International Journal of Intelligent Engineering and Systems

1

Authors: Balaji Sreenivasulu, Anjaneyulu Pasala, Gaikwad Vasanth

In computer vision, domain adaptation or transfer learning plays an important role because it learns a target classifier characteristics using labeled data from various distribution. The existing researches mostly focused on minimizing the time complexity of neural networks and it effectively worked on low-level features. However, the existing method failed to concentrate on data augmentation time and cost of labeled data. Moreover, machine learning techniques face difficulty to obtain the large amount of distributed label data. In this research study, the pre-trained network called inception layer is fine-tuned with the augmented data. There are two phases present in this study, where the effectiveness of data augmentation for Inception pre-trained networks is investigated in the first phase. The transfer learning approach is used to enhance the results of the first phase and the Support Vector Machine (SVM) is used to learn all the features extracted from inception layers. The experiments are conducted on a publicly available dataset to estimate the effectiveness of proposed method. The results stated that the proposed method achieved 95.23% accuracy, where the existing techniques namely deep neural network and traditional convolutional networks achieved 87.32% and 91.32% accuracy respectively. This validation results proved that the developed method nearly achieved 4-8% improvement in accuracy than existing techniques.

20

Authors: Reza Fuad Rachmadi, I Ketut Eddy Purnama, Supeno Mardi Susiki Nugroho, Yoyon Kusnendar Suprapto

Faces is a unique region in our body that can be used as a biometric identity. Furthermore, the face between two people that have a kinship relationship may share the same face features which can be used to decide whether two people have a kinship relationship or not. In this paper, we proposed a family-aware convolutional neural network (CNN) for the visual kinship verification problem. Our proposed classifier is constructed by paralleling the state-of-the-art face recognition model and attaching two additional networks, a family-aware network, and a kinship verification network. The family-aware network weights adjusted by learning features specific to the family using deep metric learning loss while the kinship verification network use softmax loss to learn the kinship verification problem. One of the advantages of our proposed classifier is that the output of the classifier is normalized and can be represented as the probability of two images being kin or non-kin. To preserve the face recognition features extraction ability in the state-of-the-art face recognition model, we freeze the weights of the convolutional layers in the classifier for the training process. In the testing process, the family-aware network is detached to construct the final classifier. Experiments on FIW (Families In the Wild) dataset show that our proposed classifier performs better comparing with classifiers that trained without a family-aware network and the ensemble version of the classifier is comparable with several state-of-the-art methods with an average accuracy of 68.84%.

42

Authors: Archana Kollu, Sucharita Vadlamudi

Energy management of the cloud datacentre is a challenging task, especially when the cloud server receives a number of the user’s request simultaneously. This requires an efficient method to optimally allocate the resources to the users. Resource allocation in cloud data centers need to be done in optimized manner for conserving energy keeping in view of Service Level Agreement (SLA). We propose, Eagle Strategy (ES) based Modified Particle Swarm Optimization (ES-MPSO) to minimize the energy consumption and SLA violation. The Eagle Strategy method is applied due to its efficient local optimization technique. The Cauchy Mutation method which schedules the task effectively and minimize energy consumption, is applied to the proposed ES-MPSO method for improving the convergence performance. The simulation result shows that the energy consumption of ES-MPSO is 42J and Particle Swarm Optimization (PSO) is 51J. The proposed method ES-MPSO achieves higher efficiency compared to the PSO method in terms of energy management and SLA.

67

Authors: Nageswar Rao Amgoth, Rajendra Naik Bhukya, Nirmala Devi Lavadya

In the field of Wireless Sensor Networks (WSNs), after the deployment of sensor nodes, transmission of data with an effective energy is becoming a critical issue due to the limited battery of sensor nodes. To solve this issue, most of the past developed approaches focused on the implementation of clustering in which the sensor nodes forward their data through Cluster Heads (CHs) to Base Station (BS). However, the main issue is to select an optimal CH or CHs such that every sensor node is ensured with at least one path to base station, directly, through CHs or through relay nodes. Towards this objective, we have proposed a new method called Multi-Objective Assisted Clustered Relay Node Placement (MOCRNP) to achieve improved energy efficiency along with an enhanced network lifespan. Firstly, to select the CH, the Multi-Objective Genetic Algorithm is introduced based on energy, distance and node density. Secondly, in the process of rely node placement, Multi-objective firefly algorithm is introduced and for this purpose, two constraints such as Coverage and Connectivity are considered. We have conducted several experimentations using several setups of the WSN and the performance is evaluated in terms of Network lifetime, average energy consumption, average residual energy, and throughput. On an average, the proposed approach has gained an improvement of 150 sec network lifetime, 104 Kbps Throughput when compared with state-of-art methods.

94

Authors: Rajeev Kumar, Mamdouh Alenezi, Md Tarique Jamal Ansari, Bineet Kumar Gupta, Alka Agrawal, Raees Ahmad Khan

Nowadays, most of the cyber-attacks are initiated by extremely malicious programs known as Malware. Malwares are very vigorous and can penetrate the security of information and communication systems. While there are different techniques available for malware analysis, it becomes challenging to select the most effective approach. In this context, the decision-making process may be an efficient means of empirically assessing the impact of different methods for securing the web applications. In this research study, we have used a methodology that includes the integration of Fuzzy AHP and Fuzzy TOPSIS technique for evaluating the impact of different malware analysis techniques in web application perspective. This study uses different versions of a university’s web application for evaluating the impact of several existing malware analysis techniques. The findings of the study show that the Reverse Engineering approach is the most efficient technique for analyzing complex malware. The outcome of this study would definitely aid the future researchers and developers in selecting the appropriate techniques for scanning the web application code and enhancing the security.

120

Authors: Neelampalli Jayanthi, Burra Vijaya Babu, Nandam Sambasiva Rao

The outlier detection technique is widely used in the data analysis for the clustering of data. Many techniques have been applied in the outlier detection to increase the efficiency of the data analysis. The Local Projection based Outlier Detection (LPOD) method effectively identifies neighbouring values of data, but this has the drawback of random selection of the cluster centre that affects the overall clustering performance of the system. In this study, the Adaptive Clustering by Fast Search and Find of Density Peak (ACFSFDP) is proposed to select the clustering centre and density peak. This ACFSFDP method is implemented with the min-max algorithm to find the number of categories that measured the local density and distance information. The density and distance are used to select the cluster centre, but density is not calculated on the existing distance based clustering techniques. The ACFSFDP method calculates cluster centre based on the density and distance during the clustering process, whereas the existing techniques randomly select the data centre. The results indicated that the ACFSFDP method is provided effective outlier detection compared with existing Clustering by Fast Search and Find of Density Peak (CFSFDP) methods. The ACFSFDP is tested on two datasets Pen-digits and waveform datasets. The experiment results proved that Area Under Curve (AUC) of the ACFSFDP is 99.08% on the Pen-Digit dataset, while the existing distance classifier method k-Nearest Neighbour has achieved 68.7% of AUC.

142

Authors: Pratik Kanani, Mamta Padole

Internet of Things (IoT) generates a myriad amount of data, which is sent over the Cloud computing infrastructure for analytics and Business Intelligence. This application scenario suffers network delays, transmission delays and delays in decision making. Due to these drawbacks, the Cloud-based IoT infrastructure is not suitable for time-critical health care applications. To overcome this problem, a smart way is introduced called “Fog Computing” - a LAN based processing approach which has multiple advantages. When IoT, Fog and Cloud Computing are combined, the resultant system’s performance is far better. Hence, the combination results in a very efficient Health Care system. Fog and Cloud Computing have their dimensions that not only support each other but also explore many new application domains. In this paper, the real-time ElectroCardioGram (ECG) based Health Care system is implemented in Cloud and Fog Computing. Different Quality of Service (QoS) parameters like memory consumption, transmission delays, computation delays, network delays, Carbon dioxide emission, data transferred and response time are measured, analyzed and improved to make the system more efficient. Based on the Fog computing characteristics and capabilities, the Raspberry Pi 3 B+ model is configured as a Health Care serving gateway by using different installation and configuration steps. Initially, the proposed system is tested for one patients ECG data analysis over cloud and Fog. In every set up all QoS parameters are measured and later the system is subjected to multiple ECG streams for varying numbers of patients to find the limitations of the Raspberry Pi node as a Fog Computing node. The obtained results show that for more number of ECG streams the Fog node is not able maintain QoS in decision making time. Every QoS parameter is explored in detail for decision-making time. In the end, the Fog computing based proposed system is concluded for its pros and cons and future aspects of the Fog node are discussed to make better systems.

168

Authors: Pyae Pyae Win Cho, Thi Thi Soe Nyunt

Differential Evolution (DE) has become an advanced, robust, and proficient alternative technique for clustering on account of their population-based stochastic and heuristic search manners. Balancing better the exploitation and exploration power of the DE algorithm is important because this ability influences the performance of the algorithm. Besides, keeping superior solutions for the initial population raises the probability of finding better solutions and the rate of convergence. In this paper, an enhanced DE algorithm is introduced for clustering to offer better cluster solutions with faster convergence. The proposed algorithm performs a modified mutation strategy to improve the DE’s search behavior and exploits Quasi-Opposition-based Learning (QBL) to choose fitter initial solutions. This mutation strategy that uses the best solution as a target solution and applies three differentials contributes to avoiding local optima trap and slow convergence. The QBL based initialization method also contributes to increasing the quality of the clustering results and convergence rate. The experimental analysis was conducted on seven real datasets from the UCI repository to evaluate the performance of the proposed clustering algorithm. The obtained results showed that the proposed algorithm achieves more compact clusters and stable solutions than the competing conventional DE variants. Moreover, the performance of the proposed algorithm was compared with the existing state of the art clustering techniques based on DE. The corresponding results also pointed out that the proposed algorithm is comparable to other DE based clustering approaches in terms of the value of the objective functions. Therefore, the proposed algorithm can be regarded as an efficient clustering tool.

189

Authors: Syahfrizal Tahcfulloh

Unlike the PMIMO radar, the transmit-receive (Tx-Rx) subarrays MIMO (TRSM) radar uses overlaping subarrays in Tx and Rx array so that it simultaneously combines the main advantage of the phased array radar (PA) i.e., high directional coherent gain, and the main advantage of the MIMO radar i.e., high waveform diversity gain. This paper has derived the radar performance formula such that Tx-Rx gain and SINR. The approach aims to overcome the beam shape loss, increase the transmit-receive gain, minimize the maximum peak sidelobe levels, narrow the half power beamwidth, increase directivity, and increase signal-to-noise-plus-interference ratio (SINR). This radar's performance is compared to the PMIMO radar in various methods such as equal subarrays, unequal subarrays, and optimum partitioning, the PA radar, and the MIMO radar. The numerical simulation and evaluation results show that the proposed radar has several advantages such as lowest the peak sidelobe level, narrow the half power beamwidth, and high directivity, so it is very robust against interference effects. When compared to the OPPM radar, as representative of the best-performing radars, this radar has an average performance improvement of MPSLL, directivity, and HPBW which are 21.5 dB, 2 dB, and 0.45 deg, respectively.

208

Authors: Manohar Pundikal, Mallikarjun Sayabanna Holi

The diabetic retinopathy is the leading cause of blindness worldwide, so early detection of diabetic retinopathy is necessary to reduce eye-related diseases. The accurate identification of microaneurysms is crucial for the detection of diabetic retinopathy, because it appears as the first sign of the disease. In this study, a new model is proposed to detect microaneurysms from the retinal images for early diagnosis of diabetic retinopathy. At first, the fundus images are collected from e-ophtha microaneurysms and DiaretDB1 datasets. Next, image pre-processing is accomplished using image normalization, low light image enhancement, gradient weighting and shade correction. The pre-processing methods significantly brighten the contrast of the fundus images for better visual quality and extract the hidden details of the dark conditions. In addition, Hessian based filter and Otsu threshold are used to extract the foreground objects like microaneurysms from the enhanced fundus images. At last, Grey Wolf Optimization (GWO) is used to predict the correctness of segmented microaneurysms candidates. The experimental results have revealed that the proposed model enhanced the microaneurysms detection up to 0.06-0.30 f-score value compared to the other existing models local convergence index features and local features with k-nearest neighbor. In addition, the proposed model has achieved 85.72% and 86.16% of accuracy respectively on e-ophtha microaneurysms and DiaretDB1 datasets.

229

Authors: Nagaraja Rao Pamula Pullaiah, Dorai Venkatasekhar, Padarthi Venkatramana, Balaraj Sudhakar

Breast cancer is one of the most occurring cancers in women due to the uncontrolled growth of abnormal cells in the lobules or milk ducts. The treatment for the breast cancer at an early stage is important using Magnetic Resonance Imaging (MRI) which effectively measures the size of the cancer and also checks tumors in the opposite breast. The deposition of calcium components on the breast tissue is known as micro-calcifications. The calcium salts deposited in the breast are involved with the cancer and were not diagnosed accurately due to the low effectiveness of existing imaging technique namely Haralick feature extraction technique. The MRI breast cancer diagnosis creates problems during classification of breast image and leads to misclassifications, such as unidentified calcium deposits in the existing K-Nearest Neighbour (KNN) classifier. The misclassification issues are overcome by an accurate classification and identification of calcium salts and checks whether deposited salt on breast tissue is involved with cancer or not. Initially, Contrast-Limited Adaptive Histogram Equalization (CLAHE) is used to remove the unwanted noise in the MRI and Morphological, Multilevel Otsu’s Thresholding and region growing techniques perform segmentation to mask unwanted breast tissues. The proposed Hybrid LOOP Haralick feature extraction technique is developed by combining the both Local Optimal Oriented Pattern (LOOP) and Haralick texture feature and the hybrid parameters are applied to the Stacked Auto Encoder based (SAE) to classify the breast MRI image as a Malignant or Benign. The performance of the proposed hybrid LOOP Haralick feature extraction shows significant accuracy improvement of 3.83% when compared to the Haralick feature extraction technique.

255

Authors: Ahmad Afiif Naufal, Chastine Fatichah, Nanik Suciati

This research developed a smart parking system through video data analysis using deep learning techniques that automatically determine the availability of vacant parking spaces. This system has two main stages. The first is the stage of marking the parking position on the image of a parking lot captured by the camera. This research proposes a Preprocessed Region-based Convolutional Neural Network (Mask R-CNN) to mark the parking position on the input image of a full parking lot. The preprocess that combining contrast enhancement using the Exposure Fusion framework, aims to overcome the problem of lighting variations in images taken in an open area. In the second stage, each parking position is examined whether the position is vacant or not using mAlexNet. A series of trials on images with varying light conditions indicate that the Preprocessed Mask R-CNN can improve marking the parking positions with an accuracy of Intersection over Union (IoU) reach 85.80%. The result of marking the parking position is then used in the trial of the availability of parking space on video data using mAlexNet, and achieving an accuracy of 73.73%.

279

Authors: Hanan A. R. Akkar, Sameem Abbas Salman

This paper proposes a new meta-heuristic swarm optimization algorithm called Cicada Swarm Optimization (CISO) algorithm, which mimics the behavior of bio-inspired swarm optimization methods. The CISO algorithm is tested with 23 benchmark functions and taken two problems engineering design, pressure vessel problem and himmelblau’s problem. The performance of CISO algorithm is compared with meta-heuristic well-known and recently proposed algorithms (Cockroach Swarm Optimization (CSO), Grasshopper Optimization algorithm (GOA) and Particle Swarm Optimization (PSO)). The obtained results showed that the proposed algorithm succeeded in improving the test functions and solved engineering design problems that could not be improved by other algorithms according to the chosen parameters and the limits of the research space, also showed that CISO has a faster convergence with the minimum number of iterations and also have an accurate calculation efficiency Satisfactory compared to other optimization algorithms.

