International Journal of Intelligent Engineering and Systems

1

Authors: Nanang Triagung Edi Hermawan, Edi Winarko, Ahmad Ashari, Yus Rusdian Akhmad

Radiation monitoring data is very important to support the safety of nuclear installation. The data should be ensured generated and supplied by an authenticated sensor. The new sensor should apply mutual authentication to server before joint the system. This research proposes a high secure authentication protocol based on Eight Prime Number of Modified RSA Algorithm (EPNR) method as certificateless mechanism to protect authenticity and confidentiality of sensor’s identity. The main advantages of the protocol are designed for simpler direct point to point connection, using one-time random keys, and implementing five-barrier data protection mechanism. Its performances in context security, running time, and Quality of Services have been compared to other models. The proposed protocol is faster 29.9 times in random key generation, 7.27 times in encryption, and 11.1 times in decryption duration, compared to the others model based on RSA scheme. It has better performances in throughput and delay time. It also has enhanced resistance to statistical, replay, and MITM attack.

25

Authors: Divya Jegatheesan, Chandrasekar Arumugam

The intelligent transport system aims to control congestion and enhance the driving experience through a variety of technologies and communication systems. They provide us a lot of data which can be fed to machine learning technologies to provide further enhanced services to the common public as well as the transport officials. In this busy modern world, the usage of vehicles is highly increased and thus monitoring and detecting these vehicles onboard with better accuracy and less time cost is quite a challenging task. We propose an intelligent traffic management solution to detect vehicles (static and onboard) which finds its set-about in the field of machine vision technology called YOLO D-NET (You Only Look Once Dilated Net). YOLO D-NET puts forward a novel architecture which incorporates the implementation of traditional YOLO model alongside of dilated Convolution Neural Network (CNN). Our paper also focuses on making a constructive comparison between the four different models namely SSD (Single Shot Multibox Detector) Inception, Faster R-CNN (Region-based CNN), YOLO ResNet (Residual Neural Network) and the proposed model YOLO D-NET. We have used the predefined COCO (Common Objects in Context) dataset and the custom dataset to train the traditional models. Also YOLO D-NET makes use of the predefined YOLO weights followed by the dilated CNN layers. Our proposed model was found to be 97.5 percent accurate with enhanced precision and accuracy than the other models particularly with less training time than the traditional YOLO model.

46

Authors: Quyen Thi Nguyen, Minh-Phung Bui

This paper presents a new method based on Equilibrium Optimizer (EO) algorithm that is inspired from the mass balance of a control volume for traveling salesman problem (TSP). For enhancing the efficiency of EO, the 2-opt movement algorithm is used to update the solution generated by EO. The efficiency of the proposed EO for the TSP problem has been compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) on different instances consisting of the 14-city, 30-city, 48-city and 52-city. The calculated results show that for the large scale instances such as 48-city and 52-city, EO has found the better tour than PSO. In comparison with GA, EO has ability finding the best tour with the smaller mean and standard deviation. The comparisons with previous methods in literature have also demonstrated that EO has ability to search the better tour than other methods. Thus, the proposed EO can be a potential method for the TSP problem.

67

Authors: Khin Hsu Wai, May The` Yu

Effective and automated lane marking detection system is one of the important part of many intelligent transportation systems. Many researches of lane detection techniques have been conducted on academic and automobile industry and applied in intelligent transportation systems. However, It is still a challenging problem because of the various environmental conditions such as shadows, lane occlusion and illumination invariance that can decrease the lane detection performance. To deal with these issues, a reliable lane marking detection system that use the block-wise Hough based features is proposed. We propose the Region of interest extraction that divides the image into sub-image blocks to detect the lane line and block-wise Hough Transform feature extraction approach to extract the lane line features. Artificial Neural Network (ANN) applys the extracted features to detect the lane. The efficiency and accuracy of our system is evaluated by using the Caltech lane dataset. The average correct detection rate is 94.67% and the computational time is 17.64 ms/frame by applying the proposed method.

88

Authors: Azeddine Loulijat, Najib Ababssi, Makhad Mohamed

The main problem of the Doubly-Fed Induction Generator (DFIG) is its sensitivity to network disturbances especially voltage dip due to its stator windings being directly linked to the network. A voltage dip in the network results from high current peaks in the stator windings, since the stator and rotor are electromagnetically coupled, so these peaks provoke inrush current hard at the delicate back-to-back converters and an overcharge of the DC-LINK capacitor. In this perspective, several researchers are realized a linear and nonlinear controller with a passive protection method (PPM). The objective of this paper is to develop modelling and control models based on a true DFIG model considering the effect of all the parameters of a stator winding. The Second- Order Sliding Mode controller (SOSMC) is the non-linear control strategy used to control the dynamics of the DFIG through the rotor-side and network-side power converters under normal network conditions. When a voltage dip is detected, the Kalman observer (KO) contributes to a non-linear controller and the latter is coupled to a passive protection method that contains crowbar and DC-chopper circuits to improve the dynamics of the DFIG system to cross the network fault. Also, a comparative study is carried out between the developed controller and other linear ,non-linear approaches exist in the literature. The results obtained, under a maximum voltage dip (100%) on the 60 kV network, confirm its robustness and superiority in terms of limiting the DFIG graders at the beginning and on termination of a fault. In terms of Ir and Vdc values during a voltage dip, our control strategy does not exceed 1.45p.u and 1.2p.u, on the contrary, the ABC with HGO which is developed recently reaches 2.2p.u and 0.4p.u.

112

Authors: Yosepu Cooli, Chitraivel Mahesh

The development of computing technology has led to enormous improvement in the medical data and machine learning processing techniques. The increasing data usability in various field leads to the term big data, which is required in various applications such as medical, finance and so on. The existing models such as Random Forest, Support Vector Machine (SVM) and Decision tree have low capacity in handling the large volume of the data. In this research, the Recursive – Parallel Random Forest is proposed to effectively classify the large volume of medical data. The recursive partition method has been used to segment the input data and process the data in segmented manner. The recursive partition method segment the data and subset features based on data centric to improve the learning and provide adaptive gain ratio value to build Random Forest. The recursive partition method provides the data with high correlation that supports the random forest to perform the Random Forest parallel. The partition the data in data centric and balance the classes that helps the proposed model to handle the imbalance data effectively. The Parallel Random Forest method has been used to improve the performance of the medical data classification and the performance of Recursive partition- Parallel Random Forest (R-PRF) method was estimated using three UCI medical dataset such as Chronic Kidney Disease (CKD), Heart Disease and Diabetes. The standard classifiers and state-of-art method has been used to compare with the proposed R-PRF method and the experimental result shows that the R-PRF has the accuracy of 92.01 % and the existing Improved SVM radial method has 89.9 % accuracy.

129

Authors: Chinthakunta Manjunath, Balamurugan Marimuthu, Bikramaditya Ghosh

Equity market forecasting is difficult due to the high explosive nature of stock data and its impact on investor's stock investment and finance. The stock market serves as an indicator for forecasting the growth of the economy. Because of the nonlinear nature, it becomes a difficult job to predict the equity market. But the use of different methods of deep learning has become a vital source of prediction. These approaches employ time-series stock data for deep learning algorithm training and help to predict their future behavior. In this research, deep learning methods are evaluated on the India NIFTY 50 index, a benchmark Indian equity market, by performing a technical data augmentation approach. This paper presents a Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and the three variants of Gated Recurrent Unit (GRU) to analyze the model results. The proposed three GRU variants technique is evaluated on two sets of technical indicator datasets of the NIFTY 50 index (namely TA1 and TA2) and compared to the RNN and LSTM models. The experimental outcomes show that the GRU variant1 (GRU1) with TA1 provided the lowest value of Mean Square Error (MSE=0.023) and Root Mean Square Error (RMSE= 0.152) compared with existing methods. In conclusion, the NIFTY 50 index experiments with technical indicator datasetTA1 were more efficient by GRU. Hence, TA1 can be used to construct a robust predictive model in forecasting the stock index movements.

154

Authors: Srinivasulu Sirisala, S. Rama Krishna

The successful routing in Mobile Ad hoc Networks (MANETs) completely depends on the cooperation established between the mobile nodes during data dissemination. The resource constraint feature of mobile nodes is the main reason behind its non-cooperativeness attributed towards other interacting nodes of the network. This degree of non-cooperation rendered by the mobile node disturbs the routing and degrades the network performance. Thus, a node cooperation enforcing approach that distinguishes genuine node from malicious node is essential for sustaining the network performance. In this paper, Gaussian Trust Factor-based Grey Decision-Making Technique (GTF-GDMT) is proposed for maintaining the network performance independent to the influence of non-cooperative malicious nodes. This proposed GTF-GDMT identified the malicious nodes based on the trust factor that integrates the parameters of link stability and node reputation determined through direct and indirect monitoring process. This computation of trust is achieved using the Gaussian probabilistic distribution model that utilizes multi-perspective parameters that forms the input to the decision-making process. It is proposed for attaining the trade-off between energy consumption, transmission performance and security. The simulation experiments of the proposed GTF-GDMT conducted using ns-2 portrayed its excellence over the baseline schemes in terms of throughput, packet delivery rate, packet delay, energy consumptions and packet drop independent to the number of mobile nodes and malicious nodes in the network.

177

Authors: Suhariyanto, Rachmad Abdullah, Riyanarto Sarno, Chastine Fatichah

Sentiment analysis can provide rough recommendations in the form of sentiment from a collection of reviews or can provide recommendations in more detail about sentiment in a particular aspect called aspect-based sentiment analysis (ABSA). Sentiment analysis based on many aspects has been carried out but its accuracy is still being developed. In previous research, most research was carried out on explicit and implicit aspects and opinions and was carried out in simple sentences. The purpose of this research is to analyze the sentiment of restaurant reviews using the rule grammar method to extract implicit aspects - explicit opinions in four sentence models, namely simple (Si-AIOE), compound (Co-AIOE), complex (Ce-AIOE), and compound-complex (CoCe-AIOE). The ABSA method is proposed using the development of a grammatical rule extraction method to extract explicit and implicit aspect words and opinion words as the basis for sentence extraction. Rules making is done to take explicit and implicit aspect words and opinion words in Si-AIOE, Co-AIOE, Ce-AIOE, and CoCe-AIOE so that the comparison of the evaluation values can be known. This research uses the Semeval 2015 dataset on Restaurant reviews from the Tripadvisor Website which has been annotated as sentence data for ABSA. The aspect categorization process is then used to categorize aspects into 4 aspect categories, namely Ambience, Food, Service, and Price using hybrid approach. The hybrid approach is combined using Elmo-Wikipedia, grammatical rule extraction, WordNet, TF-ICF, and semantic similarity methods. The results of the aspect extraction obtained value of precision, recall, and f1-measure of 0.80, 0.84, and 0.82, respectively. Meanwhile, the ABSA process uses SentiCircle to classify sentiments into two, namely positive and negative. The results of the ABSA showed that the performance of proposed method achieve for precision, recall, and f1-measure were 0.84, 0.89, and 0.87, respectively.

199

Authors: Reza Fauzi Iskandar, Edi Leksono, Endra Joelianto

This paper presents a control strategy of maximum power point tracking and focuses on hybrid q-learning and type-2 fuzzy logic control strategy. Photovoltaics have non-linear voltage and current characteristics which influenced by temperature and exposure of solar irradiation so that the maximum power point (MPP) can change at any time. Unfortunately, operating conditions outside the MPP could reduce the efficiency of electrical power transfer from the photovoltaic system to the load. Q-learning (QL) has the potential to provide action decisions of duty cycle signal based on the state of the power gradient percentage range. The Q-learning hybrid method with Type-2 fuzzy logic control is proposed to provide correction of QL control signal by considering the uncertainty direction of power point shifting. The proposed strategy is important particularly to avoid a sudden change in solar irradiation exposure. The simulation results show that the Q-learning hybrid type-2 fuzzy logic control based MPPT response in the photovoltaic control system has tracking efficiency about 97%, rapid rise time of 0.08s, settling time of 0.23s, low overshoot and stable response for handling the change of irradiation and temperature exposure simultaneously.

226

Authors: Manjunath Hanumanthappa Rangappa, Guruprakash Chirathahalli Dyamanna

The combination of both static sensors and mobile sensors is called as Hybrid Wireless Sensor Network (HWSN). Static sensors monitor the environment and report events occurring in the sensing field. Mobile sensors are then dispatched to visit these event locations to conduct more advanced analysis. These sensor nodes have major energy constraints as they are small and their battery can’t be replaced and collaborate together in order to gather, transmit/forward the sensed data to the base station. Consequently, data transmission is one of the biggest reasons for energy depletion in HWSN. Clustering is one of the most effective techniques for energy efficient data transmission in HWSN. This paper proposes an Adaptive Hybrid Cuckoo Search (AHCS) algorithm. AHCS improves the Lévy flight method and population evolution strategy of the cuckoo search algorithm, and introduced a mutation operation operator. Inspired by the idea of position update of Grey Wolf Optimization (GWO) algorithm, this paper introduces the inertia weight w in the Lévy flight method of CS algorithm, and gives the new dynamic adjustment methods of parameters α and β respectively. The proposed algorithm is evaluated in terms of response time, packet drop ratio, energy consumption, end to end delay and dead time. Furthermore, the performance of AHCS-GWO is evaluated with existing methods such as classical algorithms, Evolutionary Multipath Energy-Efficient Routing protocol (EMEER) and Yellow Saddle Goatfish Algorithm (YSGA). The residual energy of the AHCS-GWO method for 200 nodes is 0.57J, it is high when compared to the existing methods.

247

Authors: VietHung Nguyen, DinhToan Nguyen, NhatTan Nguyen

The trend of Industry 4.0 development in the maintenance field requires the big data analysis of system operation for predicting, or identifying any possible problem. This study proposed an automatic fault diagnosis method for rolling bearing. Ensemble empirical mode decomposition (EEMD) is used to preprocess the vibration signals of rolling bearing which solved the problem of mode mixing existing in empirical mode decomposition (EMD). The features are then extracted from the first IMFs in the time and frequency domain to form an initial fault feature of the complex vibration signal. The Laplacian score (LaS) method for selecting features is then utilized to construct a new sensitive feature according to their significances, named EEMD-LaS. An optimized pattern classification method is constructed to achieve an automatic diagnosis of bearing fault. This method uses a backtracking search optimal (BSO) algorithm to optimize the parameters of the least-squares support vector machine (LSSVM) classifier model, named BSO-LSSVM. Finally, the EEMD-LaS and BSO-LSSVM base proposed method for bearing fault diagnosis is applied to analyze the experimental data. The result indicates that the proposed EEMD-LaS – BSO-LSSVM method can effectively diagnose the fault categories with 100% accuracy of the outer-race fault and normal bearing condition; and 97% minimal accuracy of the severe conditions of the inner-race fault and roller fault of rolling bearing.

