segmentation process is the unbalanced numbers of data between object and background classes. This problem often led to misclassifications, especially at pixels on the object boundary. This research proposed a new architecture based on residual fully convolutional network (RFCN) by considering the loss values in the region and boundary of the mandibular canal segmentation. Dual auxiliary loss (DAL) functions are introduced to optimize RFCN, so that the network performs object segmentation better. For this research, DAL used focal loss to calculate the loss value in region and boundary to overcome the unbalanced class between the mandibular canal and the background. There are 2 datasets used for this research. The first dataset contains 200 images with mandibular canal and the second dataset contains 300 images consisting of 200 images with mandibular canal and 100 images without mandibular canal. We tested our network and compared it with state-of-the-art segmentation methods. The experiment showed that the proposed method outperforms all the comparing methods with Dice Similarity Score of 0.914 on the first dataset and 0.868 on the second dataset.

234

Authors: Khulood Eskander Dagher, Mohammed Najm Abdullah

An airborne computer system is avionics-based model that consists of an electronic system and a specifically designed equipment. In this paper, we propose a collision-free path finding flight strategy that enhances the output performance of a drone system for free-navigation between buildings. This strategy is based on a reactive method that requires metaheuristic algorithms to process environmental information during drone flying. In addition, a robust controller is designed to achieve drone flight and reach the target point. The goal of this paper is to discuss the determination of the shortest flight path with the minimum cost function evaluation and this flight path should avoid the collision by using four heuristic algorithms including: Chaotic Particle Swarm Optimization (CPSO) algorithm, Fire-Fly (FF) algorithm, Bees (B) algorithm and a hybrid optimization algorithm that combines the CPSO, FF and B algorithms. To fly the drone and to follow the generated flightpath, six adaptive PID controllers are designed in order to control the highly nonlinear model and under-actuated drone system using an on-line CPSO algorithm to learn and tune the eighteen control gain parameters. The purpose of this control design is to precisely and quickly obtain robust thrust forces control actions to control the attitude and altitude of the drone model. The numerical results of the proposed flightpath-finding algorithms and robust control strategy confirm that the hybrid (CPSOFFB) flightpath algorithm has the minimum number of iterations and evaluation functions as well as free navigation and the shortest flight path length generated. Moreover, the proposed six adaptive PID controller results show that the four thrust forces control actions are smooth and accurately generated making the drone take off and follow the desired flight path quickly with the minimum number of cost function and around ±10 cm minimum error tracking translation location. The maximum overshoot of the altitude did not exceed 10 cm in the transient state and the orientation error of the drone is approximately zero in the steady-state. To demonstrate the effectiveness of the proposed flight path finding and control strategy, a comparison is made with the results other types of algorithms.

258

Authors: Dedy Kurnia Setiawan, Mochamad Ashari, Heri Suryoatmojo

This research attempted to control a Four-Leg Inverter (FLI) on microgrid rooftop solar (MGRS), which connects to a distribution network (grid) via a distribution transformer. The connected load on an MGRS system comprises two loads: nonlinear load and unbalanced linear load. Rooftop Solar (RS) injection current on each grid phase fluctuated depending on irradiation value. Load and irradiation fluctuations and RS capacity differences on every phase caused the transformer’s current unbalance and harmonic. Since the pulled current load varied between grid phases, the current load’s instantaneous fundamental power demand (active and reactive) also differs for each phase. Optimized Constructive Neural Network (OCNN) with single-phase PQ theory was utilized to independently control FLI in every phase determined by fundamental power demand. Therefore, a transformer would perceive load and RS injection as balanced despite varied and unbalanced conditions. OCNN builds networks by self-constructive methods. Each training session enables the addition of new hidden layers and neurons inside each layer. The OCNN network compares the error value associated with the training results to the error value associated with the temporary best network (TBN). Throughout each training session, this comparison is made to determine the network with the lowest error value or the global best network (GBN). The frequent irradiation fluctuation indicated that the system often stayed in a transient rather than a steady-state. In high transient conditions, the performance of the proposed controlling method had been tested in simulations. The result revealed that the OCNN controller obtains the lowest peak values under high transient conditions, namely 2.62% for PCU and 6.73%, 7.33%, and 6.63% for THDi, respectively, at phases A, B, and C.

290

Authors: Syavira Tiara Zulkarnain, Nanik Suciati

Face recognition is a personal identification system based on facial biometric data. By looking at the characteristics of the image data that has lighting variations and contains Gaussian/Poisson/Quantization noise when taken using a camera, facial recognition systems working in the real world with uncontrolled environmental conditions is a challenge. Local Ternary Pattern (LTP) is a representation of image features that are invariant to lighting, resistant to noise, but require manual threshold determination. In this study, we propose an automatic calculation of adaptive LTP’s threshold using the statistical characteristic of image histograms. We also propose a Multi-scale Block Adaptive Local Ternary Pattern (MBALTP) which combines local features extracted using adaptive LTP and global features captured using multi-scale block for face recognition. We experimented on the Extended Yale-B dataset, which were collected under different lighting variations and contains noise. The experimental result demonstrated that the proposed method can improve the recognition performance with an accuracy of 98.87%.

311

Authors: Gouramma Halidoddi, Rubini Pandu

Wireless Sensor Networks (WSNs) attain much attention in different areas due to their self-configurability, easy maintenance and scalability feature. WSN is configured with more nodes to transfer the data inside the network. The sensor networks are usually characterized based on low bandwidth, limited energy, limited power supplies and small size memory, which leads to demand for the environment to provide security. Recently, the objective is to accomplish network security with low energy utilization is a challenging task in WSN. In this paper, an appropriated node selection and secure route path generation are achieved by using Grasshopper Optimization Algorithm (GOA) and Elliptic Curve Cryptographic and Diffie Hellman (ECCDH) based key exchange algorithm. The main aim of using the GOA-ECCDH method is to select the optimal route path with minimum energy consumption and improving the network lifespan in WSN. The performances of the GOA-ECCDH method are evaluated and compared to the existing Secure and Energy-aware Heuristic-based Routing (SEHR) method and Secure Routing Protocol based on Multi-Objective Ant-colony-algorithm (SRPMA) method in terms of packet delivery ratio, average energy consumption, packet loss rate, and routing load. The packet loss rate of the GOA-ECCDH method is 7.2% for the network with 8 black hole nodes, which is lesser when compared to the SEHR method and SRPMA method.

333

Authors: Tabarek A. Abdulabbas, Lwaa Faisal Abdulameer

FSO link suffers from a deep fade in some ranges of frequencies and it is very difficult to ensure the availability for this link especially in sever channel conditions such as dense fog, while RF link with mmWave working otherwise. FSO/RF hybrid system has emerged as a powerful solution for signal unavailability due to these conditions. The specific objective of this paper was to drive the error performance for the proposed coded hybrid communication system operating over atmospheric turbulence channel, considering gamma-gamma turbulence model for FSO and mmWave channel for RF. Error correction coding such as LT code, Polar code and Raptor code were used over FSO/RF link to mitigate turbulence fading and multipath effects. The numerical results of this paper show that the hybrid FSO/RF MIMO system implementing raptor code has a significant improvement over the system implementing LT or polar codes, 16×16 MIMO system implementing raptor code has a gain of 5 dB SNR when compared with the same system without implementing code. Additionally, the 16×16 MIMO hybrid system implemented Raptor code required 6 dB SNR to obtain BER of 10-3, while the 8×8 and 4×4 MIMO system with the same code required 8 dB and 9 dB SNR to achieve the same BER.

357

Authors: Arif Muntasa, Muhammad Yusuf

Acute Lymphoblastic Leukemia (ALL) is one of the most dangerous cancer diseases. Therefore, it is necessary to classify this disease accurately. We proposed a method to classify ALL using multi-distance models of the Gray Level Co-occurrence Matrix (GLCM). We employed sixteen distance models to obtain several features of the main object. We applied three channels of the image's enhancement results, where each channel has been extracted using second-order statistic models, which are homogeneity, entropy, energy, and contrast. We have obtained one hundred and ninety-two features for each object. Moreover, the feature extraction performances have been classified using Canberra and Chebyshev techniques. We have evaluated our proposed method using the ALL Image database and produced accuracy at 96.97%, where 2.27% false positive and 0.75% false negative. Our developed method has been compared to other approaches, such as Support Vector Machine (SVM)-Linear Binary Pattern (LBP), SVM Shape, Naïve Bayes Deterministic, Fuzzy based Leukemia detection, Color Correlation, Hausdorff SVM-based Leukemia detection, Automated Differential Learning Vector Quantization (LVQ), and Multi-Level Perceptron (MLP). The results show that our proposed method outperformed the others.

377

Authors: A. Vijaya Lakshmi, P. Mohanaiah

Facial Expressions are one of the most essential and general means for human beings to express their feelings and communicate their emotions; thus, Facial Expression Recognition (FER) has gained a significant research interest and an attractive topic in Human-Computer Interactions (HCI). Towards such, this paper proposes a novel face descriptor, Gradient Direction Pattern (GDP), for facial expression recognition. With the help of gradients, GDP encodes the structure of the facial image in a more compact way such that the FER is robust to pose variations. Furthermore, the GDPs are measured for edge feature maps, extracted from Gaussian filtered and Gabor filtered facial images at different orientations. These edge feature maps make the recognition system robust to noise, illumination, scaling, and orientational variations. Initially, the facial image is divided into small regions, and GDPs are extracted from them. Then these patterns are concatenated, and Histograms are measured, called GDP Histograms (GH). Simulation experiments were conducted over two standard datasets, such as CK+ and JAFFE, and the average accuracy is observed as 95% and 91% approximately.

402

Authors: Hasib Daowd Esmail Al-ariki, Mohammed Hamdi

Energy efficiency and security are the most vital requirements in Wireless Sensor Networks (WSN). Therefore, modern routing protocols have been forced to consider these issues as the primary challenges. This study proposes the develop meat of an Energy Efficient Secured Fault-Tolerant Routing (EESFTR) protocol that improves routing efficiency and also increasing network reliability. The proposed EESFTR protocol consists of cluster head (CH) selection, optimal route selection, fault detection and recovery process. First, the clustered network utilizes Fuzzy logic to select optimal energy surplus CH. The routes are then discovered and the optimal routes are selected based on energy, link quality and network lifetime designing a Modified Butterfly Optimization Algorithm (MBOA). To ensure security and fault-tolerance, the efficient Elliptical Curve Digital Signature Algorithm is enhanced through unknown parameter signature verification. Finally, the faults which are detected to be malicious to the network are either repaired or eliminated based on a disruptive process. The proposed EESFTR protocol has been evaluated and compared with the existing routing protocols to highlight its efficiency. The results showed that the proposed EESFTR protocol has better a routing performance with 13% less time consumption, 8.3% less energy consumption, 20% increased lifetime, and 12% reduced packet drops to ensure higher fault tolerance and security when compared to the performance of the efficient existing routing protocols.

427

Authors: Chadaporn Sookpuwong, Chow Chompoo-inwai

This paper presents a model and simulation results of the active noise control (ANC) system in a wide-area virtual environment using the acoustics finite-difference time-domain (FDTD) propagation approach defined by particle velocities and sound pressure level (SPL) within a certain absorbing boundary conditions called a perfectly-matched layer (PML) technique. The ANC excitation applies a single-frequency noise source with an adaptive feedforward configuration. The FDTD algorithm is used to model the area of interest acoustically, including a desired quiet zone, considering the effects from a primary path, a secondary path, and a feedback path. A processing unit of the ANC system based on the least mean-squared (LMS) algorithm is utilized to synthesize a cancelling noise using a secondary loudspeaker. A single-channel feedforward ANC system used in this paper is modified from the proposed multi-channel models and forms. Acoustics FDTD propagation results can be used to determine the optimum placements for ANC sensors and actuators. The SPL numerical and graphical results are plotted to demonstrate the performance of the proposed ANC system. All of the simulation results confirm that the proposed modeling approach can be combined with wide-area acoustic simulations and a feedforward LMS adaptive algorithm. The proposed model also provides a way for the optimum placement of the ANC sensors and actuators before being used in a more-complex practical environment.

453

Authors: Chetana Reddy, Virendra Shete

The rapid development of wireless communication technology makes Orthogonal Frequency Division Multiplexing (OFDM) technology and Multiple Input Multiple Output (MIMO) technology, key candidates for the next generation mobile communication system. OFDM system has high frequency utilization and effectively resists multipath and narrowband interference. MIMO technology can effectively improve system capacity and system separation. This paper presents a hybrid Linear-Quadratic Estimation (LQE) technique for Channel Estimation in MIMO-OFDM system. In channel estimation, the noise covariance matrix (Q) plays a key role. In general, the value of Q depends on the previous state values. In a dynamic system, the previous values are not always known. The proposed hybrid model uses two techniques to address this issue. When the previous state values are known, Fast Adaptive Hybrid - Linear Quadratic Estimation (FAH-LQE) is used and when the previous state values are unknown Discrete Multivariate Hybrid - Linear Quadratic Estimation (DMH-LQE) is used. This enables the channel estimation to perform effectively in both scenarios. The FAH-LQE method uses covariance channel noise estimation equations and updates the threshold limit. DMH-LQE finds previous values using discrete generate matrix. The switching between FAH-LQE and DMH-LQE, based on the availability of previous state values, has increased the performance of the system effectively. The proposed algorithm produced lower BER values. For a mobility of 55km/h at SNR 15, BER values of proposed algorithm are 22%, 10%, 7%, 6%, and 3% lower than existing methods namely CE with and without EEP-Kalman tracking, perfect channel estimation, FAH-LQE, and DMH-LQE respectively. Similarly for a mobility of 115km/h at SNR 15, BER values of the proposed algorithm are 45%, 35%, 20%, 15% and 5% lower than above mentioned existing methods namely CE with and without EEP-Kalman tracking, perfect channel estimation, FAH-LQE, and DMH-LQE respectively. The SER has been reduced by 5% when no compensation method is applied. By using the proposed method, the Peak to Average Power Ratio (PAPR) has also reduced.

476

Authors: Yuslena Sari, Hargokendar Suhud, Andreyan Rizky Baskara, Ricardus Anggi Pramunendar, Iphan F. Radam

The increasing ownership of four-wheeled vehicles creates a new problem: difficulty finding available parking spaces in significant places. Issues citizens often experience these in the city centre who use their cars for transportation. This causes the detection of parking lots to attract the attention of researchers. Unfortunately, this system fails when the vehicle occupies more than one place or when the parking lot has a different parking space. In this study, an automation method is proposed that utilises a combination of gray level co-occurrence matrix (GLCM) and support vector machine (SVM) methods with genetic algorithm (GA) optimisation techniques or called SVMGA. The results showed that the SVMGA could provide an accuracy performance of 96.99% for training data and 94.36% for test data. The results of this study are expected to help the queue of people to find a parking space and reduce the density of lines in the parking lot.