307

Authors: Hoai Phong Nguyen, Dinh Tuyen Nguyen, Minh Thuyen Chau

In this paper, a novel approach to realize the carrier-based pulse width modulation (CBPWM) method for indirect matrix converter (IMC) is presented. The advantage of the proposed CBPWM method is that only one triangular carrier signal with constant gradient falling and rising edges is used to generate the PWM to the power switches in both the rectifier and the inverter stages of IMC. The analysis of the proposed CBPWM method is based on the space vector approach and the relationship between CBPWM and space vector pulse width modulation (SVPWM) methods is provided. In this paper, four CBPWM methods called: sinusoidal PWM, third harmonic injection PWM, symmetrical PWM and discontinuous PWM are presented. These different CBPWM schemes are established according to the distribution of zero vectors and the switching pattern of the SVPWM method. The comparison of output voltage performance with different CBPWM schemes are investigated. Compared to the conventional method the calculation time of the proposed method is reduced around 50%. Concretely, the calculation time of the SVPWM method is 45 μs, while the calculation time of the proposed method is 22 μs. The experiments have been carried out to show the effectiveness of the proposed method.

330

Authors: Edi Jaya Kusuma, Guruh Fajar Shidik, Ricardus Anggi Pramunendar

Classification is one of the data mining techniques which considered as supervised learning. Classification technique such as Backpropagation Neural Network (BPNN) has been utilized in several fields to increase human productivity. BPNN can give better results (more natural) compared with other statistical techniques. However, the learning process of BPNN could give an inefficient synapse weight of each hidden layer. This ineffective weight can affect the performance of the network. In this research, BPNN optimization using Nelder Mead to identifying the appearance of breast cancer is proposed. The datasets used are Breast Cancer Coimbra Dataset (BCCD), and Wisconsin Breast Cancer Dataset (WBCD). The testing result using accuracy and k-fold validation presents better performance compared with the original BPNN. Best average performance can be seen in the fifth fold of BCCD with 76.5217% of accuracy. Moreover, the highest average result of WBCD presented in the fourth fold with 91.1765% of average accuracy.

349

Authors: I Wayan Agus Surya Darma, Nanik Suciati, Daniel Siahaan

The preservation of Balinese carving data is a challenge in recognition of Balinese carving. Balinese carvings are a cultural heritage found in traditional buildings in Bali. The collection of Balinese carving images from public images can be a solution for preserving cultural heritage. However, the lousy quality of taking photographs, e.g., skewed shots, can affect the recognition results. Research on the Balinese carving recognition has existed but only recognizes a predetermined image. We proposed a Neural Style Geometric Transformation (NSGT) as a data augmentation technique for Balinese carvings recognition. NSGT is combining Neural Style Transfers and Geometric Transformations for a small dataset solution. This method provides variations in color, lighting, rotation, rescale, zoom, and the size of the training dataset, to improve recognition performance. We use MobileNet as a feature extractor because it has a small number of parameters, which makes it suitable to be applied on mobile devices. Eight scenarios were tested based on image styles and geometric transformations to get the best results. Based on the results, the proposed method can improve accuracy by up to 16.2%.

374

Authors: Vivaram Veera Raghavulu, Ande Prasad

Video analytics is widely used to automatically analyze the videos to extract the required information and detect the various events object identification and traffic analysis. The segmentation of the image is referring to extracting the required region from an image. The major objective of the segmentation process is to cluster the images without being affected by the noises. The detection of the moving objects is a challenging task in video analysis due to the dynamic background of the video. The major drawback of the existing Kernel Fuzzy C-Means (KFCM) clustering is initialization of random centroids which increases the execution time to identify the segmented portions. In this research, the Grey Wolf Optimization (GWO) algorithm used to initialize the centroids of required clusters in KFCM and Level Set (LS) Algorithm is used to segment the objects in video sequence. The proposed KFCM-GWO-LS is implemented for moving and static object detection in the videos obtained from SBM-RGBD dataset. For object detection, determining central clusters are important which is performed by using KFCM. GWO helps in finding the best centroids clusters by matching with KFCM. The centroids clusters are segmented by using LS algorithm which undergoes over segmentation problem that is overcome by GWO. As all the three techniques are dependent on one another hybrid of all these techniques obtains better results. The proposed KFCM-GWO-LS is evaluated in terms of Recall, Specificity, Precision and F-measure and the experimental results showed that the proposed method improved the system performance from 0.3 % to 14.35 % compared to existing methods Multi-Sensor Scheduler Algorithm and Statistical Inference Theory model.

393

Authors: Hossam A. Nabwey

This work reports how to utilize rough set theory and group method analysis for generating a set of rules to investigate heat and mass transfer of mixed convection stagnation point flow of a non-Newtonian nanofluid towards a vertical stretching surface. By utilizing group method analysis, the main partial differential equations which describe the flow are rehabilitated to nonlinear Ordinary Differential Equations (ODEs). Then, the resultant nonlinear ODEs are solved numerically by applying the implicit finite-difference scheme. The numerical values thus obtained are depicted in tabular form and the basic principles of rough sets are applied to get all reducts and finally a group of generalized rules are extracted to predict the value of local Nusselt number and local skin-friction coefficient. The outcomes demonstrate the novelty of the current work which can be summarized as hybridization of group method analysis and rough set theory to use in the field of fluid dynamics effectively. The resultant set of generalized classification rules which performed with basic logic functions can be considered as knowledge base with high accuracy and may be valuable in many engineering applications like power production, thermal extrusion systems and microelectronics.

419

Authors: Irya Wisnubhadra, Safiza Suhana Kamal Baharin, Nanna Suryana Herman

Business Intelligence (BI) technology with Extract, Transform, and Loading process, Data Warehouse, and OLAP have demonstrated the ability of information and knowledge generation for supporting decision making. In the last decade, the advancement of the Web 2.0 technology is improving the accessibility of web of data across the cloud. Linked Open Data, Linked Open Statistical Data, and Open Government Data is increasing massively, creating a more significant computer-recognizable data available for sharing. In agricultural production analytics, data resources with high availability and accessibility is a primary requirement. However, today’s data accessibility for production analytics is limited in the 2 or 3-stars open data format and rarely has attributes for spatiotemporal analytics. The new data warehouse concept has a new approach to combine the openness of data resources with mobility or spatiotemporal data in nature. This new approach could help the decision-makers to use external data to make a crucial decision more intuitive and flexible. This paper proposed the development of a spatiotemporal data warehouse with an integration process using service-oriented architecture and open data sources. The data sources are originating from the Village and Rural Area Information System (SIDeKa) that capture the agricultural production transaction in a daily manner. This paper also describes the way to spatiotemporal analytics for agricultural production using a new spatiotemporal data warehouse approach. The experiment results, by executing six relevant spatiotemporal query samples on DW with fact table contains 324096 tuples with temporal integer/float for each tuple, 4495 tuples of field dimension with geographic data as polygons, 80 tuples of village dimension, dozens of tuples of the district, subdistrict, province dimensions. The DW time dimension contains 3653 tuples representing a date for ten years, proved that this new approach has a convenient, simple model, and expressive performance for supporting executive to make decisions on agriculture production analytics based on spatiotemporal data. This research also underlines the prospects for scaling and nurturing the spatiotemporal data warehouse initiative.

442

Authors: Rudy Dikairono, Setiawardhana, Djoko Purwanto, Tri Arief Sardjono

The Convolutional Neural Network (CNN) is an object classification method that has been widely used in recent research. In this paper, we propose CNN for use in the self-localization of wheeled soccer robots on a soccer field. If the soccer field is divided into equally sized quadrants with imaginary vertical and horizontal lines intersecting in the middle of the field, then the soccer field has an identical shape for each quadrant. Every quadrant is a reflection of the other quadrants. Superficially similar images appearing in different positions may result in positioning mistakes. This paper proposes a solution to this problem by using a visual modelling of the gyrocompass line mark and omni-vision image for the CNN-based self-localization system. A gyrocompass is used to obtain the angle of the robot on the soccer field. A 360° omni-vision camera is used to capture images that cover all parts of the soccer field wherever the robot is located. The angle of the robot is added to the omni-vision image using the visual modelling method. The implementation of self-localization without visual modelling gives accuracy rates of 0.3262, and this result is increased to 0.6827 with the proposed methods. The experiment was carried out in the robotics laboratory of the Institut Teknologi Sepuluh Nopember (ITS) with the ITS Robot with Intelligent System (IRIS) robot.

460

Authors: Ahmed Rafid Hashim, Hazim G. Daway, Hana H. kareem

Haze causes the degradation of image quality. Thus, the quality of the haze must be estimated. In this paper, we introduce a new method for measuring the quality of haze images using a no-reference scale depending on color saturation. We calculate the probability for a saturation component. This work also includes a subjective study for measuring image quality using human perception. The proposed method is compared with other methods as, entropy, Naturalness Image Quality Evaluator (NIQE), Haze Distribution Map based Haze Assessment (HDMHA), and no reference image quality assessment by using Transmission Component Estimation (TCE). This done by calculating the correlation coefficient between non-reference measures and subjective measure, the results show that the proposed method has a high correlation coefficient values for Pearson correlation coefficient (0.8923), Kendall (0.7170), and Spearman correlation coefficient (0.8960). The image database used in this work consists of 70 hazy images captured by using a special device, design to capture haze image. The experiment on haze database is consistent with the subjective experiment.

487

Authors: Hanan A. R. Akkar, Suhad Qasim G. Haddad

The most significant challenge facing the researcher in the field of robotics is to control the robot manipulator with appropriate overall performance. This paper focuses mainly on the novel Intelligent Particle Swarm Optimization (PSO) algorithm that was used for optimizing and tuning the gain of conventional Proportional Integral Derivative (PID), and improve the parameters of dynamic design in Sliding Mode Control (SMC), which is considered a strong nonlinear controller for controlling highly nonlinear systems, particularly for multi-degree serial link robot manipulator. Additional modified Integral Sliding Mode Controller (ISMC) was implemented to the design of dynamic system with high control theory of sliding mode controller. Intelligent Particle Swarm Optimization (PSO) algorithm was introduced for developing the nonlinear controller. The algorithm demonstrates superior performance in determining the appropriate gains and parameters value in harmony with robot scheme dynamic layout in order to achieve suitable and stable nonlinear controller, besides reduce the chattering phenomenon. PUMA robot manipulator that was used as study case in this work, shows perfect result in step response, with acceptable steady state, and overshoot, besides, eliminating the disadvantage of chattering in conventional SMC. Matlab / Simulink presents to increase the speed of matrix calculation in forward, inverse kinematics and dynamic model of manipulator. Comparison was made between the proposed method with existing methods. Result shows that integral sliding mode with PSO (ISMC/PSO) gave best result for stable step response, minimum mean square error with best objective function, and stable torque.

512

Authors: Mohamed Taha, Mohamed Ibrahim, Hala H. Zayed

Vein detection is an important issue for the medical field. There are some commercial devices for detecting veins using infrared radiation. However, most of these commercial solutions are cost-prohibitive. Recently, veins detection has attracted much attention from research teams. The main focus is on developing real-time systems with low-cost hardware. Systems developed to reduce costs suffer from low frame rates. This, in turn, makes these systems not suitable for real-world applications. On the other hand, systems that use powerful processors to produce high frame rates suffer from high costs and a lack of mobility. In this paper, a real-time vein mapping prototype using augmented reality is proposed. The proposed prototype provides a compromised solution to produce high frame rates with a low-cost system. It consists of a USB camera attached to an Android smartphone used for real-time detection. Infrared radiation is employed to differentiate the veins using 20 Infrared Light Emitting Diodes (LEDs). The captured frames are processed to enhance vein detection using light computational algorithms to improve real-time processing and increase frame rate. Finally, the enhanced view of veins appears on the smartphone screen. Portability and economic cost are taken into consideration while developing the proposed prototype. The proposed prototype is tested with people of different ages and gender, as well as using mobile devices of different specifications. The results show a high vein detection rate and a high frame rate compared to other existing systems.

533

Authors: Nuha H. Abdulghafoor, Hadeel N. Abdullah

Multi-object detection and tracking systems represent one of the basic and important tasks of surveillance and video traffic systems. Recently. The proposed tracking algorithms focused on the detection mechanism. It showed significant improvement in performance in the field of computer vision. Though. It faced many challenges and problems, such as many blockages and segmentation of paths, in addition to the increasing number of identification keys and false-positive paths. In this work, an algorithm was proposed that integrates information on appearance and visibility features to improve the tracker's performance. It enables us to track multiple objects throughout the video and for a longer period of clogging and buffer a number of ID switches. An effective and accurate data set, tools, and metrics were also used to measure the efficiency of the proposed algorithm. The experimental results show the great improvement in the performance of the tracker, with high accuracy of more than 65%, which achieves competitive performance with the existing algorithms.

560

Authors: Ike Fibriani, Widjonarko, Aris Prasetyo, Angga Mardro Raharjo, Dasapta Erwin Irawan

The COVID-19 pandemic has become the focus of world problems that need to be resolved. This is because the rate of spread is speedy and able to take down the world's health system. Therefore, many researchers are focusing their research on solving this problem by doing an initial screening on the X-Ray image of the subject's lungs. One of them is by using Deep Learning. Several articles that talk about implemented Deep Learning for classifying X-Ray images have been published. But most of them are comparing different architecture CNN (Convolutional Neural Network). In this study, the authors try to create a multi-classifier Deep Learning system that consists of nine different CNN architectures and combined with three different Majority Vote techniques. The target of this research is to maximize the performance of classification and to minimize errors because the final decision is a compilation of decisions contained in each CNN architecture. Several models of CNN are tested in this study, both the model which used Majority Vote and Conventional CNN. The results show that the proposed model achieves an accuracy value average F1-Score 0.992 and Accuracy 0.993, according to 5 K-Fold test. The best model is CNN, which used Soft Majority Vote.

1

Authors: Vamsidhar Talasila, Chitturi Prasad, Guttikonda Trinesh Sagar Reddy, Allada Aparna

Agriculture planning plays a significant role in economic growth and the food security of agro-based country. Crop yield prediction and selection of crops are the most challenging tasks in agricultural domain and it depends on different parameters such as production rate, market price and government policies. Among the two primary tasks, the crop yield prediction is one of the most demanding tasks for every nation. Due to uncertain climatic changes, farmers are struggling to attain a satisfactory amount of yield from the crops. Many researchers have studied on the prediction of weather, prediction of yield rate of crop, crop classification and soil classification for agriculture planning using statistical methods or machine learning techniques. This study focuses on the prediction of major crops in Andhra Pradesh region and presents an enhanced algorithm known as Deep Convolutional Regression Network (DCRN), which is trained and tested on agricultural data collected from farmers. The experimental results showed that the DCRN method achieved nearly 97% prediction accuracy when compared with existing methods like Decision Tree (DT), Self-Organizing Map (SOM).

19

Authors: Prakash Annamalai

Face recognition is an emerging research area in biometric identification systems, which attracted many researchers in pattern recognition and computer vision. Since, the human facial images are high dimensional in nature, so a new optimization algorithm was proposed in this paper to reduce the dimension of facial images. At first, the input facial images were collected from ORL, YALE, and FAce Semantic SEGmentation (FASSEG) databases. Then, feature extraction was then performed by using Local Ternary Pattern (LTP) and Binary Robust Invariant Scalable Key points (BRISK) to extract the features from the face images. In addition, an enhanced fire-fly optimizer was used to reject the irrelevant features or to reduce the dimension of the extracted features. In enhanced fire-fly optimization algorithm, Chi square distance measure was used to find the distance between the fire-flies and also it uses only a limited number of feature values for representing the data that effectively reduces the “curse of dimensionality” concern. Finally, Deep Belief Network (DBN) was used to classify the individual’s facial images. The experimental outcome showed that the proposed work improved recognition accuracy up to 7-20% compared to the existing work.