269

Authors: Awan Uji Krismanto, Indra Soegiarto, Abraham Lomi, Irrine Budi Sulistiawati, Herlambang Setiadi, Muhammad Abdillah

One of the main focus in integrating large scale wind power plant is how to maintain voltage stability under different power injection from wind farm. In this paper, effects of large-scale wind power plants on voltage profile of power system is investigated. Practical test system of South-West Sulawesi, Indonesia with integration of two large scale wind power plants are considered. It was monitored that the increasing wind enhanced the voltage profile of the system. Impacts of wind power integration on power system voltage profile were investigated in this paper. Probability analaysis based on MCS were conduted to observe the impacts of uncertain power injection from wind farm on voltage fluctuation. It was clearly monitored that the probability distribution of bus voltage varied accordingly depending to location and capacity of wind farm. It was also monitored that enhancement of voltage profiles increased in proportion with power injection from wind farm. Thus, having more power production from wind farm results in better load-ability and eventually improved voltage stability condition of power system as shown in bus 31 (the voltage magnitude increased from 0.942 to 0.952 pu).

294

Authors: Rachmad Abdullah, Suhariyanto, Riyanarto Sarno

TripAdvisor is one of the most popular e-commerce platforms in the tourism sector in Indonesia. TripAdvisor give Traveler Choice Award every year in Indonesia through user reviews. However, online text-based reviews are often associated only with evaluation scores that do not pay attention to the context and meaningful content of the review itself, either explicitly or implicitly. Moreover, the sentence structure of the review can have an impact on the goal of the target sentiment which is nothing but an aspect of the review itself. This research discusses aspect based sentiment analysis for explicit and implicit aspects. This research starting with taking the TripAdvisor website restaurant product review dataset to measure customer satisfaction based on four aspect categories of Ambience, Food, Service, and Price. Furthermore, the aspect word extraction and opinion word extraction processes in the case of explicit sentences for simple, compound, complex, and compound-complex sentence structures are carried out using grammatical rule extraction. This research also works on implicit sentence cases for simple sentence structures. Aspect categorization process uses hybrid approach. Aspect and opinion keyword extraction process uses the ELMo-Wikipedia. Then WordNet and TF-ICF are used to expand the meaning of aspect and opinion that has been taken. The last stage is the aspect based sentiment analysis process, both explicit and implicit sentences using SentiCircle. This research can produce two evaluations of sentiment classification, namely positive and negative. The results of the aspect extraction obtained an evaluation of the aspect categorization for each precision 0.82, recall 0.87, and f1-measure 0.86. Meanwhile, the results of the sentiment analysis showed that the respective evaluations for precision 0.87, recall 0.92, and f1-measure 0.89.

320

Authors: Antono Adhi, Budi Santosa, Nurhadi Siswanto

Studies and research related to the Vehicle Routing Problem (VRP) are mostly carried out on simple constraints. The study was conducted to find the shortest route that the carrier will take. Effective algorithms are sought to find better solutions. Another study will develop VRP cases to be closer to real conditions. In actual conditions, the VRP cases are very complex. Several solutions that are considered good can be implemented to solve this problem. VRP in shipping bulk products, such as oil, cements, and fertilizers is one of the daily logistical cases carried out in an archipelago country. Bulk products are shipped from the depot port city to other islands. The optimum solution is needed to make the delivery shorter distance and the products can be sent faster. The solution to the VRP case with a single product can no longer solve the problem of bulk product shipment. Bulk product shipment involves more than one product with more than one ship compartment. The number of ship compartments that are smaller than the number of products will further increase the complexity of the VRP of bulk product shipment. The combination of several metaheuristic methods and strategies of product selection to fill undedicated compartment of ship is used to find the shortest route. Modified Nawas Enscore Ham (M-NEH), Particle Swarm Optimization (PSO), and 3-Opt algorithms are combined to solve VRP. This hybrid algorithm called Hybrid Particle Swarm Optimization (HPSO) is improved and supports each other. HPSO provides a better solution than those algorithms separately and other metaheuristics namely Tabu Search (TS) and Genetic Algorithm (GA). The implementation of the algorithm to solve real data of bulk cement transportation supports the conclusion that HPSO is superior to the other algorithms. It has 0.99% more effective than the second best and about 3.42% more effective than the average total distance of five other compared metaheuristic methods.

348

Authors: Hussein A. A. Al-Khamees, Nabeel Al-A'araji, Eman S. Al-Shamery

Many different applications in the real world can generate huge amount of data, that has unconventional features including massive size, fast access, besides the evolving in its nature; this is data stream. Data stream clustering algorithms began to grow at breakneck speed. evolving Cauchy (eCauchy) is a significant algorithm of density-based data stream clustering. The major limitation of eCauchy is the high number of clusters generated in dynamic environments. This paper presents an evolving model for data stream by optimizing e-Cauchy algorithm to decrease the number of clusters and reach to an ideal number by implementing evolving mechanisms (adding, merging, splitting clusters) based on a specific membership function. Model is tested by two real datasets NSL-KDD99 and keystroke. Proposed model outperforms two other algorithms, e-Cauchy and FEAC-Stream. Model constructs five and four clusters with less time to implement 1.30 and 2.30 minutes respectively for each dataset.

369

Authors: Seyed Alireza Bashiri Mosavi, Seyed Yashar Banihashem

Monitoring transient excursions of power system dynamic response is the most significant task for a reliable power supply. Hence, pursuing the best-laid features alongside a fit learning model for precise mining on the inherent characteristic of fast transient phenomena is considered a great concern on transient stability analysis (TSA). To this end, first, we discussed the root cause involved in selecting the cross-relevance (CR) features that influenced TSA. Furthermore, we regarded the geometric form of CR (GCR) features to measure the partial-whole manner of power system stability limits. Next, we triangulated Python technology, PSS/E, and MATLAB for generating the GCR multivariate trajectories. In the learning phase, to shroud the linear alignment weakness, the elastic kernel plugged into the support vector machine is considered for transient stability prediction (TSP). The results show an average accuracy near 97% with an acceptable average processing time of 691.7 milliseconds on test cases for TSP.

395

Authors: S. Beghin Bose, S. S. Sujatha

Cloud computing is the on-demand availability of internet-based computing services, especially software, large amounts of data storage, operating systems, and other computing resources. Service Level Agreement (SLA) violation is the most critical problem in cloud computing. SLA violation creates many problems for cloud service providers and cloud customers. Due to this, cloud customer gets low-quality cloud service. Thus, designing an effective cloud service recommendation algorithm is a critical research problem in cloud computing. The primary objective of this research is to determine the optimal cloud service from functionally equivalent cloud services that better fit the user's requirements (latency, throughput, response time, and cost). The efficiency of cloud services varies according to the time and location of the virtual machine. First, this proposed method determines the correlation between active user requirements and cloud services. Second, strongly correlated services are separated into two clusters based on the virtual machine's location and the cloud service's data transmission rate. For this purpose, two lightweight clustering algorithms have been proposed. A modified multilayer perceptron algorithm has been developed to recommend the optimal cloud service to the active user from the two clusters. The open-source WS-Dream dataset is used to train and validate the proposed MLP. The training efficiency, prediction accuracy, and performance of the proposed MLP-based cloud service recommendation system are experimentally compared to the existing cloud service recommendation systems analysed in the literature study [20, 2, 22, 23]. Compared to existing cloud service recommendation approaches, the MAE and RMSE values of the proposed cloud service recommendation system are less than one. In terms of accuracy, the suggested method obtains a precession of 94 %, a recall of 97 %, and an F1-Measure of 96 %, all of these are significantly better than the existing cloud service recommendation methods. Finally, experimental results prove that the overall performance of the proposed method's throughput (increase 10 MBPS), latency (reduce 10 ms), the response time (reduce 17 ms) and service recommendation time (reduce 5ms) is more robust than existing methods.

422

Authors: Jai Bhagwan, Sanjeev Kumar

Cloud computing technology is becoming popular in both academia and industries these days because of its dynamic and flexible infrastructure which provides good computing facilities through web networks without a place specification. In order to process the workflows, the cloud technology uses datacenters which consist of hosts having various homogeneous and heterogeneous virtual machines. Virtual machines play the role of real machines with multiple operating systems environments. Due to a wide acceptance of the cloud, the load on the cloud is increasing regularly. So, scheduling plays a key role to manage a machine load by mapping several workflows with available virtual machines. The scheduling can be divided into two types: dynamic and static scheduling. Dynamic scheduling can be done effectively by meta-heuristic computing techniques. In the field of cloud computing, many scientists have developed various algorithms for workflow scheduling in order to schedule the workflows. The Cat Swarm Optimization works fine as compared to the Max-Min algorithm, Particle Swarm, and Ant Colony Optimization algorithms. In this research, a new algorithm is designed named H-CSO by using the concept of Heterogeneous Earliest Finish Time and Cat Swarm Optimization algorithms. After experiments, it is found that the proposed H-CSO algorithm gives efficient makespan at realistic costs as matched to the Cat Swarm Optimization. The newly designed H-CSO algorithm is 2.99%, 2.87%, 3.35%, and 5.77% efficient for CyberShake_1000, Montage_1000, Inspiral_1000, and Sipht_1000 datasets respectively as compared to the standard Cat Swarm Optimization in terms of average makespan reduction. In case of cost reduction, the proposed algorithm is 4.03%, 5.12%, 2.42%, and 3.93 effective as compared to the Cat Swarm Optimization for CyberShake_1000, Montage_1000, Inspiral_1000, and Sipht_1000 datasets respectively.

446

Authors: Lindung Parningotan Manik, Zaenal Akbar, Hani Febri Mustika, Ariani Indrawati, Dwi Setyo Rini, Agusdin Dharma Fefirenta, Tutie Djarwaningsih

Knowledge-based chatbot (KBC) has grown in popularity in recent years and has been widely used for various use cases. Building KBC from scratch using deep learning (DL) is challenging since no prior historical data exists. Meanwhile, DL systems need a vast Volume of data to be trained. This paper proposes a novel framework to create an intent classifier of the KBC used to detect in-scope (IS) and out-of-scope (OOS) intents. We introduce an automated queries generator to create IS intents employed as the training data from an ontology input. We utilize Bidirectional Encode Representations from Transformers (BERT) fine-tuning as the backbone of our DL system. Moreover, we present a Bayesian approach as an extension of the BERT to classify OOS queries with minimal OOS training data. The experiments result show that the proposed method manages to achieve an F1 score of 100% for IS intents and 86% for OOS queries.

469

Authors: Ali Saeed Alfoudi, Ali Hakem Alsaeedi, Mohammed Hamzah Abed, Abayomi M. Otebolaku, Yasmeen Sajid Razooqi

Palm Vein Identification (PVI) is a modern biometric security technique used for enhancing security and authentication systems. The key characteristics of palm vein patterns include its uniqueness to each individual, its unforgettability, non-intrusiveness and its ability for disallowing unauthorized persons. However, the extracted features from the palm vein patterns are huge with high redundancy. In this paper, we propose a combined model of two-Dimensional Discrete Wavelet Transform, Principal Component Analysis (PCA), and Particle Swarm Optimization (PSO) (2D-DWTPP) that feeds wrapper model with an optimal subset of features to enhance the prediction accuracy of -palm vein patterns. The 2D-DWT extract features from palm vein images, using the PCA to reduce the redundancy in palm vein features. The system has been trained to select high recognition features based on the wrapper model. The proposed system uses four classifiers as an objective function to determine PVI which include Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT) and Naïve Bayes (NB). The empirical results proved that the proposed model has the best results with SVM. Moreover, our proposed 2D-DWTPP model has been evaluated and the results show remarkable efficiency in comparison with AlexNet and other classifiers without feature selection. Experimentally, the proposed model has better accuracy as reflected by 98.65% whereas AlexNet has 63.5% accuracy and the classifier without feature selection process has 78.79% accuracy.

492

Authors: Mohammed Fadhil Ibrahim, Huda Khurshed Shawkat Aljader, Nawar Abbood Fadhil

Covid-19 pandemic affected our daily activities. Education is the fastest aspect that has been hindered due to the pandemic. The majority of the schools and universities were prone to global closure. The world turned toward using e-learning as an alternative to traditional learning due to the strict lockdown procedures. This study presents a model to measure the students’ intention to use e-learning during the pandemic in Iraq. Technology Acceptance Model (TAM) is utilized with some external variables. Task Technology Fit (TTF) is also adopted as a moderator for the proposed model. The model is implemented on a sample of students studying at Technical College of Management – Baghdad who have completed the academic year using e-learning. Eighteen hypotheses were formulated; out of them, only two were rejected. The model is tested and evaluated using PLS Tool. The results showed that the students have a positive intention to use e-learning during the covid-19 pandemic; also, most of the external variables were statistically significant. Furthermore, using TTF positively moderates the proposed model. The results open the way to perform more investigations in adopting e-learning during unusual situations.

517

Authors: Prabhakaran Mathialagan, Malathy Chidambaranathan

Upper Aero Digestive Tract (UADT) cancer is one of the most common cancer types in any gender. Early detection and diagnosing such type of cancer will reduce the risk of death in human. In this paper we addressed the state-of-the-art solutions for two major pathological constraints like, dysplasia type tumour grading and tumour grade classification with artifact’s present in biopsy images. A new Whole Slide and patch-based CNN model was proposed which involves different pre-processing techniques to detect and normalize the stain artifacts present in biopsy tissue samples. The proposed CNN model is developed to classify different oral cavity tumour sites and its gradings automatically. It was observed that the proposed CNN model achieved best accuracy of 98 % with zero classification error rate. These findings will throw a light for pathologists to handle digital biopsy images with artifacts and also will classify dysplasia type tumour grades automatically.