498

Authors: Shilpa VenkataRao, Vidya Ananth

Nowadays, Wireless Sensor Networks (WSNs) are extensively utilized as the communication system in the Internet of Things (IoT). Security of the sensor nodes is considered as one of the important aspects of the IoT-based WSN because the nodes in the network are susceptible to malicious attackers. The main objective of this investigation is to generate secure routing and mutual authentication over the IoT-based WSN. In this paper, the Hybrid Optimization Algorithm (HOA) based secure Cluster Head (CH) selection and routing path generation is obtained for secure data transmission. The HOA is the combination of the Grey wolf optimization (GWO) and Moth Flame Optimization (MFO) whereas the fitness functions are trusted value, residual energy, distance and node degree. Additionally, the Shamir Secret Sharing (SSS) method is used for providing mutual authentication between the nodes. The performance of the HOA-IoT-WSN is analyzed in terms of Packet Delivery Ratio (PDR). Packet Loss Ratio (PLR), Average End-to-End Delay (AEED) and network overhead. An existing method such as Energy-efficient and Secure Routing (ESR) protocol, Hybrid Harris Hawk and Salp Swarm (HHH-SS), and Light Weight Trust Sensing (LWTS) methods are used to evaluate the proposed HOA-IoT-WSN method. The PDR of the HOA-IoT-WSN method is 99.848% for 100 nodes, it is high when compared to the ESR, HHH-SS and LWTS.

518

Authors: Sharon Rose Victor Juvvanapudi, Pullakura Rajesh Kumar, Konala Veera Venkata, Satyanarayana Reddy

In this study, a new watermarking system was proposed to address two major concerns: slow learning and computational capability. At first, cover image was transformed into wavelet environment utilizing Integer Wavelet Transform (IWT) that makes the cover image free from false errors. Then, Grey Wolf Optimizer (GWO) was utilized for selecting the image pixels to embed the secret image in the cover image. GWO effectively selects the pixels utilizing fitness function, which calculates entropy, pixel intensity and edge of the cover images. Besides, the secret image was encrypted by using chaos encryption with hybrid mapping (logistic and henon map) that improves computation efficiency and security with good embedding capacity. After the cover image transformation and secret image encryption, Least Significant Bit (LSB) was utilized to deliver self-recovery features and also for locating the tempered region in the digital image. Experimental results showed that the developed system attained a secure transmission network with low complexity in light of Unified Averaged Changed Intensity (UACI), entropy value, Structural Similarity Index (SSIM), Normalized Cross Correlation (NCC), and Peak Signal-to-Noise Ratio (PSNR). Compared to the existing systems, the proposed system showed 3dB to 4.4 dB improvement in PSNR and 0.32 value improvement in SSIM.

539

Authors: Ayad Mohammed Jabbar, Ku Ruhana Ku-Mahamud

Grey wolf optimization (GWO) algorithm is one of the best population-based algorithms. GWO allows sharing information in the wolf population based on the leadership hierarchy using the hunting mechanism behavior of real wolves in nature. However, the algorithm does not represent any key exchange information sharing for the traveling salesman problem because of two issues. The candidate solutions are improved dependently, similar to local search concepts, losing their capability as a population-based algorithm. The algorithm is limited in its search process in finding only the local regions and ignoring any chance to explore search space effectively. This study introduced an adaptive grey wolf optimization algorithm (A-GWO) to solve the information-sharing problem. The proposed A-GWO maintains sufficient diverse solutions among the best three wolves and the rest of the population. It also improves its neighborhood search by obtaining more locally explored regions to enhance information sharing among the wolves. An adaptive crossover operator with neighborhood search is proposed to inherit the information between the wolves and provide several neighborhoods to find more solutions in the local region. Experiments are performed on 25 benchmark datasets, and results are compared against 12 state-of-the-art algorithms based on three scenarios. The credibility of the proposed algorithm produces approximately 53%, 58%, and 63% better tour distance in the first, second, and third scenarios, respectively. The proposed A-GWO achieves approximately 87% better minimum tour distance compared with the GWO algorithm.

568

Authors: Mohamed S.S. Basyoni, Hesham A. Hefny

Fuzzy rules have been used extensively in data mining. Our model is proposed to construct Fuzzy Rules with considering criteria of accuracy. The model consists of five phases: (1) Automatic attributes fuzzification, (2) Feature selection via core and reduct, (3) Fuzzy rules extraction via SQL statement, (4) Calculates Accuracy and Confidence for each rule, (5) Genetic coding of fuzzy rules. The algorithm determines automatically the parameters width of each attribute then uses the rough set to reduce the number of attributes (Feature Selection) via core and reduct. The model then extracts fuzzy rules using SQL statements and calculates Accuracy and Confidence. Finally, our genetic model represents each fuzzy set by “Real number” to improve the accuracy. The model applied on Iris and Wine datasets and the result will be better than the other models in term of the number of fuzzy sets and classification rate for evaluating the accuracy of training and test instances.

588

Authors: Khalissa Behih, Salah Eddine Saadi, Ziyad Bouchama

This paper addresses the design of synergetic control (SC) for synchronizing two chaos systems represented by Takagi-Sugeno (T-S) fuzzy models. Unlike many works published, the present paper provides a new synchronization algorithm of two different hyperchaotic systems which corroborate the given analytical stability proof of the closed-loop systems. The main idea is to represent the chaos systems as an aggregated fuzzy model which compromises a set of linear models in the first place. Afterwards, the synergistic controller is applied to synchronize a nonlinear hyperchaotic (slave) system with another hyperchaotic (master) system. The proposed approach achieves hyperchaos synchronization such that it has a simple structure leading to an easy implementation. Then, theoretical analysis and numerical simulation results are compared to those obtained with a conventional sliding mode (SMC) approach clearly showing the effectiveness of the proposed strategy.

605

Authors: Purba Daru Kusuma, Meta Kallista

Multi-depot capacitated vehicle routing problem is a derivative of vehicle routing problem and a well-known area that has been studied widely with various applications and constraints. Many studies in it focused on minimizing cost and distance with assumption that all nodes are visited. Ironically, number of unserved customers (nodes) in a single trip was rarely explored and often ignored. Based on this problem, this work aims to minimize the total travel distance and the number of unserved customers in single routing cycle. The solution is developed by combining the stable marriage algorithm and k-means clustering in the clustering process. The nearest neighbour algorithm is used in the routing process. This work proposes two contributions. The first is the usage of the stable marriage and k-means clustering. The second is concerning the predetermined distribution of the vehicles in the solution to reduce the number of unserved customers. In the simulation, this proposed model is compared with the hybrid evolutionary algorithm (HEA), partition-based algorithm-nearest neighbour algorithm (PBA-NN), genetic algorithm-nearest neighbour algorithm (GA-NN) and simulated annealing algorithm (SA). Based on the simulation result, it is found that the proposed model performs moderately as a trade-off between the SA and PBA-NN. In the number of unserved customers aspect, when the number of customers is low, the proposed model creates 71 percent lower than the PBA-NN model. Meanwhile, when the number of customers is high, the proposed model creates 73 percent lower than the PBA-NN model. In the total travel distance aspect, when the number of customers is low (50 persons), the proposed model creates 63 percent higher than the GA-NN model and 48 percent lower than the SA model. Meanwhile, when the number of customers is high (100 customers), the proposed model creates 52 percent higher than the PBA-NN model and 54 percent lower than the SA model.

629

Authors: A. N. Revathi, S. Mohanaselvi

This work will be illustrating a model for dealing with multi level four dimensional fractional multi item transportation problem whose parameters in both objectives and constraints are considered as uncertain variables. The Fractional programming technique is useful in dealing with real life situations where the decision maker has to maximize or minimize the ratios of two objective functions. In any hierarchical system or decentralized system, multi level programming problem can be used to obtain efficient solution based on the preferences of each decision maker at each level. The usage of fractional method and level wise preference in real life problems helps us to obtain the efficiency of system and also enables hierarchical decision making which is common in day to day transportation firms. An equivalent deterministic model has been obtained for the multi level four dimensional fractional multi item transportation problem under uncertain environment by using expected and chance constrain model of uncertainty theory. Then the deterministic model is solved by using the modified fuzzy goal programming technique to obtain the compromise solution of the proposed model. The validity of the proposed model has been explained using the numerical example.

656

Authors: Naga Siva Jyothi Kompalli, Dasika Sree Rama Chandra Murthy

The texture is an important feature in the visual content of the images and plays a significant role in the computer vision system. Many existing methods were involved in applying the feature selection and classifiers for texture classification. Existing methods have the limitation of poor convergence in the feature selection and overfitting problems in the classification. In this research, the Elite FireFly – Long Short Term Memory (EFF-LSTM) model is proposed to improve the efficiency of the texture classification. The EFF method has the advantage of replacing the low fitness firefly with a high fitness firefly in the search process and the LSTM model has the advantage of a store the important features in long term for the classification. This process provides good convergence in the feature selection and important features are applied to overcome the overfitting problem. Two datasets such as UIUC and KTHTIPS-2b were used to test the performance of the proposed EEF-LSTM model. The Min-Max normalization method is applied to enhance the quality of the images. The Local Directional Ternary Pattern (LDTP), Dual-Tree Complex Wavelet Transform (DTCWT), and Grey-Level Co-occurrence Matrix (GLCM) were the extracted features from the pre-processed images. The EFF method is applied to select the relevant features with good convergence. The LSTM method is applied to classify the images based on the EFF selected features. The proposed EFF-LSTM model has an accuracy of 96.5 % and the existing Convolutional Neural Network (CNN) has 94.3 % accuracy.

678

Authors: Veena Seenappa, Narayanappa Chikkajala Krishnappa, Pradeep Kumar Mallesh

In telemedicine applications, the images pertaining to medicines and diseases can undergo tampering and forging, where it increases risk in diagnosis. The reversible watermarking technique is capable to extract the embedded information and retrieve the original images completely from watermarked images. It has some specific features like lower distortion of images and high capacity of payload. To solve such an issue, a reversible watermarking method is proposed to provide security for the medical images using hyperchaotic with Deoxyribonucleic Acid (DNA) sequence. The proposed reversible watermarking method utilized standard images (lena, Barbara, boat and medical images) for encryption and decryption. Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) are used to compress the cover images and to decompress the stego images. Further, the thresholding with morphological transform is employed for segmentation of Region of Interest (RoI) in secret images. In addition, the encryption and decryption of secret image is done by hyperchaotic system with DNA sequence. Lastly, the embedding and extraction of secret images and cover images from the stego images is performed by reversible watermarking with differential expansion and modulus function. Simulation result shows that the proposed reversible watermarking method achieved Peak Signal to Noise Ratio (PSNR) of 99 dB for secret image construction, which is better compared to other traditional watermarking techniques.

15

Authors: Jaladi Vivek, Baswaraj Gadgay

Steganography is also known as data hiding, is a method of ensuring the security and confidentiality of digital data by hiding sensitive data in digital media. In video steganography, compression is the most demanding research area in block based video encoders. With the improvement of video coding equipment, the High Efficiency Video Coding (HEVC) delivers better coding efficiency. However, a large proportion of video frames showed system complexity and required a larger Coding Unit (CU) during encoding. Also, as the larger number of video frames were encoded, the time consumption was increased. To overcome such an issue, this research introduced the chaos with enhanced mapping technique to reduce computational complexity and fast encoding. In this research study, the position of each pixel of the secret video frame is calculated by the ELSB technique, where the existing LSB techniques have not considered this secret video frame’s position which leads to high video distortion. In proposed chaos encryption with mapping method, the input video frames are encrypted by using logistic and henon mapping that simultaneously maintains quality and efficiency of the model. The HEVC technique compresses the input video frames and the proposed ELSB performs two functions namely replacement of LSB and matching of LSB which effectively embeds the secret video frames into cover video frames. Finally, the secret video frames are retrieved by HEVC technique with chaos decryption which provides security to the model. The results proved that proposed compression technique achieved PSNR of 35.81dB with average execution time of 40 seconds, but the existing H.264 compression technique achieved PSNR of 32.72dB with average execution time of 45 seconds.

35

Authors: Gundeboyina Srinivasalu, Hanumanthappa Umadevi

Wireless Sensor Network (WSN) is an immense collection of low-power, intelligent and multifunctional sensor nodes for sensing and monitoring the environmental conditions. The information collected from sensors are transmitted to the sink or Base Station (BS). The sensors in the WSN use the battery energy and the energy consumption of the nodes are considered as an important constraint in the network. In order to overcome the issue related to the energy consumption, a cluster based routing protocol is developed in the WSN. In this paper, an appropriate Cluster Head (CH) selection and route generation are obtained using the Energy Centric Multi Objective Salp Swarm Algorithm (ECMOSSA). The main objective of using ECMOSSA is to improve the network lifetime of the WSN by minimizing the node’s energy consumption. The performance of the proposed ECMOSSA method is evaluated by means of alive nodes, total energy consumption, total packets received by the BS, throughput and network lifetime. Moreover, the ECMOSSA method is evaluated with one classical approach namely Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol as well as this ECMOSSA is compared with Grey Wolf Optimizer (GWO)-Dual Hop Routing (DHR) method and Cat- Salp Swarm Algorithm (C-SSA) to evaluate the efficiency of ECMOSSA. The last node dies (i.e., network lifetime) of the ECMOSSA is 1704 that is high when compared to both the LEACH and GWO-DHR method.

56

Authors: Ola M. El Zein, Mona M. Soliman, A. K. Elkholy, Neveen I. Ghali

Pneumonia is a lung infectious disease caused by viral bacteria. It damages one or both lungs in humans. Expert radiotherapists must evaluate chest X-ray to detect pneumonia. As a result, designing an automated system for detecting pneumonia would be valuable for quickly treating the disease, especially in remote areas. This paper introduces a convolutional neural network-based model for reliably detecting pneumonic lungs from chest X-rays. This model can be used by doctors to treat pneumonia in the real world. This study proposes a hybrid model of EfficientNetB0 as a transfer learning-based model and support vector machine (SVM) hinge loss. The functionality of the pre-trained EfficientNetB0 model is used as feature-extractors followed by SVM classifier for the classification of abnormal and normal chest X-Rays. The statistical findings show that using a pre-trained EfficientNetB0 model and supervised classifier algorithm to evaluate chest X-ray images, specifically to detect Pneumonia, can be very beneficial. The proposed model achieves higher classification accuracy, precision, recall, and AUC values outperforming other state of art models with an overall accuracy of 97%.

78

Authors: Ritanshi Agarwal, Neha Mittal, Hanmandlu Madasu

Facial expressions are indicative of one’s mood that serves to communicate his/her the status of mind. Facial expression recognition poses problems when we attempt to use Convolutional Neural Network (CNN) architectures. The choice of a particular CNN architecture/model and the design of its internal architecture are two problems that need to be addressed. We have chosen a simple CNN architecture comprising two convolutional layers and two pooling layers. To improve its performance the input facial images displaying emotions are subject to two kinds of filtering: One by Gaussian filter and another by LoG. The classification of seven emotions by the CNN model on the filtered facial images from Extended Cohn-Kanade (CK+) dataset comprising 981 images has led to the attainment of 100% recognition accuracy thus vindicating the effectiveness of the proposed approach.