38

Authors: Petch Nantivatana, Pattarapong Phasukkit, Supan Tungjitkusolmun, Keerati Chayakulkheeree

This paper presents the optimal antenna slot position and sizing (OASPS), for microwave (MW) hepatocellular carcinoma (HCC) treatment, using multi-objective fuzzy decision making (MOFDM). In the optimal design problem formulation, the multi-objective for, (1) achievement for a near-spherical zone for ablation defined by axial ratio (AR), (2) the minimum reflection coefficient (S11), and (3) maximum volume of destroying (VD), are desired. The finite element method (FEM) is used for MW distribution simulation for coordination analysis with the proposed MOFDM based OASPS. The investigation on the design results shown that the temperature distribution of the proposed MOFDM for OASPS is in the more controllable shape comparing to the existing design, due to the sharp beam of temperature distribution to the target at the front side of slot with the less temperature distribution at the rare side of slot. The results also show that the proposed MOFDM for OASPS for OASPS design is easier to manage with the less effect to the rare side of the slot. Moreover, the proposed MOFDM for OASPS resulted in the temperature distribution shape closer to round shape than the existing design. The MOFDM for OASPS is tested on designing of the antenna build up from the coaxial cable. The coaxial cable used is Semi-rigid 141 (RG402 M17/130-RG402 Copper Jacket) including Inner conductor, Dielectric, and Outer conductor. In the simulation, the slot distance from conductor end (Lts), representing the slot position, is varied as 2.3mm, 3.3mm, 4.3mm, 5.3mm, 6.3mm, 7.3mm, and 8.3mm. Meanwhile, the slot size (Wd), is varied as 1mm, 2mm, 3mm, 4mm, 5mm, and 6mm. The input power (Pi) used is 50 W with the duration of 300 seconds. The comparison of obtained by different Lts and Wd, is investigated. The simulation results show that the proposed MOFDM based OASPS can efficiently and effectively provide the near-spherical zone of ablation result with simultaneously trade-off between S11 minimization and VD maximization.

63

Authors: Suhaila N. Mohammed, Fatin Sadiq Alkinani, Yasmin A. Hassan

COVID-19 is a vital zoonotic illness caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). COVID-19 is a very wide-spread among humans thus the early detection and curing of the disease offers a high opportunity of survival for patients. Computed Tomography (CT) plays an important role in the diagnosis of COVID-19. As chest radiography can give an indicator of coronavirus. Though, an automated Computer Aided Diagnostic (CAD) system for COVID-19 based on chest X-Ray image analysis is presented in this article. It is designed for COVID-19 recognition from other MERS, SARS, and ARDS viral pneumonia. The optimal threshold value for the segmentation of a chest image is deduced by exploiting Li s' method and particle swarm intelligence. Laws' masks are then applied to the segmented chest image for secondary characteristics highlighting. After that, nine different vectors of attributes are extracted from the Grey Level Co-occurrence Matrix (GLCM) representation of each Law's mask result. Support vector machine ensemble models are then built based on the extracted feature vectors. Finally, a weighted voting method is utilized to combine the decisions of ensemble classifiers. Experimental findings show an accuracy of 98.04 %. It indicates that the suggested CAD scheme can be a promising supplementary COVID-19 diagnostic tool for clinical doctors.

85

Authors: Fatna Elmendili, Younès El Bouzekri El Idrissi

The rapidly growing online social networking sites have been infiltrated by a large amount of spam. Spammers are a particular kind of ill-intentioned users who degrade the quality of OSNs information through misusing all possible services provided by OSNs. Social spammers spread many intensive posts/tweets to lure legitimate users to malicious or commercial sites containing malware downloads, phishing, and drug sales. Given the fact that Twitter is not immune towards the social spam problem, different researchers have designed various detection methods, which inspect individual tweets or accounts for the existence of spam contents. Today, social networks are exposed to various threats that exploit their vulnerability. However, although of the high detection rates of the account-based spam detection methods, these methods are not suitable for filtering tweets in the real-time detection because of the need for information from Twitter’s servers. At tweet spam detection level, many light features have been proposed for real-time filtering; however, the existing classification models separately classify a tweet without considering the state of previous handled tweets associated with a topic. First, they propose the identification of spam tweet by the security approach based on social honeypots and then they propose a method based on an algorithm "content filtering" in order to detect those that are similar to spam tweet detected by the approach of honeypots. Our approach has greatly improved the quality of abstraction in terms of performance and design. The algorithm is also fast and simple to implement. Experimental results show the stability and accuracy (over 99%), F-measure 98% of our approach.

111

Authors: Murugesan Suman, Vadugapalayam Ponnuvel Sakthivel, Palanigounder Duraisamy Sathya

In this paper, a new meta-heuristic algorithm, called Squirrel Search Optimizer (SSO) is applied to solve various types of economic load dispatch (ELD) problems. The SSO mimics the foraging behavior of squirrels which is based on the dynamic jumping and gliding strategies. In SSO algorithm, predator presence behavior and a seasonal monitoring condition are employed to increase the search ability of the algorithm, and to balance the exploitation and exploration. The key idea of the suggested approach is to determine the optimal generation scheduling by minimizing total generation cost of units while satisfying various constraints such as power balance constraint, prohibited operating zones (POZ), ramp rate constraints and operating limits of generators. The different generating unit’s characteristics, quadratic fuel cost function, non-convex fuel cost function and multiple fuel options (MFO) are also considered. The feasibility of the proposed algorithm is tested on four different power test systems having different sizes and intricacies. The results are examined in terms of both solution quality and the computational efficiency, and compared with the other approaches in the literature. The comparisons prove the robustness and effectiveness of the proposed algorithm and show that it could be used as a consistent optimizer for solving various ELD problems.

133

Authors: Rafika El idrissi, Ahmed Abbou, Mohcine Mokhlis

The temperature and radiation changes lead to obvious fluctuations in photovoltaic panels ' output power. To optimally exploit the PV system, maximum power point tracking (MPPT) is needed. Various offline and online methods for monitoring the MPP have been implemented. In this work, a maximum power point tracking (MPPT) technique was created which is primarily based on backstepping integral sliding mode controller (BISMC) design. The control scheme incorporates two parts: the first is based on an artificial neural network that provides the reference voltage, that supplies the maximum power regardless the environmental factors, which is given to the proposed BISMC control that is responsible on regulation of the duty cycle of the DC-DC boost converter's PWM applied switch (Mosfet). This strategy offers very low tracking error, and chattering improvement in tracking the MPP of a PV system when the environmental disturbances occur. The added integral action is very important in the control closed loop because it removes the steady state error. The method is compared with the ANN-sliding mode, PSO-backstepping and P&O-backstepping controllers in order to demonstrate its efficiency. In this later, the P&O (perturb & observe) and the PSO (particle swarm optimization) algorithms serve to generate reference voltage, that corresponds to the MPP, while the backstepping controller tracks this reference voltage. To reduce the PV system cost, a high-gain observer is designed, it requires only the PV output voltage sensor which enables the unmeasured PV system state variables on-line. This allows the minimization of the number of sensors in practical case because they have many disadvantages. The use of these sensors can lead to bulky system. Moreover, they are expensive. The simulation study is carried out under Matlab/Simulink. The results of the suggested MPPT reveal outstanding dynamic response under rapid changes of irradiation and temperature. The suggested method is more accurate and has fast convergence time about 2.2 ms and 1ms depends on the meteorological condition changes and 98% of efficiency to track the maximum power point.

159

Authors: Chaiyut Sumpavakup, Kritsada Mongkoldee, Tosaphol Ratniyomchai, Thanatchai Kulworawanichpong

This article proposed a computer-based simulation approach to estimate touch and step voltages due to a lightning strike at a mass rapid transit railway station. A purely resistive circuit model of a station lightning protection, and earthing & bonding system is formed in MATLAB/Simulink with Yaek Nonthaburi 1 passenger station of MRT purple line in Bangkok, Thailand as a case study. The simulation investigated the consequence of a lightning strike at four different locations on the roof of the station and examined whether or not the lightning protection system met the standard requirements. The results showed the touch voltages, step voltages and body currents in a steady state together with the impact on the human body standing in the station. The simulation can guarantee the effectiveness of the proposed model, and also proved that the lightning protection system of the station is able to conform to the standard and there is no harm for the passenger in the station.

181

Authors: Mamdouh Alenezi, Mohammad Nadeem, Alka Agrawal, Rajeev Kumar, Raees Ahmad Khan

Security and design tactics estimation of web application for ensuring the security, efficiency and design tactics of web applications is necessary. A survey conducted by the security research team, Micro Focus, of the USA reveals that 80% of the vulnerability defects occur due to the coding defect, validation causes 60% of the errors, 70% errors are due to encapsulation and path traversal. Such statistics call for a more efficacious design to enhance software security. The primary research goal of this study is to compute or evaluate the security threats of the software and web applications from the perspective of design tactics. Towards this intent, we have employed the methodology of Fuzzy Analytic Hierarchy Process (F-AHP) to evaluate the security factors or obtain the weight of different factors. The different design tactics of web application have also been selected according to the factors that affect the security. In this article, researchers have used a crossbreed technique of fuzzy based Multi Criteria Decision Method (MCDM) technique, i.e., F-AHP and Fuzzy Technique for Order of Preferences by Similarity to Ideal Solutions (F-TOPSIS) Technique. The results of the assessment of security will be helpful for developers or experts in designing the security tactics of software or web applications. We have also compared the results of classical and Fuzzy approach to determine the weight of alternatives or attributes and rank of the factors. This process is an effective and conclusive methodology for the developers working for more enhanced secure design tactics of software and web application design.

211

Authors: Andreas Andreas, Mochamad Hariadi, Mauridhi H. Purnomo, Kunio Kondo

Lighting is one of the very important things in cinematography. Lighting in cinematography is used not only to illuminate the objects, but it has many important roles such as to make the object looks sharper, has tone, depth and separation, and so on. There are some parameters that affect the lighting result, such as light source type that is used, its colour, intensity, position, and direction. Unfortunately, lighting has so many permutations and variations and there is more than one right way to light the scene. In this research, a designer designs some scenes that will be used as inputs. In this study, the authors found that lighting set is strongly influenced by five important things, which are: the situation of the scene, Point-of-View, the intensity of the attached light sources, camera position, and emotion of the characters, that we called as the SPICE system that is the abbreviation from situation, PoV, intensity, camera, and emotion. some additional light sources will be added into the scene automatically according to the scenario and director’s behaviour using the SPICE system that we propose. The SPICE framework has two fuzzy inference systems in calculating the intensity of light that hits an object and some additional light sources that are added to the scene automatically to make it looks much better. This research will make a calculation to gain the best scenes as an output. There are 20 scenes used in this research. As the final result, designers assess 80% outputs have been as expected.

238

Authors: Hazim G. Daway, Esraa G. Daway, Hana H. kareem

Colour image enhancement plays an important role in image processing, computer vision and pattern recognition. Fuzzy logic techniques one of the methods used for digital image enhancement. In this study, Fuzzy Logic Based-on Sigmoid Membership Function (FLBSMF) was developed to improve lightness and contrast in coloured images. The FLBSMF algorithm was applied to the lightness component by using only the YIQ colour space, and the colour compounds were unchanged. The suggested algorithm was compared with other algorithms, such as fuzzy logic enhancement using membership function modification based on square operation, fuzzy logic based on histogram, histogram equalisation and multiscale retinex with colour restoration by using different criteria. Form the result the FLBSMF succeeded in enhancing colour images and it had good average values for entropy (7.44), mean square error in saturation (2.2E-08) and hue (8.11E-07), nature image quality (2.97) and lightness order error (0.90).

257

Authors: Suhaila N. Mohammed, Alia K. Abdul Hassan

Speech emotion recognition finds many applications in the daily life like conversational agents, human robot interaction, call centres etc. However; the task of emotion recognition from speech signal is not trivial due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the signal in an accurate manner. Image processing techniques are exploited in this paper to solve speech emotion recognition problem. After converting the signal into 2D spectrogram image representation, four forms of Extended Local Binary Pattern (ELBP) are generated to serve as a source for feature extraction stage. The histograms of multiple blocks from ELBP variants are computed and fed to Deep Belief Network (DBN) for classification purpose. Different tests were performed using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the achieved results showed that when using combined vectors of MELBP, the system gives the best accuracy which is 72.14%. The achieved result outperforms state-of-the-art results on the same database.

277

Authors: Smita Chaudhari, Gandharba Swain, Pragnyaban Mishra

In most practical cloud computing applications such as e-voting, auctions, health, and financial applications or cloud services in common, to prove the exactness of outsourced data is one of the major needs today. Most of the time, third party auditing is employed for this task. This auditing work is controlled by assigning the secret inputs to an entity trusted third party, or worker, who is liable for performing computations and hand over the result of the computation to the cloud users or clients. To verify the integrity of computations using traditional cryptographic techniques, the time required to generate and validate the proof is a major computation issue. This paper proposes an improved public auditing technique for multi-party computation to check the integrity of outsourced data using a cryptographic solution. Many researchers have given auditing protocols that generate and verify proof using a cryptographic solution. Most of these scheme uses Non-Interactive Zero-Knowledge Proof (NIZK) which are basically built on bilinear map technology. The verification time using these existing technique is computationally expensive which affect the performance of the auditing system. We propose an efficient protocol that verifies the result correctness using modern cryptographic technique Indistinguishability Obfuscation. The proposed system works in two phases, (i) auction and (ii) audit. During the auction phase, multiple clients share their encrypted bid value to the worker. The worker generates auction result and proof using Pedersen Commitment Scheme. The audit phase starts only after the completion of the auction phase which results in reduced verification time. During the Audit phase, clients can verify the integrity of results using NIZK with the IO technique. The results for reduced verification time in auction system have been presented. It is found that the performance of the proposed system has improved compared to the pertinent NIZK Proof technique. In our setting, we assumed that a worker is one of the trusted entity. By this notion, our protocol also guarantees privacy to the clients during the audit phase.

295

Authors: Yashesh Joshi, Hiren Mewada

Object detection and tracking is one of the active areas of studies because of variation in object location, pose, illumination, scale and occlusion. Singular object tracking (SOT) is an important task in human-computer interaction, anonymous tracking in the robotic application and security analysis. This paper proposes single visual tracking in a complex environment. We propose SOT using patch sparse formulation that is robust to pose, scale, log-term occlusion and low- and high- luminance conditions. A sparse dictionary using the patches from the previous frames is used to track object in the current frame under such a clutter environment. Contrast modeling, patch resizing and likelihood measurement in the proposed sparse framework allows the selection of key patches to tackle long-term occlusion and extreme illumination variation. We evaluate the proposed method on different challenging datasets involving Illumination variation, heavy occlusion and pose variation. The result analysis indicates that the proposed patch wise joint sparse occlusion and contrast (PJS-OC) tracking method outperforms other methods with the maximum precision rate is 99%. Also for the sampling value of 0.5, the proposed tracker shows excellent results compared with the other different sampling values. The proposed sparse formulation provides excellent performance in various quantitative parameters, achieving better precision and speed. The comparative analysis with state-of-art methods using the benchmark database shows the outstanding performance of the proposed method.

317

Authors: Yohanes Setiawan, Chastine Fatichah, Riyanarto Sarno

Globus Pallidus is an object in Magnetic Resonance Imaging (MRI) with severe intensity inhomogeneity and noises, such as small, low contrast, and weak boundary. Existing active contour methods fall into false boundaries while segmenting objects, which is smaller than the background like Globus Pallidus. This paper proposes a new local gaussian variational level set (NGVLS) for Globus Pallidus segmentation. We developed an energy term to create a smooth curve and prevent evolving curve into the false boundary. In the experiment, we compare NGVLS qualitatively and quantitively with existing methods such as Chan-Vese (CV), Region Scalable Fitting (RSF), Improved Region Scalable Fitting (Im-RSF), Local Pre-Fitting (LPF), and Local Gaussian Distribution Fitting (LGDF). We use Dice Similarity Coefficent (DSC) and Misclassification Error (ME) to measure the accuracy and error of the segmentation, respectively. The experiment is using 40 MRI datasets from Rumah Sakit National Hospital Surabaya, Indonesia. Qualitatively, the results show that NGVLS shows the best segmentation compared to the existing methods. Quantitatively, NGVLS achieves the highest average, minimum, and maximum value from DSC in 0.8291, 0.7119, and 0.9050, respectively. Also, NGVLS achieves the lowest average, minimum, and maximum value from ME in 0.0050, 0.0023, and 0.0155, respectively.

337

Authors: Abdelmoujoud Assabir, Jamal Elmhamdi, Ahmed Hammouch

Indoor communication system has known an exponentially growth due to high demand of wireless data service ratio. As an alternative way, the visible light zone of the electromagnetic spectrum is explored to fulfil the future wireless data traffic requirements. Among the main issues that can be handled in the visible light communication (VLC) system are the resources allocation and interferences management. In this paper, we aim to enhance the throughput of the edge user based on the joint optimization of the power and bandwidth in the VLC downlink indoor cellular network. We propose a new geometry design that alleviates the inter-cell interference (ICI) of the edge users which occurs especially in the overlapping areas between the adjacent cells. An orthogonal frequency division multiple access (OFDMA) based on the direct current optical (DCO) is regarded as the channel access protocol for supporting the multiusers at the same time. At the edge of the coverage area, we evaluate the performance of the proposed method in terms of the achievable throughput and signal-to-interference-plus-noise ratio (SINR).Furthermore, based on the least square (LS), the joint optimization of the power and bandwidth result to a parameter ρ_0 which varies between 0 and 1. The results show that the new optimization with ρ_0=0.9 depict better improvements in term of throughput 〖R(ρ_0)〗_(D,δ)=65Mbps at r_3,1=3m.We further notice that the signal-noise-ratio (SNR) is increased with ρ_0=0.9 at the expense of the error probability falling (P_z≅〖10〗^(-4)).