539

Authors: Dipak Patel, Kiran Amin

Nowadays, the rapid growth of internet has led to the way for easiest data generation. Sentiment Analysis (SA) is necessary to scrutinize the user-generated data for classifying the sentiment polarity into anyone of the two classifications (negative and positive). SA is implemented by machine learning techniques and lexicon oriented techniques. However, the traditional SA models could not deal with missing labelled data and internet slang data. This paper aims to propose a new multi-source sentiment analysis model that includes six stages to perform the task of domain adaptation. For checking similarity between statements, higher order statistics based on text features is exploited. The higher order statistics based features extraction includes use of modified cross entropy measure. Remaining features are then given to proposed classifier that predicts the polarity of target domain in a precise way. For classification purpose Neural Network (NN) is exploited. Particularly, the weights of NN are tuned in an optimal manner using Improved Grey Wolf Optimization (IGWO) algorithm, which is the enhanced version of GWO algorithm. Finally, the performance of the proposed model is compared over several conventional methods of domain adaptation and gained an improvement of 28% to 6% in accuracy.

559

Authors: Mohammed Amine Hassoine, Fouad Lahlou, Adnane Addaim, Abdessalam Ait Madi

This paper investigates the optimal layout of wind turbines (WTs) within a wind farm. Finding the best placement of WTs in a wind farm is a challenging process due to the existence of multiple wake effects. A biogeography based optimization (BBO) algorithm method is proposed to search for the optimal location of WTs in a wind farm (WF), to maximize the power produced by the WF and improve the annual economic performance of the WF. A wind turbine (WT) that operates in the wake of one or more other turbines is subject to lower flow and therefore produces less power. When designing a wind project, the arrangement of the turbines with each other in a wind farm is a very important factor. The best layout of a wind farm is to achieve the optimal placement of the turbines in relation to each other in a given area to maximize the efficiency of the whole wind farm and reduce its cost. A dense configuration would result in considerable power losses. Each turbine must have a sufficient distance from other turbines in the WF where the optimal number of turbines should be placed. The BBO approach is conducted on a 2 km x 2 km wind farm assuming a constant wind speed of 12 m/s with a fixed wind direction, for solving the wind farm layout optimization problem in two different configurations which include 26 and 30 WTs respectively. A comparison of the results obtained with the previous studies shows that the BBO is more efficient in terms of maximizing power output and economic profitability of the same wind farm model, which validates that BBO performs effectively in optimizing WTs placement within WF. BOO provides the greatest improvement in the optimal layout, for example, in the case of the layout for 30 WTs. The power output reaches 15,383 KW, the agreement between the ideal and the optimal layout is more favorable. The difference in output power is only 169 KW (1%). Knowing that the ideal layout is reached if all WTs receive a wind flow with a maximum wind speed of 12 m/s. Furthermore, a case study of wind turbine layout optimization using the BBO program on the Alta X wind farm has been performed under variable wind speed and variable wind direction. The results indicate that the optimized layout of the Alta X wind farm achieves a 12% increase in the power output for a similar cost when compared to the original layout of Alta X. It is also more appropriate for evaluating the wind farm layout in the Wind Power Project (WPP).

583

Authors: Abdelghani Aib, Djalal Eddine Khodja, Loutfi Benyettou, Salim Chakroune

This article provides a hardware implementation of torque and flux estimators for direct torque control (DTC) of an induction motor (IM) on the Field Programmable Gate Array (FPGA). Due to its high processing frequency, the FPGA circuit presents an alternative strategy for achieving a high performance DTC implementation. This cannot be achieved by Digital Signal Processing (DSP) or Microcontroller application. Optimized hardware architecture is also proposed for implementation of a complete estimator with explicit determination of all the quantities such as the flux phase, the flux amplitude and the torque amplitude. This work presents a precise hardware estimator of the torque and the flux, it developed with negligible estimation error (0.0002 [Wb] RMS estimate error for the stator flux and 0.0005 [Nm] RMS estimate error the torque), in addition to that this estimator is implemented on the Virtex-4 FPGA board with minimal resources(less 5% of slices registers, 5% of DSP48Es and around 17% of LUTS). This estimator is developed and synthesized independently of the command and can be reused for any task which requires the estimation of these quantities. After this the estimator was used to complete a DTC control in a hardware co-simulation procedure with the Xilinx system generator and MATLAB/SIMULINK tools on the Xilinx ML402 development kit.

1

Authors: Rashed Salem, Wafaa Abdel-Moneim, Mohamed Hassan

Large graphs are available in everywhere such as social networks, web link analysis, and computer networks. Traditional methods of clustering are not suitable to tackle the problem of clustering large graphs because the computation is very costly, which is solved by local graph clustering using a given vertex set as input to detect an accurate cluster. SimpleLocal (SL) algorithm detects a best conductance cuts close to seed vertices set. In this paper, a new Parallel SimpleLocal (PSL) system is proposed using multicore CPUs. OpenMP parallel library is utilized to parallelize the first and second stages of 3StageFlow algorithm whereas the SL algorithm is used for enhancing the runtime. The experiments are performed on various applications from different domains, which are image segmentation and community detection. From the experiments, the proposed method improves the runtimes with 75.43% using 4-cores and 81.01% when using 8-cores over the sequential single core.

22

Authors: Phyu Phyu Khaing, May The` Yu

Image captioning is systematically generating the caption of the image with a sentence description. In the past few years, the automatic process of creating image caption has fascinated the great interest in Artificial Intelligence (AI) field. Image captioning defines as the basic process of building the conjunction of image processing and natural language processing at input and output position. All image processing tasks, such as the segmentation of image, object tracking, object detection, image recognition, and many others, are mostly performed using Convolutional Neural Networks (CNNs). To perform the natural language processing tasks, just as semantic role labelling, neural machine translation, speech recognition, question and answering, and many others, Recurrent Neural Networks (RNNs) and long-term memory networks (LSTMs) are essential for some of the biggest breakthroughs. This paper proposes the efficient encoder-decoder framework-based image captioning model, namely Two-Tier LSTM (TT-LSTM) Model. The TT-LSTM model is designedly implemented upon the encoder-decoder framework with two LSTM layers. This research is implemented on the MSCOCO, Flickr30k, and Flickr8k datasets; and evaluated with standard evaluation matrixes such as ROUGE-L, CIDEr, and four BLEU scores. The outcomes of the experiments on the typical datasets reveal that the proposed model generates meaningful natural language sentences. The proposed model also improves the sentence generation efficiency and can achieve better performance for image caption generation.

43

Authors: Nitesh Sureja, Avani Vasant, Nandini Chaudhari

Data clustering is a collection of data objects similar to one another within the same cluster and dissimilar to the objects in other clusters. The shuffled frog-leaping algorithm is a nature-inspired algorithm that mimics the natural biological evolution process of frogs. This algorithm also consists of elements like local search and exchanging information globally. This algorithm faces the problem of converging in local optima due to the limitations of the local search method used to explore search space. In this paper, a hybrid shuffled frog-leaping algorithm is introduced for clustering. The proposed algorithm uses a simulated annealing search method instead of a simple local search to improve the search behavior for selecting fitter solutions required in each iteration. Six benchmark datasets are used to validate the performance of the proposed algorithm. Quality measures used are purity, entropy, completeness score (CS), homogeneity score (HS), and FMeasure (FM). Fitness functions used to optimize are total within-cluster variance (TW) and the Silhouette coefficient (SC). Results obtained are compared with the results of twelve other state of the art algorithms. Results stored in the tables clearly shows that our proposed algorithm outperforms other algorithms in terms of quality. Results also prove that the proposed algorithm converges in the significantly less amount of time and eliminates local optima problem also.

56

Authors: Santhosh Kumar Kenkere Basavaraj, Bangalore Ramchandra Sujatha

Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is widely used to provide high speed data transmission and spectrum efficiency in modern wireless communication systems. Specifically, the transmission of Electrocardiogram (ECG) signal plays a main role in health monitoring systems. The privacy and security of the patient identification and medical records are considered as a main concern in health monitoring systems. In this paper, the Lightweight cryptography (LWC) is proposed to secure the ECG signal transmission from unauthorized users through the MIMO-OFDM system. The LWC is mainly used to minimize the amount of logical elements using the gate level architecture and simple key schedule in the MIMO-OFDM. The turbo code is used in MIMO-OFDM is due to its error correcting capacity that minimizes the amount of error caused during communication under the constraints of burst error and Inter Symbol Interference (ISI). Here, the ECG signals from the MIT arrhythmia database is used to analyse the secure ECG signal transmission of LWC-MIMO-OFDM method. The performance of the proposed LWC-MIMO-OFDM is taken by means of area, delay, power, number of slices, flipflops and LUTs. The LWC-MIMO-OFDM method is compared with Advanced Encryption Standard (AES) to evaluate the efficiency of LWC-MIMO-OFDM. The delay of the LWC-MIMO-OFDM for Virtex 5 device is 13.3ns, it is less when compared to the delay caused by the AES.

77

Authors: Hicham Bouzakri, Ahmed Abbou, Khalid Chennoufi

The optimal installation of a photovoltaic panel in fixed mode, allows us to receive the maximum of solar radiation at noon, when the sun is in front of the panel, although the output drops in the morning and afternoon. In order to maintain this efficiency as much as possible, the photovoltaic panel must be oriented by a solar tracker. However, the introduction of such a mechanism makes the installation expensive and complicated during maintenance, especially since a part of the energy produced by the panel will itself be consumed for its operation. This paper proposes a simple and less expensive system allowing the photovoltaic panel in fixed mode to maintain its efficiency almost at the maximum all day long with the introduction of a simple reflection system based on two reversible mirrors at noon. After a theoretical study and on the basis of a simulation, the proposed system will have an efficiency of more than about 35% compared to that in fixed mode and more than 90% compared to that in tracking mode, these results will be validated via an experiment at the end of the document with a prototype produced.

105

Authors: M. Ganesh Karthik, M. B. Mukesh Krishnan

In recent decades, the Internet of Things (IoT) is a growing technology in smart applications, where it is highly susceptible to security breaches and the resources are constrained in nature. Hence, the growth of IoT devices allows the hackers to take benefit of the communication capabilities to detect different types of attacks such from NSL-KDD such as Denial of Service (DoS), Probe, Remote to Local (R2L) and User to Root (U2R) attacks. The existing deep networks used larger data in training because the size of non-predictive parameters for learning required huge potential to attack detection. In order to overcome the problem occurred in the existing models, an efficient Post Pruning Decision Tree-Synthetic Minority Over-Sampling Technique (PPDT-SMOTE) is proposed in the research work. The proposed PPDT-SMOTE eliminates or pruned the non-predictive parameters from the huge data samples and SMOTE solves the data imbalance problems as they use the prominent samples from the non-pruned regions. Thus the PPDT models improves the learning rate of the system for huge data and SMOTE will overcome the problem of class imbalance problem as the learning rate is improved. The results obtained from the proposed PPDT-SMOTE approach effectively detects the DoS, Probe, U2R, and R2L attacks in terms of accuracy as 98.04% better when compared to the existing shallow models of 95.22%, Routing Protocol for Low Power and Lossy Networks (RPL) of 91.5 % and Multi-Convolution Neural Network (CNN) fusion model of 64.81% in the IoT environment.

123

Authors: Phyu Myo The, May The` Yu

Hand gesture detection and recognition is a way to communicate between deaf-mute person and rest of society. In this study, the static sign and the dynamic sign recognition system based on hand gesture is presented. The dynamic sign gesture is more challenging than static sign gesture which contains the movement. The vision based sign language recognition using hand gesture consists of the following main steps: acquisition the sign data, detection the hand region, extraction the features and recognition. The data acquisition stage, the data acquired by using web camera without using any data gloves and special markers. In hand region detection process, this study proposed hybrid hand region detection process by combining CbCr channel from YCbCr colour space and motion detection by using mean filter background subtraction to definitely segment the hand region from the background. In hand feature extraction, the scale, shape, texture and orientation features extracted by using GIST (Generalized Search Tree), HOG (histogram of gradient) and HOG-LBP (Local Binary Pattern) methods. Finally, Quadratic Support Vector Machine (QSVM), Cubic Support Vector Machine (CSVM) and Linear Discriminant recognized the static and dynamic hand gesture. The recognition accuracy achieved 86.7% and 99.22 % acceptable accuracy on self-construct dynamic Myanmar sign word dataset and MUDB dataset.

146

Authors: Bhagirathi Bai Vittal Rao, Hanume Gowda Chandrakanth

Presently, industrial automation has become a popular field because of its various advantages includes higher production rates, more efficient use of materials, better product quality, improved safety, and requires minimum labours. This is achieved by utilizing Local Networking Standards (LNSs), remotely monitoring and controlling industrial devices by utilizing Raspberry Pi and Embedded Web Server (EWS) technology. This proposed research provides an idea of utilizing Internet of Things (IoT) for monitoring and controlling the automation process through Wi-Fi or wireless medium by utilizing Raspberry pi as a server system. Additionally, the prediction and error detection utilized in the machine learning process for this Improved Random Forest (IRF) method. The proposed IRF uses Out of Bag (OoB) bagging estimation technique to randomly select sub-datasets for overcoming the optimization problem. The OoB estimation is the technique used to find prediction error in IRF as every samples are not used when each tree in IRF is trained. So for all those bags unused samples estimate the prediction error for a particular bag in prediction process. IRF recursively partition the data into categories and the derived RF model and maps the results. The proposed IRF method overcomes the overfitting problem, receives the command and applies action according to it. The IRF method uses only eight devices to control and monitor more than thirty devices and this entire process is represented as IoT based Machine Learning (ML) Automation Algorithm. The experimental results show that the IRF method provides better outcomes in terms of accuracy, precision, F-measure, and recall. The RMSE values obtained for the proposed IRF model shows 0.0386 lesser error values when compared with the existing models that had achieved RMSE as 3.607 for Long Short Term Memory- Recurrent Neural Network (LSTM-RNN), Artificial Neural Network (ANN) as 1.226 and Fuzzy Gain Scheduling (FGS) of 0.4247.

166

Authors: Vasudha Bahl, Anoop Bhola

WSNs embedded device can be extended to a wide range of implementation in reality. Clustering is efficient way to lessen the energy utilization and improve WSN's lifespan. To improvise network lifespan numerous clustering approaches, implement various parameters for election of CH. An effective clustering algorithm depends upon the number of factors such as number of CHs, uniform cluster size, CHs distribution, energy of the CHs etc. In our research we strengthen our methodology for election of cluster head in HWSN depending on multiple node parameters such as distance, density and residual energy. This paper aims to optimize energy and improve network with Energy Balanced Cluster Technique (EBCT). In our research we reformulated for probability estimation to identify the CHs in each round characterized by node parameters: distance, density, residual energy and node dormancy mechanism. Mathematical analysis and simulations show the proposed method extends the service life by around 8% to 53% relative to the other protocols and optimizes energy utilization of HWSNs.