102

Authors: Anitha Krishnaiah, Parameshachari Bidare Divakarachari

Automatic Raga identification is an important step in computational musicology as it is helpful for indexing Indian music, classifying, recommending tunes, etc. However, automatic raga recognition is a difficult task as it requires guidance from the expertise training and each of the ragas is identified based on the characteristic phrase and pitch collection. However, the identification or extraction of appropriate musical sample features was difficult to enhance the success rate of the classifier. The feature identification and extraction were considered as the major issue in the existing models. To overcome such an issue, the proposed hybrid spectral feature extraction technique extracted and combined the spectral features of audios such as Spread, centroid, skewness, Mel Frequency Cepstrum Coefficients (MFCCs) and Linear Predictor Coefficients (LPCs) reduced the dimensionality complexity enhanced the success rate of the Multi-Support Vector Machine (MSVM) classifier for mood classification. The CompMusic dataset is utilized for this research work, where 4 types of ragas namely Sindhu Bhairavi, Darbari, Saveri, and Sri Raga, are considered for the mood classification of ragas. From these ragas, tone based features are extracted based on the amplitude of each raga samples and are fed to the MSVM classifier for mood classification of 4 different types are Sympathy, Serious, Peace, and Sad. The classification results for the proposed hybrid spectral feature extraction obtained better accuracy of 97.53% when compared to the existing Hidden Markov Model (HMM) obtained accuracy as 95.3%, Convolution Neural Network (CNN) model as 94%, and Gaussian Mixture Model (GMM) as 95 %.

121

Authors: Fatemeh Ahmadi Zeidabadi, Mohammad Dehghani, Om Parkash Malik

One of the most important and efficient methods in providing suitable solutions for various optimization problems is population-based optimization algorithms. The main contribution and innovation of this paper is to present a new optimization method called Three Influential Members Based Optimizer (TIMBO) which is used for implementation in solving optimization problems. The main idea in designing the proposed TIMBO is to use three important population members with the titles of best member, worst member, and member as mean population in updating the position of population members of the algorithm in the problem search space. The most important feature and advantage of the TIMBO is that it does not have any control parameters, which means that there is no need to control the parameter in this algorithm. TIMBO has been mathematically modeled for use in solving various optimization problems. The efficiency of the TIMBO is analyzed in order to provide suitable quasi-optimal solutions on a set of twenty-three standard objective functions of different types unimodal, high-dimensional multimodal, and fixed-dimensional. Evaluation of unimodal functions indicates the high exploitation power of the proposed TIMBO and evaluation of multimodal functions indicates the appropriate exploration power of the TIMBO. Also, the results obtained from the TIMBO are compared with the performance of eight other well-known optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimization (GWO), Grasshopper Optimisation Algorithm (GOA), Hide Object Game Optimizer (HOGO), and Flow Direction Algorithm (FDA). The results of optimization of standard objective functions indicate the high capability of the TIMBO in providing quasi-optimal solutions suitable for various optimization problems. In addition, analyzing and comparing the performance of the other eight optimization algorithms shows that the TIMBO has a more effective ability to solve optimization problems and is much more competitive.

142

Authors: Vibha Patel, Krishna Warhade

With an increase in defined carrier frequency for the 5G new radio system, the need for synchronization also increases. The transceiver loses synchronization due to the occurrence of timing and carrier frequency offsets. Carrier frequency offsets often occur due to mismatch between transmitter and receiver oscillator frequency as well as the occurrence of doppler shifts due to transmitter/receiver movements. When frequency offsets exceed subcarrier spacing, integer frequency offset occurs that results in performance loss due to subcarrier indices shifts. Conventional approach i.e. maximum likelihood and sequential method is already employed to estimate integer frequency offset and to detect sector id. In this paper, the deep learning-based method is demonstrated to estimate the integer frequency offset and sector id detection. The neural network containing multiple convolution layers with activation layers is used to find the optimum received signal. Then, by calculating the number of cyclic shifts in the optimum received signal, the integer frequency offset is estimated. Using the corrected optimum received signal, the primary synchronization signal is also detected that gives sector id. This proposed estimator is tested for different profiles of tapped delay line models with different desired delay spread and compared with conventional methods i.e. maximum likelihood estimation method and sequential estimation method. Simulation results show that the proposed Neural Network based estimator outperforms in all delay profiles.

166

Authors: Purba Daru Kusuma, Meta Kallista

Dispatching system plays a crucial role in today smartphone based online taxi system. There are three common models that are used in online taxi dispatch system: (1) first come first served, (2) stable marriage, and (3) agent-based models. Unfortunately, these existing models accommodate passenger’s waiting time and/or driver’s pickup cost only. Meanwhile, there are several other important aspects: price, driver’s service level, and passenger’s behaviour. Based on this problem, this work aims to develop a dispatching model for online taxi system that accommodates these aspects too rather than waiting time and pickup cost only. This model is developed by combining stable marriage and first price sealed bid auction. The stable marriage is used to satisfy both passenger and driver. The first price sealed bid auction is used to offer a competitive price for the passenger that is still accepted by the driver. Based on the simulation result, overall, the proposed model performs the best among the previous models, except in the average pickup distance. In the extreme undersupply condition, the proposed model creates 20 percent higher in the average customer’s satisfaction score and equal in the average net distance. In the extreme oversupply condition, the proposed model creates 28 percent higher in the vehicle’s satisfaction score. The proposed model creates up to 21 percent lower travel cost. Although the proposed model creates the worst in the average pickup distance, the average pickup time is 3.9 minutes with the assumption that the vehicle’s speed is 10 km per hour.

188

Authors: Herlambang Setiadi, Dimas Anton Asfani, Tigor Hamonangan Nasution, Hadi Suyono, Lesnanto Multa Putranto, Awan Uji Krismanto, Muhammad Abdillah

This paper introduces the influence on integrating battery energy storage system (BESS) as ancillary controller of wind power plant in small signal stability. Modified two area power system is used as the test system. The modification has been made by replacing one synchronous generator with wind and PV generation. Modal analysis and phase portrait analysis are used to investigate the influence of adding BESS on wind power plant. From the simulation results it is found that the small signal stability performance can be enhance by adding BESS as ancillary controller of wind power plant. This result indicated by increasing damping performance on the critical mode. Furthermore, the oscillatory condition is more damped compared to the base case scenarios. These results indicated by the smallest circular on the phase portrait analysis. Moreover, by adding BESS as ancillary controller the structured singular value (SSV) of the investigated system is 0.8321. It can be stated that the proposed method is robust against uncertainty (SSV must below 1).

209

Authors: Wiwik Anggraeni, Eko Mulyanto Yuniarno, Reza Fuad Rachmadi, Pujiadi, Mauridhi Hery Purnomo

Dengue fever is an endemic disease that occurs throughout the year. Forecasting cases of dengue fever based on actual data is needed for monitoring and taking action. Recently, developing countries have faced problems related to the dengue fever surveillance system caused by the data delay factor. On the other hand, availability and access to health-related information on the internet have changed people’s behaviors and habits. However, the effect of internet data usage has not been widely studied, especially in areas with different levels of internet penetration. This study examines the impact of dengue fever case reported data, Google Trends, Twitter, and climate data in areas with many cases and varying levels of internet penetration to forecast dengue fever cases. Split time-series cross-validation (STSCV) and blocked time-series cross-validation (BTSCV) are used to obtain various training and testing results. The hybrid Decomposition-Bidirectional Long Short-Term Memory(D-BiLSTM) method is proposed. D-BiLSTM applied to eight different scenarios across multiple level areas. According to the results of the experiments, the D-BiLSTM model with STSCV outperforms the BTSCV. In the high internet penetration area, the average error is 9,517, while in the low internet penetration area, it is 5,188. In areas with high internet penetration, adding the variables Google Trends and Twitter does not significantly reduce the error forecasting. However, in the low penetration area, the inclusion of Google Trends and Twitter significantly decreases errors. In general, the D-BiLSTM model performed well. Then, when compared with other approaches, the D-BiLSTM model as a whole can reduce the average RMSE and the average MAE of the comparison model by 94,120 and 45,132, respectively, in areas of high internet penetration with the best SMAPE model of 0.310. In the low internet penetration area, the average decline in RMSE and MAE was 54,390 and 19,362, with the best SMAPE model performance of 0.183.

238

Authors: Maryamah Maryamah, Agus Zainal Arifin, Riyanarto Sarno, Rarasmaya Indraswari, Rizka Wakhidatus Sholikah

Pseudo-relevance feedback (PRF) is an effective query expansion method for searching candidate terms based on top-ranked documents. PRF uses a statistical approach that relies heavily on term-based document retrievals. It causes a semantic gap between query and document that causes vocabulary mismatch problems, such as synonyms and polysemy. In this paper, we proposed pseudo-relevance feedback that combines statistical and semantic term extraction using Term Frequency–Inverse Document Frequency (TFIDF), Bidirectional Encoder Representations from Transformers (BERT), and Yet Another Keyword Extraction (YAKE) for searching Arabic documents. Pseudo-relevance feedback will produce top-ranked documents that are relevant to the query. Term extraction on top-ranked documents using TFIDF, BERT, and YAKE was conducted to obtain candidate terms related statistically and semantically, thus understanding the context of the query sentence. The term extraction results will be re-ranked using Borda ranking to ensure that the Top-K candidate terms are relevant and used for searching the final document with the original query. From the experimental results, the proposed method obtains Precision 5 (P5), P10, Recall, Mean Reciprocal Rate (MRR), and Success Rate (SR@K) of 21%, 14%, 42%, 42%, and 58%, respectively. These results indicate that PRF with a combination of statistical and semantic term extraction for searching Arabic documents can improve the relevant document results.

259

Authors: Eisha Akanksha, Pamarthi Rama Koteswara Rao

In the field of computer vision, object detection is getting more attention due to its huge applications in visual monitoring. Multiple object detection identifies the position of objects or regions of objects in the image or videos. Many methods were developed for detecting multiple objects, but the overall detection accuracy of those methods was limited due to the congested environment, complex background, and similarities between the objects. To solve such an issue, this research study proposed the feature extraction method for multiple object detection using Histogram of Oriented Gradient (HOG) with Local Ternary Pattern (LTP). The Caltech 101 dataset is used in the proposed method where the images are converted to LAB. The process of feature extraction takes place by using the proposed HoG and LTP to detect prominent regions from the image. Further, the obtained features are fused by using Deep Convolutional Neural Network (D-CNN) and then forwarded to Region-based Convolutional Neural Network (R-CNN) to detect the multiple objects. The proposed HoG and LTP feature extraction method has the advantages of improving the classification accuracy by effectively extracting the oriented features and texture features. The proposed method achieved better accuracy of 92.48%, whereas the existing Multi-Object Detection and Tracking (MODT) method achieved an accuracy of 76.23% for the detection of multiple objects.

283

Authors: Mohammed Abdallah Khodja, Sami Mahfoudhi

Fuzzy gain-scheduled PID (FuGSPID) controllers have attracted significant interest in contemporary research. This paper provides mathematical description help to reduce the code program for a Fuzzy gain-scheduled controller FuGSPID that is subsequently used to stabilize the altitude of a home-constructed Quadcopter. The subject particle swarm optimization approach was employed to optimize the fuzzy output sets of the controller. MATLAB was used to generate the dynamic model, the FuGSPID, and the optimization. A simulation exercise revealed that the new parameters employed in the FuGSPID that were generated as a result of the particle swarm optimization produced fewer trajectory tracking errors. Fuzzy systems are required to process large volumes of tasks and processes. This subsequently impedes the performance of the microcontroller. In light of this, this paper outlines a mathematical description that can potentially reduce the algorithm code employed in the FuGSPID controller and, thereby, making it more intuitive and reducing the processing speed of the microcontroller and reducing the sampling time of the controller and the whole flight controller. The findings of this study revealed that the mathematical description it be useful to reduce instruction of fuzzy controller program to implement it in low cost microcontroller and tested effectively for a quadcopter altitude stabilization.

306

Authors: Ali Mohammed Jaleel, Mohammed Kdair Abd

The electrical power system, especially that of the Electric Distribution Network (EDN) is more complex for the rapid deployment and penetration of Distributed Generation (DG). The DGs in the EDN are vulnerable to faults, and the reliability index considered is a critical factor in the work continuation of the EDN. The Particle Swarm Optimization (PSO) is modified to restrict the particle velocities when it runs to obtain the optimum solution for DG placement and capacity in the distribution network. This modification prevents the velocities from reach an acceptable level within a few iterations. This paper presents a new approach and good analysis to the evaluation of reliability and estimates the optimal location and capacity of the DGs units with multi-objective functions for power loss reduction and improves voltage profile. The optimization approach is based on the new Modified Particle Swarm Optimization (MPSO) for decision-making on strategic distribution system points for location and capacity of DGs using Matlab software.In this study, reliability is evaluated using the "Electrical Transient Analyzer Program, ETAP" and applied on an IEEE 33-bus test system. The obtained results of the proposed approach show superior on the other methods a reduction in real power losses by (60.13%) and an improvement in voltage profile by (88.34%).

336

Authors: Imane Dbibih, Imad Iala, Fatima Zahra El Biach, Ouadoudi Zytoune

The continuous evolution of new wireless technologies has given birth to new application areas for the Internet of Things (IoT) such as Smart Cities, Smart Home, Intelligent Transport Systems, Wireless Sensor Networks, etc. IoT is a network of networks that enables, via small wireless electronic devices called "sensor", to identify and communicate with physical objects. The main IoT objective is to measure and exchange data between physical and virtual worlds. However, the use of these sensors in applications characterized by the coexistence of different traffic loads in the same network could lead to power consumption and latency problems. In this article, we present a new efficient algorithm called (BMPriority-based CSMA/CA) based on message priority and battery energy level of sensor to manage access to the transmission channel. More precisely, our algorithm uses a weighting function to calculate the contention window during which a sensor node must wait before starting its data transmission. Simulation results show that our proposed protocol outperforms S-MAC, IEEE 802.15.4, and ECA-MAC protocols in terms of optimizing Packet Delivery Ratio (PDR), end-to-end latency, and throughput.

359

Authors: Rajesh Saturi, Parvataneni Premchand

Breast cancer is the second most cause of cancer deaths in women after lung cancer, and the treatment and diagnosis of breast cancer at an earlier stage is an important requirement for the reduction of the mortality rate. The detection of cell nuclei is the core operation involved in Computer-Aided Diagnosis (CAD) that is used in breast cancer detection. The existing deep learning methods used contour information for accurate detection. But, the existing cancer detection method extracted only the semantic information from the former layer and was not provided the details about the semantic shallow layers in detecting cancer. To solve such an issue, a multi-objective feature selection method was proposed by using Ant Colony Optimization (ACO) with Particle Swarm Optimization (PSO) for the detection of breast cancer. The proposed method utilized the BreakHis dataset and data augmentation was applied to analyze the internal morphological features of the images. The features were extracted by using Convolutional Neural Network (CNN), Histogram of Gradients (HoG) and Local Ternary Pattern (LTP). Then, the features are selected by the proposed multi-objective feature selection method by using ACO with PSO. The selected features are forwarded to the Long Short Term Memory (LSTM) network for the process of classification. The proposed multi-objective feature selection method has the advantage of minimizing the features and reduces the rate of error classification, through a selection of optimal features to increase the rate of breast cancer detection. The proposed multi-objective feature selection method achieved a higher accuracy rate of 95.72% in the detection of breast cancer. Whereas, the existing method showed an accuracy of 93.68% in the detection of breast cancer.