368

Authors: Khairiyyah Nur Aisyah, Chastine Fatichah, Riyanarto Sarno

Most of neuroimaging applications tend to still rely on expert knowledge in determining anatomies of the brain. For example in Parkinson’s disease surgery, detection of the anterior commissure (AC) and posterior commissure (PC) are still done manually by doctors. Previously, various methods have been developed related to the automatic detection of AC and PC. However, the majority of previously methods have several drawbacks, such as only compatible on T1-W or T2-W, only compatible for data with the same matrix size, and requires a time-consuming training process. This study proposes a new strategy by combining a multilevel thresholding and morphological relationships approach for automatic detection of AC and PC. The process divided into 4 main stages: preprocessing, multilevel thresholding, segmentation, and detection of AC and PC. The segmentation is performed on several anatomies of the brain including corpus callosum, fornix, and colliculus. From the experiment, it can be concluded that the use of multilevel thresholding and morphological relationship was successfully detecting AC and PC with the mean error were 1.02 mm and 1.06 mm, respectively. The proposed method can perform an automatic detection of AC and PC with simply algorithm, does not require a large of diverse data sets for the training process, without training process that takes up time, and reliable on the diversity of MRI since it is compatible for T1-W and T2-W with various matrix sizes of 256 x 256 and 512 x 512 pixels which cannot be handled by previous researches.

392

Authors: Esther Irawati Setiawan, Ferry Ferry, Joan Santoso, Surya Sumpeno, Kimiya Fujisawa, Mauridhi Hery Purnomo

The user’s feedback on healthcare services is usually based on ratings from post-service questionnaires. However, in order to get a clear view of the user's perspective, online text reviews need to be analyzed. We combined targeted and aspect-based sentiment analysis by multi-level attention to get a specific user sentiment on a target of an aspect. The multi-level attention consists of Target-level and Sentence-level attention. Our proposed framework is based on Bidirectional Gated Recurrent Unit. Bi-GRU is commonly known to have comparable results compared to LSTM while having lesser computational complexity. We also utilized Bidirectional LSTM based Character-Enhanced Token-Embedding to handle out of vocabulary words and misspelling to avoid error in detecting sentiment. We created a dataset of online healthcare reviews from 2018-2020, targeting the name of the hospital or department, with ten aspects: cleanliness, cost, doctor, food, nurse, parking, receptionist and billing, safety, test and examination, and waiting time. To improve the results of our proposed method, we calculated polarity weight to handle imbalanced aspects in the dataset. We classified these reviews into three polarities, which are positive, negative, and neutral. Based on our experiments, we achieved the best F1-Score of 88%.

416

Authors: Khamla NonAlinsavath, Lukito Edi Nugroho, Widyawan, Kazuhiko Hamamoto, Somphone Kanthavong

The growing research interest in the localization tracking system has prompted development alongside availability positioning techniques to apply to the real environment and current situation. An integrated system that can support navigation and tracking in both indoor and outdoor conditions has been equipped with location information to improve the requirement and fulfil the demand of a seamless localization system based on existing technologies. This paper proposes a system called an integrated location-aware computing system using received signal strength indicator to support the direction and location information that works together with a smart devices and integrates an indoor-outdoor environment seamlessly by combining both conditions to be the system without any obstacle. Furthermore, the technologies of indoor and outdoor will be integrated into the users. Then, they move from different conditions; the system can provide as an indicator for the complication that arises between indoor and outdoor locations, as well as location-based service technology itself. The transition between indoor and outdoor is less than 5 seconds switching at the overlapped area, the certainty value of the performance when moving from outdoor to indoor is 98.4% accuracy, from indoor moved to outdoor is 97.7% accuracy at the first floor. Moreover, having this seamless location system makes a lot of benefits to assist the users in discovering interesting things from the system, enable the seamless in real-time operation based on Android platform and providing direction maps and multi-pathways from current locations to destinations, the combination between different environments is generated with higher performance and smoothly works when users have many alternatives to choose the available signals from both indoor and outdoor technologies, transmission computation of switching from indoor to outdoor and vice versa has been decreased time consumption with excellent reliability and continuity service by proposed system.

441

Authors: Gede Angga Pradipta, Retantyo Wardoyo, Aina Musdholifah, I Nyoman Hariyasa Sanjaya

The umbilical cord is one of the important organs on the growth and development of the fetus in the womb. Umbilical cords are associated with an adverse perinatal outcome such as intrauterine deaths, preterm delivery, repetitive intrapartum fetal heart deceleration, operative delivery for fetal distress, meconium staining, and chromosomal abnormalities. Initial screening stages of the fetal umbilical cord are carried out by analyzing the coiling pattern of two umbilical arteries. In this study, we propose a relevant feature extraction for classifying this organ based on texture and morphological approach. However, this study is facing an imbalanced class problem, which leads to the inability of the traditional classifier to predict data in the minority class. To deal with the emerging issues, this study proposed a model by optimizing data and algorithmic levels using a combination SMOTE method and Multiclassifier Voting. At the data level, the SMOTE method is used to generate new synthetic data and to balance the skewed data distributions directly. Subsequently, the classification uses a multiclassifier method that combines several traditional classifier methods in making final decisions based on voting schemes. The first experiment was conducted on imbalanced and small size data with a total of 62 umbilical cord images from 3 classes namely hypercoiling, hypocoiling, and normalcoiling. The results showed the multiclassifier voting method was able to achieve the best results with an accuracy of 73%, average recall of 72%, and ROC of 48% compared to other classifier methods such as SVM, Random Forest, KNN, Naïve Bayes, and Decision Tree (C.45). However, all classifiers failed to predict the normocoiling class because of the limited amount of normocloing data in the trainning phase. Then the second experiment was carried out by adding synthetic data using the SMOTE method with the total data increasing to 111 images spread evenly in each class. The results show a combination of multiclassifier voting and SMOTE methods ultimately leading and produced higher performance than other classifiers, which yielded and accuracy of 81.4%, average recall of 80%, average precision of 81.5%, and ROC of 89.1%.

463

Authors: Abdullah Baz, Hosam Alhakami, Ebtihal Alshareef

The whole world is reeling under the brunt of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2 virus or COVID-19 disease) at present. Ever since the first case of this highly contagious virus was detected in Jinyintan Hospital in Wuhan, in December 2019, the disease has spread rapidly in Saudi Arabia and entire world. The current situation is extremely critical and as per the data released by WHO (the World Health Organisation), till 22 May 2020, there were 5,265,750 confirmed cases in the world. In Saudi Arabia alone there were 67,719 COVID-19 patients till 22 May, 2020 and the numbers continue to rise as more people get afflicted by this virus. Generating appropriate, knowledgeable and consistent estimates for the pandemic COVID-19 disease is an enormous challenge. Tackling this virus has become the key objective of both the government as well as the entire medical and scientific community across the world. Responding to the compelling contingency of COVID-19 pandemic, this research pursuit intends to develop a computational model that can be used to gauge the spread of Coronavirus and predict the most vulnerable regions that are likely to be affected by it. Furthermore, the framework of the model also analyzes the impact of Coronavirus on various societal factors. Once identified, these factors can be prioritized so as to device accurate and efficient countermeasures. For developing the expected model, the study has adopted the Fuzzy-AHP technique of computational modeling. Moreover, a decision making technique and severity analysis technique along with the prioritization Fuzzy-AHP approach has been employed to validate the efficacy of the suggested model. Through rigorous study of different models of COVID-19, the proposed framework provides the best model to use according to the social factors prevalent in a country. After evaluation through fuzzy AHP, the priority of proposed model is found to be the highest amongst the seven other models used for comparison. Though the present study is aimed at adding to the government’s initiatives to counter the current health emergency in the Kingdom of Saudi Arabia, the proposed computational model can be an effective aid for the specialists across the world working on containment strategies to minimise the spread of Coronavirus.

490

Authors: Aola Yousfi, Moulay Hafid El Yazidi, Ahmed Zellou

Schema matching is critical for applications that manipulate data across heterogeneous, autonomous and scattered data sources. We pick the schema matching approach based on the total number of data sources we wish to integrate: holistic matching approaches are ideally used for a big to a huge total number of data sources, while pairwise matching approaches are ideally used for a small to a medium total number of data sources. Nonetheless, the state of the art matching approaches obtain a very moderate (sometimes poor) matching accuracy. Furthermore, the state of the art holistic schema matching approaches proceed in a series of two-way matching steps. In this paper, we present hMatcher, an effective approach to holistic schema matching. To perform collective schema matching, hMatcher generates frequent schema elements before proceeding with the matching. To reach high matching accuracy, hMatcher employs a context-based semantic similarity measure. Experimental results on a real-world domain dataset show that hMatcher performs holistic schema matching properly, reaches a high matching accuracy (Precision=0.89;Recall=0.66;Overall=0.57), and outperforms the state of the art matching approaches in terms of matching accuracy.

514

Authors: Mohammad Dehghani, Mohammad Mardaneh, Josep M. Guerrero, O. P. Malik, Vijay Kumar

Heuristic optimization algorithms are widely used to solve problems in different fields of science. In this paper, a new game based optimization method called football game based optimization (FGBO) is presented which simulates the game of football. The population of FGBO are clubs and the variables of the problem are the players belonging to the clubs. FGBO has four phases: a) league holding, b) player transfer, c) practice, and d) promotion and relegation. The power of FGBO in solving optimization problems has been investigated on several benchmark test functions. The result of FGBO and other algorithm are obtained from implantation of these algorithms on unimodal, multimodal, and fixed-dimension multimodal benchmark test functions. Eight optimization algorithms called Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO) have been used to compare these results. The proposed FGBO algorithm is also used to solve the energy commitment (EC) problem. Based on the simulation studies and obtained results, FGBO has a higher efficiency than a number of other algorithms. The results and data obtained from applying FGBO and other mentioned algorithms on unimodal test functions, multimodal test functions, and energy commitment problem show that FGBO is able to provide better results in comparison with other well-known optimization algorithms.

539

Authors: Marwa Sayed Moustafa, Sayed Ahmed, Amal Ahmed Hamed

Recently, deep hashing dominated single label image retrieval approaches. However, the complex nature of remote sensing images, which likely contains multi-labels, hardly benefits from the above approaches. To overcome single-label image retrieval limitations in remote sensing domain, we address this problem by proposing a multi-label remote sensing image retrieval (MLRSIR-NET) framework. Specifically, the proposed MLRSIR-NET composed of two main sub-networks: multi-level feature extraction and deep hash. The multi-level feature extraction network predicts multi-level features to exploit different levels of Convolution Neural Network (CNN (characteristics. To suppress discriminative feature representation, the multi-level features are aggregated and feed to Convolutional Block Attention Module (CBAM) to amplify the representation of relevant multi-label features. CBAM is flexibly integrated into our network with end-to-end training. The hash network stacked two fully connected layers aimed to learn multiple hashing functions to encode the feature vector into a compact hash code. Finally, we conduct experiments on two benchmarks for multi-label images: Dense Labelling Remote Sensing Dataset (DLRSD) and Wuhan Dense Labeling Dataset (WHDLD) to systematically assess the performance. The results show that the proposed framework improved the accuracy in terms of Mean Average Precision (MAP) by a considerable margin of 85.4%, 87.2%, 90.8% and 92.9% for 12-bit, 24-bit, 32-bit and 48-bit code lengths respectively on DLRSD. For WHDLD, it can be noted that the proposed framework supers the DCH by 93.8%, 98.7%, 91.9%, and 94.6% on average respectively.

560

Authors: Adiba Sabilla Ajrina, Riyanarto Sarno, Hari Ginardi, Aziz Fajar

Indonesia is one country that is rich in natural resources, both natural, marine and air resources. The existence of abundant resources is not properly utilized by the people of Indonesia. Improper use of resources will cause environmental damage. One form of environmental destruction is natural Sandy Gravel (Sirtu) mining activities in Kediri Regency. This study aims to map potential place for mining areas assessed using Geographic information system (GIS) and Multi criteria choice Making (MCDM) using Simple Additive Weighting Method (SAW), Multi Objective Optimization on the basis of Ratio Analysis Method (MOORA) and Complex Proportional Assessment Method (COPRAS). The weighting of the criteria used in this study is based on the Fuzzy Analytical Hierarchy Process (F-AHP) method. GIS is used to score the criteria and build the decision matrix needed by the MCDM method. The criteria used in this study are based on UU Republik Indonesia 4/2009. Those criteria are grouped into 3 categories which are natural factors, environmental factors, and aesthetic factors. Natural Factors have sub-criteria such as geomorphology, lithology, and hydrology. Environmental factors are comprised of vegetation, wildlife, distance from main road, distance from settlements, and population density. While aesthetic factors consist of natural features and touristic places. The results showed that from the three methods used which are SAW, MOORA and COPRAS methods, the most suitable method was the COPRAS method with 19/19 aggregation values. The order of sub-districts in Kediri Regency with the most suitable potential for Natural Sandy Gravel (Sirtu) is as follows: Kras, Bulan, Papar, Tarokan, Purwoasri, Mojo, Pare, Ngadiluwih, Kukung, Kandatel, Gampengrejo, Semen, Grogol, Plosoklaten, Ngancar, Puncu, Wates, Kepung, Kandangan.

1

Authors: Rachapalli Sunanda, Joseph Beatrice Seventline

The rainy effect normally decreases the visual quality of the images that highly affects the outdoor vision system’s performance. Due to light scattering, the rain streaks generate the haziness and blurring effect. So, an effective model is required to remove rain streaks from the single image that assists an extensive range of applications such as object tracking, image enhancement, etc. In this research work, a new model has been proposed for rain streak removal in the single image. In the proposed work, a global sparse model with Singular Value Decomposition (SVD) and Huber loss function were used to remove rain streaks from the synthesized images. In the proposed model, three sparse terms (characteristics of image background information, structural knowledge and intrinsic direction of rain streaks) were used for depicting the directional smoothness of rain-free and rain streak images and also described the intrinsic and latent properties of rain streaks. The rain streaks sparsity was also enhanced by employing l_1 norm that effectively avoids the undesired features on the rain free regions. Then, Alternating Direction Method of Multipliers (ADMM) was used for tackling the proposed model in order to achieve optimal solutions. In the experimental phase, the proposed model was compared with the existing models like Cascading Attention Aggregation Network (CAAN) and Directional Global Sparse Model (DGSM) in terms of Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Root Mean Square Error (RMSE). Through the experimental simulation, the proposed model almost showed 0.52-1 dB improvement in PSNR value.

21

Authors: Saha Tanangsanakool, Pongsathorn Reangsuntea, Kazuo Mori, Pisit Boonsrimuang

Orthogonal Frequency Division Multiplexing (OFDM) signal would be damaged significantly by inter-carrier interference (ICI) in higher time-varying fading channels. The ICI leads to fatal degradation of bit error rate (BER) performance due to the loss of orthogonality among subcarriers. To solve this problem, this paper proposes a high accuracy time-domain channel impulse response (CIR) estimation method and low-complexity based time-domain equalization (TDE) method for solving the simultaneous equations instead of using an inverse matrix calculation which can achieve better BER performance and lower computation complexity even in higher time-varying fading channels. The salient features of proposed method are to employ a time-domain training sequence (TS) in the estimation of channel impulse response (CIR) instead of using pilot subcarriers in the frequency domain and to employ the time domain equalization (TDE) method with maximum likelihood (ML) estimation instead of using a conventional frequency domain equalization (FDE) method. This paper also proposes a low-complexity iterative method for solving the simultaneous equations instead of using an inverse matrix calculation, which remains the computation complexity up to 7.8% of inverse matrix calculation with the same BER performance but achieves the BER performance when compared with the conventional method. This paper presents various simulation results in higher time-varying fading channels (vehicle speed ≈ 381 km/hrs) to demonstrate the effectiveness of the proposed method as compared with conventional FDE and TDE methods.