190

Authors: Abdelmoula Saggai, Samir Zeghlache, Loutfi Benyettou

This work presents a reliable fault-tolerant tracking control system (FTTCS) for actuator faults in a octorotor unmanned aerial vehicle. The proposed FTTCS is designed based on adaptive fuzzy global fast dynamic terminal sliding mode control (AFGFDTSMC) that guarantees the global asymptotic stability of a octorotor system. To mitigate the negative impacts of model uncertainties and enhance system robustness in faulty operation, an adaptive fuzzy system is incorporated into the global fast dynamic terminal sliding mode control (GFDTSMC) scheme for adaptively identifying the model uncertainties online and compensate the actuator faults effect. Lyapunov stability analysis proofs that the developed control method design maintains the stability in the closed loop dynamics of octorotor UAV in faulty operation. Simulation results show that the proposed (FTTCS) enables the octorotor to track the desired reference commands in the presence of actuator faults with satisfactory performance.

217

Authors: Aris Marjuni, Oky Dwi Nurhayati

Image watermarking is a technique to embed a piece of information or data, which also can be an image, into the host or medium image for specific purposes, such as authentication, security, or copyright protection. A good image watermarking method should have a good perceptibility after adding the watermark. It should also robust against attacks and the watermark should be able to be recovered although the watermarked image has distorted. This paper presents the proposed lifting image watermarking scheme based on wavelet transform through singular value and Schur decomposition. This method is proposed to improve the perceptibility and the robustness of the image watermarking scheme, especially against non-geometrical attacks. The lifting scheme is performed by lifting wavelet transform to produce a better imperceptibility. The use of singular value decomposition is used to increase the intactness of the embedded watermark from the distorted image, which is performed on the triangular matrix of low-frequency sub-band produced by Schur decomposition. In perceptibility performance, experimental results show that the proposed method could produce a watermarked image very close to the host image with more than 99% of similarity level. In robustness performance, the proposed method can recover the embedded watermark even though the watermarked image has been distorted by non-geometrical attacks with more than 90% of normalized correlation average.

241

Authors: Pradeep Krishnadoss, Gobalakrishnan Natesan, Javid Ali, Manikandan Nanjappan, Parkavi Krishnamoorthy, Vijayakumar Kedalu Poornachary

Services are delivered promptly and efficiently managed over the Internet using cloud computing. Data and storage services are offered to users through cloud computing without regard to the actual physical location of the users. The cloud environment includes huge number of tasks and computing resources. Identification of appropriate virtual machine (VM) for allocation of resources to complete any submitted task is done through the task scheduling algorithms, which play a significant role in the cloud computing process. Task scheduling techniques improvise the makespan of the schedule and account for considerable reduction of cost expended. In this paper, an efficient hybridized scheduling algorithm that replicates the parasitic behaviour of the cuckoo and food gathering habit of the crow bird, named Cuckoo Crow Search Algorithm (CCSA) had been presented for improvising the task scheduling process. The crow bird always stares at its neighbours looking for a better food source than the one it currently possesses. At some circumstances the crow even goes a step further and steals its neighbour’s food. The CCSA had been inspired from such characteristics of these birds and had been designed to be applied in the cloud environment for identifying a suitable VM for carrying out the task scheduling process. The proposed CCSA accounts for reduction in makespan and cost and its performance had been experimentally verified by comparing it with other existing algorithms like Multi objective - Ant Colony Optimization (MO-ACO), ACO, Min-Min and the results had been presented. The proposed CCSA technique had produced an improvement of 3.14%, 10.70%, and 21.60% for makespan and had reduced the overall cost to the tune of 4.56%, 11.19% and 19.35% when compared with MO-ACO, ACO, Min-Min algorithms respectively when used with 10 VMs. The detailed description of results obtained had been described in result section.

264

Authors: Sethakarn Prongnuch, Suchada Sitjongsataporn

This paper presents a spherical magnetic robot named ‘CE-R1’ propelled by an inner drive unit and a magnetic balancing unit rolling without slipping on a plane. A spherical magnetic robot ‘CE-R1’ consists of two parts as a domed head and a ball-shaped body with contactless and controlled via Bluetooth. A non-holonomic model and wheel-drive locomotion are interested in a spherical mobile robot. Sphere movement is controlled by a differential drive from the inner drive unit inside the sphere. Firstly, the magnetic interaction in a magnetic balancing unit is applied to the connection of a domed head and a ball-shaped body with contactless. Secondly, a differential drive of proposed spherical magnetic robot ‘CE-R1’ with an inner drive unit inside is derived and deployed with the multi-single computer board controller. Hardware of prototype ‘CE-R1’ is designed including the multi-single board computer for wireless charger, voice recognition system and controlling the ‘CE-R1’. The explicit experiments of proposed ‘CE-R1’ spherical magnetic robot are tested as a faculty-assistive guide robot controlled through application at the faculty lobby area in the Suan Sunandha Rajabhat University. This work proposed here is a step towards the overall goal of an autonomous spherical mobile robot.

289

Authors: Ayad Mohammed Jabba

The wide amount of data in the modern applications available on the Internet make it very complicated to deal with the knowledge behind these data. The data classification task is a useful tool that used to deal with a huge amount of data by classify these data into coherent groups. The data size decreases the performace of the classification technique, especially when contain uninformative data (i.e., irrelevant, noisy). The stochastic local search algorithm is an optimization approaches employed to find the informative data to build the classification model. These methods are used to search for the optimal patterns to construct the classification algorithm. Thus, this research introduces a stochastic local search algorithm for rule induction called Iterated-Miner (Iterated local search-based rule induction). The purpose of the algorithm is to construct classification rules from data. This classification algorithm is inspired by the concepts and principles of stochastic local search and rule induction. The performance of the proposed classifier evaluated with a well-known and state of art classification algorithms called, Ant-Miner, ACO/PSO2, PART FURIA, and ACO/GA on 10 UCI datasets. The results demonstrate that our classifier is superior compare with all classifiers respect to classification accuracy; and the model sizes by our classifier are considerably competitive with those discovered by other classifier.

312

Authors: Hilal Rahali, Samir Zeghlache, Loutfi Benyettou

In this study, an adaptive fuzzy type-2 backstepping controller (AFT2BC) has been proposed for an industrial PUMA560 manipulator robot with a variable load and actuator faults. The method realizes an accurate tracking of the trajectory at the end effectors of the manipulator and improves its robustness toward model uncertainty (payload variation) and actuator faults when controlling the position of the robot. By using the advantages of the backstepping control, the convergence speed of the control algorithm has been improved, and its steady-state error has been reduced. Also, integration of the continuous approximation law has been used to eliminate the real-time chattering during the control process without affecting the robustness of the system. A type-2 fuzzy adaptive control law has been designed in order to guaranty faults and uncertainties compensation, small tracking error, and fast transient response, where the prior knowledge of uncertainties and external disturbances is not required, without disappearing the tracking precision and robustness property. Finally, the stability of the controller has been proved by the Lyapunov theory, and comparative simulations in faulty operation are conducted to show the superiority of the developed control strategy.

337

Authors: Aradea Aradea, Rianto Rianto, Husni Mubarok

Internet of Things (hereafter, IoT) service is a complex system because it should meet miscellaneous domain forms represented physically and virtually. The main challenge of IoT is to provide an inference model to resolve the dynamic context on a run-time basis. The system should have the ability to catch instances or concrete IoT services. On the other side, it should have the capability to adapt to the newest evidence of contexts. This paper introduces an inference model consisting of an IoT structure service artifact, a subsystem of contextual knowledge, and a subsystem of run-time adaptability reasoning. The results of model implementation on monitoring system of coronavirus disease revealed that the ability to adapt continuously and provide various alternative solutions to handle uncertain contexts, which is refered to sensor, network and server failure. The example of experiment result when a sensor failure occurs, the data received by the main server from the node is the average of the three previous data.

373

Authors: Nahlah Abdulrahman Alkhalidi, Fouad A. Yaseen

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational characteristics of traffic flow types; by considering only the position of the selected bits from the packet header. The proposal a learning approach based on deep packet inspection which integrates both feature extraction and classification phases into one system. The results show that the FDPHI works very well on the applications of feature learning. Also, it presents powerful adequate traffic classification results in terms of energy consumption (70% less power CPU utilization around 48% less), and processing time (310% for IPv4 and 595% for IPv6).

393

Authors: Bishoy Sameeh Sawiris, Sayed Abd El-Gaber, Manal A. Abdel-Fattah

Centralized big data is an emerging field that manages information in one common repository. Currently, Internet of Things (IoT) devices are increasing massively for sensing and transmitting data to the big data environment. This information is sensitive and easily traced by attackers due to the centralized data management. To address the security and privacy issues and avoid the system in a single point of failure, in this paper the researchers proposed a new model, namely, BOBS CRABID i.e. Blockchain Based Secure Centralized Big Data model using distributed computing approach. In big data, Hadoop MapReduce is one of the distributed computing approaches which handles all information in a distributed manner. The BOBS CRABID model is designed by three ideas as Multi-Factor authentication, IoT Devices Data Collection and Processing, and Optics based MapReduce for Data Clustering. In multi-factor authentication, a lightweight camellia key generation algorithm (LCKGA) is used for their authenticating all devices based on ID, IP address, MAC and PUF. These credentials are saved in Blockchain Security Entity for authentication verification. All transactions are hashed and stored in the blockchain using Keccak hash algorithm which performs better than the traditional hashing (SHA) 2. Then the researchers conducted IoT devices data collection and pre-processing using Cross Correlation and Min-Max Normalization for redundant data pruning and normalization, respectively. Finally, clustering is performed in a distributed way for optimizing the storage and scalability performance. For that MapReduce is used in which OPTICS algorithm is applied for clustering based on the data size, type of context and nature of data (sensitive or non-sensitive). In this way, data is stored in the big data environment. Experiments were conducted for the proposed BOBS CRABID model and compared with the well-known methods using Hadoop 2.7.2. The proposed BOBS-CRABID model achieves better performance in terms of throughput (increase 1500mbps), response time (reduce 150ms), energy consumption (reduce 35%), attack detection rate (increase 30%), accuracy (increase 20%), computation overhead (300 kb), and time reduce 4s).

422

Authors: Anuruk Nosan, Suchada Sitjongsataporn

In this paper, a proposed absolute sort delta mean (ASDM) method obtaining the speech feature extraction for noise robustness is developed from mel-frequency cepstral coefficients (MFCC) named ASDM-MFCC, in order to increase robustness against the different types of environmental noises. This method is used to suppress the noise effects by finding a rearranging average of power spectrum magnitude combined with triangular bandpass filtering. Firstly, the spectral power magnitudes are sorted in each frequency band of the speech signal. Secondly, the absolute-delta values are arranged and then a mean value is determined in the last step. The purpose of proposed ASDM-MFCC algorithm is to require the noise robustness of the feature vector extracted from the speech signal with the characteristic coefficients. The NOIZEUS noisy speech corpus dataset is used to evaluate the performance of proposed ASDM-MFCC algorithm by Euclidean distance method with the low computation complexity. Experimental results show that the proposed method can provide significantly the improvement in terms of accuracy at low signal to noise ratio (SNR). In the case of car and station at SNR=5dB, the proposed approach can outperform in comparison with the conventional MFCC and gammatone frequency cepstral coefficient (GFCC) by 80% and 76.67%, respectively. Obviously, some experimental results of the proposed ASDM-MFCC algorithm are more robust than the traditional one.

452

Authors: Krit Chantarasamai, On-Uma Lasunon

We describe a Differential Evolution (DE) algorithm for solving U-shaped Assembly Line Balancing Problems – Type 2 (UALBP-2). The minimum cycle time in a just-in-time production line for producing a single product with a certain number of workstations was investigated by developing solution methods and testing on 15 problem sets (101 instances). The problems were divided into 10 medium-scale problem sets (50 instances) and 5 large-scale problem sets (51 instances). When comparing the results with those obtained from the rule-based heuristic; three rules and two rules, the DE algorithm could generate 14 better solutions (28%) in the medium-scale problems, and 3 better solutions (about 6%) in the large-scale problems. In terms of computational time, DE algorithm was faster than rule-based heuristic methods in medium-scale problems. In large problems, our DE algorithm used computational times that were shorter by 73% (for two rules) and 98% (for three rules).

472

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

There are numerous optimization problems in various sciences that need to be solved using the appropriate technique. One of the most widely used techniques for solving optimization problems are population-based optimization algorithms. The innovation and contribution of this paper is to design a new optimizer called Mixed Leader Based Optimizer (MLBO) to solve optimization problems. The main idea in the proposed MLBO is to create a new member as a leader by mixing the best population member and a random member to guide the algorithm population. The main advantage and feature of the proposed MLBO is that it has no control parameters and therefore no need to adjust the parameter. The proposed MLBO algorithm is mathematically formulated to implement in solving various optimization problems. The capability of the proposed optimizer in optimizing and providing appropriate solutions has been tested on a set of twenty-three standard objective functions. These objective functions are selected from three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal in order to analyze different aspects of optimization algorithms. Also, in order to analyze the obtained optimization results, the performance of the MLBO is compared with eight other well-known algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Teaching Learning-Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Gray Wolf Optimizer (GWO), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). The obtained optimization results from the MLBO show the proper performance of the proposed algorithm in solving various optimization problems. On the other hand, comparing the performance of the MLBO with the other eight optimization algorithms indicates the superiority of the proposed optimizer over the compared algorithms.

500

Authors: Seyed Alireza Bashiri Mosavi

The corrective action for power systems over transient space requires the transient stability prediction (TSP) in an accurate and timely manner. To this end, applying data mining techniques to achieve high performance on TSP is an inevitable approach. One of the influential factors on TSP performance is extracting the most relevant features (MRFs) of transient data. In this paper, first, we constructed the transient dataset in the form of reactive power-based two-variate time series (RP2vTS). Next, for exploiting the optimal feature subset, the MRFs of RP2vTS are obtained by the 1-persistence parallel fragmented hybrid feature selection scheme (PFHFS) based on filter and wrapper methods. Finally, to evaluate the efficacy of the proposed framework, experimental comparisons on inter-connected New England and New York systems (NETS-NYPS) were applied using the support vector machine (SVM) classifier. The results showed that the proposed framework by selecting MRFs of RP2vTS offers high-performance capacity on TSP.