386

Authors: Manikandan Ramamurthy, Shankar Pushpa

Blockchain system has various advantages in transaction data storage. Due to the distributed nature of blockchain, security becomes a concern to store private data. In literature, many techniques to maintain security in the blockchain is proposed. But the conventional data security schemes are not alone sufficient to incorporate complete security in blockchain. Hence in this paper, a blockchain management system with three layers of security is proposed. The proposed blockchain system is suggested for E-health record (EHR). The proposed system includes three layers, and the first layer contains a user authentication wall, which is used to verify the user based on unique user id, password and usage pattern. A tri-factor mechanism is used to verify the usage pattern of the user. In the second layer, the EHR manager is used to verify the query based on policy. The policy contains 48 rules, which is used to analyse the query to grand or denied access. The final layer consists of lightweight 16-bit XOR cryptography used to store the data as cybertext. The proposed system is evaluated using three medical datasets, and its performances are evaluated based on turnaround time, tardiness, actual time delay and average actual time delay. The simulation performance of the proposed technique, that turnaround time is 55ms, tardiness is 150ms, and actual-time delay is 160ms. The encryption time of Data 1, Data 2 and Data 3 is 550ms, 50ms, and 1150ms, respectively. On the other hand, the proposed technique consumed time for decryption of Data 1, Data 2 and Data 3 is 1750ms, 65ms, and 1450ms, respectively. The performance analysis proves the effectiveness of the proposed system. Ultimately, the proposed blockchain management system is more suitable for enhancing the security for handling health records.

410

Authors: Noof T. Mahmood, Mahmuod H. Al-Muifraje, Sameer K. Salih

The recent revolution in the biomedical field carried out the researchers to work on the prosthetic technique because it reflects the amputee's need. Therefore, the electromyography (EMG) signals generated by muscle contractions are used to implement the prosthetic human body parts. This paper presents a pattern recognition system based on two EMG data; the first EMG data represents the general body movements collected from biceps and triceps muscles for six different motions, i.e., bowing, clapping, handshaking, hugging, jumping, and running. The Root Mean Square, Difference Absolute Standard Deviation Value, and Principal Component Analysis extract the raw signal data features and enhance classification accuracy. The machine learning method is applied, i.e., Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) are used for data classification; the results show high accuracy reached 94.8% and 98.9%, respectively. Whereas, the second EMG data is selected to be more specific in hand movements, i.e., cylindrical, spherical, palmar, lateral, hook, and tip motions, because these significant motions are the first step implementing any prosthetic hand. Consequently, the mean, Standard Deviation Value, and Principal Component Analysis extract the raw signal feature. Meanwhile, the same algorithm used in the first data classification is also used to classify the second data because it shows high accuracy and good performance. SVM algorithm is used to classify the data and achieved high training accuracy, reaching 89%. The high training accuracy for different hand movements is considered an essential step toward implementing human prosthetic parts to help the people who suffer from an amputee.

435

Authors: Malik Jasim Farhan

In modern wireless and portable devices, reconfigurable antennas could be utilized to operate across many frequency bands and scan radiation patterns according to user requirements. This paper presents a low-profile planar multiple-input multiple-output (MIMO) antenna system-based frequency-reconfigurable multi-band with high isolation and pattern diversity properties. It applies to various wireless technologies, including UMTS, LTE, WiMAX, Bluetooth, Wi-Fi, and sub-6 GHz. The MIMO arrangement uses two identical antenna elements that are symmetrically arranged and cover an overall size of 45 x 65 x 1.6 〖mm〗^3while also providing frequency shifting and pattern reconfigurability. The proposed antenna functions in four distinct modes, depending on the state of the two lumped element switches per radiator, and so operates at eleven different frequencies: 2.2, 2.4, 2.8, 3.2, 3.3, 3.4, 3.5, 5.2, 5.3, 5.4, and 5.5 GHz, which are reserved for practical wireless applications (3G/4G/sub-6 GHz). The antenna's impedance bandwidth is between 9 to 30.28 %, while its peak gain and efficiency range between 1.8 to 3.26 dBi and 42 to 75.4 %, respectively, depending on the mode of operation. The antenna scattering and far-field characteristics are determined numerically using a technique known as Finite Integration (FIT) in Computer Simulation Technology (CST). The measured results agree with the simulated results, confirming the antenna's frequency and pattern diversity capability for portable wireless devices.

458

Authors: Lamia Moudoubah, Abir El yamami, Khalifa Mansouri, Mohammed Qbadou

The objective of this paper is to provide an ontological overview of the semantically rich structure of best practice repositories for IT (Information Technology) project alignment. It emphasizes the importance of integrating IT management repositories with IT project management repositories to achieve high performance. It aims to present a semantic conceptual framework for IT project alignment that combines Project Management Body of Knowledge (PMBOK) , Project Management Institute (PMI's) risk management frameworks for improving project performance, and value from IT investment (VAL IT), which is an overarching framework for optimizing the business value of a project. We develop an ontology integrating these frameworks using the Ontology definition metamodel (ODM) approach. We use Unified Modeling Language (UML) for Resource Description Framework Schema (RDFS) representation to model the knowledge related to the principles of strategic alignment of IT projects and their interactions to visualize this field and give a relevant study, presented by an ontological model, that will serve the professionals of the domain as well as the researchers interested in the governance of information systems and IT project management. We continued the process of building our ontology from business objects based on our UML business model in order to populate a structured knowledge base that will be validated and shared in servers. We have also validated the consistency of the proposed ontology with the HermiT 1.4.3. 456 reasoning engine integrated in PROTÉGÉ 5 and we have also validated the structure of the proposed ontology with the OOPS! tool. The resulting ontology of this contribution is therefore validated by solid tools that allowed us to obtain a confirmed ontology. We confirm that the results of this contribution appear in the metrics of the resulting ontology which are summarized in the following data: 202 axioms (101: logical axiom count & 101: lexical axiom count), 64 Class count, 37 object property count, 26 subClassOf, 37 ObjectPropertyDomain and 38 ObjectPropertyRank. These results are inferred from the reasoning applied to the proposed ontology using the HermiT reasoner.

479

Authors: Awan Uji Krismanto, N Mithulananthan, Abraham Lomi, Herlambang Setiadi, Muhammad Abdillah

This paper investigates effects of wind speed and solar irradiance uncertainties on oscillatory stability of Microgrid (MG). Novel estimation technique based on Student’s t-Copula is applied to estimate the probability distribution function (PDF) of wind speed and solar irradiance by taking into account the stochastic dependencies between those energy resources. A detailed MG system comprising of Wind Energy Conversion System (WECS), PV system and Diesel Engine (DE) based distributed generation (DG) units is considered to provide a complete dynamic response of the investigated MG. Distribution of critical modes damping ratio is thoroughly investigated by means of Monte Carlo Simulation (MCS). Probabilistic study of small signal stability in MG implies that the renewable energy resources (RES) uncertainties result in a dynamic change of power-sharing strategies and introduce adverse effects on small signal stability. In addition, the damping value is variated from critical damped (with probability around 0.004), well damped and under damped. Moreover, it was monitored that wind speed fluctuation brings more severe impact on system damping than irradiance variation. Consequently, the MG experienced more oscillatory conditions and even lead to unstable situation at high wind speed circumstances. While under solar irradiance change, the investigated MG system can maintain its stable operation.

504

Authors: Ali Sohofi, Aliasghar Amidian, Yaghoub Farjami

The core vision of the Internet of Things (IoT) is to connect objects into a broad network and establish interactions between them to enhance society and human life through the society of objects. As this network expands and the number of connected objects increases continuously, more efficient, secure, and scalable architectures will be needed. Social IoT (SIoT) is a promising IoT architecture, which is based on establishing social relationships between objects. However, being centralized is one of its drawbacks, which leads to challenges like scalability, latency, and privacy issues. Distributed ledger technologies, especially blockchain, are distributed architectures that have received attention in recent years. This paper proposes a fully decentralized SIoT-based architecture for the IoT, exploiting a two-layer distributed ledger structure. This architecture is privacy-preserving and scalable; besides, it provides social relationships between objects. These social relationships are stored as transactions on the ledger. This study also calculates the required space to store ledger data locally. The number of established relationships between the objects is estimated by simulation. The results indicate that less than 10% of all possible relationships are based after 1000 days. Hence, it is possible to store ledger data locally. This approach leads to ten times less latency comparing to previous SIoT-based architectures for 10000 devices.

529

Authors: Fatemeh Ahmadi Zeidabadi, Mohammad Dehghani, Om Parkash Malik

Designed optimization problems in different disciplines of science should be solved using appropriate techniques. Optimization algorithms are among the most effective and widely used methods in solving optimization problems that are able to provide suitable solutions for these problems. Innovation and scientific contribution of this article in designing a new optimizer called Random Selected Leader Based Optimizer (RSLBO) in order to be used in optimizing the objective functions of optimization problems and achieving the desired solutions. The main idea of the proposed RSLBO is to increase the search power using a random leader. In fact, instead of the algorithm population update relying on several specific members, such as the best member or the worst member, any ordinary member of the population can be a leader in guiding and updating the algorithm population. RSLBO is described, then mathematically modelled to be used in solving optimization problems. The main feature and advantage of the proposed RSLBO is that its implementation and relationships are very simple and understandable, and it also lacks control parameters. The performance of the proposed RSLBO is evaluated on a set of twenty-three standard functions. Also, in order to analyse the quality of the proposed RSLBO in providing appropriate solutions to optimization problems, the results obtained from the proposed algorithm are compared with eight well-known algorithms including Flow Direction Algorithm (FDA), Hide Object Game Optimizer (HOGO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Teaching-Learning-Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). The results of optimization of objective functions indicate the high ability of the RSLBO to solve optimization problems and provide appropriate and acceptable solutions. Also, the analysis and comparison of the performance of the eight well-known optimization algorithms against the proposed RSLBO shows that the RSLBO has provided more appropriate solutions and is superior and much more competitive than the eight compared algorithms.

550

Authors: Yoga Samudra, Tohari Ahmad

Information technology has multiplied for some decades. It has affected the way how the public interacts with their environment. Specifically, this technology helps us to work more efficiently than before. Nevertheless, some problems arise due to the exploitation of its weaknesses, like capturing confidential data. Some schemes have been introduced, such as embedding the secret in a multimedia file. However, the quality of the file after the embedding process may be different from its original form significantly. Moreover, there is a limitation on the secret size that can be hidden. In this research, we propose to improve the existing method by taking audio as the carrier. Specifically, we explore the mirroring technique after the audio samples are interpolated. It is to reflect the samples to the specified mirroring point, purposing to minimize the difference between before and after the payload is embedded. The more similar the stego to the cover file, the higher the quality; besides, the embedding space has been enlarged. The experimental result shows that this proposed method can increase the quality of the stego file by about 20 dB for various payload sizes.

571

Authors: Farouk A. Emara, Ahmed. A. A. Gad-Elrab, Ahmed Sobhi, K. R. Raslan

Recently, resource management is the major issues in cloud computing (CC) environment because of dynamic heterogeneity of cloud computing environment. The task scheduling and virtual machines (VMs) allocation play a vital role in resources management of CC. Most of existing works for these issues aim to achieve single objective as maximizing resource utilization, load balancing, or power management. Currently, the big challenge in CC is building task scheduling and virtual machines (VMs) allocation algorithms that consider all these objectives in the same time. This problem is called task scheduling with VMs allocation multi-objectives problem which is NP completeness problem. In this paper, a new task scheduling algorithm is proposed for achieving efficient resource management based on these objectives. This proposed algorithm uses a modified genetic algorithm (GA) to find the optimal solution for choosing the most appropriate VMs for executing received tasks and their appropriate servers that will deploy these VMs. This proposed algorithm uses a matrix structure for representing the chromosome of GAS which combines the ids of tasks, VMs, and servers. Simulation results show that the proposed algorithm achieves better performance than ETVMC, TSACS, and ACO algorithms in terms of makespan, scheduling length, throughput, resource utilization, energy consumption, and imbalance degree.

596

Authors: Abubakar Usman Othman, Aisha Yahaya Umar, Maryam Maishanu, Hauwa Abubakar, Boukari Souley, Abdulsalam Ya’u Gital

Finding approximate nearest neighbour (ANN) is essential in huge database for efficient similarity search to return the nearest neighbour of a given query. Many hashing algorithms have been designed to improve retrieval accuracy and storage requirements of data in a large-scale database through long code word which increases the time complexity in loading data into memory, but do not consider the search time which is an important parameter in the field of information retrieval and pattern recognition. To address the aforementioned problem, this research therefore proposes an improved search time algorithm for improving the retrieval time of data from a database in cloud computing environment by optimising both the search accuracy and search time simultaneously. We improved the minimum search time by the use of balance partitioning algorithm for the even distribution of data points into hash buckets to minimise search time, and similarity preserving algorithm for search accuracy were designed for fast and accurate retrieval of data in a database. An extensive experiment conducted on a cloud simulator and the result obtained when the code length is 8, 96 bits, the retrieval time for the proposed system is 0.030sec, 0.260sec, and that of Density Sensitive Hashing is 0.040, 0.400sec. Therefore, the retrieval difference is 0.010sec and 0.140sec. Also, the result obtained for the rest of the code lengths of 16, 32 and 64 show that the improved minimum search time algorithm outperforms the compared techniques in terms of the velocity of big data retrieval.

11

Authors: Anil Kumar Chatamoni, Rajendra Naik Bhukya, Praneet Raj Jeripotula

In this paper, a novel scheme for secure image transmission based on compressive sensing (CS) and Fractional Wavelet Transform (FrWT) is proposed. The scheme uses CS and a multi chaotic pseudo random Sine-Tent- Hénon (STH) map based measurement matrix, in the first stage of encryption. Therefore, it provides a simultaneous compression and encryption. Then the security is further enhanced through the novel approach in the second stage of encryption with an iterative procedure, in which a combination of FrWT and random pixel exchange method is applied. The key parameters used in the generation of random matrix, measurement matrix and fractional orders of FrWT are served as keys. This approach has the larger key parameters provided by the STH map and high degree of scrambling and diffusion with FrWT. With the simulation results, the novel proposed scheme has better compression performance as the PSNR value is 35.6631 dB with 0.75 compression ratio and above 25dB with only 4950 coefficients in the reconstruction of image. When the different types of attacks applied, the value of PSNR in the range of 20-30dB shows the good reconstruction robustness of the proposed scheme. Numerical value comparison with other recent CS based encryption schemes, represents the superiority of the proposed scheme in terms of security.

35

Authors: Badra Khellat Kihel, Souad Khellat Kihel, Samira Chouraqui

The sizes of medical datasets are increased with an exponential rate. Extract useful knowledge from those data, analyzing and searching on this very vast amount of data is become a hard task for the scientists. Several metaheuristic algorithms for solving such problem were proposed for dimensionality reduction. In this work, a new stochastic search strategy inspired by the Grey Wolf optimization theory is proposed for feature subset selection. Grey Wolf Optimization algorithm (GWO) is a new metaheuristic optimization technique. Its principle is to reproduce the behavior of grey wolves in nature to hunt in a cooperative way. In order to improve the disadvantage of Grey Wolf Optimizer when solving the feature selection problem, three immune operators (selection, cloning and mutation) are embedded into the standard GWO and an Hybrid Immune Grey Wolf Algorithm (IGWO) is proposed. To ensure the diversity of the population of wolves and avoid premature convergence, we cloned and mutated Alpha, Beta and Delta wolves before selection of the new first, second, and third best solutions. The experiments were performed using ten medical datasets: Parkinson, Leukemia and heart diseases, Breast, Colon, and Prostate Cancers, and the results show that the proposed approach is able to explore different regions of a search space and avoid local optima reducing the dimensionality of datasets than can significantly improve the performances of the system for diseases recognition and decrease the required classification time.