44

Authors: Anny Kartika Sari

Concept mapping between two different ontologies in the biomedical field are commonly conducted. When one of the ontologies changes, the changes should be propagated to the other ontology in the mapping. This propagation process is an issue that must be addressed because the knowledge in biomedical field develops rapidly. In this paper, we present the change propagation method from Gene Ontology (GO) to Medical Subject Headings (MeSH). The change propagation is conducted by mapping GO change operations into MeSH change operations. There are three steps that must be performed. Firstly, the change operations on each ontology are identified and represented formally. Secondly, change operations in both ontologies are classified and compared. Finally, each GO change operation is mapped into a MeSH change operation based on the semantic meaning of the change operation. A proof of concept is also presented to show how the process works. In the experiment, the method has been applied to ten concept deletion operations of GO, with 80% of operations being successfully propagated to MeSH. The 100% success cannot be achieved because some GO concepts do not have relevant corresponding MeSH concepts due to the specificity of GO concepts.

66

Authors: Ali Hayder Abdul Kareem

This paper introduces the concept of designing all optical orthogonal frequency division multiplexing (AO-OFDM) techniques to increase data rates to obtain a greater range of transmission. Arrayed waveguide grating (AWG) was proposed in this work as inverse fast fourier transform / fast fourier transform (IFFT/FFT) circuits in all optical orthogonal frequency division multiplexing system for its structure is simplified and capacity in large subcarriers. AWG combining with dense wave division multiplexing (DWDM) technique with 20 GHz channel spacing. The proposed system is simpler than others, which can obtain AO-OFDM and have efficiency in the spectrum. The output investigated for the proposed system is evaluated by applying different input power and transmission lengths to assess the quality of the signal via the bit error rate (BER) parameter. The simulation results show the constellation diagram and BER of the received signal with 8 channels, using quadrature amplitude modulation (QAM) and quadrature phase shift keying (QPSK) at (240 Gb/s) per channel.

90

Authors: Kamal A. El Dahshan, Eman K. Elsayed, Ashraf Aboshoha, Ebeid A. Ebeid

Classification of human face images into emotion categories is a hard challenge. Deep learning is mostly effective for this task. In this paper, we propose a facial emotions recognition system based on Deep Belief Networks (DBNs) and Quantum Particle Swarm Optimization (QPSO). The proposed methodology is composed of four phases: first, the input image is preprocessed by cropping the Region Of Interest (ROI) in order to get the desired region and discard non-significant parts. Second, the ROI is divided into many blocks and then the integral image is utilized to determine the superior (most efficient) blocks. Third, image down sampling algorithm is adapted to reduce the size of the new sub image in order to improve the system performance. Fourth, the emotion's class is identified using the DBN. Instead of adapting DBN parameters manually, QPSO is used to automatically optimize DBN parameters' values. The proposed algorithm has been applied to the Japanese female facial expression (JAFFE) and FER-2013 datasets. By applying the proposed algorithm, the computation time is reduced effectively by about 62% on the whole JAFFE and 82% on the whole FER-2013 datasets. Whereas, the accuracy is retained at 97.2% and 68.1% on JAFFE and FER-2013 datasets respectively.

114

Authors: Riyadh Toman Thahab, Tahani H. M. Al-Mhana

Furnished with many features, modular multi-level converters (MMC) have various applications in power systems. The main application considered is high voltage direct current transmission (HVDC), where the MMC operates as back to back converters. In this work, an MMC is studied as an interfacing converter, between a distributed energy source and an unbalanced grid. Therefore, at the point of common coupling (PCC) the voltage and current is a composite of positive, negative and zero sequence components. A mathematical relationship for the MMC arm current is developed that accounts for the presence of the PCC voltage and current zero sequence in addition to the positive and negative components. Since current is injected towards the grid, the profile of the converter output voltage is an important factor. This voltage is implicitly affected by the sub-module capacitor voltage; hence, the balancing scheme adopted is pivotal in grid connected applications. Therefore, two balancing techniques are investigated, where one depends on simply rotating the carriers between sub-modules of the MMC. The other is based on the conventional sorting algorithm. Results show that the former method, which requires less calculation efforts, provides sufficient results in-terms of the current shape and ripple injected by the converter compared to the sorting based benchmark scheme. This is attributed to the converter output voltage, for which simulation results confirm that the rotation method contributes to small deviations in this voltage. Evaluations of balancing methods presented in this work prove that the rotation method is a sufficient balancing platform for interfacing MMC applications.

145

Authors: Ayat Taha, Ashraf Darwish, Aboul Ella Hassanien, Ahmed ElKholy

In this paper, the identification system for the Arabian horse based on the biometric features is presented. This system used the Local Binary Pattern (LBP), Speed Up Robust Features (SURF) to extract the biometric features from the muzzle print images of the Arabian horse and to increase the robustness of the features against noise, illumination and rotation a fusion of SURF and LBP is presented to form LS features. The system also used the Support Vector Machine (SVM) classifier to perform the identification and gender determination process using the extracted features. To improve the accuracy of the proposed system Gray Wolf Optimization Algorithm (GWO) is used to optimize the SVM parameter. The experimental results represent that the proposed system achieved a promising accuracy of 96% using LS features without optimization. After Applying the GWO algorithm the accuracy of the system using LS features achieved the best accuracy 99.6% compared to SURF and LBP features.

171

Authors: Khamla NonAlinsavath, Lukito Edi Nugroho, Widyawan Widyawan, Kazuhiko Hamamoto

Location-based service has become an important element in indoor positioning with increasing precision. Indoor technologies help with user navigation and positioning in specific locations. Many location-based services and applications have been developed to improve indoor positioning, especially to deal with the complexity of location awareness systems. This study proposes the use of Wi-Fi Fingerprint (Wi-Fi), pedometer and Bluetooth Low Energy 4.0 (BLE) beacon to measure and pinpoint user’s indoor locations using received signal strength-based indoor localization. The system tracks, directs and guides user’s indoor movements using an android application and assists first-time visitors to find a particular location. The networks are comparable and can be detected using smartphones. The phones will detect the strongest signal from the available networks. Wi-Fi, pedometer and BLE 4.0 beacon were able to identify the direction and location in indoor environments and assist user to find their specific destination easily. The map provided by the system based on position information has an accuracy of 91.5% at the third floor and 97.1 % at the second floor, and the signals have seamlessly worked together in integrated fashion when user is in indoor environment. The system is also able to highlight pathway according to user’s current location and destination, in addition to being fast responding, secure and robust. The application enables seamless service in real-time operation and provides multiple pathways, guidance and highlighted map for indoor localization. The display of distance between the user and the destination as the user near the target destination is an extremely helpful feature in the application.

196

Authors: Nam-Soo Kim

Most of the coordinated multipoint (CoMP) transmission studies have been focused on the sum capacity. However, the increase of the sum capacity does not guarantee the individual user’s required outage performance. In this paper, we consider the outage performance of non-orthogonal multiple access (NOMA) networks with CoMP transmission under multi-cell environments. We adapt the Alamouti code for CoMP transmission to improve the outage performance of a cell-edge user. Also to provide the spatial diversity gain, cell selection with maximum channel gain is adapted. Outage probabilities of users are derived in closed-form and verified through Monte Carlo simulation. It is noticed that the outage performance improves as a near user approaches a base station, however, the error floor which does not improve the performance as the transmit power increases is observed. Since the inter cell interference (ICI) from the selected cell with NOMA causes the error floor, the selected cell with orthogonal multiple access (OMA) shows better outage performance for the near user than the cell with NOMA. Furthermore, the performance is more susceptible to the distance between the base station and the near user rather than power allocation to the user. Also, the performance of the cell-edge user shows significant improvement with the combination of the Alamouti code and the cell selection compared to the cell selection only.

216

Authors: Mohammad Dehghani, Zeinab Montazeri, Shahrzad Saremi, Ali Dehghani, Om Parkash Malik, Kamal Al-Haddad, Josep M. Guerrero

A number of metaphysical algorithms have been developed in recent years. Most of these algorithms are inspired by physical processes or living beings' behaviour. In this paper, a new algorithm namely “Hide Objects Game Optimization (HOGO)” is presented to obtain quasi-optimal solution. It is inspired by an old game and the searcher agents who try to find a hidden object in a given space. In this game, any player must notice the following points: (a) pay attention to the voices made by the coach for players, (b) get closer to the best player for whom the coach made the loudest voice, (c) take influence from the voices made by the coach for other players, (d) compare the new voice after a move with the old voice before the move and return back in case the voice gets lower. HOGO is tested on 23 well-known benchmark test functions and is compared with eight optimization algorithms: Genetic Algorithm, Particle Swarm Optimization, Gravitational Search Algorithm, Teaching Learning Based Optimization, Grey Wolf Optimizer, Grasshopper Optimization Algorithm, Spotted Hyena Optimizer, and Emperor Penguin Optimizer. The results and data obtained from applying HOGO and other said algorithms show that HOGO is able to provide better results in comparison with other well-known optimization algorithms.

237

Authors: Nora Niazy, Ahmed El-Sawy, Mahmoud Gadallah

The capacitated vehicle routing problem is the most popular type of vehicle routing problem and is a kind of NP-hard optimization problems. The purpose of this problem is to decrease the total distance travelled by vehicles with respect to restrictions of vehicles’ capacity. In this paper the capacitated vehicle routing problem solved by Chicken Swarm Optimization algorithm, Tabu Search and a new hybrid algorithm. The main idea of the proposed algorithm is to use the hieratical order of Chicken Swarm Algorithm and Tabu Search for finding best neighbourhood to find shortest path with minimum cost, after that we using the moving equations of the two algorithms on each chicken to construct the paths then we choose the shortest path which has the minimum cost. Results from a computational experiment on 10 different datasets show that the hybrid algorithm can be considered as an efficient approach and overcome the best known results in 9 datasets which means that it is 90% better than known results.

260

Authors: Ari Endang Jayati, Wirawan Wirawan, Titiek Suryani, Endroyono Endroyono

Recently, various application scenarios have emerged in new communication networks that have a variety of new requirements such as: ultra-low latency, low out-of-band emission and long battery life. Generalized Frequency Division Multiplexing (GFDM) is one of the nonorthogonal multicarrier techniques that meets these requirements. We study the analysis of nonlinearity effects in a High Power Amplifier (HPA) and crosstalk on Multiple Input Multiple Output (MIMO)-GFDM systems and its compensation. Moreover, proposed is a recurrent receiver compensation method for the joint RF interference in the MIMO-GFDM system. The assumptions made at the receiver considered the characteristics of the HPA. This proposed technique also has the ability to compensate for the combined effects of nonlinear and crosstalk distortion. The simulation results show an improvement in the bit error rate for small iteration numbers, i. e. 1 - 4 iterations for 16-OQAM mapping. The receiver-based mitigation techniques were used as an option to maintain low-level transmitter complexity. The main advantage is the possible application at the base station without increasing the computational complexity of the mobile terminal as well as the terminal costs or battery life. This means the advanced nature of the technique ensured there was no need to make any changes on the cellular terminal side. Therefore, the performance of the iterative receiver technique proposed in the MIMO-GFDM system is better than other orthogonal multicarrier systems.

283

Authors: Dhafer R. Zaghar, Hadeel N. Abdullah, Ikhlas M. Farhan

This paper investigates the application of advanced forward error correction techniques mainly: Raptor code, Low-Density Parity Checks (LDPC) code, and Luby Transform (LT) for wireless communication systems in the low power regime (LPR). Raptor codes are a significant theoretical and practical improvement over LT codes, which were the first actual class of fountain codes. These codes are under consideration for many recent technologies. Different code parameters such as code rate and several decoding iterations are used to show their effect on the performance of digital systems. In this paper, we propose a systematic Raptor code that enabled transmission of information with different code rates in LPR. Simulation results showed that the suggested design is appropriate for data transmission in a low SNR regime and can provide a reasonable bit error rate (BER) against the additive white Gaussian noise(AWGN) with the optimum selection rate1/4 and 1/9. A more significant improvement in the system's BER performance for the SNR up to -5dB over binary input additive white Gaussian noise (BI-AWGN) channels that showed through simulation results. Also, the BPSK modulation with systematic Raptor code is performed better than QPSK modulation in a limited power region, for the same coding rate and the number of decoding iterations.

305

Authors: Yulindon Yulindon, Noor Azah Samsudin, Riswan Efendi

The antenna simulation is generally done manually where certain antenna parameters are changed by trial and error method until the desired performance is obtained. In this study, the E-shaped microstrip antenna was designed to work in wideband frequency using the Particle Swarm Optimization (PSO) algorithm. The E-shaped patch antenna with ground follows the subtraction size. The type of substrate used in this study is FR4 Epoxy with a relative dielectric constant value (r) = 4.6 and thickness (h) = 1.6 mm. Feeding technique used in the connector is a probe feed technique to get matching impedance that matches the impedance of the connector which is 50 ohms. The working frequency of the E-shaped microstrip antenna after the use of the PSO algorithm generated bandwidth of 295 MHz with a working frequency of 340-635 MHz. From several stages of optimization performed, an ideal gain is obtained in accordance with the characteristics of the E-shaped antenna, where the resulting gain is 3.246 dBi. The characteristic obtained is omnidirectional, where the donut-shaped radiation pattern with the direction of the wave is in all directions.

333

Authors: Sujatha Arun Kokatnoor, Balachandran Krishnan

Online ratings and customer feedback on hotel booking websites support the decision-making process of the customer as the reviews provide a deeper understanding about all aspects of a hotel. Consequently, review and rating analyses are of great interest to consumers and hotel owners for the hotel related social media services. The key challenge, however, is to make the wide variety of information accessible in a simple, fast and relevant way and the solution is Topic Modelling and Opinion Mining. Common approaches like Latent Semantic Analysis (LSA) and Hierarchical Dirichlet Process (HDP) have order affects. If the input dataset is shuffled then different topics are generated leading to misleading results. To overcome this, a two-stepped feature engineering process is used: first step is to use a TF-IDF with modified trigrams calculation followed by the second step in removing weak features from the corpus thereby reducing the dimensionality of the Vector Space Model (SVM) for efficient Topic Modeling and sentiment analysis of the considered corpus. Sentiment score is calculated using VADER tool and Topic Modeling is done with Batch Wise Latent Dirichlet Allocation (LDA) using Stochastic Variational Inference (SVI) model. The modified trigrams included calculation of probabilities of words not only in the backward direction but also the probability calculation of the next two words of the target word thereby retaining its context information. The proposed method using Batchwise LDA with SVI along with two-stepped feature engineering process considerably improved its performance when compared to LSA and HDP models due to the fact of identifying hidden and relevant topics in terms of their optimized posterior distribution in hotel reviews dataset. The Batchwise LDA with SVI improved its performance by 3% in terms of its coherence values by using two-stepped feature engineering process and by 9% and 4% increase when compared with LSA and HDP models respectively.

358

Authors: Azad Raheem Kareem, Ali Majeed Mahmood

Direction of Arrival (DOA) estimation represents one of the key issues in the field of signal processing for the present and future 5G and radar engineering applications. The problem of the inaccuracy of DOA estimation has attracted considerable interest from academia. One of the advantageous common techniques that have been followed to improve the accuracy of DOA is the Estimation of the Signal Parameters via the Rotational Invariance Technique (ESPRIT) algorithm, albeit ESPRIT suffers from the challenge of estimation bias at short separation distances when two sources are very close to each other. Hence, in this paper, a modified-ESPRIT algorithm is proposed to improve the accuracy of the DOA. The modification of ESPRIT is presented by increasing the phase difference between the received signals. The idea is to add a pre-processing before the frame of the ESPRIT algorithm; this is done by introducing a double-balanced mixer and 90o signal splitter at the output of each antenna. The performance evaluation shows that using only the ESPRIT algorithm exhibits large estimation bias when two sources are very close to each other, while the performance is improved noticeably with the proposed modified-ESPRIT. It can detect and resolve two sources with an enhancement of 83.09% over traditional ESPRIT when the number of snapshots is very short, while an enhancement of 54.4% is achieved when source separation between (1 to 4) degrees and 77.8% achieved when Signal to Noise Ratio (SNR) between 0-10 dB and of 79.04% when four or fewer elements are used. The obtained results validate the performance efficiency of the proposed algorithm in terms of less Root Mean Square Error (RMSE), and a smaller number of snapshots.