524

Authors: Shahbaa I. Khaleel

Estimating models in software engineering are used to estimate some important and future characteristics of the software project, such as estimating the developed project effort, and that failure in the program is mainly due to wrong project management practices, so estimating the software effort is a very important step in the software management process for large projects. In this research, a software estimation tool was built to find an efficient and accurate method for estimating the effort. The average COnstructive COst MOdel COCOMO was used, which is classified as one of the best traditional methods among arithmetic estimation models. Four methods of swarm intelligence were used, the first of which is the Glowing worm Swarm Optimization GSO method and the second is the Bird Swarm Algorithm BSA and the third is the first proposed method hybrid BSA-GSO Method1 BSA-GSOM1, where the GSO and BSA algorithms were hybridized, and the performance of the third method was improved to form the fourth method, represented by the second hybrid new method, which called hybrid BSA-GSO Method2 BSA-GSOM2. The new tool was implemented with all its methods on the NASA data set and satisfactory results were obtained by the first and second swarms intelligence, and excellent results were obtained in the first proposed method, but the results of the second proposed method were better and more accurate than the previous ones. many measurements of performance were used for all the methods, the second proposed method yielded the best results from everyone.

548

Authors: Muhamad Asvial, Agatha Cracias, Muh. Asnoer Laagu, Ajib Setyo Arifin

In this paper, we create a network for IoT applications that able to reduce power to a minimum. The system uses a combination of objective function zero and the modified E-Trickle (ME-Trickle) algorithm to reduce power usage. The design and simulation are conducted with the Contiki OS, Cooja simulator. The system performance is evaluated based on the parameters of power consumption, packet delivery ratio, latency, and convergence time on the network. The proposed system in the simulation result can reduce the convergence time by 15%, power consumption by 8%, and latency by 9%. This system also continues to provide efficient performance with an average PDR of 93.8%. The simulation concluded that MRHOF provides better performance in network quality, whereas OF0 has a faster network convergence and consumes less power than MRHOF. Therefore, the use of ME-Trickle and OF0 will be appropriate for network scenarios with limited resources.

571

Authors: Nurul Zainal Fanani, Adri Gabriel Sooai, Khamid, Festa Yumpi Rahmanawati, Alex Tormasi, Laszlo T. Koczy, Surya Sumpeno, Mauridhi Hery Purnomo

The increase of the classification accuracy level has become an important problem in machine learning especially in diverse data-set that contain the outlier data. In the data stream or the data from sensor readings that produce large data, it allows a lot of noise to occur. It makes the performance of the machine learning model is disrupted or even decreased. Therefore, clean data from noise is needed to obtain good accuracy and to improve the performance of the machine learning model. This research proposes a two-stages for detecting and removing outlier data by using the covariance estimator and isolation forest methods as pre-processing in the classification process to determine fine motor skill (FMS). The dataset was generated from the process of recording data directly during cursive writing by using a digitizer. The data included the relative position of the stylus on the digitizer board. x position, y position, z position, and pressure values are then used as features in the classification process. In the process of observation and recording, the generated data was very huge so some of them produce the outlier data. From the experimental results that have been implemented, the level of accuracy in the FMS classification process increases between 0.5-1% by using the Random Forest classifier after the detection and outlier removal by using covariance estimator and isolation forest. The highest accuracy rate achieves 98.05% compared to the accuracy without outlier removal, which is only about 97.3%.

1

Authors: Girija Vani Gurram, Noorullah Shariff Chowdary, Rajkumar Laxmikanth Biradar

Security of Wireless Sensor Node (WSN) is an important issue due to inherited nature and uncontrolled operation, which makes it vulnerable to many attacks Sybil attacks, denial of service attacks, etc. The main issue related to WSN is the key management system, as it plays an important role in providing security services for WSNs. However, developing a distribution/establishment scheme for WSNs is challenging due to limited sensor sources. In this study, three schemes namely proxy-based path key establishment (PPK), friend-based path key establishment (FPK) and disjoint path key establishment (DPK) are developed. The main problem of path key explore is minimized by passing the nodes with high value of attack coefficient. In PPK model, the Grey-Wolf Optimization (GWO) is used to optimize the threshold value for k-key fragment number and categorization of nodes. The nodes having the common key with destinations are identified by FPK and the best path to reach the destinations is selected by DPK. According to the path compromise ratio and probability of key recovery, the resistance to the effectiveness of three proposed schemes are verified. The results proved that the proposed PPK-GWO achieved probability of key recovery 0.8 for 100 captured nodes, where the DPK achieved the probability of path compromise 0.95 for 10 captured nodes.

23

Authors: Hady Abdel-Maksoud

DC motor drive is still widely employed in manufacturing and transportation systems. The control schemes require an accurate model for recognizing the motor performance. Some or completely the motor parameters may be unknown for the designer. In this research, the application of farmland fertility optimization (FFO) algorithm to identify the parameters of DC motor is presented. This paper purposes to develop an efficient DC model to characterize its performance under various working conditions. The experimental system to validate the proposed method in identification the motor parameters has been built. The parameters of DC motor have been estimated based the actual motor speed response. Then, the proposed technique has been examined at different motor operation as starting, load change and speed change. Subsequently, the experimental results are compared with results obtained from the FFO. It is found that a good convergence between the actual and estimated results is appeared and the convergence rate is continuously decreased until highly acceptable value. Furthermore, a comparison between the results of FFO and those based recent optimization technique has been occurred. It can be assured that the FFO performs well and has good capability of recognizing the unknown DC motor parameters with fewer errors.

43

Authors: Widi Aribowo, Supari Muslim, Bambang Suprianto, Subuh Isnur Haryudo, Aditya Chandra Hermawan

A power system is a collection of equipment that has characteristics. Power system stability is maintaining how the system can return to its position. Power system stabilizer (PSS) is a device used to maintain the stability of the power system due to load changes. The archimedes optimization algorithm (AOA) is a metaheuristic method based on the force that occurs in the fluid due to a load. The method is based on the laws of physics. A neural network is a concept to duplicate the work function of the human brain. In this study, the archimedes optimization algorithm (AOA) will be proposed to improve the performance of the feed forward neural network (FFNN). This hybrid method is called AOA-NN. The hybrid method is used to improve the performance of power system stabilizers. To test the ability and effectiveness of the AOA-NN method, a comparison with the conventional PSS, feed-forward neural network (FFNN), Cascade-forward neural network (CFNN), Distributed time-delay neural network (DTDNN) and Sine Tree-Seed Algorithm – Feed-forward Neural network (STSA-NN) method are applied. From the research, it can be concluded that the method proposed by AOA-NN has the best ability. The AOA-NN method has the ability to reduce the overshoot speed with an average value of 85.43972% and the overshoot rotor angle with an average value of 38.9278%.

72

Authors: Yashaswini Doddamane Kenchappa, Kwadaki Karibasappa

Recently, the multimedia complexity is noticeably increased due to the growth of internet and multimedia technologies and it has become one of the prime research topics in multimedia content retrieval. In this research, a dual phase model is proposed to enhance the performance of content based image retrieval. Initially, normalization technique is employed to improve the visibility level of the images that are collected from Wang and COREL datasets. Next, color moments and Alex-Net convolutional neural network are used to extract the feature values from the normalized image. By combining low and high level features, the semantic space between the extracted feature values is less that helps in retrieving the relevant content images significantly. Finally, the Manhattan distance measure is applied to measure the distance between the query image and an image dataset to retrieve the relevant images. The experimental result showed that the proposed dual phase model achieved better performance in content based image retrieval by means of recall, precision and f-score. From the comparative analysis, the proposed dual phase model showed a maximum of 0.43 precision value and 0.06 recall value improvement in content based image retrieval compared to the existing works like Bi-layer system, spatial and frequency domain features with Euclidean distance, and local directional pattern with color histograms.

97

Authors: Somkiat Khwanpruk, Chalida U-tapao, Kankanit Khwanpruk, Laemthong Laokhongthavorn, Seksun Moryadee

The Bangkok–Nong Khai high-speed railway is the first high-speed line in Thailand and is currently under construction. The project is due to be completed by 2023, with ticket prices starting at 80 baht plus 1.8 baht per km which can be considered as a straight-line fare set with a fixed rate increase. Different from the current price policy, this paper examines the application of dynamic pricing to Thailand’s HSR. Dynamic Pricing Optimizer proposed in this paper is a unique system of dynamic pricing by considering the changes in the amount of service requirements and the time of purchasing tickets designed to solve the problem of pricing that is appropriate. First, The Dynamic Pricing Optimizer (DPO) system will integrate the user data from the latest trip or historical information. After that, there will be a forecast for users of high-speed trains based on historical data in order to create a demand function that is linear. After that, the price will be determined by the optimization method to find the most suitable price in order to maximize the revenue. Lastly, we compare both fare policies and found that The Dynamic Pricing policy can increase revenue up to 6.5%.

119

Authors: Aye Aye Aung, Zin Mar Win

Bone age assessment methods have been replaced from manual to automatic evaluation in medical field. Bone age is also needed for pediatricians and endocrinologists to compare chronological age for growth disorders and endocrinological problems. Recently, bone age estimation has gained remarkable ground in academia and medicine. Automated bone age assessment (BAA) methods are needed to improve the accuracy of the bone age because manual BAA methods are time-consuming and inefficient. Hand radiograph is the most widely applied to calculate the bone age of children. There are several challenges in bone age estimation such as the less of accuracy to segment region of interest (ROI), to identify boundaries and to classify bone age. The aim of the proposed system is to improve robustness and accuracy of the BAA system. The system applies Histogram of Oriented Gradient (HOG) features for middle finger and Hu moments features for carpal bones. Then, the system uses Support Vector Machines (SVM) by applying extracted features from middle finger and carpal bones for bone age classification. The proposed system uses the dataset from University of Southern California. Combination of HOG features from Epiphysis/Metaphysis region of interests (EMROIs) and Hu moments features from carpal region of interest (CROI) achieved better results than other methods. Running time of combining HOG and Hu moments features is faster than other methods and this method reduces feature dimensions than using HOG features from EMROIs and CROI. As the experimental results, the accuracy of the system achieves 89% of Asian (ASI) and African American (BLK) female radiographs and 86% of ASI and BLK male radiographs in age range from one year to ten years old. In the experiment, this method obtains 1.2717 years of Mean Absolute Error (MAE) compared with radiologist 1 and 1.1997 years of MAE compared with radiologist 2 in testing ASI and BLK females. Then, the system also obtains 1.3681 years of MAE compared with radiologist 1 and 1.3311 years of MAE compared with radiologist 2 in testing ASI and BLK males.

139

Authors: Dheyaa Jasim Kadhim

Massive multiple-input-multiple-output (MIMO) technology offers high spectral efficiency (SE) using simple schemes while it is also especially expected to offer high energy efficiency (EE). Then to capture promising gains expected from the 5G networks, this work proposes two improved versions of famous linear precoding schemes, i.e., MRT (maximal-ratio-transmission) and ZF (zero-forcing). To enhance and evaluate the energy efficiency performance in massive multi-user mimo (MU-MIMO) system, we considered here imperfection base station (BS) channel state information. Additionally, our proposed improved versions of linear precoding schemes are taking into account power circuit consumption and transmit power. Simulation results have shown that the improved ZF precoding scheme achieves higher EE as compared to improved MRT precoding scheme, while the results showed that improved MRT precoding outperforms ZF precoding within small BS number of M-antenna deployment comparatively to number of K terminals (from 20 to approximately 40 antennas). Previous results had been further shown that without circuit power consideration, the energy-efficiency was increasing without limit. In contrast to this belief in the literature, our model shows that energy efficiency is not unbounded when BS antennas increase very-large. We found that around 100 active antennas serving tenth of autonomous users simultaneously showing that energy-efficiency cannot be optimal at low signal to noise ratio (SNR) region, but in region where an appropriate interference-canceling processing, ZF precoding is extremely suitable over an interference-limited processing, MRT precoding. Finally, the results demonstrated and confirmed that an efficient-energy can be achieved by allowing so more and more users to access the channel simultaneously, thanks to massive MIMO that illustrates 5G promising gain expectations.

160

Authors: Irwan Budi Santoso, Yudhi Adrianto, Anggraini Dwi Sensusiati, Diah Puspito Wulandari, I Ketut Eddy Purnama

High performance in the epileptic electroencephalogram (EEG) signal classification is an important step in diagnosing epilepsy. Furthermore, this classification is carried out to determine whether the EEG signal from a person's examination results is categorized as an epileptic signal or not (healthy). Several automated techniques have been proposed to assist neurologists in classifying these signals. In general, these techniques have yielded a high average accuracy in classification, but the performance still needs to be improved. Therefore, we propose a convolutional neural network based on multi-segment of EEG signals to classify epileptic EEG signals. This method is built to overcome data limitations in the convolutional neural network training process and add the ensemble combination process. The multi-segment of EEG signal is formed by splitting the signal without overlapping each channel and converting it into the spectrogram image based on the short-time Fourier transform value. The spectrogram image is then used as input for the convolutional neural network in in-depth training and testing. The convolutional neural network model of the training results is used to classify each EEG signal segment on each test channel before entering the ensemble combination stage for the final classification. To evaluate the performance of our proposed method, we used the Bonn EEG dataset. The dataset consists of five EEG records labelled as A, B, C, D, and E. The experiments on several datasets (AB-C, AB-D, AB-E, AB-CD, AB-CDE, and AB-CD-E) which were arranged from the dataset showed that our proposed method (with segment) performs better than without segment. Our proposed method yielded the best average of classification accuracy which is 99.33%, 100%, 100%, 99.5%, 99.8%, and 99.4% for the AB-C, AB-D, AB-E, AB-CD, AB-CDE, and AB-CD-E. By these results, the proposed method can outperform several other methods on the same dataset.