44

Authors: Malikhah Malikhah, Riyanarto Sarno, Shoffi Izza Sabilla

Pork and beef are the main resources of red meat in the world. However, not everyone can eat pork because of their religious background or other reasons. Therefore, it is very important to ensure the purity of the meat prior to being consumed. This research applied ensemble learning to optimize the classification on Electronic Nose Dataset for Pork Adulteration in Beef. Ensemble learning is one of a method that is widely used and the most successful method to improve performance. This research used several traditional machine learning algorithms and chose a machine learning algorithm, which produced the best result as the base classifier for ensemble learning to optimize the classification on Electronic Nose Dataset for Pork Adulteration in Beef. There were three ensemble learnings used in this research, namely hard voting, stacking, and bagging. The steps conducted in this research comprise (1) pre-processing by de-noising of the raw signals, (2) statistical feature extraction, (3) feature selection, (4) classification, (5) using ensemble learning to improve the performance, and (6) performance evaluation. The experiment result shows that hard voting ensemble learning using K-nearest neighbors (KNN) as base classifier is able to distinguish well between beef, pork, and pork adulteration in beef to seven classes which obtained 98.33% accuracy.

66

Authors: Ediga Sathyanarayana Phalguna Krishna, Thangavelu Arunkumar

Internet of Things (IoT) is a network that provides security for physical objects such as smart home appliance, smart machines and many more. The physical objects are assigned to a unique Internet address known as Internet Protocol (IP) that is used for data communication with the external entities of the network through the internet. The IoT devices are facing security issues due to the rapid increase in attacks that are launched by the intruders during data sharing through the internet. The detection of attacks is essential to provide a strong security mechanism for such threatening attacks. The proposed hybrid optimization algorithm utilizes the combination of Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) in this research. The PSO is known for its fast computation speed and has found extensive utility in data training as well as data estimation. The GWO is developed as an intrusion detection approach to classify data and to efficiently detect several of intrusions. The proposed hybrid GWO-PSO uses NSL-KDD data set with binary and multi class problem respectively for showing the effectiveness of the present work. The results obtained better accuracy value of 99.97 % when compared to the existing LSTM-RNN that achieved 97.72% of accuracy, whereas the multi class SVM obtained 98 % and modified rank-based information gain feature selection method showed 99.8 % of accuracy.

91

Authors: Moufida Achour, Abderrahim Belmadani

Research on the privacy of human genomic data has significantly increased these last years to find out effective solutions to protect the most critical information resulting from an alignment operation such as the genomic position, cigar and Read content. In this paper, the authors propose an encoding technique based on homomorphic encryption to protect genomic positions of DNA sequences for a secure storage of BAM file. This technique uses two Discrete Gaussian Sampling algorithms for comparison purposes which is Gauss and Knuth-Yao algorithm. The framework was implemented on Java from scratch using container data structure with standard java HashMap for the representation of duplicates positions elements exiting in BAM file. The proposed method was assessed on the NA12878 dataset with initially 2 million positions to encrypt. Obtained results show that the FV-Knuth-Yao technique with HashMap architecture storing 165667 positions and a polynomial degree equal to 64 ensures a fast encryption time estimated to 6,5 minutes. The decryption operation needs 1 hour with zero loss of information. The acceptable security level reached 264 operations to break the cryptosystem. To demonstrate the efficiency, security and reliability of our approach, the FV-Knuth-Yao was compared with the OPE Method as implemented in SECRAM tool which is based on a deterministic encryption scheme. Our work mainly focuses on the security of genomic data without compression of BAM file.

115

Authors: Tanairat Mata

Time-domain OFDM (Orthogonal Frequency Division Multiplexing) signal has various different levels from many subcarriers in one frame which are fatal effect in the non-linear OFDM channel. Because of the signal distortion occurring at the output of non-linear power amplifier, the performance of system has failure. This issue is called that Peak to Average Power Ratio or PAPR which is the main issue of concern for OFDM system. One of the effective PAPR reduction technique known as partial transmit sequence (PTS) with non-uniform phase factors investigating between 0 and 2π was proposed to solve this issue which can suppress the PAPR value competently by clustering their data subcarriers in frequency-domain and multiplying data subcarriers in each cluster by non-uniform phase factors in time-domain after IFFT (inverse fast Fourier transform). However the computational complexity is increased in proportion to the increasing of cluster numbers. By carefully reducing the PAPR of time-domain OFDM signal with small computational complexity, this paper proposes a modified ABC (Artificial Bee Colony) with PTS (ABC-PTS) scheme which can reduce the PAPR of time-domain OFDM signal by applying non-uniform phase factor in the weighting factor sequences with small computational complexity of PAPR reduction process by performing the modified ABC algorithm. The simulation results in this paper can demonstrate that the proposed scheme outperforms approximate 0.4dB and 0.8dB over the original PTS for adjacent and interleaved respectively and approximate 3.2dB over the conventional OFDM at CCDF=10-2 (Complementary Cumulative Distribution Function) with smaller computational complexity which is 19.14% approximately as compared with the original PTS. Moreover, the radix-2, radix-4 and split-radix DIF-IFFT (Decimation-in-Frequency IFFT) algorithms are employed to reduce the computational complexity which are approximate 50%, 62.5% and 66.67% for PAPR reduction process without affecting the optimum PAPR performance.

136

Authors: Shaymah Akram Yasear, Ku Ruhana Ku-Mahamud

The ant colony system (ACS) algorithm is one of the metaheuristics in solving combinatorial optimization problems. The control parameters namely, pheromone coefficient, heuristic coefficient, decision rule, and evaporation rate of the pheromone in ACS are important in determining the performance of the algorithm. However, the initialized values of these parameters stay constant during the search process which leads to performance degradation of the algorithm. This paper aims to tune the parameters of the ACS algorithm by using Harris’s hawk optimization (HHO) algorithm in solving the travelling salesman problem (TSP). The proposed hybrid algorithm is called Harris’s hawk optimizer ant colony system (HHO-ACS). The final process of HHO-ACS will output the path distance. The performance of the HHO-ACS has been evaluated by using different symmetric TSP instances of various scale size. The results showed the HHO-ACS provided superior performance compared to other well-known metaheuristics namely black hole, particle swarm optimization, dragonfly, genetic and ant colony optimization algorithms. Thus, it can attain a better solution with higher accuracy. The proposed algorithm was able to achieve best known optimal solution in solving bayg29, att48 and berlin52 instances and near optimal solution in solving bays29, eil51, st70, eil76 and eil101 instances. Compared to other algorithms, the HHO-ACS algorithm showed superior performance in solving the TSP instances which indicates its effectiveness. This is possible because, in the HHO-ACS algorithm, the main control parameters of ACS algorithm namely, the pheromone coefficient, heuristic coefficient, evaporation rate of the pheromone, and decision rule were tuned according to the problem instance at hand. Thus, the HHO-ACS algorithm can be used to solve problems of travelling salesman nature with minimum customization.

157

Authors: Roshan Shetty, Prasad Narasimha Sarappadi

Thoracic diseases are the various collections of diseases that are associate with the cavity of the thorax such as lungs, heart, oesophagus, chest wall, and diaphragm. The Chest X-Ray is the widely used radiological examination in the diagnosis of such diseases. Many existing methods used entire Chest X-Ray images for the process of training however, it suffered from few limitations. The misalignment of potential or exist of unrelated objects in the Chest X-Ray images resulted in irrelevant noises which limited the network performance. The pre-processing steps of existing methods during training the neural network resulted in lower resolution images. Due to loss of information, it is very difficult to find the tiny lesion regions from the images. To solve such issues, the Self-Sequential Attention Layer based DenseNet (SAL-DN) model is proposed to enhance thoracic disease prediction on Chest X-Ray. The SAL uses the co-relation among the class labels and abnormal pathology by analysing the known feature maps from DenseNet-121.The SAL-DN has the advantages of providing the best representation of images and the capacity to handle large imbalanced datasets like Chest X-Ray14. The performance of the proposed SAL-DN model is compared with existing methods in-terms of the achieved AUC score. The experimental result shows that the proposed SAL-DN outperforms the Thorax-Net method by obtaining an Average AUC score of 0.8715, whereas Thorax-Net obtained an Average AUC score of 0.7876 in patient-wise official split.

177

Authors: Issa Mohammed Saeed Ali, Mukunthan Balakrishnan

The analytics techniques in Big Data are extensively employed as an alternative to generalized for data mining due to the huge volumes of large-scale high dimensional data. Feature selection techniques eradicate the redundant and inappropriate features to decrease the data dimensionality and increase the classifiers’ efficiency. However, the traditional feature selection strategies are dearth of scalability to handle the unique characteristics of large-scale data and extract the valuable features within the restricted time. This article proposed a feature selection algorithm centered on the Population and Global Search Improved Squirrel Search Algorithm (PGS-ISSA) that tackles the problem of local optimum and reduce convergence rate in standard Squirrel Search Algorithm (SSA). The novelty of this proposed PGS-ISSA is the introduction of chaos theory to improve population initialization so that the search space is increased. Then the acceleration coefficients are used in the position update equations to improve the convergence rate in local search process while inertia weight is also applied to optimally balance the exploration and exploitation in SSA. PGS-ISSA employs the fitness function based on the minimum error rate for ensuring the selection of best features that improve the classification accuracy. The proposed PGS-ISSA based feature section algorithm is evaluated by using Support Vector Machine (SVM) classifier implemented in MATLAB tool to address the big data classification problem. The experiments performed on both small and large-scale datasets illustrated that the suggested PGS-ISSA enhances the classification accuracy by 1.7% to 5.4% better than other compared models through effective handling of the big data problems. The results obtained for the bigger Higgs dataset shows that the proposed PGS-ISSA achieved high performance than the standard SSA, existing ISSA models and other prominent optimization-based feature selection algorithms with 64.72% accuracy, 67.3194% precision, 62.1528% recall, 62.3026% f-measure, 82.7226% specificity and consumed less time of 140.1366 seconds. PGS-ISSA also achieved comparatively better results for the other benchmark datasets with 0.3% to 6% improvement on statistical metrics and 10% to 25% reduction in execution time.

204

Authors: Eman H. Zaky, Mona M. Soliman, A. K. Elkholy, Neveen I. Ghali

In the field of health care, one of the most important problems is predicting the possibility of hospital readmission due to its important role in caring for patients with chronic diseases such as diabetes. Such predictions affect the health care costs and the hospital’s efficiency and reputation. In this paper, an intelligent-based model is developed to predict the reintroduction of the patient into the hospital. This model is based on using some Machine Learning (ML) algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Also, it proposes the use of a Deep Learning (DL) based network such as a convolutional neural network (CNN). Both ML and DL are used as classifiers to predict hospital readmission. The main problem is the input noisy data to these classifiers. These noisy data reduce the accuracy of the readmission prediction model. Sequential pre-processing steps are proposed to get over such a problem. These pre-processing steps provide solutions to missing values, feature engineering, and normalization problems. The main contribution of this work is improving readmission prediction rate by solving the data normalization problem. Two types of data normalization (e.g. z-score and min-max normalization) are applied, results show there is a difference in accuracy, z-score normalization is better than min-max normalization when comparing ML methods and DL models, CNN is the best with an accuracy of 0.894% in case of z-score normalization. Moreover, the model performance is improved with an accuracy of 0.924% when non-normalized data is used as input to the model. The proposed Non-normalization technique successes in providing superior results compared to some previous techniques which are displayed data by using Ensemble, Normalization, and Ensemble by age group techniques.

231

Authors: M. J. Niegil Francis, Mihika Preethi Keran, Rachana Chetan, B. Niranjana Krupa

This study aimed to find an optimal feature-extraction method for real-time electroencephalography-based naviga-tion. A comparative study of promising methods was done on two-class motor-imagery data. A novel combination of Hjorth parameters and Welch’s-power-spectral-density produced the highest two-class accuracy of 0.84 across five subjects. The method is computationally inexpensive and provides short training and execution times that best suit real-time applications. The method yielded an average accuracy of 0.66 on three-class data. The real-time fea-sibility of the method was tested by implementing an object-retrieving robot and running a navigation experiment. The robot successfully navigated a three-circuit maze using three commands – right, left, and forward – and auto-matically retrieved the desired object. Along with being ideal for real-time applications, this method utilized only eight electrodes, making it compatible with cheap, portable BCI devices. The method and the applica-tion-framework can find commercial utility in the field of healthcare for the elderly and physically-handicapped.

251

Authors: Awan Uji Krismanto, Irrine Budi Sulistiawati, F Yudi Limpraptono, Ardyono Priyadi, Herlambang Setiadi, Muhammad Abdillah

The increasing penetration of wind power plant introduces a various effect on power system stability, operation and control. Unpredictable, uncertain and fluctuating circumstances can significantly affect the performance of power system. Moreover, employment of novel technologies in wind power plant significantly alter the control characteristic and operation procedure of power system to deal with load variations and fast changing of generated power from wind-based power plant. One of the main focus in integrating large scale wind power plant is how to maintain voltage stability under transient and steady state scenarios. In this paper, effects of large-scale wind power plants on voltage stability of power system is investigated. Practical test system of South-West Sulawesi, Indonesia with integration of two large scale wind power plants are considered. Hence the novelty of this paper is to introduce new and practical test system for power system stability study considering renewable energy integration. The simulation results suggest that the additional power injection from wind power plant introduces beneficial effects on power system voltage stability performance. It was monitored that the increasing power injection from wind power plant enhanced the voltage profile (0.96 pu to 0.975 pu), voltage margin and load-ability (200 MW to 215 MW) of the system. Furthermore, it was also observed that additional power injection from wind power plant significantly improved the dynamic voltage stability performance of the power system, ensuring stable operation under transition stages when the power system was subjected to disturbances.

276

Authors: Joe Siang Keek, Ser Lee Loh, Yan Chiew Wong, Xiu Juan Woo, Wei Wen Lee

Interference minimization in cellular network has and will always be top priority, whether in current or future generation of cellular technology. Therefore, cellular channel assignment problem (CAP) requires continuous study and research. This paper presents the study and comparison of Genetic Algorithm (GA) and Particle Swam Optimization (PSO) for CAP in minimizing interference. GA with three variants in term of population selection – roulette wheel selection (RWS), tournament selection (TS) and stochastic universal sampling (SUS) were studied, and then compared with classic PSO. Two CAPs were derived and used to comprehensively evaluate the performances of the PSO and GAs. It was found that GA-TS is ~11% and ~7% faster than GA-RWS and GA-SUS, respectively. Although the difference is small, but it allowed GA-TS to run for few more iterations and eventually achieved better interference minimization. Moreover, it was also found that GA-SUS has less noise and produce a more consistent result. On the other hand, PSO is slower than GA-TS, but has higher potential to converge on smaller minimum value.