378

Authors: Murtadha Al-Kaabi, Layth Al-Bahrani

Artificial Bee Colony (ABC) simulates the behaviour of intelligent foraging for a honeybee swarm. This article deals with one of the best swarm-based algorithms that has been used to solve the Optimal Power Flow (OPF) problem. Minimization of the objecting function can be satisfied by choosing a suitable optimal control variable while maintaining an acceptable system performance of the state variables in terms of their limits. The control variables that used in this article are the magnitude voltage of the generator, the tap changer of the transformer, the injection reactive power of compensative devise and the active power of the generator except the slack generator. The state variables are the reactive power of the generator, the load bus voltage and slack generator active power. The proposed algorithm modifies the classical Artificial bee colony by replacing the worst solutions obtained from the employee bees’ phase and the onlooker bees’ phase by the best solutions in the swarm size. The percentage of swarm sources that have been selected for the worst solutions is 33%, 50%, and randomly selection from the total source of the swarm size. This update contributes to improve the quality of solutions and determine the optimal settings of OPF control variables. The propose algorithm deals with minimization four different objective functions, the total fuel cost of the thermal units, the total active power losses in the transmission lines, the total emission caused by fossil-fueled thermal units and the total voltage deviation at the load buses. The modified ABC reduced the fuel cost by 11.34%, active power losses by 49.26%, voltage deviation by 91.34% and the emission by 16.70% satisfying all the constraint of the state variables in their limits. The proposed algorithm has been applied on the IEEE 30 bus system and gives good result when compare with other optimization techniques.

399

Authors: Mohammed K. Al-Haddad, Hadi T. Ziboon

Due to the inefficient use of the licensed frequency spectrum, a significant portion of the spectrum is left unutilized. This has led to the emerging of the Cognitive Radio (CR) as new technology that can access the white spaces in the licensed spectrum for improved spectrum utilization efficiency. In this paper, we present a scheme for detecting cyclostationary feature embedded in multicarrier signals like Orthogonal Frequency Division Multiplexing (OFDM) and Filter Bank Multicarrier (FBMC). The cyclostationary feature is generated due to the presence of pilot signals at certain subcarriers commonly used for channel estimation and frequency offset estimation. Therefore, no especial restrictions on the Primary User (PU) signal are imposed in order to generate the desired Cyclostationary Signature (CS). Both cases of scattered pilots and continuous pilots are considered. The algorithm is based on observing a periodic pattern in the cyclic autocorrelation function created by the pilot subcarriers, this periodic pattern is considered the cyclic signature and it can be extracted without prior knowledge of the signal parameters. The performance is evaluated under different impairments conditions like timing offset, frequency offset and multipath channel in addition to AWGN. Results show that FBMC has about 2-3dB signal-to-noise ration advantage over OFDM in continuous pilots mode and both modulations perform comparably in scattered pilots mode.

423

Authors: Berlian Al Kindhi

The stipulation of the COVID-19 (Corona Virus Disease 2019) as a global pandemic by the WHO (World Health Organization) made a number of countries lockdown. Countries like Italy, Denmark, China, and Ireland have taken lockdown steps to prevent this disease from spreading and taking many lives. COVID-19, SARS (Severe Acute Respiratory Syndrome), and MERS (Middle-East Respiratory Syndrome) are viral infections in the respiratory tract that can be fatal. SARS first became an epidemic in China in 2002, while MERS first appeared in the Middle East in 2012. At the end of 2019, a new disease appeared in China called COVID-19. These three viruses are still in the same family so they have very similar nucleotide sequences. The tested COVID-19 primer was able to adhere well with a similarity level of more than 70% in all DNA SARS and MERS isolates tested. To distinguish DNA samples between MERS, SARS, and COVID-19 using the basic local alignment sequence nucleotide approach alone is not enough. We propose an optimization of machine learning methods to predict the COVID-19, the optimization method depends on the method we improved. In Discriminant Analysis, we use Wilks Lamda's approach and change Linear into Diagonal Discriminant Matrix. In the Decision Tree method, we make optimization by making gain formulation to minimize the entropy value to get more information on the result. We optimized K-NN with add weighted distance optimization, and in SVM we try several kernels and optimize the hyperplane with SRM (Structural Risk Minimization) approach to looking for the best result. Besides that, in preparation for input features, we use Edit Levenshtein Method with the calculation of the optimum similarity from each DNA sequence. The results of our test, optimization of the Decision Tree method produces an accuracy of 98.3%, optimization of Discriminant Analysis 98.3%, and optimization of SVM and KNN 100% respectively. We also find a fact in the DNA Alignment process, when the primer being compared is 'R', the nucleotides in the COVID-19 sample data are always 'A' and this approach from the bioinformatic side can be used as analytical material in the medical world.

446

Authors: Salah Mahdi Hamzah, Ibrahim A. Murdas

Frees Space Optical (FSO) communication is one of the communication systems types that uses a free space to transmit information carried by light. It is previously known that the single input single output (SISO) technique is susceptible to atmospheric attenuation because of the effect of weather conditions. The proposed system is a combination of dense wavelength division multiplexing (DWDM) and multiple input multiple output (MIMO) techniques with Fork components. The main Fork component is configured in the Optisystem simulator to connect to another Fork components to increase the power of transmitted signals and ensure the arrival of the transmitted signals to the receiver of the FSO system. The key idea in the proposed system is to mitigate the attenuation that will be happened to the transmitted optical signal which propagated through free space due to different weather conditions. The study is done based on using the Optisystem simulation toolbox that is used to emulate different weather attenuation conditions in two types of FSO systems. The first is the traditional DWDM-SISO and the second is a proposed system named Hybrid DWDM-MIMO. A comparison between the proposed system and the traditional system is made in terms of the quality factor under different weather attenuation. The proposed system shows promising results in the performance and the quality of the received signal. The transmission path length of the proposed system under dense fog attenuation of 260 dB/km is enhanced to (30.43%) in comparison with traditional DWDM-SISO, also transmission path length of the proposed system under heavy rain attenuation of 9.29 dB/km is enhanced to (55.55%) in comparison with traditional DWDM-SISO. The transmission path length of the proposed system under heavy dry snow of attenuation of 131.835 dB/km is enhanced to (26.19%) in comparison with traditional DWDM-SISO.

1

Authors: Oskar Natan, Agus Indra Gunawan, Bima Sena Bayu Dewantara, Junaedi Ispianto

The balance of the aquatic ecosystem is an influential factor in the world of aquaculture, especially in shrimp cultivation. The one that plays a role in that ecosystem is aquatic microorganisms such as vibrio, bacteria, and algae. Therefore, farmers need to know their number and ratio to maintain the shrimp growth. Thus, in this research, models that can estimate vibrio-bacteria ratio and number of algae are developed. These models are formed from aquaculture datasets which are modeled using machine learning algorithms named Gaussian process regressor (GPR) and gradient tree boosting (GTB). Other processing techniques like data pre-processing, feature decomposition, and optimization are also applied to improve model performance. Moreover, these models are also compared to other models which are modeled using another machine learning algorithm like support vector regression (SVR), Lasso, and kernel ridge regression (KRR), so that the best models can be determined. Based on k-fold cross-validation, the GPR model has the best performance in estimating the vibrio-bacteria ratio with mean absolute error (MAE) value of 0.02482 and explained variance score of 0.96515. Then, in the algae estimation, the best performance is achieved by the GTB model with MAE value of 6.55554 and explained variance score of 0.33001.

26

Authors: Vutukuri Sarvani Duti Rekha, Pokkunuri Pardhasaradhi, Boddapati Taraka Phani Madhav, Yalavarthi Usha Devi

A Coplanar waveguide (CPW) fed printed monopole antenna with dual notch band characteristics is proposed for Ultra wideband (UWB) applications. The Ultra wideband antenna operates from 2.48 GHz to 18.35 GHz. Dual notch band is achieved by incorporating double H-slot structure in the radiating element. It achieved notch bands at 3.5GHz (WiMAX) and 8.3GHz (military/radar). The proposed antenna is investigated for the notch bands and operating band as well for various performance parameters like voltage standing wave ratio (VSWR), surface current distribution and far field radiation characteristics. It achieved a peak gain of 5.2dB in the operating band. The proposed antenna is fabricated on fr4 substrate of dimensions 37 x 40 x 1.6 mm3. By analysing the simulated and measured results of the proposed antenna it is observed that the proposed antenna is good for UWB applications by eliminating WiMAX and military/radar application interferences.

46

Authors: Ummadi Sathish Kumar, Edara Srinivasa Reddy

A recent development of a linear dimension reduction (DR) algorithm that is often used in face recognition and other applications has been used to preserve the Locality preserving projection algorithm (LPP). However, in LPP the projection matrix is not orthogonal, so rendering and other apps are not easily reconstructed. Hybridization of Enhanced Orthogonal and Uncorrelated Locality Preserving Projection (HEOULPP) attempts to discover the subspace that best distinguishes distinct face classes by maximizing the gap and reducing the uncertainty within class. In the design of a similarity matrix, HEOULPP algorithm assumes both local data and label data, and needs statistically improved and orthogonal output-base vectors, in order to enhance the OLPP life grade extraction performance. We propose HEOULPP to minimize the locality in an orthogonal projection matrix and to maximize the globality. This investigation is conducted using three datasets, such as YALE face dataset, ORL face dataset, and AR face dataset. Our proposed HEOULPP techniques achieves higher facial recognition rate under three conditions such as occlusion by 70.6%, noise by 69.1% and original by 76.2% than the existing techniques. In addition, dimensionality also reduced in proposed HEOULPP method comparing with traditional methods, including PCA, LPP, OLPP, ULPP and FOLPP. Experimental findings indicated a stronger depiction of data and much greater accuracy in the suggested HEOULPP method.

69

Authors: Sinan Majid Abdulsatar

Coherent Optical Orthogonal Frequency-Division Multiplexing (CO-OFDM) is widely used in optical communication systems, these systems suffer from many problems, including the high Peak-to-Average Power Ratio (PAPR). In this paper, a new method is proposed, it is based on Rotation Odd Constellation (ROC), this is done by dividing the subcarriers into two halves. The first half includes the Odd and the second consists of the Even subcarriers. A phase rotation is added to the first half, and no change is made in the second half. A simulation model has been accomplished by using Matlab platform and VPI transmission maker software to verify the efficiency of the proposed method and the improvment concerning Bit Error Rate (BER), Error Vector Magnitude (EVM) and Quality Factor (QF). The results have been collected for128 subcarriers, 4QAM, 16QAM and bit rate of 10Gb/s, shows the best performance is obtained when /4 rad is the rotation angle.

90

Authors: Ahmed Vall Hemeyine, Ahmed Abbou, Naoual Tidjani, Mohcine Mokhlis, Anass Bakouri

This paper proposes a robust fuzzy logic method based on the Takagi-Sugeno fuzzy controller model. In fact, this one is designed in order to control a Doubly-Fed Induction Generator (DFI-Generator) for the variable wind speed turbine. On the one hand, the main aim of the proposed strategy is the real-time tracking of optimal operating point, extracting the maximum output power, and controlling the pitch. This later is applied in order to limit the output power of the system to its nominal value. On the other hand, the proposed controller can extract the maximum of power under variable wind speed. While the existing methods deals with the case of the fixed wind speed. Thus, in this case, the controller can diverge and cause considerable drop of power, whenever the wind speed is changed. Moreover, the proposed control strategy can increase the efficiency and improve the tracking performance of the system. The model is established based on the Parallel Distributed Compensation technique. The stability and tracking analysis properties are ensured by using the Lyapunov approach theory. In addition, the feedback gains of the strategy controller proposed are determined by Linear Matrix Inequalities. A numerical simulation is carried out under Matlab software for comparing the proposed Takagi-Sugeno fuzzy controller with the conventional PI controller. Effectively, these both controllers are applied to controlling the efficiency of the 3 MW DFI-Generator based on Wind Energy Conversion System in order to test their tracking performances and robustness against rapid changes of the reference signal and for testing the proposed controller strategy as well. Indeed, the simulation results prove the robustness of the proposed controller and show the drawbacks of PI controller, which makes high overshoot under the rapid changes of the reference that can reach 20A in the worst cases. While, the proposed controller strategy tracks rapidly accurately and without any overshoot, the reference signal.

112

Authors: Sethakarn Prongnuch, Suchada Sitjongsataporn

This paper proposes an adaptive step-size approach based on the stochastic gradient-based algorithm for Hammerstein spline adaptive filtering (HSAF).With the good convergence and low complexity computation, a normalized least mean square (NLMS) algorithm is developed with HSAF. A nonlinear Hammerstein adaptive filter consists of memoryless function modified during learning and the spline control point is automatically controlled by gradient-based method. An adaptive step-size approach based on NLMS algorithm with the HSAF is introduced how to derive using the direction of steepest descent method for tracking performance. Hence, the stability and steady-state performance are detailed. The simulation results of trajectories of both update step-size parameters are shown that the initial step-size are reduced, all convergence rates are correspondingly reduced to each own optimum over 100 independent trials of the experiment. Experimental plots of learning curves of mean-squared error of the proposed algorithm for varying the initial step-size parameters are obtained that the proposed algorithm conducts more robust performance and fast convergence compared with the HSAF based on least mean square algorithm, even the large variations of 100-fold initial step-size parameters are used.

134

Authors: Hady Abdel-Maksoud

The main drawback of the switched reluctance motor (SRM) is the torque ripples that restricts its suitability for high-performance applications. Several control techniques have been used to mitigate these torque ripples. Torque sharing function (TSF) is one of the most significant control techniques used for minimizing the torque ripples in SRM. Different TSFs have been developed for reduction SRM torque ripples. However, the difference between the incoming and the outgoing torque responses along with changing of the commutation interval (CI) with changing the motor speed makes the tracking operation difficult. In this paper, a hybrid TSF is proposed. The proposed TSF is formulated based on a linear function for the incoming phase and a nonlinear exponential function for the outgoing phase. Also, the control procedure of the TSFs is developed where the motor speed is become an input along with the rotor position and the reference torque. The CI is accurately adjusted enough for the commutating process. A look-up table model based finite element analysis (FEA) for 6/4 SRM is built. The results of the proposed method are compared with those of conventional TSFs. The effectiveness of the proposed control technique is verified by the simulation results.

154

Authors: Sara H. Muhi, Hadeel N. Abdullah, Bassam H. Abd

This work aims to design an intelligent model capable of diagnosing and predicting the severity of the hepatitis of illness that assists physicians to make an accurate decision. The main contribution is achieved by adopting a new multiclass classifier approach based on a collected real database with new proposed features that reflect the precise situation of the disease. In this work, three artificial neural networks (ANNs) methods, namely, Back Propagation neural network (BPNN), Radial Basis Function Network (RBFN) and, K-nearest neighbor (KNN), used to forecast the level of hepatitis intensity. Real data Collected from the Gastroenterology and Liver Education Hospital of the City of Medicine in Baghdad used as modeling and forecasting samples, respectively, to compare the results of forecasting. The results show that the prediction result by the KNN network will be better than the two other methods in time record to reach an automatic diagnosis with an error rate of less than 1%. Diagnosis accuracy was 99.33% for 2-class and 88% for 5-class, which considered excellent accuracy.

177

Authors: Abdulrahman Sabra, Mohamed Taha, Dina S. M. Hassan

Clustering is considered a critical process to make a fast and reliable connection establishment of ad-hoc networks. In this paper, a new clustering algorithm is proposed, namely Stick Clustering Algorithm (SCA). The proposed SCA is used to overcome the low connectivity, low energy and delay to enhance the performance of large-scale ad-hoc networks. The simulation results showed the efficiency and the reliability of the proposed clustering algorithm against many greatest challenges such as high intensity of nodes under small network area. Also, the results demonstrated that the proposed SCA is very competitive algorithm compared with RCA, CDS, Z-Degree and ZID. The proposed SCA obtained 3.4306 approximation ratio. Also, the efficiency of the SCA is of O(n) for both message complexities and time.