189

Authors: Nuchanart Santalunai, Samran Santalunai, Piyaporn Meesawad, Chanchai Tongsopa, Samroeng Santalunai

This research focusses on the methods for expanding the working space to radiate the electromagnetic fields of 2.45 GHz within a dielectric load by using only one square patch radiator together with a plus-like electromagnetic band gap (EBG). One port of square patch generates a high transmission power to distribute the symmetrically electromagnetic field distribution. Electromagnetic band gap structure has a unique band gap characteristic where it can suppress the surface wave and continuously increase electric fields distribution on dielectric load under test with dimension of 300×300 mm2 when the thickness dimension is optimized. A dielectric heating applicator using microstrip patch with EBG structure has a low-loss dielectric because of the structure of system which has no gap between applicator surface and dielectric load. This paper provides a comparison of the simulation of electric distribution in microwave chamber when the applicators are copper plate, square patch microstrip and square patch generator surrounded by EBG. In the simulation results, an electric distribution in the dielectric load was compared when the dielectric constant was 4.7. The maximum percentage of electric fields distribution that was evenly distributed in dielectric load was about 99.97% at middle surface with a thickness of 5 mm by using square patch generator surrounded by EBG as the applicator. In addition, the simulation results were confirmed with the results of the dielectric heating measurement of the dielectric load as rice which is a popular grain in the commercial application of dielectric industrial drying.

210

Authors: Chandrashekhar Ghuge, Vudatha Chandra Prakash, Sachin Ruikar

The popularity of video recordings either on mobile devices or video surveillance has contributed to the demand for video data applications. As a result, the management of video has become significant in the object retrieval process. However, the video object retrieval is considered as a major issue in the video objects management. This paper proposes an integrated optimization-based classifier for video object retrieval. Here, the input video is subjected to optical flow estimation using a spatio-temporal feature descriptor, named Histograms of Optical Flow Orientation and Magnitude (HOFM) for extracting the events and establishing the HOFM indexed database using the detected objects. Hence, object tracking is a major step, which detects and tracks the video objects using a hybrid model named Nearest Search Algorithm-Support Vector Regression (NSA-SVR). For the video retrieval, the training of Naive Bayes (NB) classifier is performed using the proposed Lion-Salp Swarm Algorithm (LSSA) on the indexed database of the tracked objects. Then, the recognized events interrelated to the query are subjected to the Naive Bayes classifier to retrieve the required video. The performance of the proposed integrated LSSA-Naive Bayes Classifier is found to be better than the existing methods with maximal precision of 82.985 percent, recall of 87.451 percent, and F-measure of 87.847 percent.

242

Authors: Dhari Y. Mahmood, Mohammed Kdair Abd, Kanaan A. Jalal

Wind Energy Output Prediction (WEOP) has an important impact on the integration of wind energy systems in the power grid, the management, power systems dispatching, safe and stable operation. Short-term probabilistic WEOP is a perfect choice to increase the stability of the power grid. Still, it has a high error because of uncertainty factors such as wind speed, and very important to find out a method to increase the fineness of predicting. Therefore, in this paper, the main contribution is to design an intelligent model capable of wind energy generation prediction. This aim is achieved by adopting a new Developed Particle Swarm optimization algorithm based on proposes a new inertia weight called DPSO and Back-Propagation Neural Network (DPSO-BPNN). The proposed models were compared and verified through MATLAB software test to prove the superiority over other models. The obtained results of the proposed model show a fast convergence and high prediction accuracy. Also, the error rate was improved compared with the published research by up to 98%.

267

Authors: Darmawan Napitupulu, Rudi Yacub, Aditya Halim Perdana Kusuma Putra

Telehealth has become an emerging technology since they offer remote healthcare services for infected patients or those who are worried about being infected. Furthermore, Telehealth can provide routine care without the risk of contracting Covid-19. Although Telehealth offers many potential benefits, the challenge was to encourage people to use them. Research related to Telehealth adoption in Indonesia is still limited, so there is still a lack of understanding regarding their behavior intentions in using Telehealth. One of the most widely used theories to explain Telehealth adoption is the Unified Theory of Acceptance and Use of Technology (UTAUT). However, doctors' opinion and computer anxiety variables have never been applied in the UTAUT model to analyze Telehealth use behavior, especially for cases in Indonesia during the Covid-19 outbreak. In addition, many researchers point out the importance of both variables in the healthcare context increasing the need for examining this feature. This paper discusses it for the first time. The survey was conducted by distributing online questionnaires via Google Docs to 118 Indonesian respondents who have used Telehealth. The results show that performance expectancy, effort expectancy, and facilitating conditions significantly affect behavior intention to use Telehealth. Social Influence is not significantly associated with behavioral intention since early adopters are considered reluctant to other views. Moreover, performance expectancy is significantly affected by doctor’s opinions and effort expectancy is strongly influenced by computer anxiety.

294

Authors: Hayder Naser Khraibet AL-Behadili

Internet usage is increasing rapidly worldwide, allowing numerous connected computer objects or devices to run and communicate with mass digital information. As Internet usage becomes pervasive, attacks against them are also rising aiming to penetrate the target network and remain undiscovered. Therefore, analyzing the behavior of Internet traffic manually is not possible due to its complexity and the large number of user activity. Incoming and outgoing Internet traffic are controlled using a firewall through an automated Internet security system using a predefined set of rules. Machine learning algorithms are used for Repeated Stemanalysis of the activities on firewall devices and to control traffic on the basis of the results. However, the output models (i.e., classification models) lack explanatory power insight into the relative influence of the main factors in the classification and thus have low accuracy. In this study, a decision tree classification algorithm with a tree-structured model is used for firewall activity analysis, which produces high classification accuracy. Empirical results on firewall access with different actions were compared against six benchmark classification algorithms, namely, SVM, OneR, ANN, Multi class classifier, PSO and ZeroR, in five popular evaluation metrics. The experimental results show that the performance of the proposed classifier in all evaluation metrics is higher than the state-of-the-art classification algorithms, such as SVM, ANN, Multi class classifier, PSO, and the most related classification algorithms that provide comprehensible models (i.e., OneR and ZeroR). The proposed classifier offers interpretation ability by presenting the classification model into a tree representation, which is a further advantage.

319

Authors: Mostafa Gamal, Ahmed El-Sawy, Ahmed H. AbuEl-Atta

The daily massive flow of information requires automated summarization methods to extract the most important information. Manual summarization of large text documents is very complicated and time-intensive for human beings. Numerous methods rate all of them decadently based on the sentence scoring that labels ratings for input sentences. The higher-rated sentences are used as a part of the summary. In an extractive-based automated text summary, locating the relevant sentences is an essential problem for the researchers. Therefore, to deal with such problems, evolutionary algorithms are applied as a solution. This paper presents a hybrid approach (CSOGA) based on the effectiveness and convergence to the solution of a (CSO) chicken swarm optimization and a (GA) genetic algorithm for text summarization to ensure the optimal solution. The evaluations of the proposed algorithms are done on the standard dataset from CNN / Daily Mail and are measured by the Recall-Oriented Understudy for Gisting Evaluation (ROUGE). The performance of the proposed method is then compared with other methods. The results show that the new approach hybrid (CSOGA) has the best performance on text summarization quality. The proposed method was capable of generating a better accuracy than other algorithms on the ROUGE-1, ROUGE-2 and ROUGE-L. The highest increase in the accuracy of the proposed method was in ROUGE-1 with a rise of 4.4%, ROUGE-2 with a rise of 12.01%, and ROUGE-L with a rise of 9.8% comparing with the highest accuracy of the other extractive models.

346

Authors: Arfinda Setiyoutami, Diana Purwitasari, Wiwik Anggraeni, Eko Mulyanto Yuniarno, Mauridhi Hery Purnomo

Since local human movements can influence dengue spread, a network-based prediction model considers the dynamic relation between dengue case incidences and their location over time. Some approaches often generated the networks in a certain period with a single spanning time until several months or in one year, called static networks. However, one annual-based dengue spread model could have different simulations depending on the selected months to show different dynamicity. Other approaches do not involve any networks for generating the spread models but employ them for validating the models with simulations. Considering the evolution of dengue circumstances that could quickly change between periods, we proposed a Dengue Spread Dynamic Network (DSDN) model with some timespan and location boundaries variants. DSDN includes five network models with nodes representing localities and links showing dengue spread which varied every day depending on infections presence and environmental conditions in a certain period. With our proposed method, daily dengue spread from one location to another can be predicted based on the location-based incidence historical data as outbreaks prevention initiative. We also analyzed how dengue spreads differently in outbreak and non-outbreak periods using Dynamic Network Link Prediction (DNLP) method. From our experiments result, Neighbor Network which modeled that dengue only spreads between neighboring localities produced an accuracy of 92.54% for the entire period. When applied only to outbreak data, there was a performance increase of 3.39 points, which suggested that link prediction performs better when dengue is rapidly spreading. In addition to that, our experiments concluded that dengue potentially spreads to a location with no current infections if local incidence often occurred in the past.

374

Authors: Giva Andriana Mutiara, Othman Mohd, Nanna Suryana, Ahmad Naim Che Pee

An effort to minimize illegal logging by utilizing advanced technology such as Wireless Sensor Network (WSN) are highly required. WSN is a communication solution between remote sensors located in isolated wide forest areas. However, the WSN challenge in a remote and wide-area has the energy resources limitation. This research proposed a weights-based energy-efficient WSN data communication protocol with firefly synchronization, namely Firefly Synchronization Multi-hop - Geographical Energy-Aware Routing (FSM-GEAR) Protocol. This protocol aimed to increase the network lifetime of each node by shortening the transmission distance. Firefly synchronization between sensors is conducted to reduce the data transmission waiting time. A weights-based approach in cluster head (CH) selection phases and energy consumption analysis are formulated according to the comparison between nearest distance to Gateway and the maximum residual energy of each node based on received signal strength indicator (RSSI). Weights-based FSM-GEAR performance analysis is carried out based on the parameters of network lifetime, residual energy, and throughput with the Modified-GEAR and enhanced Modified-GEAR protocols as a comparison. The simulation result stated that FSM-GEAR protocol showed better network lifetime, residual energy, and throughput than both protocols.

403

Authors: Mohammed Najm Abdullah, Khulood Eskander Dagher

A model avionics system is a type of electronic system and equipment specifically designed for use in airborne computers. This article proposes an improvement of the output performance of Unmanned Aerial Vehicle (UAV) system based on multi-PID-controller with on-line swarm optimization algorithm. The main goal of this research is to design six PID controllers to control the high nonlinear UAV quadcopter system by using an on-line Particle Swarm Optimization (PSO) algorithm that uses to learn and tune the eighteen control gain parameters based on multi-objective function. The task of the proposed on-line multi-PID-PSO feedback path-tracking controller is to obtain precisely and quickly the robust rotation speed of rotors which are used to control the attitude and altitude of the UAV quadcopter system. The results of the proposed strategy show that the on-line multi-PID-PSO controllers are accurate in terms of the UAV quadcopter take-off and follows the Spiral and Cyclone desired paths quickly through fast obtaining of the multi-controller’s parameters and smooth rotation speeds actions generating for UAV system with a minimum number of multi-cost function evolutions that minimized the tracking translation location error around ±5 cm and the overshoot of altitude did not exceed 1cm. Finally, we confirm the effectiveness of the simulation results of the proposed controller through comparison of other types of controller simulation results.

425

Authors: Kelly R. Sungkono, Riyanarto Sarno, Fransisca D. Amadea, Ariqoh F. Irbah

Process discovery is a study to form a process model based on an event log. The output of process discovery is a process model that contains linked activities based on performed business processes. There are several kinds of relationships and additional invisible tasks in forming a process model. The current invisible tasks, called invisible prime tasks, are divided into three conditions, i.e. skip, redo and switch. However, the need for invisible task is not only in those conditions but also in a situation, which is hybrid choice-parallel relationships. Hybrid choice-parallel relationships occur when there are choice relationships, namely XOR, pile up with parallel relationships for all branching activities (AND) or parallel relationships for several branching activities (OR). This research proposes graph-based Invisible-Task Prime and Non-Prime (ITPNP) algorithm that mines invisible prime tasks and invisible non-prime task in the cased of hybrid parallel relationships. Graph-based ITPNP algorithm creates rules for detecting invisible task needs based on relationships occurring in the graph model. The evaluation is implemented with seven processes and four comparison methods, i.e. Alpha#, Alpha Mining, Fodina, and Graph-based Invisible Task. Among all algorithms, graph-based ITPNP obtains one of fitness for all event logs and one of precision in four event logs. On the other hand, Fodina algorithm has 0.6 precision which is lower than graph-based ITPNP in the event logs requiring invisible non-prime tasks of hybrid XOR-OR. Besides, Graph-based Invisible Task has low fitness, i.e. 0.67 in Hazardous Waste Handling processes that depict choice relationships with additional invisible tasks for hybrid XOR-AND relationships.

447

Authors: Md Shoaibuddin Madni, C. Vijaya

Currently, hand gesture recognition becomes a promising system for the digital entertainment field due to the recent development of sensor innovations and machine learning. However, hand gesture recognition is a challenging task in most of the existing models, due to background clutter, motion blur, illumination variations, and occlusions. In this research, a dynamic hand gesture recognition system is proposed to improve the performance of hand gesture recognition. At first, the normalization method is used to enhance the visibility level of the gesture images which are collected from the Indian sign language dataset. Then, semi vectorial multilevel segmentation method is employed to segment the exact gesture regions from the normalized images. Further, improved reliefF algorithm and K-nearest neighbour classifiers are used to select the optimal features and to classify the 16 gesture classes or symbols. In the experimental phase, the proposed improved reliefF- K-nearest neighbour model performance is analysed in light of Matthew’s correlation coefficient, accuracy, sensitivity, specificity, and f-score. For overall 16 distinct alphabets; A, B, D, E, F, G, H, K, P, R, T, U, W, X, Y, Z, the proposed improved reliefF-K-nearest neighbour model achieves average accuracy of 98.95%. Hence, the proposed improved reliefF-K-nearest neighbour model showed maximum of 7.97% and minimum of 0.15% improvement in recognition accuracy compared to k means-neural network model, and discriminant correlation analysis based unimodal feature-level fusion model.