299

Authors: Fatima Chanaa, Radouane N'ait Zarri, Rachid Bendaoud, Houssam Amiry, Elhadi Baghaz, Said Bounouar, Bouchaib Zohal, Charaf Hajjaj, Said Yadir, Abderrahim El-abidi, Mohammadi Benhmida

The islanding of grid-connected photovoltaic (PV) installations is a difficult problem that leads to many protection and security problems. A variety of islanding detection methods (IDMs) are proposed in several studies and these techniques are categorized into passive, active and communication based methods. This study describes the IDM adopted to detect islanding situation in the new PV installation connected to LVs grid of OCP Group (Cherifian Office for Phosphates in Morocco), global leader in the phosphate and phosphate derivatives markets. After having defined the way in which the PV generators will be integrated within the OCP Group grid, the causes that could lead to islanding situation are established. The comparison on the most used methods for islanding detection in the grid connected photovoltaic system (PV) is based on three parameters: the Non-Detection Zone (NDZ), the Detection Duration Time (DDT) and Total Harmonic Distortion (THD) of each method. These performance criteria are compared with the limit thresholds authorized by the IEEE 1574 standard, which are 5% for THD and 2 seconds for DDT. The Slip Mode Frequency Shift (SMS) method is demonstrated to be the most effective, for our installation, among the simulated methods, with THD and DDT of 1.3% and 0.28s, respectively.

326

Authors: Samir M. Hameed, Sinan M. Abdulsatar, Atheer A. Sabri

Visible Light Communication (VLC) becomes a promising technology in the Optical Wireless Communication OWC field. VLC has been developed based on Optical Orthogonal Frequency Division Multiplexing (O-OFDM) in recent years. The Bit Error Rate (BER) performance for various O-OFDM is studied in this paper. The non-linearity characteristic of Light-Emitting Diode (LED), modulation constellation size, O-OFDM scheme, and channel types impacts the BER performance of VLC systems significantly. The proposed model utilizes the conVolutional encoder of 2/3 rate and seven memory cells in the transmitter with O-OFDM for BER enhancement. At the receiver, the Viterbi algorithm system is employed with hard or soft-decision decoding. The proposed system is evaluated in Line-of-Sight and Non-Line-of-Sight (NLOS) channels. In the NLOS model, Zero Forcing Equalizer (ZFE) with channel estimation is added at the receiver path for equalizing the multipath diffused components. This study proves the capability of the proposed system in mitigating the LED non-linearity and reducing BER. The simulation results show the Signal-to-Noise Ratio (SNR) enhancement of the exanimated O-OFDM-based soft-decision between 7 to 10 dB for the BER 10-5 in the LOS model, and for the NLOS model, the BER can be improved from 0.02 up to 10-5 at SNR 45dB.

350

Authors: Dedy Trisanto, Nofita Rismawati, Muhamad Femy Mulya, Felix Indra Kurniadi

The development of credit card use in Indonesia has not been matched by the security provided by credit card service providers. This resulted in significant losses both in terms of banking and customers. The difficulty in finding the characteristics of credit card fraud is one of the biggest challenges. Currently, many are developing machine learning models that can identify credit card fraud to help banks. Unfortunately, the model created is mostly biased towards the class which has more dominant data. This problem is caused by the imbalance of the data on the available dataset. In the previous research, Wang et al found the implementation of Focal loss in XGBoost improve the precision, recall of imbalanced data. However according to Qin et al, the parameter loss from Focal loss have poor judgement in several case of imbalanced data and to handle this weakness, they proposed Weighted – Cross Entropy Loss (W-CEL) loss in Focal loss. According to previous research, we propose a Modified Focal Loss method for Imbalanced XGBoost by entering another parameter from W-CEL loss to Focal Loss to improve the ability of Focal Loss. Focal loss itself is a method that is often used to give weight to classes that are often misinterpreted, so that with the use of imbalance parameters (φ) from W-CEL loss is expected to improve the weighted value of the Focal Loss. We tested our proposed method in credit card fraud dataset from Université Libre de Bruxelles (ULB) machine learning group. Our proposed method produced 100 % accuracy, 0.97 precision, 0.56 recall and 0.72 MCC score in scenario 1 with extreme imbalanced data, in scenario 2 with the mild imbalanced data, the result delivered 99% accuracy, 0.88 precision, 0.87 recall and 0.89 MCC and in scenario 3 with the medium imbalance data, the result delivered 100% accuracy, 0.97 precision score, 0.72 recall score and 0.83 MCC score. The results obtained in this study proved that the proposed method makes machine learning models valid and unbiased, especially in mild-imbalanced data. However, many improvements still need to be made to medium and extreme imbalanced data.

384

Authors: Sajjad Amiri Doumari, Fatemeh Ahmadi Zeidabadi, Mohammad Dehghani, Om Parkash Malik

Numerous designed optimization problems in different disciplines of science should be solved using appropriate techniques. population based optimization algorithms are one of the powerful tools in solving optimization problems. The innovation of this paper is to present a new optimization algorithm called Mixed Best Members Based Optimizer (MBMBO) that can be used to solve various optimization problems. The main idea in designing the proposed MBMBO algorithm is to create a mixed member of several top members of the population in order to guide and update the algorithm population. The main feature and advantage of the MBMBO is the lack of control parameters. Therefore, the proposed MBMBO does not need to adjust the parameter. The various steps of the MBMBO are described and then mathematically modeled for implementation in solving optimization problems. The performance of the MBMBO in solving optimization problems is evaluated on a set of twenty-three standard objective functions. These objective functions are of three different types, including seven unimodal objective functions, six high dimensional multi-model objective functions, and ten fixed dimensional multi-model objective functions. The results of evaluation of single-model objective functions indicate the high exploitation power and also the results of evaluation of multi-model objective functions indicate the high exploration power of the proposed MBMBO algorithm. Also, the results obtained from the simulation of the MBMBO are compared with the results of eight other well-known optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Emperor Penguin Optimizer (EPO), Hide Objects Game Optimization (HOGO), and Shell Game Optimization (SGO). The results of optimizing the objective functions of unimodal and multi-modal types using MBMBO show the acceptable ability of the proposed algorithm to provide suitable solutions. Comparison of the simulation results shows that the proposed MBMBO is much more competitive than the other eight optimization algorithms.

410

Authors: Ahmed Hesham Mostafa, Hala Abdel-Galil El-Sayed, Mohamed Belal

Facial Expression Recognition (FER) is one of the most important research problems in computer vision and Artificial Intelligence (AI) due to its potential applications, many studies were proposed for the FER, whether based on using handcrafted (Craft) features with traditional machine learning techniques or using end to end convolution neural network (CNN). In this paper, we proposed a new model called CNNCraft-net based on combining the advantages of CNN and traditional models by concatenating features outputs from CNN, autoencoder, and handcrafted features such as scale-invariant feature transform (SIFT), speed up robust feature (SURF) and Oriented Fast Rotated Brief (ORB), computed by the bag of visual words (BOVW) to recognize eight facial expressions for static RGB images. For the comparative analysis, multiple metrics were used such as Accuracy, Loss, F-measure, precision, and recall. The high imbalanced AffectNet and FER2013 datasets were used to evaluate the proposed model where the proposed model achieves accuracy 61.9% for eight expressions and 65% for seven expressions for AffectNet and 69% for FER2013.

437

Authors: Hapsari Peni Agustin Tjahyaningtijas, Dewinda Julianensi Rumala, Cucun Very Angkoso, Nurul Zainal Fanani, Joan Santoso, Anggraini Dwi Sensusiati, Peter M.A van Ooijen, I Ketut Eddy Purnama, Mauridhi Hery Purnomo

Brain tumors are among the most common diseases of the central nervous system and are harmful. Early diagnosis is essential for patient proper treatment. Radiologists need an automated system to identify brain tumor images successfully. The identification process is often a tedious and error-prone task. Furthermore, brain tumor binary classification is often characterized by malignant and benign because it involves multi-sequence MRI (T1, T2, T1CE, and FLAIR), making radiologist's work quite challenging. Recently, several classification methods based on deep learning are being used to classify brain tumors. Each model's performance is highly dependent on the CNN architecture used. Due to the complexity of the existing CNN architecture, hyperparameter tuning becomes a problem in its application. We propose a CNN method called en-CNN to overcome this problem. This method is based on VGG-16 that consists of seven convolutional networks, four ReLU, and four max-pooling. The proposed method is used to facilitate the hyperparameter tuning. We also proposed a new approach in which the classification of brain tumors is done directly without priorly doing the segmentation process. The new approach consists of the following stages: preprocessing, image augmentation, and applying the en-CNN method. Our new approach is also doing the classification using four MRI sequences of T1, T1CE, T2, and FLAIR. The proposed method delivers accuracy on the MRI multi-sequence BraTS 2018 dataset with an accuracy of 95.5% for T1, 95.5% for T1CE, 94% for T2, and 97% for FLAIR with mini-batch size 128 and epoch 200 using ADAM optimizer. The accuracy was 4% higher than previous research in the same dataset.

463

Authors: Mohammed K. Hussein, Nasser N. Khamiss

The 5G approximately provides 1000x higher mobile data traffic and 10 to 100x numbers of connected devices than the 4G. The 5G is becoming a reality after deploying it by some of the main cities in the world. Millimeter-wave (mm-wave) communications play a crucial role in the 5G networks owing to its enormous available bandwidth. The hybrid beamforming (HBF) presents better multiplexing gains than analog beamforming. It is a trade-off between cost and performance. As a result, the HBF is a promising approach that exploits the small digital beamforming and the high analog beamforming to meet these challenges. This study proposes and simulates a limited feedback hybrid beamforming for outdoor environments, including the analog precoding/combining based on a quantization codebook and the digital precoding based on Lagrangian optimization with the mathematical model in detail compared with other algorithms in the literature. It achieves an increase in the spectral efficiency satisfied, such as at 20dB SNR, it is approximately 11.75 bps/Hz while the other solutions are 11.18, 11.4, and 11.8 bps/Hz. Also, it presents work well in all possible and satisfies the quality of the results for all cases, such as increasing the number of users, number of multi-path, the SNR, and the BS antennas. Also, it offers the BER approximately 10-4 at 10.3 SNR, better than other solutions.

480

Authors: Ali A. Hasan, Hussain F. Jaafar, Mahmoud Shaker

In modern digital control system such as model reference control and model predictive control, where the real time calculation is the main challenge, the model order reduction has become very important issue to minimize the execution time. In this work, our aim is to construct a novel technique for reduction of high order discrete time systems. This could be achieved by computation algorithm model from a given high order pulse transfer function. The proposed model is based on matching the weighting sequence of the original parameters with those adopted in the low-order model. The generalized least squares method is then used to determine the reduced model parameters. The efficiency of the proposed algorithm is validated by using the integral squared error minimization between the original system and the reduced model. An example is presented and discussed to validate the efficiency of the proposed low-order model. Performance comparisons with many recent related works showed that the proposed model is promising in terms of low error indices and time responses.

514

Authors: Kaoutar Ouarid, Abdellatif El Assoudi, Mohamed Essabre, El Hassane El Yaagoubi

This paper aims to present a new approach in the design of observers for a Class of Discrete-time Takagi-Sugeno Singular Models (DTTSM) in the case of unmeasurable premise variables, allowing to estimate simultaneously the states of the system, and the faults in the actuators and sensors. The approach used is based on augmenting the state vector. The exponential convergence is studied using the Lyapunov theory. The stability conditions are presented as Linear Matrix Inequalities (LMIs). Lastly, a numerical application is used to evaluate the efficiency of the proposed dynamic system. It shows that the estimated variables catch up rapidly (around 0.3s), and accurately the real variables during the application time of the actuator and sensor faults.

539

Authors: A K M Zakir Hossain, Win Adiyansyah Indra, J. A. Jamil Alsayaydeh, Safarudin Gazali Herawan

In this article, a microstrip-feed planar monopole ultra-wide band (UWB) antenna is proposed for the chipless RFID (CRFID) tag-reader systems with the intension to reduce the size of the tag. This antenna is keyhole shaped and small in size of 32 × 20 mm2 compared with other existing antennas in the retransmission-based chipless CRFID system. The partial ground plane technique is applied here to obtain the bandwidth of 6.81 GHz within the UWB frequency spectrum. The results show it is suitable for both identification and sensing application in the retransmission-based CRFID. The antenna has a good omni-directional radiation pattern with realized gain up to 4 dBi and the realized gain to antenna size ratio goes as high 0.39/cm2 which is comparatively higher than the other existing antennas in retransmission-based CRFID. The other feature of this antenna is it has the radiation efficiency up to 99% whereby it never goes below 96% throughout the working bandwidth and the measured antenna efficiency goes as high as 95%. Furthermore, to check the suitability to apply in CRFID sensor application, the proposed antenna has been realized on three flexible substrates of polyimide, PET and paper, and very little deviations are found compared to the rigid Rogers RT 3003 substrate in terms of S11, realized gain, efficiency and radiation pattern which is a proof of robustness of the design. This proposed antenna will motivate the researcher to develop a smaller sized UWB CRFID tags.

561

Authors: Eman A. Radhi, Mohammed Y. Kamil

The task of segmenting breast tumours in mammograms is very difficult, as its difficulty lies in the lack of contrast between the tumour and the surrounding breast tissue, especially when dealing with small tumours that are not clear boundaries and hidden under the tissues. Segmentation algorithms often lose the path of tumor boundaries in an attempt to determine the position of them. Active contours are used widely for segmentation as a high-level technology for boundary recognition. The main aim to create a clear contrast between the tumour and the normal breast region. In this study, two approaches to active contour are applied: snakes and level sets. The proposed methods were applied to all abnormal mammogram images taken from mini-MIAS database. The first approach showed a weakness in the segmentation of this type of image, while the other approach was able to segment all the mammogram's tumours. The Chan-Vese method was the most superior of all the active contour segmentation methods. The proposed models were tested in two ways, the first is statistical the best result was for the Chan-Vese method and it came as follows (90%,95% 98%, 97%, 97%) for Jaccard, Dice, PF-Score, Precision, and Sensitivity respectively. And the other is based on the segmented region's characteristics, Chan-Vese was able to accurately determine the location and shape of the tumor. The proposed Chan-Vese approach is appropriate in adopting computer assisted detection systems to predict tumor boundaries and locations in mammography for its reliability and superior performance over other algorithms.

11

Authors: Haneen Qteat, Mohammed Awad

Diabetes mellitus is one of the deadliest and chronic diseases that affect persons who have an increase in their blood glucose levels. Type 1 Diabetes Mellitus “T1DM” is considered one of the most dangerous types of diabetes as it is the reason why diabetes is called the silent killer. Due to the common symptoms of type 1 and 2 diabetes, diabetes specialists face doubts about their diagnosis of the type of diabetes in the patient where the uncertainty about the diagnosis of the disease may lead to delays in controlling the potential complications, especially if they have T1DM. The prime motto of this work is to classify the diabetes types accurately. In this work, we have collected a local Palestinian dataset “DataPal” With the assistance of the Palestinian Diabetes Institute. The "DataPal" dataset was applied using machine learning algorithms to predict diabetes types. The “DataPal” consists of 9 predictors used to diagnose diabetes types. The dataset consists of 314 instances of diabetic females. Thus, our samples were for females with both types 1 and 2 diabetes, aged between 5 and 89 years. The local dataset “DataPal” was preprocessed using the K Nearest Neighbor (KNN) algorithm to fill their missing values, wherein medical diagnosis there is no room for error. The Support Vector Machine (SVM) algorithm was applied to the dataset to select the most optimal features to predict diabetes types. Both the two-fold and four-fold cross-validation methods were applied to the datasets to evaluate the applied models fairly. A hybrid Model Particle Swarm Optimization with Multi-Layer Perceptron Neural Networks (PSO-MLPNNs) uses the PSO evolutionary algorithm to train an MLPNNs and find the optimal weight values of the trained network. The PSO-MLPNNs model was applied to the preprocessed dataset. Then the performance of the model was evaluated using different metrics such as the overall accuracy, recall, specificity, and others. The obtained results show that the proposed PSO-MLPNNs model outperformed all models applied in this work in the classification of diabetes types with an overall accuracy (ACC= 98.73%).