201

Authors: Moine El Hajji, Hassane Mahmoudi, Moussa Labbadi

Air pollution caused by the transport sector has become a very worrying subject. To minimize toxic gas emissions, people start to prefer rail transportation. This high demand has caused high energy consumption in the electric railway. Thus, Moroccan Railway Company was obliged to use different traction locomotives of different technologies which can generate electromagnetic interferences between systems. Reason why the study of electromagnetic pollution has become a necessity to avoid these inter-system electromagnetic disturbances by analysing the experimental measurements on the railway site and by proposing a solution which is the implementation of an active filter capable of eliminating the parasites caused during the connection of the locomotive. However, the authorization to put into operation remains a real subject of study since different parameters must be taken into account, namely the weight, the location, the size and the electromagnetic interferences (EMI) of the filter. This paper discusses the electromagnetic noise by analyzing the impact of the auxiliary power supply quality generated by electric locomotive E1250 on the sound systems when it is coupled to multi-unit Corail cars and by detailing an overview of the conversion chain located in the electric locomotive under test and the sound system installed in Corail car which keeps passengers constantly informed when approaching and arriving at a station, and gives them other indications about the train. Next, static test bench and dynamic experimental tests will be measured in order to determine the Electromagnetic Compatibility (EMC) disturbances in the Common Mode (CM) by studying the harmonics generated by the static converter that feeds the Corail cars, and analyzing their impact on all operating systems especially sound system since it is considered as a very sensitive organ to the least source of noise. In the end, the conversion system will be modeled and a proposed solution will be implemented to mitigate the electromagnetics noises.

222

Authors: Rakesh Kumar Yadav, Rajendra Prasad Mahapatra

Clustering is one of the most significant methods for maximizing the lifetime of the network in wireless sensor networks (WSNs). The technique involves the first grouping of sensor nodes to clusters and second selecting appropriate cluster heads (CHs) for each cluster. In fact, CHs gather data from corresponding nodes and transmit those aggregated data to Base Station (BS). However, the major shortcoming drawn in this technique is to choose optimal CHs. More research works have been already under the investigation on selecting the appropriate CH with respect to different angles. In this scenario, this paper aims to propose a new energy-aware CH selection by hierarchical routing in WSN via proposing a new hybrid optimization technique. More importantly, the selection of CH is carried out under the consideration of stabilization of energy, minimization of the distance between nodes and minimization of delay during data transmission as well. With this, a non-linear objective function is framed, which intent to optimize the CH in such a way that the network lifetime has to be prolonged. Here, for attaining optimal CHs, a new hybrid algorithm is introduced that integrates the concept of Lion Algorithm (LA) and Grey Wolf Optimizer (GWO), which is named as Lion Updated GWO (LU-GWO). Finally, the performance of the proposed model is evaluated by comparing other conventional models with respect to certain performance measures. The simulation analysis shows that the proposed scheme for 50 nodes and 1000×1000 distance was 2.7%, 2.7%, 5.56%, 5.56%, and 5.56% better than GWO, LA, GA, PSO and ABC algorithms with respect to the alive node analysis and the overall analysis proves the superiority of proposed work.

246

Authors: Mohammad Dehghani, Zeinab Montazeri, Om Parkash Malik, Hadi Givi, Josep M. Guerrero

This article presents a new game-based optimization method entitled Shell Game Optimization (SGO). The novelty of this article is simulating the rules of a game known as shell game to design an algorithm for solving optimization problems in different fields of science. The key idea of the SGO is to find the ball hidden under one of the three shells, which should be guessed by players. The main feature and advantage of SGO is that it does not have any control parameters and hence, there is no need to set parameters. SGO is mathematically modeled and implemented on 23 well-known benchmark test functions as well as on a real life-engineering problem entitled pressure vessel design problem. Moreover, SGO is compared with eight optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Spotted Hyena Optimizer (SHO), and Emperor Penguin Optimizer (EPO). The results and data obtained from applying SGO and other mentioned algorithms on unimodal test functions, multimodal test functions, and pressure vessel design problem show that SGO is able to provide better results in comparison with other well-known optimization algorithms. Moreover, results of Wilcoxon signed rank test confirm that SGO achieves more accuracy in comparison with the mentioned algorithms.

268

Authors: Ahmed Aldhahab, Sarmad Ibrahim, Wasfy B. Mikhael

Classification of a brain tumor is a critical step in the design of computer-aided diagnosis systems for Magnetic Resonance Image (MRI) analysis. This work presents an efficient algorithm to classify a tumor in brain MRI images using statistical-based features and deep neural network. Data, within the region of interest, is transformed into two-dimensional discrete Gabor filter and wavelet transform. These filters are combined in this algorithm as directional transformation methods for utilizing all information in all orientations of the MRI input image. MRI Features are extracted based on the first and second order statistics from both domains. Two types of neural network classifiers are employed: Stacked Sparse Autoencoder (SSA) and Softmax Classifier (SMC). Two regularization functions are used in the training of the SA, sparsity regularization and L2-weight regularization. Sparsity regularization controls the firing of the neurons in the hidden layer, whereas L2-weight regularization reduces the effect of the overfitting and improves the performance of the SA. Two datasets are used to evaluate the proposed algorithm. The first dataset consists of 3,064 of T1-weighted MRI slices with three kinds of tumors: Pituitary, Glioma, and Meningioma. The second dataset consists of 200 MRI slices with low-grade and high-grade Glioma tumor collected from the BRATS dataset. The performance of the proposed algorithm is validated using the experimental results in terms of accuracy, specificity, and sensitivity compared to the existing algorithms. For the first dataset, the accuracy obtained is 94.0%, the sensitivity of Meningioma, Glioma, and Pituitary is 87.44%, 97.29%, and 94.27%, respectively, and the specificity of Meningioma, Glioma, and Pituitary is 98%, 96.89%, and 96.78%, respectively. For the BRATS dataset, the accuracy, the specificity, and the sensitivity achieved are 98.8%, 100%, and 100%, respectively.

293

Authors: Lailil Muflikhah, Widodo Widodo, Wayan Firdaus Mahmudy, Solimun Solimun

Liver cancer can be caused by hepatitis B virus (HBV) infection. This genome virus inserts genetic material into its host. Moreover, each gene has numerous HBV DNA sequences due to its high mutation rate in replication. Thus, detecting virus is a difficult task. A support vector machine (SVM) is a robust machine-learning algorithm for detecting liver cancer disease. However, a high data volume can reduce its computation speed and performance measures. Therefore, we propose data simplification using Kernel k-means clustering method to construct the SVM classifier model by minimizing objective function as object distance. Based on experimental results, the proposed method’s performance evaluation was higher than SVM algorithm without kernel k-means, especially for the sensitivity significantly increased. The accuracy rate and AUC of the proposed method were around 98% and 0.95. Furthermore, the performance of proposed method is also predominant of the other machine learning: Random Forest, Naïve Bayes, Naural Network and C5.0.

327

Authors: Kiruthiga Gurumurthy, Mary Vennila Selvadurai

The cloud environment comprised of distributed resources in a dynamic fashion, so this necessitates the need for developing optimal scheduling in cloud environment with the satisfaction of Quality of Service necessitated by the cloud consumer with the maximum profit to cloud providers. But the presence of impreciseness while scheduling cloud resources is the challenging issue of traditional scheduling policies. The main objective of this paper is to treat the vagueness in scheduling of cloud resources by designing Neutrosophic inference system for prioritizing incoming tasks and optimizing the resource usage by applying quantum cuckoo search cache management (INIS-QCSCM). This proposed work represents the parameters involved in resource scheduling by the means of Neutrosophic representation with the aim of reducing the response and execution time with maximize throughput which favours the profit of cloud service providers. Once the job is passed to the virtual machine (local schedulers) it is fine grained at the local host level by utilizing cache segmentation with the knowledge of quantum cuckoo search algorithm. The incoming task request is prioritized using neutrosophic inference model by considering impreciseness as the important factor. In local host memory management specifically cache assignment is done by cuckoo search-based model which selects the best cache allocation policy to optimize the resource utilization with quality of service. To prove the performance of this proposed INIS-QCSCM, its make span, degree of imbalance handling, energy consumption, execution time of each task and the hit ratio of the cache management are used as the evaluation metrics. The efficacy of INIS-QCSCM is proved by comparing it with existing classification models support vector machine, Fuzzy inference system and intuitionistic fuzzy inference system. As the outcome of the simulation results proved that INIS-QCSCM model provides potential resource scheduling with optimized resource usage in cloud computing by treating hesitancy, impreciseness and vagueness in adversarial situation of cloud paradigm.

350

Authors: Sinan M. Abdulsatar, Hadi T. Ziboon, Humam F. Majeed

An essential element and one of the challenging tasks in cognitive radio is spectrum sensing. In this paper, a new method to enhance the sensing is presented and implemented, in this scheme eigen-values are obtained from the sample covariance matrix of the signal that received at the secondary user’s receivers. Random Matrix Theory (RMT) derives the expression of the thresholds required for effective sensing. Low signal to noise ratio cases have been overcome by the new method which bestead the traditional energy detection method as well as the conventional eigenvalues based on single threshold. The simulation results based on randomly generated signals show that the proposed method exhibits a detection probability of (91.3%) when signal to noise ratio is (-19dB) while it’s (78.5%) for classical eigenvalues method. The results also show that the samples’ number that needed for reliable sensing is less than the energy detector and the conventional eigenvalues with single threshold method. An implementation of the the proposed method based on FPGA using myRIO-1900 kit is presented in this paper, the results obtained from the simulation and the hardware implementation are identical.

373

Authors: Salem Abdulla Awadh Ba Hmaid, Vasanthi Varadharajan

Integration of Wireless Sensor Networks (WSN) with the Internet Protocol (IP) has led to the development of Internet-of-Things (IoT) networks with high connectivity and improved data transmission with limited power supplies. Hence, it is necessary to utilize a high performance multi-path routing protocol to avoid energy constraint problems and network connectivity issues. This paper presents an optimal multi-path routing protocol using multi objective algorithms namely Fractional Firefly algorithm with Chicken Swarm Optimization (FFA+CSO) to resolve the energy constraint problem. First the network is clustered and a Cluster Head (CH) is selected to initiate inter-cluster and intra-cluster communication. Fractional Firefly algorithm (FFA) has been developed for this purpose by overcoming the slow convergence and local optima problem of firefly algorithm. FFA selects CH based on energy, delay, link quality and lifetime. Then the routes are formed and optimal route is selected using Chicken Swarm Optimization (CSO) based on energy, inter and intra-cluster delay, link quality, lifetime and hop count. The proposed FFA+CSO routing protocol is evaluated using Network Simulator-2. Results obtained for 100 nodes showed that FFA+CSO provided efficient multipath data transmission for WSN based IoT networks with 24% less delay, 28% greater throughput, 18.7% lesser energy consumption, 21.6% longer lifetime, 20% higher PSNR and 37.5% less number of hops than the existing routing models.

397

Authors: Tenia Wahyuningrum, Azhari Azhari, Suprapto Suprapto

Usability evaluation focuses on how well users can learn and use products to achieve their goals. Evaluation of e-commerce website usability can be done using the Fuzzy Analytical Hierarchy Process (FAHP) method. Usability is considered a multi-criteria problem due to its complex structure. Logarithmic Fuzzy Preference Programming (LFPP), a non-linear programming approach, is one of the FAHP methods for weighting criteria. However, deficiencies in the LFPP method, in some cases, result in a consistency value of 0 from a consistent fuzzy comparison matrix, which should be greater than 0. This study modifies the LFPP method with the Modified LFPP (MLFPP) models. Modifications were made as a development of the LFPP method in improving the accuracy of the consistency index (λ^*) before weighting criteria. The MLFPP makes improvements to the functional constraints, while at the same time improving the determination of criteria weights. The MLFPP model uses arctan to limit functions to improve consistency index. The test results show that the MLFPP can improve the accuracy of the consistency index values in the pairwise comparison matrix given by experts to be more than 0, with an average increase of 14.9%. The amount of consistency index (λ^*) changes has improved the usability score by 6.59%.

422

Authors: Kethavath Prem Kumar, Thirumalaisamy Ragunathan, Devara Vasumathi, Pamulapati Krishna Prasad

In recent years, cloud Load balancing (CLB) is the significant research area because vast data are stored on the servers leads to increase the loads on cloud servers. A trade-off on servers are maintained by LB techniques by distributing less power with equal load. In this paper, Cuckoo Search with Firefly Algorithm (CS-FA) is proposed for LB in a cloud environment. Initially, the capacity and load of each virtual machine are calculated. If the load of the virtual machine is greater than the balanced threshold value then, the LB algorithm is used for allocating the tasks. The CS-FA algorithm selects the best Virtual Machines (VMs) for assigning the tasks and migrate the overloaded VMs tasks to under-loaded VMs task. This algorithm majorly avoids the imbalanced workload performances in a cloud environment. The performance of proposed CS-FA method is compared with existing LB techniques such as Dynamic LB (DLB), Hybrid Dynamic LB (HDLB) and Honey Bee Behavior LB (HBB-LB) to evaluate the capacity and load of methods. The results showed that the existing HDLB method migrate seven tasks whereas the CS-FA method migrates only two tasks. The experimental result shows that proposed CS-FA method migrate only two tasks when number of loads is 40 and existing method migrate 6 tasks.

446

Authors: Sujatha Arun Kokatnoor, Balachandran Krishnan

Online Social Media (OSM) produce enormous data related to the human behaviours based on their interactions. One such data is the opinions expressed and posted for any specific issue addressed in the OSM. Majority of the opinions posted would be categorized as positive, negative and neutral. The lighter group’s opinions are termed anomalous as it is not conforming the regular opinions posted by other users. Though, lot of conventional classification and clustering based learning algorithms works well under supervised and un-supervised environment, due to the inherent ambiguity in the tweeted data, anomaly detection poses a bigger challenge in text mining. Though the data is un-supervised, for the learning purpose it is treated as Supervised Learning by assigning class labels for the training data. This paper attempts to give an insight into various anomalies of OSM and identify behavioural anomalies for a Twitter Dataset on user’s opinions on demonetization policy in India. Through Self-Supervised learning, it is observed that 86% of the user’s opinions did agree to the demonetization policy and the remaining have posted negative opinions for the policy implemented.

1

Authors: Sahirul Alam, Selo Sulistyo, I Wayan Mustika, Ronald Adrian

Vehicular ad hoc network (VANET) is the emerging wireless communication technology in vehicular domain to support the safety and non-safety purposes in transportation. Despite the potential implementation of VANET, some challenges mainly due to the dynamic environment need to be addressed and tackled. In this paper, the horizontal handover especially in vehicle-to-vehicle (V2V) connection is investigated. The dominant handover decision method in wireless network, namely received signal strength (RSS) based method, is actually inappropriate for the implementation in V2V VANET. Therefore, a utility based handover decision method is proposed in this paper. The utility function is formulated based on the value of signal to noise ratio (SNR) and the load of network. Despite the fluctuation of SNR, the proposed method can make the better handover decision i.e. by reducing the ping pong effect and improving the throughput by selecting the network with lower load. In this research, the proposed method is evaluated through simulations. The results of simulations show that the proposed method can improve the performance of V2V handover in terms of throughput and handover rate compared with the RSS based method.

20

Authors: Akram Saeed Aqlan Alhammadi, Vasanthi Varadharajan

Virtual Machine (VM) consolidation is a crucial process in improving the utilization of the resource in cloud computing services. As the cloud data centers consume high electrical power, the operational costs and carbon dioxide releases increases. The inefficient usage of the resources is the main reason for these problems and VM consolidation is a viable solution. VM consolidation includes host overload/under-load detection, VM selection and VM placement processes. Most existing host overload/under-load detection approaches of VM consolidation uses CPU utilization only for the determining host load. In this paper, three resources namely CPU utilization, memory utilization and bandwidth utilization are used for host overload detection and an adaptive regression based model called Multiple Regression Multi-Objective Seven-Spot Ladybird Optimization (MR-MOSLO) is proposed. This model is based on combining the benefits of adaptive threshold based and regression based host overload detection algorithms. This approach of combining these features provide more advantages for threshold setting in dynamic environments with accurate prediction of host overloading. For this purpose, initially, Multiple Regression (MR) algorithm is used which relay on CPU utilization, memory utilization and bandwidth utilization for estimation of the host load conditions. Then a Multi-Objective Seven-Spot Ladybird Optimization (MOSLO) algorithm is introduced to select the upper and lower threshold limits for host utilization. Based on these algorithms, the host overload/underload is detected with high accuracy and less power consumption. The simulations are conducted in CloudSim tool and the empirical results shows that the proposed MR-MOSLO algorithm detects the host overload efficiently. The results obtained for 25 hosts, 30 VMs and 500 tasks, are: SLATAH is 20.0434, PDM is 8.7E-4, SLAV is 3.7E-5 and ESV is 10.962 which are lesser than the other methods. Though the energy of 15.4 kWh and SLA of 0.00757 are negligibly higher than some of the existing methods, the proposed approach provided comparatively better performance.