468

Authors: IBG Manuaba, Muhammad Abdillah, Ramon Zamora, Herlambang Setiadi

A disturbance such as a small load change in the electrical power grid triggers the system to oscillate and may lead to performance degradation of the control system in the generation source unit. To overcome this problem, a power system stabilizer (PSS) is added as a supplementary control in the power system grid to damp the oscillation caused by the disturbance. This paper proposes a kernel extreme learning machine (K-ELM) method to adjust the parameters of PSS for a wide range of operating conditions. The proposed control scheme in this research work is PSS based on K-ELM called KELM-PSS. To examine the robustness of KELM-PSS, a single machine infinite bus (SMIB) is utilized as a test system. The simulation results showed that the KELM-PSS provided a satisfactory result in both the training and testing phases. In the training process, KELM-PSS has a smaller mean square error (MSE) value, mean absolute error (MAE) value, and sum square error (SSE) value in terms of accuracy criterion compared to PSS based on machine learning such as extreme learning machine (ELM), support vector machine (SVM), and least square support vector machine (LS-SVM). Also, in terms of computation time, KELM-PSS has faster CPU time than other machine learning methods to obtain the parameters of PSS. The parameters of K-ELM including the regularization coefficient Cr and kernel parameter σ obtained from the training phase are utilized in the testing process. In a testing phase, the K-ELM approach will obtain the appropriate parameters of PSS (Kstab, T1, and T2) when there are changes of generator real power output P, reactive power output Q, terminal voltage Vt, and equivalent reactance Xe. In this stage, KELM-PSS could decrease the overshoot and compress the settling time better than other approaches utilized in this study. Moreover, KELM-PSS has the best performance index based on integral time absolute error (ITAE) compared to other techniques utilized in this research work.

493

Authors: Musaab Riyadh, Basim Jamil Ali, Dina Riadh Alshibani

The rapid growth of computer networks has led to massive flow of data every second. Some of these data flow is a malicious activity and cannot detect by anti-malware and firewalls. Therefore, an intrusion detection system is an urgent issue aims to distinguish between non relevant and relevant data in order to maintain data availability and integrity. Due to this, a hybrid intrusion detection system is proposed in this study based on machine learning techniques to tackle various challenging issues in data set such as mixed type data, incomplete, and uncertain data. The proposed system has achieved its objectives by supporting: Firstly, the density based clustering approach due to its robustness to noise removal. Secondly, the K means and K-nearest neighbour algorithms to transform n dimensional data into one dimensional data in order to deal with mixed type data and minimize the running time. Finally, a special type of dissimilarity measure has been supported to tackle the problem of missing data. The experimental results illustrate that the proposed classifier has better or similar classification accuracy (92.9%) as compared with support vector machine (91.5%), CANN(92.2%), Random forest(93.3%), and EIDS-ACC- OD(91.9%) classifiers in KDD Cup99 data set. However, the proposed classifier has the best performance (92.2%) and (91.2%) when randomly removing 5% and 10% of the KDD Cup99 data set in spite of decreasing the overall accuracy classification for all the classifiers.

516

Authors: Vincentius Raki Mahindara, David Felipe Celeita Rodriguez, Margo Pujiantara, Ardyono Priyadi, Eduard Muljadi, Mauridhi Hery Purnomo

Integration of inverter-based resources (IBR) as a distributed generator (DG) in a microgrid has been a trend. Considering that DG integration transforms the short circuit behavior in terms of magnitude and direction in the system, the conventional protection scheme becomes no longer relevant. The major concern of the traditional protection scheme in a microgrid is the relay operating time to clear the fault in a coordinated manner. This paper presents a novel voltage-current model for directional over-current relay (DOCR) that gives the relay capability to reduce the overall relay time operation. The state-of-the-art of the proposed relay architecture relies on the adaptive pickup current threshold, which varies with the voltage magnitude, to anticipate the small fault current caused by the IBR. The improvement of relay sensitivity offers faster tripping time in a coordination scheme. The proposed method is assessed in the IEEE 13 bus distribution system with the integration of DG. The outcome of this study indicates that the total relay time operation using the proposed method is 2.363s. Compared to the conventional method based on the standard equation, the proposed technique significantly reduces the total relay operating time up to 47.67%.

537

Authors: Tanongsak Kongsin, Sakon Klongboonjit

This study aims to introduce a new method for modular design, which is called Connection Correlation Method (CCM), to clustering machine parts with inter-part connection conditions to classify independent modules for the Industrial Printer Powertrain set with 105 components. With the CCM technique, module members would be appropriately assigned for each module so that machine modules are independent of their function. The CCM technique should first calculate the distance coefficient matrix with surface contact conditions of bonded, rough, separation, and frictionless for machine components. Finally, this distance coefficient matrix is used to generate the machine dendrogram with the dependency coefficient between 2.4945 and 3.5593. At the dependency coefficient of 2.7913, the 105 components of the Industrial Printer Powertrain set are clustered into 7 modules: Module 1 with 75 interconnection surfaces and 8 parts, Module 2 with 136 interconnection surfaces and 14 parts, Module 3 with 176 interconnection surfaces and 17 parts, Module 4 with 61 interconnection surfaces and 8 parts, Module 5 with 200 interconnection surfaces and 21 parts, Module 6 with 209 interconnection surfaces and 19 parts, and Module 7 with 18 interconnection surfaces and 18 parts. The results show that the CCM technique can apply to design a modular machine like DSM technique, and multitudinous connectivity factors can also be analysed together with general factors.

555

Authors: Girish Nagaraj, Nandini Channegowda

In recent decades, frame duplication is a common inter frame tampering operation in the digital videos. To find the duplicate frames with better computational time, multi scale local oriented feature descriptors are proposed in this paper. Initially, histogram equalization is used to improve the visual quality of the images or videos, which are collected from surrey university library for forensic analysis dataset. Then, feature extraction is accomplished utilizing binary robust invariant scalable keypoint, speeded up robust features, and maximally stable extremal regions to extract feature vectors or key points from the enhanced images. After identifying the keypoints, matched keypoints are evaluated by hamming distance and k-means clustering algorithm from the source and moving video frames. The multi scale local oriented feature descriptors with two step feature matching significantly decreases the computational time and effectively determines forged and non-forged frames in the video sequences. Simulation results showed that the proposed model achieved better performance in passive video forgery detection in terms of accuracy, sensitivity, f-score and specificity. Compared to the existing approaches like spatio temporal context learning, inter-frame forgery detection algorithm and adaptive parameter-based visual background extractor algorithm, the proposed model obtained better detection accuracy of 95.10%, and average detection time of 1.04 seconds per frame.

580

Authors: Saritha Ibakkanavar Guddappa, Rajeshwari Mahabhaleshwar Hegde

Cyber-Physical Systems (CPS) is an emerging technology of intelligent systems with the united computational performance and physical capacities. Due to the growing system complexity and system directness the CPS is affected by the malicious attacks which lead to improve the CPS performances with an efficient data transmission method. In this paper , CPS making use of the Orthogonal Frequency-Division Multiplexing (OFDM) to achieve the high data rate requirement of modern communications. The Priority Aware Frequency Domain Polling (PAFDP) protocol is proposed to detect the priority based node which identified by using the throughput and data rate from the group of nodes in the network and also the priority based nodes are identified as malicious nodes which are avoided to minimize the packet loss in the existing network. The performances of the PAFDP-OFDM-CPS are analysed in terms of various QoS parameters such as delay , energy , Packet delivery ratio , packet loss ratio , through put and overhead so that the proposed method is assessed by three existing methods such as Adaptive Duty Cycle Control Based Opportunistic Routing (ADCCOR) protocol , Trust Aware Routing Framework (TARF), Trust Aware Wireless Routing Protocol (TAWRP) and It was found that the performance of the PAFDP-OFDM-CPS is improved much compared to the above said three protocols.

1

Authors: Naim Ahmad

Cloud computing is a matured platform to host the information and communication technology (ICT) infrastructure and services. All the top leading companies have made investments in cloud infrastructure including US Department of Defence’s contract of USD 10 Billion to Microsoft for cloud migration. Cloud computing helps in conserving the resources and improving the performance of ICT services, hence making the computing more sustainable. Migration to cloud needs careful planning, strong business case, credible migration strategy and robust migration frameworks. This research has reviewed the area of cloud computing migration to identify important factors. The author keywords of 299 research articles downloaded from the web of science database have been analyzed using co-word analysis and resulted in 10 most significant factors in the form of cloud migration tuple. This study applies the interpretive structural modeling (ISM) technique to study the interrelationship on these factors. Application of ISM followed by Matriced’ Impacts Croise’s Multiplication Appliquée a UN Classement (MICMAC) analysis has categorized these factors into independent and dependent sets. The resultant model demonstrates that organizations need to incorporate the dimensions of service orientation, virtualization, multi utilization, dynamic allocation and mobile support into their data, application and security stacks. Migration of these stacks with sound strategy, policy and frameworks will lead to post-migration performance improvements in a synergistic fashion. The adoption of cloud migration tuple with understanding of interrelationships will help organizations make strategies and plan to migrate to cloud in an efficient manner.

22

Authors: Aravind AR, Rekha Chakravarthi

Wireless sensor network (WSN) employs the mobile sink to collect the information from the sensor nodes that are deployed in the network environment in such a way to reduce the energy hole issues. However, the placement of mobile sinks optimally in the network without degrading network lifetime result in a great challenge. Thus, the adjacency-based cell score factor is adopted in this research in order to place the mobile sink optimally in WSN. Here, the network is transformed into the cell using the Voronoi transformation. For each uniform-sized cell, the cluster head (CH) is selected using Sparse-Fuzzy C-means clustering. The optimal placement of the mobile sink is carried out using the adjacency-based cell score, which effectively places the mobile sink using the constrained factors, such as energy, Euclidean distance, and fairness. The proposed adjacency-based cell score obtained better performance with a minimal distance of 22.4615m, maximal energy, fairness, network lifetime, and throughput of 97.2182, 92.6064, 145, and 95.8852, respectively, while considering 400 nodes.

43

Authors: Pradeep Kumar, Anita Kanavalli

Clustering plays a major role in the data mining application, because it divides and groups the data effectively. In the pattern analysis, two major challenges occur in real-life applications that includes handling the categorical data and the availability of correctly labeled data. According to the characteristics of homogeneity, the clustering techniques are designed to group the unlabeled data. Some important issues such as high memory utilization, time consumption, overhead, computation complexity and less effective results are present in various existing algorithms of numerical data. Therefore, the research study implemented clustering techniques based on the similarity of categorical data. Simultaneously, the attributes of inter and intra-clusters’ similarities are identified, and then the performance of proposed method is improved by integrating those similarities. The noises are also removed by performing the pre-processing techniques, so the similarity between noise-free elements are estimated. Once these similarities are identified, the insignificant attributes are removed and the relevant attributes are chosen from the pre-processed elements. The overhead is reduced by developing the Similarity-based K-means Clustering (SKC) approach for clustering the attributes that depends on divergence distance. The efficiency of SKC is tested in the experimental analysis by means of precision, f-measure, accuracy, error rate of clustering and recall. The results state that the developed study achieved 98.45% accuracy for the publicly available dataset when comparing with the existing techniques: variations of Particle Swarm Optimization (PSO) and semi-supervised clustering system.

67

Authors: Sammaiah Seelothu, Konakanchi Venugopal Rao

The automatic Face Expression Recognition is very important in computer vision since it has widespread applications in real time. However, the main issue is the design of an efficient descriptor that describes the appearance changes on the face. Towards such design, in this paper we have introduced a novel binary compact coding called as Edge Adaptive Local Directional Binary Pattern (EALDBP) that encodes the texture features of facial image. The proposed EALDBP is resilient to noise and illumination variations as well as it encodes the directionality of expressions. The directionality is discovered by the computation of edge responses through different compass masks. Further to encode intensity variations, we have considered the relationship between gradient pixels in the local neighborhood. Finally, a compact face descriptor is constructed with the help of Histograms computation over the encoded face image. To evaluate the performance, we tested out approach on two datasets; they are CK+ and JAFFE. The average recognition accuracy of these two datasets is observed as 90.3898% and 93.5922% for JAFFE and CK+ respectively. These accuracies demonstrate that the proposed approach outperforms the several state-of-art methods.

95

Authors: Srinivasa Suresh Sikhakolli, Asha Kiran Sikhakolli

A program design can be understood by the developers through detecting the design patterns in object-oriented programming source code. The main advantage of the design pattern detection includes maintainability, understand-ability and reusability of object-oriented programs. An object-oriented program is used as the input to the design pattern detection techniques, which judges the candidate roles of the patterns. The existing pattern detection techniques use the machine learning (ML) techniques, namely Support Vector Machine (SVM) and Decision Tree with all feature metrics (more than 70 metrics) to identify patterns, which increases the computation time. To minimize the issues of using all feature metrics, an effective feature selection technique is used in the research study. The most important relevant features are selected by proposed Bacterial Foraging Algorithm (BFA) and given as input to ensemble classifiers namely SVM, Decision Tree and Random Forest (RF) classifier to design pattern detection. By using BFA technique, the method used only five metrics for the identification of patterns, where existing techniques uses 70 to 80 metrics for same pattern detection. The simulations are conducted to test the effectiveness of BFA with ensemble classifiers on the Python Software platform in terms of average accuracy and precision. The results stated that BFA-RF achieved 88.57% of average accuracy, where BFA-SVM technique achieved 81.43% of average accuracy, which shows the RF achieved better results among other ensemble classifiers.

121

Authors: Shimaa Ouf, Noha Hamza

Objectives: this paper aimed to first, exploring the relationship between multiple physical measurements. Second, predicting the treatment course of hospitalized patients with COVID-19 disease from physical measurements. Third, investigating the primary symptoms and the average duration of each symptom’s disappearance. Fourth, provide the physicians with the prediction model to help them determine the best combination of drugs for Covid-19 patients’.　Methods: this paper first, apply correlation analysis on the dataset to identify the relationships between the dataset attributes and selecting model features more efficiently to improve the accuracy results. Second, implements seven machine learning algorithms with four cross-validation techniques to predict the most appropriate treatment course for COVID-19 hospitalized patients. Two treatment courses were identified; course-LR was patients treated with Lopinavir-ritonavir and course LR + AR were patients treated with Lopinavir-ritonavir combined with arbidol for antiviral treatment. Results: by applying correlation analysis between dataset attributes, we found that there is no relationship between the presence of chronic diseases or the patient’ age and the Covid-19 clinical classification. The prediction model results show that 10 fold cross-validation with Naïve Bayes and neural network achieving the highest accuracy of 85.71%.　Conclusion: this paper has exploited correlation analysis and machine learning based approaches to identify relevant attributes in the COVID-19 patients’ dataset and predicting the most appropriate treatment course.