31

Authors: Shimaa Ouf

This paper introduces an architecture to improve the pharmaceutical supply chain's security by using the Internet of Things, semantic web, and blockchain. This architecture increases transparency and visibility of drug flows and improves the representation of the increasing amounts of data that have been generated from pharmaceutical supply chain transactions. The pharmaceutical companies have problems dealing with the complexity of their supply chains, which represent the full life cycle of drugs from extracting raw materials, production, distribution, tracking of drugs, and quality assurance to use by patients. They need to create an efficient, effective, transparent, immutable, and secured supply chains to achieve a competitive advantage in a fast-changing market. A proposed architecture which applies the semantic web technology is implemented to enhance the representation capability of the IoT-blockchain based pharmaceutical supply chain data by annotating them with semantically rich languages to conduct formal reasoning, and aggregating data from heterogeneous sources in easy way and an interoperable manner. The proposed architecture integrates IoT, blockchain, and semantic web to help pharmaceutical companies improving their supply chains in transit and storage, improving patient satisfaction, trust through transparency, preventing drug counterfeit and sharing and reusing knowledge related to the pharmaceutical supply chain with other systems.

54

Authors: Kun Nursyaiful Priyo Pamungkas, Waskitho Wibisono, Supeno Djanali

Wireless sensor networks (WSN) have been widely applied to monitor objects in harsh and hazardous environments. The limited energy resource in WSN is a major problem. Energy efficiency is required for WSN to operate for a long time. The cluster in WSN is a strategy that can help to extend the life of the network. However, the optimal cluster formation and cluster head (CH) selection is a non-deterministic polynomial-time hard problem. Besides, many clustering-based protocols build clusters after defining the cluster heads among all sensor nodes on the network. This process triggers inefficient energy consumption. Furthermore, the unbalanced load between CH and cluster members (CMs) also affects the network lifetime. This paper proposes an advanced clustering protocol based on a modified differential search algorithm to save energy consumption and extend network life. To form clusters, this protocol determines the optimal number of clusters. In this proposed protocol, CH selection takes place at the cluster level. The process of cluster formation and CH selection is proposed by utilizing a modified differential search algorithm. Furthermore, the rotation system and the determination of relay nodes are proposed to control the load distribution. The proposed protocol's performance is compared with various data gathering protocols for WSN, namely LEACH, PEGASIS, LEACH-GA, and HFAPSO. The experimental results show that the proposed protocol can increase network lifetime in terms of First Node Dies (FND), Half of Nodes Die (HND), and Last Node Dies (LND). On average, the MDSAC protocol increases FND by 1596 rounds, 2456 rounds, 1211 rounds, and 1060 rounds compared to LEACH, PEGASIS, LEACH-GA, and HFAPSO. The MDSAC protocol also increases the average HND by 5764 rounds, 4918 rounds, 5221 rounds, and 4308 rounds compared to other comparison protocols. Even the MDSAC protocol managed to improve the average LND of 7784 rounds, 8739 rounds, 9001 rounds, and 7161 rounds, compared to the four comparison protocols. These results highlight the contribution of the proposed approach in maintaining network lifetime in WSN as one of the important challenges in WSN research and development.

82

Authors: Yohanes Artha Setiawan, Amirullah Amirullah

This paper aims to design and implement a single-phase dynamic voltage restorer (DVR) supplied by battery energy storage (BES) using load voltage controlled by the unit vector template generation (UVTG) method. The UVTG method on the DVR system is implemented using the Arduino-Uno hardware. LabVIEW-based interface simulation is used for monitoring the parameter in real-time during 80% voltage sag period i.e. source voltage, injection voltage, load voltage, source current, load current, and DC-link voltage. The true power factor measurements are also carried out to obtain total harmonic distortion (THD) of the source current and load current. The single-phase DVR system is connected to load i.e. a 5-watt bulb lamp (linear-load), 5-watt fluorescent (FL) lamp (non-linear load), and 5-watt light-emitting diode (LED) lamp (non-linear load). During voltage sag, the single-phase DVR-BES system is able to maintain load voltage i.e. bulb lamp, FL lamp, and LED lamp of 216 volts, 256 volts, and 185 volts, respectively. The percentage of load sag voltage deviation for each load is 6.4%, 26.13%, and 8.87%, respectively. The measurement of source power-factor with voltage sag results in source true power factor of three loads of 0.985 pu leading, 0.985 pu leading, and 0.990 pu leading respectively. On the same system with voltage sag, single phase DVR-BES is able to result in source current THD for three loads of 1.523%, 1.523%, and 1.010% respectively. The proposed study is able to give the best performance because it is able to produce source current THD lower than the previous studies. By using the Arduino-Uno interface, this model can be run and monitored in real-time with LabVIEW better from the simulations conducted by previous researchers which were still carried out off-line using Matlab/Simulink.

109

Authors: Ayomi Sasmito, Asri Bekti Pratiwi

This paper proposes an efficient optimization algorithm called Chaotic Student Psychology Based Optimization (CSPBO) to solve bi-objective permutation flowshop scheduling problem (BPFSP). The SPBO algorithm does not require any tunning parameters which makes it simpler in computational experiments. The original SPBO classifies students related to their efforts in improving their performances. In this paper, we use chaotic maps to enhance student efforts in each category and present different strategic approaches. Logistic, iterative, sine, tent, and singer maps are integrated using proposed strategies to find the best map and also its strategy, named CSPBO. To prove its performance, the CSPBO is compared with several well-known metaheuristic algorithms. The comparison results are evaluated in regard to mean, standard deviation, best terms and ARPD. Since BPFSP is an NP-hard problem, computational experiments on BPFSP instances are carried out according to type size of the dataset. Three types of dataset, small, medium and large are tested. Based on the experiment result, the SPBO integrated with logistic map has significantly given better performance compared to other chaotic maps. Furthermore, CSPBO shows compatible performance compared with other metaheuristic algorithms in solving BPFSP.

128

Authors: Dheyaa T. Al-Zuhairi, Abbas Salman Hameed, Isam Salah Hameed

In this paper, a system for the purpose of signals encryption using the technique of independent component analysis has been proposed. The proposed system mixes the original signal with arbitrary number of random signals in order to obtain a highly encrypted signal like noise. The number of random signals is indicated by an attached key signal preceding the encrypted signal. In order to increase the encryption strength, the number of random signals is changed at each signal transmission. An independent component analysis technique is utilized to separate signals and select the original signal by a designed selector. A set of parameters has been adopted to measure the encryption quality and decryption capability. Promising encryption results represented by up to - 48.35 dB for segmental spectral signal to noise ratio and 0.00037 correlation coefficient between the original and the encrypted signals are obtained. As well, an efficient signal decryption is achieved with 45.85 dB signal to noise ratio and nearly 1 correlation coefficient value between the original and reconstructed signals. In conclusion, the proposed system is a good candidate to be adopted in developing highly reliable security transceiver systems.

150

Authors: Ameer A. Badr, Alia K. Abdul-Hassan

Lately, with the rapid growth of various technologies, identifying the information of gender and ‎age give short speech utterances has become a necessity for many applications in daily life like ‎human-robot interaction, targeted ‎marketing, identifying suspects in criminal cases, etc.‎‏ ‏Despite ‎the comprehensive studies carried out to extract descriptive features, the recognition accuracy is ‎still not satisfactory.‎‏ ‏In this study, an automatic system is proposed to classify age and gender in ‎short speech utterances without depending on the text. Firstly, two groups of features are ‎extracted from each utterance frame, followed by measuring ‎10 statistical functionals for each ‎extracted feature dimension. After that, the extracted features dimensions are normalized using ‎the Quantile technique. Then, the CatBoost machine is utilized as an important features detection ‎to select the most discriminatory features for speaker age and gender recognition tasks. For ‎classification purposes, the selected feature dimensions are fed into the Support Vector Machine ‎‎(SVM). Experiments are conducted on the aGender data-set for measuring the suggested ‎system's performance. The unweighted accuracies (UA) of the proposed system for gender, age, ‎in addition to gender & age is ‎89.62%, 72.29%, and 71.96%, respectively. The achieved results ‎outperform recent results on the same data-set.

177

Authors: Mohammed El Mahfoud, Badre Bossoufi, Najib El Ouanjli, Mahfoud Said, Mohammed Taoussi

This research paper deals with the improved direct torque control (DTC) of a doubly fed induction machine (DFIM) operating in motor mode. The conventional DTC is a powerful tool to ensure robustness and good torque dynamics. However, the variable switching frequency as well as the presence of torque and flux ripples due to the use of hysteresis controllers are the major drawbacks of this strategy. For this purpose, the control by space vector Modulation (SVM) is developed to improve DTC performance, by replacing hysterisis controllers by PI controllers to reduce electromagnetic flux ripple and torque ripple in order to minimize mechanical vibration and acoustic noise while maintaining the benefits of DTC control. The proposed control algorithm is simulated and tested in MATLAB/Simulink. A comparative analysis between this technique and conventional DTC is carried out, highlighting the efficiency of the proposed improvement approach. The performance indexes and comparative results show the effectiveness of the proposed control in reducing torque ripple and stator and rotor flux ripple by up to 43%, 42% and 31%, respectively, resulting in Total Harmonic Distortion (THD%) 61% lower, in addition the switching frequency is controlled, and the Rejection Time is improved by more than 76%.

201

Authors: Mahmoud O. Elish, Karim Elish

Software defect prediction is one of the most important quality assurance activities during software development. This paper contributes empirical insights into the effectiveness of three resampling ensemble methods (bagging, boosting, and dagging) of Deep Learning Neural Networks (DLNN) for cross-project software defect prediction, compared to individual DLNN. An empirical study was conducted using five datasets. The results indicate that the bagging ensembles of DLNN offer only 0.24% increase in accuracy, on average, compared to the individual DLNN models, whereas the boosting and dagging ensembles degrade the accuracy. Furthermore, the results show that the three resampling ensembles of DLNN outperform the individual DLNN models in precision; with a maximum improved precision by 25.15% on average using the boosting ensembles. The results however indicate that none of the resampling ensembles improve the recall. Lastly, for a balanced performance in terms of both precision and recall, the results indicate improvements ranging from 0.98% on average by applying the bagging ensembles to 11.67% on average by applying the boosting ensembles.

222

Authors: Muhammad Fuad, Trihastuti Agustinah, Djoko Purwanto

The need for mobile robots that can operate in the human environment is increasing during the Covid-19 pandemic. To accomplish this task, robot navigation must be supported by collision avoidance to maintain human safety and comfort. Collision avoidance methods generally only maintain a safe distance from surrounding objects. Some methods that provide comfort still have difficulty in overcoming pedestrians that move with unexpected direction, changing speed, and unknown trajectories. This paper proposes Hybrid Velocity Obstacle-based modified Headed Social Force Model (HVO-based modified HSFM) to avoid disturbed groups of pedestrians while navigating in the complex and dense workspace. HVO is used to calculate linear and angular velocities to avoid obstacles with nonlinear trajectories. The linear velocity that computed by HVO acts as desired velocity for generating target force of modified HSFM that drive robot to target location. While interaction force of modified HSFM that guide robot to circumvent obstacles is determined by static and moving objects in the surrounding of robot. The angular velocity from HVO is used to produce steering command to avoid collision. For evaluating the proposed method, several simulation scenarios had been run by implementing HVO-based modified HSFM into a two-wheeled differential-steering mobile robot that navigate in the indoor human environment. The results show that our approach is capable to avoid collision by maintaining safety and comfort with disturbed groups of pedestrians with average value 0.14 of Threat Level Index (TLI) from two scenarios. It is envisioned that proposed method can be implemented into real transport robot that operate in human environment.

255

Authors: Salah Mortada, Yuhanis Yusof

Artificial bee colony (ABC) is one of the widely used swarm intelligence algorithms in solving combinatorial optimization problems. In this study, the existing Modified ABC (MABC) algorithm is revised to solve the vehicle routing problem with time windows (VRPTW). The reason is that even though MABC is reported to be successful, its exploitation process lacks a selection neighborhood structure during the intensification process. The proposed algorithm, termed as enhanced Modified ABC (E-MABC), offers a neighborhood search that exchanges neighborhood structure between two different routes in the same solution, rather than one route. To evaluate the effectiveness of E-MABC, experiments are performed on 56 instances of VRPTW. The results of E-MABC are compared against the ones produced using MABC and other metaheuristic algorithms. Based on the total travel distance and number of vehicles, the proposed E-MABC is shown to be a solution for VRPTW. The interchange neighborhood search, which is implemented by bees during the exploitation process, improves the solution quality, hence producing an optimal outcome. The proposed E-MABC results are better as compared to MABC in terms of the total travel distance by 71.42 % and the number of vehicles by 35.71%.

282

Authors: Rafika El idrissi, Ahmed Abbou, Mohcine Mokhlis, Mahdi Salimi

The environmental condition changes lead to obvious fluctuation in photovoltaic panels’ output power. Therefore, to make efficient use of photovoltaic (PV) systems the maximum power point tracking (MPPT) controllers are required. Many classical methods are proposed to track the MPP, but they will lead to a high power drop when rapid changes in the atmospheric conditions occur, which necessities a robust controller with high performance. In such a manner, the proposed controller is designed for this purpose. There are two stages of the proposed controller: The artificial neural network (ANN) based the first stage that generates the PV panel optimal voltage and the second phase consists of a non-linear adaptive backstepping control, which is able to follow this optimum voltage by acting on the DC/DC boost converter’s duty cycle. The input-output linearization technique is based the suggested controller. The last is robust and safe from the parameters fluctuation, load variation, and the atmospheric condition changes. In the proposed control design, unknown and estimated converter parameters are assumed, especially the inductor and input capacitor, considering an adequate Lyapunov function. A single-phase inverter connects the boost converter to the grid. However, for this connection to be made the power factor should be unified and the inverter current should be synchronized with the grid voltage. The sliding mode controller (SMC) is designed to operate on the inverter duty cycle for the resolution of these tasks. The controller is used to enhance the robustness and the system's rapidity. Besides, the DC bus is regulated using the proportional and integral (PI) controller. Matlab/Simulink software is used to simulate the overall system. Furthermore, for accurate results, the proposed controller is compared with perturb & observe (P&O), ANN-backstepping sliding mode (ANN-BSMC), and ANN-backstepping integral sliding mode (ANN-BISMC) techniques . The findings show that the proposed method outweighs other methods in terms of tracking rapidity, steady-state error and the fluctuations around the MPP under severe assumptions.