40

Authors: Amal Fouad, Hossam M. Moftah, Hesham A. Hefny

Brain cancer importance emanates from the importance of the brain as an organ and its functions. It has a great effect on the whole human body. Identification brain cancer according to its type, it refers to a multiclass classification problem in the machine learning world. In the real-world, object detection and classification face numerous challenges. The object has a large variation in appearances. In this research, a Haar Discrete Wavelet transforms hybrid with the Histogram of Oriented Gradients (HDWT-HOG) features descriptors are proposed by the local gradients in MR image as shape information. The whale optimization algorithm (WOA) plays a great role to reduce the numbers of HOG and Harr features from 38,640 to 120 features only which are less than .01% from all features. This reduction doesn't affect the system performance but it saves time in the classification phase. The test image is matched with its learned class by performing a Bagging ensemble learning classifier. Bagging achieves 96.4% in average accuracy but when Boosting is used, it achieves 95.8%.

63

Authors: M. Rao Batchanaboyina, Nagaraju Devarakonda

The hybrid Improved monarch butterfly optimization- mutual nearest neighbor (IMBO-MNN) is proposed for outlier detection in high dimensional data. It is a challenge to detect outliers in high dimensional information. The external behavior of the data points cannot be detected in high-dimensional data except in the locally relevant data sub-sets. Subsets of dimensions are called subspaces, and with an increase in data dimension, the number of those subspaces grows exponentially. In another subspace an information point that is an outlier can appear ordinary. It's essential to assess its outlier behavior according to the amount of subspaces in which it appears as an outermost part to characterize an outlier. Data is scarce in high-dimensional space and the concept of closeness does not preserve meaning. In fact, the sparsity of the high-dimensional data means that every point is nearly equal from the point of view of closeness-based finishes. As a result, for higher dimensional information finding is more complicated and non-obviously significant outliers. An enhanced MBO (IMBO) algorithm is offered for enhanced search precision and run time efficiency by a fresh adaptation provider. Statistical results indicate that the elevated local optimal prevention and quick convergence rate of the improved monarch butterfly optimization (IMBO) algorithm helps to exceed the basic MBOs in outlier detection. Comparatively, IMBO produces very competitive outcomes and tends to surpass present algorithms. Optimal value k remains a task, affecting the efficiency of kNN straightforwardly. We are presenting a fresh learning algorithm under kNN in this paper to alleviate this issue called mutual nearest neighbor (MNN). The main feature of our method is that the class marks of unknown instances are defined by mutually next to one another, instead of by closest neighbor. The advantage of mutual neighbors is that in the course of the prediction process pseudo close neighbors can be identified and taken not into account. The performance of the suggested algorithm has been examined with a number of studies. For 100 data, IMBO-MNN is in 4897 milliseconds, PSO is in 5239 milliseconds, random forest is 5347 milliseconds, PNN is 5278 milliseconds and KNN is in 5166 milliseconds. For 250 data, IMBO-MNN is in 5984 milliseconds, PSO is in 6132milliseconds, random forest is 6145 milliseconds, PNN is 6124 milliseconds and KNN is in 6152 milliseconds. For 500 data, IMBO-MNN is in 6416 milliseconds, PSO is in 6636 milliseconds, random forest is 6634 milliseconds, PNN is 6719 milliseconds and KNN is in 6710 milliseconds. For 1000 data, IMBO-MNN is in 6913 milliseconds, PSO is in 7111 milliseconds, random forest is 7019 milliseconds, PNN is 7134 milliseconds and KNN is in 7162 milliseconds. The proposed IMBO-MNN performs better with minimum time taken. The findings indicate that the technique can identify outliers in high dimensional data efficiently in a decreased calculation moment.

83

Authors: Nattapon Jaisumroum, Jirarat Teeravaraprug

This paper is dedicated to the study of electricity generation expansion planning considering uncertainty. The method used for this problem is utilizing a highly constrained uncertainty optimization issue, as it were be solvable and complete classification. The demonstration uses a toll function to minimize environment effect and operational costs. A Neural Network is utilized to long-term forecast in electricity. The stochastic optimization model consists of finding the optimal long-term electricity generation planning for the construction of new generation capacity subject to various economic and technical constraints. The model can be showed experimental results that enable long-term planning of electricity generation with optimization of the costs, pollution emission and uncertainty of these generation resources in determining the best solution. The findings can help decision the government policy not only adjust the existing electricity generation but also coordinate the conflict interaction among system cost, energy supply, pollutant mitigation as constraint violation risk.

108

Authors: Shahryar Sorooshian, Nor Filiani Aziz, Noor Azlinna Azizan

The Performance Measurement Systems (PMSs) seen as part of a factor that indicates the successful and unsuccessful company; especially in the Small and Medium Enterprises (SME’s). The main objective is to analyze most suitable PMSs for Malaysian SMEs based on applicability. In the methodology, qualitative design with applied Analytic Hierarchy Process (AHP) is used. The expert panel is selected from practitioners linked with PMSs and SME’s in Malaysia. The findings show the Balanced Scorecard (BSC) as first ranked of PMS in SMEs. Followed by BSC are the Theory of Constraints (TOC) and Result and Determinate Framework (RDF). The last three lowly ranked PMS which are the Medori and Steeple Framework (MDF), Dynamic Multi-dimensional Performance Framework (DMPF) and Holistic Performance Measurement Framework (HPMF). At the end of the study, the researcher makes the discussion and conclusion; it is also advised to improve and implement the suitable models in the Malaysian SME’s for further success.

127

Authors: Indra Waspada, Riyanarto Sarno, Kelly Rossa Sungkono

The existing method of graph-based process model discovery has weaknesses in detecting parallel relationship (XOR, AND, and OR). The algorithm only works on a particular graph structure, so it must be reconfigured when applied to other different structures. To answer this problem, this paper proposes an improved method of parallel model detection, which is designed in two phases. The first one consists of three steps; firstly is to count and record the value of relationship frequency into every node in a graph model. Then, the second step implements the algorithm to discover the concurrent relationship. The third step detects all possible split and join relationships. Based on the first phase, then a consistent and robust parallel discovery algorithm can be developed. The first parallel algorithm is to identify the XOR relationship. This algorithm is designed with the rule that the XOR pattern cannot have a concurrent relationship between its branch nodes. Next, the algorithm for detecting AND and OR must detect the existence of any concurrent relationship in its branches. Then, AND and OR pattern is differentiated by their unique characteristic of relationship frequency at branch nodes. To verify the ability of the proposed methods in which the existing method fails, we have designed four scenarios. Scenario 1 and 2 consecutively were arranged with two and three branches parallel model. Scenario 3 located the AND and OR inside the XOR pattern. In scenario 4 the sequence relationships were inserted between split and join of parallel patterns. The experimental results show that the proposed method successfully recognizes and differentiates XOR, AND and OR patterns correctly in all scenarios. It also sounds in all discovered model and get 100% fitness.

151

Authors: Bambang Setiawan, Supeno Djanali, Tohari Ahmad

Imbalanced class is one of the main problems for intrusion detection models that use machine learning methods. The classifiers generally are designed to minimize the global error rates, which have not considered the condition of imbalanced class. The amount of training data for each type of imbalanced attack can cause those intrusion detection models to have high accuracy but can also lead to difficulty in identifying minority class attacks. In this research, we propose an intrusion detection model using a combination of the feature selection method for imbalanced class and weighted support vector machine classifier. We apply a composite performance index in the features selection and the optimization of the weight of the minority class. The experimental result using the NSLKDD dataset shows that this model produces overall accuracy, sensitivity, and specificity that reaches more than 99%, with false alarms below 0.5% and false-negative rates below 0.7%. The sensitivity of the U2R and R2L classes are 56% and 92%.

173

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

Credit card fraud is an issue that has affected Indonesia payment system over a decade. Sometimes, the result of the fraud used for terrorism and other crimes. Financial loss is not the only problem that is affected caused by credit card fraud but also Indonesia images in international trade, e-commerce, and the merchant. Currently, a trusted and secured banking payment system is crucial for both customers and banks. The problem from credit card fraud dataset is the data have many features and imbalanced class, this problem leads the paper to propose undersampling technique and feature reduction methods. In this paper we proposed two stage-feature reduction technique because a stage feature reduction could not find the optimal features. On the other hands, we are also applied Instance Hardness Threshold sampling and Random undersampling to deal with imbalance data. The two-stage feature reduction is chosen to eliminate the ineffective feature that cannot eliminate using only one feature reduction. The model from the implemented machine learning methods is evaluated using accuracy, specificity, recall, and Matthews Correlation Coefficient. We implemented our proposed approaches in the ULB credit card fraud detection dataset. According to the result, the undersampling gives a boost in performances which improve the recall and MCC score, the IHT undersampling provide goods results, and in some cases, the result can predict all the test set correctly. However, the two-stages feature reduction fails to improve the accuracy, precision, recall, and MCC score. In one case, the method reduced the accuracy score to 0.302.

196

Authors: Ryan Setiawan, Tohari Ahmad

The internet is widely applied to facilitate the transferring process of data as a means of communication. Some of this transferred message has confidential information and must be protected. One of the techniques to protect is data hiding. It uses carrier media as a cover to hide information inside it, one of which is audio. However, there are some problems in the use of audio, namely the limited capacity and sound quality after data insertion. In this paper, we address these problems by interpolating the audio samples using Modification Lagrange Interpolation (MLI) and exploring Reduce Difference Expansion (RDE) for embedded the secret. Next, samples are smoothed to improve the quality based on three sampling signal points of the stego audio. The testing of proposed method is carried out on fifteen mono audio data sets from various genres the covers and ten files as confidential information data with various file sizes. The results show that using the proposed methods, the performance increases that the average quality of each audio genre raises by about 4dB and able to increase the capacity of message data that can be hidden by 20%.

216

Authors: Istiyo Winarno, Mochamad Ashari, Heri Suryoatmojo

This paper proposed a Hybrid PI-Fuzzy based Static Var Compensator for minimizing voltage deviations as the integration of wind power generation to the distribution network. Hybrid PI-Fuzzy Logic controller constructed by the cascade-connected conventional PI controller and Fuzzy Logic controller. This algorithm combined the advantages and minimized the disadvantages of each conventional controller. The control method applied to the Static Var Compensator, which functioned as a dynamic reactive power source. The controlled dynamic reactive power source influenced the system with proper reactive power value, which resulted in the voltage regulation. Two scenarios were carried out to investigate the efficacy of the proposed method. The first scenario demonstrates the performance of the proposed system in wind speed variations and the second scenario in load variation conditions. Finally, the comparisons with Fuzzy Logic Controller-based self-tuning PI control were made to investigate the performance of the proposed control method. Simulation results verified that the proposed control algorithm outperformed the FLCbased self-tuning PI controller, in terms of the steady-state voltage deviation minimization by 1.47% and the settling time of the voltage respond decrement by 92.78%.

239

Authors: Aye Mya Hlaing, Win Pa Pa

The main objective of this paper is to improve the naturalness of Myanmar Text-to-Speech (TTS) system without using time-consuming and expensive annotation of training corpus. Recently, word embedding, which has the advantage of training directly from a large amount of raw text data, has been used as the additional input features with the conventional input features or as the replacement of that conventional input features on the acoustic modelling of TTS systems. In this paper, the effectiveness of applying word vectors as the additional input features are investigated for Myanmar speech synthesis on the three acoustic modelling techniques, Deep Neural Network (DNN), Long Short- Term Memory Recurrent Neural Network (LSTM-RNN), and a hybrid of DNN and LSTM-RNN (Hybrid-LSTMRNN). For the purpose of achieving better TTS performance, we built our own word vectors for Myanmar language. We further explore the best modelling method and vector dimension of word embedding for Myanmar TTS systems. Both objective and subjective evaluations are done on DNN, LSTM-RNN and Hybrid-LSTM-RNN based Myanmar TTS systems with and without additional input features such as Part-of-Speech (POS) and word vectors. According to the subjective results, applying additional input features in DNN, LSTM-RNN, and Hybrid-LSTM-RNN based Myanmar TTS systems can improve the naturalness of the synthesized speeches though objective results cannot lead to the significant improvement in LSTM-RNN and Hybrid-LSTM-RNN based systems. To the best of our knowledge, this is the first attempt to apply word vector features in neural network based Myanmar TTS systems.

259

Authors: Dwaram Jayanarayana Reddy, Akepogu David Donald, Kothapalli Seshadri Ramana, Karanam Srividya Lakshmi, Pullala Cheruvu Sai Divya

In the financial stock market, a sequence of prices obtained from the share market with respect to the time series is usually examined. Generally, time series in finance, particularly shows importance in predicting investment in today’s share market. Since there are too many factors such as public opinions, general economic conditions, or political events, vulnerability in the economy are directly or indirectly reflects on the evolution of financial time series. The desire of the investor is to predict the future stock prices neglecting whether the investor is a long-term investor or a day-trader. A major challenge is to develop and design an efficient predictive model that guides investors to make appropriate decisions. In this research work, Long Short Term Memory-Recurrent Neural Network (LSTM-RNN) is developed to overcome such disputes and contributing an efficient technique for predicting the future stock prices financially. In addition to the model, cross entropy is calculated using a Mutual Information feature selection model to minimize the optimization problems that create the time complexity in the system. The proposed LSTM-RNN has achieved best accuracy of 61.33% of prediction accuracy compared to state-of-the-art techniques.

278

Authors: Agung Wiratmo, Chastine Fatichah

Distributed representation of a sentences cannot only be seen with the sequence but also with dependency. In this paper, we proposed an answer assessment model that considers with dependency relational each word. The dependency relational is obtained by universal dependency modelling from CoNLL format data. The dependency relational is used in Long Short-Term Memory (LSTM) architecture by modified the hidden state, which is called dependency tree LSTM. The proposed method has an improvement on QWK and accuracy in 2.38% and 2.05%, respectively, that compared with the LSTM state of the art in the English short essay. Furthermore, the proposed method with an Indonesian short essay shows the evaluation of QWK and accuracy of 68.07% and 82.51%, respectively.

299

Authors: Eman El-Sayed Mahmoud, Ashraf Soliman Mohamed

Search engines’ capabilities are improved continually for enhancing the retrieved results and satisfying users’ needs. Despite the great efforts in the information retrieval field, the user spends long time in changing the search keywords; though the retrieved results may be out of user’s expectation. This may be due to the huge number of web resources, the lack of search keywords for specific domains, and the gap between the user and author in specific domain. This paper proposes a model for improving the search engines capabilities through adding level of awareness to the web resources. The model targets the human view about web resources in addition to author perception. This is through automatically extraction of the user’s perceptions and mapping them to the resources’ based on semantic aspects. The experimental studies realized 36% precision improvement compared to other search engines, and user satisfaction was up to 92%.

317

Authors: Theerawat Rojanapitoon, Jirarat Teeravaraprug

Warehousing can be expensive because order picking requires considerable vehicular movement and labor hours. Although many previous studies have focused on order picker routing, there exists a lack of research on the simultaneous increase of order picking speed and energy reduction in rectangular warehouses with varying levels of traffic in each aisle. This study accordingly developed and evaluated a mathematical model for determining optimal picker routes considering the total travel time and energy consumed. The results were validated using the brute-force search method and benchmarked with the time-staged (TS) model. The energy savings were determined by comparing a time-optimized use-case (T) with one optimized for both time and energy (TE). Both use-cases provided routes up to 44% faster than the TS and avoided more than 50% of congested paths, and TE which represents the full functionality of our model provided a possible energy savings of up to 17% over T.