303

Authors: Hanaa Khater, Ahmed ElSawy, Assem Tharwat, Ihab El-Khodary

The max-min separable optimization problem is depended on max-min algebra. This optimization problem focuses on the worst case for minimization of objective function. Additionally, this problem has separable max-min constraints. It was solved in another article by using difficult and complicated algebraic method for finding one solution only. In this paper, the Max-Min Separable problem under Max-Min Separable Linear Constraints (MMSMMLC) is solved by hybrid PSO. The main idea of the presented algorithms is combining GA processes with PSO algorithm to be suitable for solving MMSMMLC optimization problem under all types of constraints (equality and/or inequality). Finally, the new algorithms applied through several benchmark functions show that the new algorithms present all optimal solutions in best result with 100% and improve the optimal solution in some cases rather than algebraic method.

332

Authors: Hassan Almazini, Ku Ruhana Ku-Mahamud

Modified graph clustering ant colony optimization (MGCACO) algorithm is an unsupervised feature selection (UFS) algorithm used in determining a subset of effective genes from microarray data. The feature subset construction is based on the ant colony optimization (ACO) algorithm, which guides the search process from clusters. However, the MGCACO algorithm is unable to choose all significant features from the clusters to form an optimal feature subset. This paper proposes an enhanced graph clustering ACO (EGCACO) to overcome the problem of feature selection in the MGCACO algorithm. A principal point of this algorithm is utilizing an adaptive selection technique that guides ACO for subset construction from the clusters of features. The adaptive technique for ant selection is based on the state of the search space. Experimental results indicated that the proposed EGCACO achieves the highest classification accuracy than five other common UFS algorithms on four classifiers, where it obtained 87.13%, 86 .19 %, 87.38 % and 90.80 % for support vector machine, k-nearest neighbor, decision tree and random forest classifiers, respectively. In particular, the proposed algorithm can select the genes of the deoxyribonucleic acid microarray with consideration of relevance and redundancy among the genes. Therefore, the proposed EGCACO can be implemented to handle the high dimension feature space, such as image processing, text classification, and microarray data processing, which is critical for good and reliable results.

359

Authors: Dinial Utami Nurul Qomariah, Handayani Tjandrasa, Chastine Fatichah

Diabetic retinopathy is a disease caused by complications of chronic diabetes and can lead to blindness. Therefore, automatic detection of microaneurysms on retinal fundus images as an early warning system is very necessary and challenging since the size of the microaneurysm is very small compared to the anatomical structures of the retina such as blood vessels and other elements, and hemorrhages as other red lesions. In this study, we propose a novel deep learning network that modifies UNet using residual units with modified identity mapping (MResUNet) to perform microaneurysm segmentation. The purpose of identity mapping modification using convolutional layers and batch normalization is to enrich features and add Residuals in UNet to overcome feature degradation as the network becomes deeper. The mean weighted loss function is used in training to solve the problem of pixel imbalance between the microaneurysms and the background. The proposed architecture is evaluated using the IDRID and DiaretDB1 datasets. The experimental results show that the proposed architecture (MResUNet) achieves higher sensitivity values of 61.96% and 85.87% on the IDRID and DiaretDB1 datasets, respectively, compared to AutoEncoder, FCN16, FCN8, and UNet.

388

Authors: Lamia Moudoubah, Kamal El Guemmat, Khalifa Mansouri, Mohammed Qbadou

In terms of mental health, COVID-19 has a great impact on people's psychology. It can cause a certain degree of fear, worry, boredom, depression, eating disorders, loneliness and sometimes self-aggression or suicidal behaviour. The purpose of this article is to explore the psychological consequences of health problems related to COVID-19. During this period, some literature appears to analyze the psychological behaviour of individuals. However, in the sense of domain ontology, there is a lack of knowledge about modelling specific knowledge in the field of psychology and to have formal semantics. Through this article, we present a psychology-based method to analyze human behaviour and frailty during COVID-19. We chose IR (Information Research) based on TOR (Termino-Ontology Resources) as the method to construct the resulting ontology modelling the state of mental health in a COVID-19 context, an ontology that avoids ambiguities in the concepts that model the domain in question. The result of our research is the ontology described in Fig. 5, 6, 7, 8 and 9. The concepts that make up our ontology are derived from the results of the calculation of scores through Eq. 6, which consists of calculating the Score (Cij), then the CI(c) through the calculation of P(c) which is 0.18 in the term ti= anxiety, and therefore (-log (0.25)) for the calculation of CI(c). These results concern the term "anxiety" in our ontology, following the same logic for the other terms. The lack of a psychological ontology modelling the concepts, those correspond to the fragility of people's mental health during the COVID-19 pandemic, so we thought about developing an ontology that would fill this gap.

412

Authors: Elly Matul Imah, Erina Seviyanti Dewi, I. G. P. Asto Buditjahjanto

Electroencephalography (EEG) is a method for recording the electrical activity of the brain. In the EEG various frequency signals can analyse the brain and the brain's behaviour. EEG can detect the abnormalities in the brain, one of the abnormality is Autism Spectrum Disorder (ASD). ASD is a condition where a person has a combination of neurological disorders in social communication and behavior, limited interest in something and sensory behavior. Joint attention (JA) is the ability in sharing attention between interactive social partner with third external elements as objects or events. Joint attention can also be said as social interaction behaviors to follow the attention of others, and direct attention of another. Joint attention is the key point affecting social communication in individuals with autism spectrum disorder (ASD). In this study, the detection of the ability of ASD sufferers to respond to instructions to see a targeted object based on the EEG signal recording was conducted. The dataset used is BCIAUT P-300 that is non-linear separable and imbalanced class with a ratio of 1: 8. In handling the imbalanced data, under-sampling was applied. The feature extraction method that compared are wavelet and principal component analysis. Based on the experiment result, Pz has the best channel to classify the Joint Attention of ASD using EEG P-300 data. The best accuracy is GRLVQ and the best G-mean is SVM. Over all the results, based on accuracy, g-mean, training and testing time show that GRLVQ is better performance than others, and SVM is the runner up. The differences of accuracy 9%, training time GRLVQ faster around 45 seconds than SVM, and testing time faster around 3 seconds.

435

Authors: Vandana Charaliparambu Pathayapuram, Ajeet Annarao Chikkamannur

Internet of Things (IoT) paradigm merges the physical world into digital world with proliferation of smart solutions in various domains. These smart solutions leverage the sensing and actuating power of heterogeneous IoT devices. The user applications must discover the right IoT resource at the right time. To serve this objective, the most important attributes of these diverse IoT devices must be identified and managed by network management systems (NMS) for effective discovery. In this paper, an ameliorated feature selection approach is proposed which selects minimum number of discriminating and service relevant features of multiple diverse IoT devices. Proposed feature selection technique has two phases where the features are ranked based on discriminating capability. Minimal subset of discriminating features is then chosen and ensemble with service relevance features. The proposed model is validated with dataset constructed from annotated IoT resources based on our previous work. Analyzes is carried out by comparing with existing state of art hybrid feature selection techniques and the proposed method has outperformed in terms of accuracy, quality and size of feature set selected. Further, these selected feature set derived from the proposed feature selection method is used to enrich IoT protocol, Constrained Application Protocol (COAP) CoRE Link format. This semantic enriched COAP protocol is used for IoT resource discovery on COOJA simulator. The results show that enriched COAP based discovery diminishes the discovery time by 33% compared to traditional COAP. Experimental work is carried out to showcase the effectiveness of the proposed feature selection model in terms of resource discovery success rate, query response time, service type detection accuracy.

458

Authors: Qasim Hadi Kareem, Malik Jasim Farhan

This paper presents a compact linear and circular polarization multi-input multi-output (MIMO) antenna for wireless local area network (WLAN) / worldwide interoperability for microwave access (WiMAX) and C-band wireless communication systems. This work has been performed with two configurations of six-port MIMO antennas with a total size of 60 x 60 〖mm〗^2 on low-cost FR-4 substrate. There are two rectangular slots - one is situated on the upper part of the circular patch to radiate left hand circular polarization (LHCP) waves, and the other is located on the lower portion of the circular patch to emit right hand circular polarization (RHCP) waves. It is a wave absorbing slot that allows electromagnetic waves on one side to be separated from the other. Another design of the four elements is the structure that consists of four orthogonal radiators. The four feed-through pair antenna elements are located orthogonal to each other for best isolation and provide vertical and horizontal polarization. The two configurations have a broad bandwidth (640MHz and 400MHz), high efficiency (over 75 %), good isolation (>-20 dB), low correlation coefficient (<0.01), high channel capacity (22.5 bps/Hz), and stable gain characteristics. The proposed MIMO antenna is simple, generalized, and can be adapted for a broad range of frequencies. It was found that there is a close agreement between the measured/calculated antenna parameters and experimental performance.

481

Authors: Ali Majeed Mahmood, Azad Raheem Kareem

Increasing the number of mobile network connections represented by humans and machines leads to a higher demand for resources, which have to be allocated for all User Equipment (UEs) with an acceptable level of fairness. However, the cost of allocating the required resources for a great number of connections may result in growing the computational complexity of scheduling algorithm and can delay the response of the Base Station (BS). In this paper, a new processing approach is proposed for Fifth Generation - Internet of Things (5G-IoT) networks. The computational complexity of the scheduling process is minimized in two folds. First, the Spark framework is used as a distributed and parallel processing paradigm, which can reduce the computational complexity from O(N^2) to O(〖N/M)〗^2. Then, to minimize the scheduling latency of the scheduler itself. The second level of complexity minimization is presented by using a low computational scheduling algorithm via modifying the traditional proportional fair scheduling algorithm to further decrease the complexity from O(〖N/M)〗^2 to O(N/M). Simulation results demonstrate the efficacy of the proposed scheduling approach by applying the concept of divide and conquer in achieving the scheduling process. Hence, the effectiveness of the new scheduling approach represented by scaling down the time of scheduling large number of UEs to the time of small sub-clusters of UEs. The desired percentage of gain in Scheduling Time (ST) depends on the size of sub-clusters such as deploying 250 UEs in form of clusters of (50, 30, or 10) UEs will reduce the ST compared to the classical Proportional Fair (PF) with a percentage of 85%, 89%, and 97% respectively. This property can be exploited to support network scalability at low scheduling latency in next-generation mobile networks.

503

Authors: Zakaria Boucetta, Abdelaziz El Fazziki, Mohamed El Adnani

Road deteriorations threaten people ‘safety as they are considered one of the major causes of road accidents leading to significant human and material losses. Automatic detection of road deteriorations can help massively in avoiding the tragic consequences. However, this task represents a real challenge. This work describes an attempt to tackle this issue by establishing a crack notification and road condition monitoring system. The proposed system will help drivers to avoid dangerous cracks and enable authorities to supervise and manage the road surface. In order to automatically detect and classify the road surface images, a convolutional neural network (CNN) is used, as this model has proven to be more suitable for this task than traditional methods due to its ability to extract features implicitly. The novelty in this paper consists on finding the optimal CNN model architecture for crack detection and classification into 10 distinguished classes. The proposed CNN model is trained and tested using historical 3D pavement images and can perform the classification with a high accuracy (above 95%). To evaluate and supervise each road segment condition, a novel method for severity indexes computation that uses the resulting classes is suggested as well as the generation of a weighted road graph in which the edge weights evolve according to the severity indexes. All data processing tasks are performed over a Hadoop-based framework using HBase and MapReduce.

528

Authors: Doan Perdana, Sarah Hafidzah, Bayu Erfianto

The IEEE 802.11ah standard was developed from IEEE 802.11, which works on a Wireless Local Area Networks (WLAN). This standard works in the sub-band 1GHz, to increase the range of an Access Point (AP) up to 1 km2, with the ability to handle approximately 8000 stations (STA). The IEEE 802.11ah standard occurs in the MAC and PHY layers. However, the high number of STAs produces high collision, therefore this standard introduces the Restricted Access Window (RAW) at the MAC layer. This research accurately examines a surrogate model to predict RAW performance under hidden nodes scenario. The solution to the surrogate model was analyzed using the Markov chain and numerically simulated with Matlab. Furthermore, the Markov chain solution is used to determine the performance measure parameters, namely throughput, energy consumption, and average delay. This research also examines the effect of hidden nodes on the IEEE 802.11ah standard network’s performance, especially in RAW, with variables obtained using Bianchi’s approach. The result showed that the simulated RAW slot duration of 52 µs on the IEEE 802.11ah standard made performance results better than 104 µs and 156 µs. The effect of the hidden nodes makes the successful transmission time probability longer due to its increment.

545

Authors: Sajjad Amiri Doumari, Hadi Givi, Mohammad Dehghani, Om Parkash Malik

There are many optimization problems in different scientific disciplines that should be solved and optimized using appropriate techniques. Population-based optimization algorithms are one of the most widely used techniques to solve optimization problems. This paper is focused on presenting a new population-based optimization approach called Ring Toss Game-Based Optimization (RTGBO) algorithm. The main idea of RTGBO is to simulate the behaviour of players and rules of the ring toss game in the design of the proposed algorithm. The main feature of the proposed RTGBO algorithm is the lack of control parameters. Steps of implementing RTGBO are described in detail and the proposed algorithm is mathematically modeled. The ability of RTGBO to solve optimization problems is evaluated on a set of twenty-three standard objective functions. These functions are selected from three different groups including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. The performance of RTGBO is also compared with eight other well-known optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Emperor Penguin Optimizer (EPO), Hide Objects Game Optimization (HOGO), and Shell Game Optimization (SGO). The results of optimization of objective functions of unimodal type indicate the high exploitation ability of RTGBO in solving optimization problems. On the other hand, the results of optimizing the multi-model type objective functions indicate the acceptable exploration ability of RTGBO. The results also confirm the superiority of the proposed RTGBO algorithm over mentioned optimization techniques.

565

Authors: Omar Abdul Razzaq Abdul Wahhab, Ahmed Sabah Al-Araji

This paper presents an improvement of the actual output trajectory tracking performance of a mobile robot based on convolutional neural network controller with off-line and on-line tuning Back-Propagation algorithms. The goals of this strategy are to find the optimal path to direct its movement and to design Convolutional Neural Network Trajectory Tracking (CNNTT) controller in order to control the nonlinear kinematics mobile robot system. Therefore, a hybrid swarm optimization algorithm uses for solving the two most important problems of path planning; the first is that the path must avoid collision with obstacles, and the second it must reduce the length of the path to a minimum. This paper will discuss the finding of the shortest path with the optimum cost function by using three optimizations’ algorithms; Chaotic Particle Swarm Optimization (CPSO) algorithm, A-star algorithm, and a hybrid swarm optimization algorithm (ACPSO). The task of the proposed feedback (CNNTT) controller is to obtain precisely and quickly the robust left and right wheels velocity which are used to control the position and orientation of the mobile robot system. These algorithms are simulated by MATLAB in a fixed obstacles environment to show the effectiveness of the hybrid swarm optimization algorithm in terms of the minimum number of an evaluation function and the shortest path length as well as the results of the proposed method showing that the (CNNTT) controller is accurate in terms of the mobile robot follows the desired paths quickly through fast obtaining the (CNNTT) controller’s parameters and a smooth linear wheels velocity actions are generated for mobile robot system with minimum number of cost-function evolutions that minimized the tracking error x-position around 4 cm and y-position around 2.5 cm and zero approximately orientation error as well as no oscillation in the responses. Finally, we confirm the effectiveness of the numerical simulation results of the proposed control strategy through comparison other types of controller simulation results.