147

Authors: Manohar Manur, Alok Kumar Pani, Pankaj Kumar

Heart disease is a serious disease that causes sudden death among 80% of the people around the world. The traditional models performed predictive analytics using machine learning techniques to make a better decision. For better decision making in heart disease prediction, the big data analysis shows the great opportunities to predict the future health status from health parameters and provide best outcomes. However, the traditional decision making models had traffic data or contained noise and uncertainty was unpredictable as the data ambiguity emerged. In order to overcome such an issue, the big data is used to ensure the medical service which is mostly needed in a timely manner and for accurate diagnosis. The pre-processing of the medical data acquired from Cleveland heart disease UCI datasets has a vast number of attributes which helps to predict the heart disease. The data are contaminated with the noise and some of the data are missing, so the pre-processing using Min max Normalization is performed to remove contaminated noise acquired in the data which is taken from the UCI repository dataset. The proposed Fuzzy Deep Convolution Network (FDCN) permits the input features for fuzzification process that uses transformed features. The fuzzification process eliminates the redundant or irrelevant fuzzified features and overcomes the system complexity problems. The proposed FDCN obtains accuracy of 95.56 % and 92 % of F-score shows better results when compared with the existing KNN-DT, Naive Bayes, and Random Forest algorithms.

167

Authors: Easpin Brumancia, Justin Samuel

Clustering is the most frequently used physical architecture in data fusion, causing a problem as it has random distribution within local clusters, we propose a density peak based clustering algorithm utilizing a hierarchical approach. As a proficient method to joint decision-making of multi sensors, data fusion contains several benefits in data mining. In this work, we use the Salp Swarm Optimizer (SSO) algorithm based Hierarchical Density Peak Clustering (DPC) method to cluster the multiple sensor nodes as per the data similitude for the process of data fusion. Here, the higher density, distance and the cut off values are determined by using the SSO algorithm and then the chosen optimal fixed values were used to further process. Hierarchical Density Peak Clustering estimates the density and distance of each point. It is validated experimentally and compared it with the datasets and the error prediction with the test case numbers. The experimental results of the method are 96% accuracy determined from different datasets.

190

Authors: Wathiq Laftah Al-Yaseen, Ammar Jehad, Qusay Abdullah Abed, Ali Kadhum Idrees

Breast cancer has been recently considered as one of the broadly spread diseases that causes death among women. Early disease diagnosis is a critical aim in building the treatment policies and is extremely related to safety of patient. Therefore, there is a necessity for computer aided detection (CAD) in order to provide accurate and rapid diagnosis for breast cancer. Recently, many classification models utilizing machine learning approaches have been adopted and modified to diagnose breast cancer disease. Moreover, the performance of each model depends on different compositions such as the number and type of data features and the parameters of model. In order to enhance the performance of classification model, this research proposes a model using modified K-means algorithm to create a new training dataset of breast cancer which can highly improve the performance of support vector machine model. A modified K-means algorithm is also proposed to build a high quality training dataset that contributes significantly to reduce the training time of classifiers, and improve the performance of classifier. The proposed model handles the noise and irregularity in data and produce high quality dataset which represents all the cases of disease. The two recognized datasets Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) have been used to examine and appraise the performance of the proposed model. The experimental results show that the proposed model has a significant performance compared to other previous works and with accuracy level of 98.067%, sensitivity of 100%, specificity of 94.811%, precision of 97.011% and finally with area under the curve related to the receiver operating characteristic of 97.406%.

211

Authors: Wayan Firdaus Mahmudy, Mohammad Zoqi Sarwani, Asyrofa Rahmi, Agus Wahyu Widodo

A supply chain process in manufacturing industries involves a distribution process that ensures finished goods distribute to their customers properly. The process may involve several parties such as manufacturing plans, distributions centres, and retailers. To maximize profit, the companies need to optimize the distribution process by minimizing costs. The multi-stage distribution problem contains several constrains so obtaining optimum solution using exact methods may require excessive processing time. Genetic algorithm is proposed the solve the complex problem. The genetic algorithm is improved by modifying chromosome representation and related genetic reproduction operators. The new chromosome is designed to address different number of distribution stages in manufacturing industries. Thus, the proposed approach could be applied for various distribution problems. Several genetic operators are tested to obtain the most suitable reproduction operator for the multi-stage distribution process. The numerical experiments prove that the improved genetic algorithm is suitable for the optimization of the multi-stage distribution process and produce better result compare to those achieved by original genetic algorithm, simulated annealing, and random search as baseline method. The improved genetic algorithm produces solution with cost of Rp 20,167.8, lower than cost of Rp 21,860.4, Rp 23,354.30 and Rp 34,328.0 obtained by original genetic algorithm, simulated annealing, and random search, respectively.

236

Authors: Michael Moses Thiruthuvanathan, Balachandran Krishnan, Madhavi Rangaswamy

Online teaching and learning has recently turned out to be the order of the day, where majority of the learners undergo courses and trainings over the new environment. Learning through these platforms have created a requirement to understand if the learner is interested or not. Detecting engagement of the learners have sought increased attention to create learner centric models that can enhance the teaching and learning experience. The learner will over a period of time in the platform, tend to expose various emotions like engaged, bored, frustrated, confused, angry and other cues that can be classified as engaged or disengaged. This paper proposes in creating a Convolutional Neural Network (CNN) and enabling it with residual connections that can enhance the learning rate of the network and improve the classification on three Indian datasets that predominantly work on classroom engagement models. The proposed network performs well due to introduction of Residual learning that carries additional learning from the previous batch of layers into the next batch, Optimized Hyper Parametric (OHP) setting, increased dimensions of images for higher data abstraction and reduction of vanishing gradient problems resulting in managing overfitting issues. The Residual network introduced, consists of a shallow depth of 50 layers which has significantly produced an accuracy of 91.3% on ISED & iSAFE data while it achieves a 93.4% accuracy on the Daisee dataset. The average accuracy achieved by the classification network is 0.825 according to Cohens Kappa measure.

262

Authors: Shreela Dash, Madhabananda Das, Dayal Kumar Behera

The proposed work suggests a high capacity steganography technique using dual steganography approach. It utilizes a Multi pass encryption system to scramble the mystery message before hiding. The scrambled mystery message is divided into two parts and concealed utilizing two duplicates of the same cover image. One copy of the image uses a modified Quotient value differencing approach to hide 1st part of secret message and another copy uses difference expansion method to embed the 2nd part of secret messages. It provides more secrecy because the suggested work uses the benefits of both encryption and steganography. The investigation shows that the two stego-pictures provide high embedding capability (EC) of 1848153 bits with Peak signal to noise ratio PSNR1 of 47.2 dB and PSNR2 of 51.1 dB. Further, examination with other existing methodologies shows that the suggested methodology is proven to be better in Structural similarity Index (SSIM), EC and PSNR.

285

Authors: Evy Hendriarianti, Aryuanto Soetedjo

This paper presents the Internet of things (IoT) technology for real-time monitoring of wastewater phytoremediation. Phytoremediation is a technique to remove pollutants from the wastewater using the plants. A conventional method to monitor the phytoremediation performance is by taking the samples of the contaminants from a site and measuring them at the laboratory. This method needs many works for data preparation and analysis. A recent development on the IoT technology may eliminate such tasks by a real-time monitoring system. In the proposed real-time monitoring system, several phytoremediation models are implemented on embedded hardware and connected to the Thingspeak IoT platform. The proposed system aims to provide a real-time monitoring system to better model the phytoremediation by examining the monitoring data time interval and fitting techniques. From the experiments, the proposed monitoring system achieves a data transfer reliability of 81.4% when the period of the data transmission is one minute, which is suitable for one-day interval real-time monitoring system. The proposed monitoring system can build a phytoremediation model using a polynomial fitting with a higher fit than the existing methods using an exponential fitting. Further, the proposed method promises a better solution in terms of the best model, the low cost, and the acceptable accuracy.

306

Authors: Shaaban M. Shaaban

Groundwater (GW) quality evaluation includes a variety of biological, chemical and physical parameters. The fundamental problem with water quality assessment is the difficulty with which a large number of parameters are evaluated. If all criteria have been used to evaluate the quality of GW, then computational difficulty will certainly increase. In this paper, a new hybrid three-stage assessment approach based on Feature Extraction Algorithm (FEA), standard deviation (SD) and Complex Proportional Assessment Method (COPRAS) was proposed. In the first stage the redundant criteria for GW quality assessment is removed using FEA. Secondly, the weights of the reduct parameters are evaluated based on SD. Finally, GW sites are ranked using (COPRAS). Sixteen GW samples were gathered from several GW wells. The collected samples were investigated for 12 various physicochemical water quality criteria to evaluate GW quality. The results reveal that sulfates (SO4), nitrate (NO3), Fluorides (F), sodium (Na), and Escherichia coli (E. coli) are the main parameters for GW quality assessment. Furthermore, the optimal concentrations of physicochemical parameters: (SO4), (NO3), (F), (Na), and (E. coli) are 18.9(mg/L), 8.18(mg/L), 0.222(mg/L), 21(mg/L), 1.9(MPN/100mL), respectively, with 40 WQI.The suggested approach is compared to three MCDM methods to validate the performance of the proposed methodology. The assessment results gained by the FEA combined with COPRAS and SD significantly minimize computational difficulty, reasonable and accurate. The approach presented in this study improves the system for evaluating GW quality.

323

Authors: Udayakumar Nandagopal, Subbulakshmi Thirumalaivelu

Malwares (Malicious Software’s) has increased rapidly in the recent years over the internet, In-order to detect the malwares many anti-malware strategies also been introduced but most of them relay on signature based techniques. But signature based technique failed to detect the new and unknown malwares, so signature based method failed to provide the better result. So it is important to find an technique that could able to detect the both new and unknown malwares without fail. Dynamic malware analysis with sandbox setting provides the best way to analyse the malware in the isolated environment. In this work, inside the cuckoo sandbox malwares were executed for observing behavioural accomplishments of the each and every Malicious Application. After executing the malware inside the sandbox, it will record the activities and finally the report will be provided in the JSON format. System calls needs to be generated from JSON file, MIST file is generated from the JSON file format. This will be organised in the N-Grams after extracting the system call, which helps construct the classifier using the Gained Information (GI) as the selection technique for the submission. Then the overall based on the N-Gram value and Byte length, various classifiers are evaluated, in that random forest has provided the best result.

344

Authors: Yeon Ji Choi, I Nyoman Apraz Ramatryana, Soo Young Shin

The technology of Unmanned Aerial Vehicles (UAV) has been the subject of pioneering research in recent years. Also, the subject has been used in various situations for both outdoor and indoor. In this paper, a cellular communication-based autonomous UAV Navigation is proposed that enables UAVs to maneuver independently in previously unknown and GPS-denied indoor environments with LiDAR sensors. The main idea of the proposed scheme is to implement LTE connection to UAV Navigation and compared it to a Wi-Fi connection. We have proposed that autonomous UAV navigation system for unknown environment like indoor. This system relies on the combination of ROS-based Hector SLAM systems, 2D-LiDAR sensor and LTE connection. The performance of proposed scheme is evaluated as the error distance and exploration time for unknown area like indoor environment. The mapping efficiency of LTE connection is 57.5% greater than the Wi-Fi connection. Computation time for flight is approximately 200 seconds.

361

Authors: Iman Fahruzi, I Ketut Eddy Purnama, Kayo Yoshimoto, Hideya Takahashi, Mauridhi Hery Purnomo

Sleep disorders are challenging to diagnose. The complexity of records obtained from electrocardiogram (ECG) recordings requires manual inspection by experienced medical practitioners. Meanwhile, ECG records are still widely used to diagnose heart problems during sleep. To resolve the issue, the fractal analysis is a promising means to help identify the characteristics of non-overlapping apnea and non-apnea events based on signal scaling behaviour and QRS wave morphologies. Therefore, we propose a new approach to develop automatic sleep disorder classification to minimalize visual inspection and manual scoring. We employed the monofractal and the multifractal analyses to generate new features such as alpha1, residue1, alpha2, residue2, Dqmin, Dqmax, hqmin, hqmid, hqmax, and hqmaxhqmin. To improve the proposed method's performance, we used the ten new features that have been extracted, which are eventually being used as inputs space to a support vector machine (SVM). Through examining the feature set, we designed an optimum SVM model classifier to explore the usability of patterns to predict potential sleep disorder corresponding to apnea and non-apnea events. Hence, our approach through SVM with radial basis function (RBF) kernel is achieved to have accuracy, sensitivity, specificity of 92.16%, 88.24%, 94.12% respectively.

386

Authors: Reshma Siddegowda, Shaila Kalanath, Venugopal Kuppanna Rajuk

The environmental monitoring application is widely used in agriculture, green house farming, floriculture to monitor temperature range in different zone. This application is an embedded application fabricated with wireless sensors, smart phones, protocols, etc. Therefore, with respect to sensor nodes, the battery lifetime and security are the greater challenge in Wireless Sensor Network. In our work, we have designed a protocol using ranking strategy and intelligence similarity function. Initially, the clusters are formed based on the radius range. The SDARIS protocol focused on training the outer nodes of the cluster to acquire balanced cluster. The outer nodes are trained using ranking strategy and are assigned based on Euclidian distance from outer node to cluster head’s and node density of the clusters. This ranking strategy assist to balance cluster size by assigning nodes evenly among all clusters. In addition to balance a cluster size, the SDARIS concentrates on aggregates and secure data during transmission using intelligence similarity function and session keys. The session key is generated for every interval, so the intruder node fails to identify the session key and thus mitigates intruder intervention in the network. Thus, the collaboration of ranking strategy, intelligence similarity function and session key concept in SDARIS protocol improve the overall performance of the network. Hence, SDARIS protocol achieved greater performance with increment of 12% data accuracy, 10% of cluster accuracy, 15% network lifetime and 26% throughput when compared with existing protocol.

410

Authors: Aryo Nugroho, Surya Sumpeno, Kimiya Fujisawa, Mauridhi Hery Purnomo

Campaigns on online social media are becoming intriguing because all parties can compete with each other. Various content can be created to support or defeat one another. Interaction is important to control the battlefield in the face of disinformation. The campaign team must be able to increase interaction and reporting it as a form of achievement. The reality is that the campaign team has not been able to provide a measurable report on the success rate of political campaign interaction on social media. This is because there is no standardization in measuring these interactions. This fact inspires to build a model for measuring campaign interaction on social media. This model will be able to help the campaign team. This study uses a dataset from a governor election campaign in Indonesia which is then tested on a presidential election campaign dataset in America. The model was built using machine learning with the k classification method Nearest Neighbor (kNN) and Distance Weight kNN (DWkNN). The research stages were arranged using the Nassi-Shneiderman Framework. This framework describes the stages and comparison of the use of the KNN and the DWkNN from the training to testing stages in an easy to understand manner. The kNN and DWkNN training stages showed excellent accuracy results of nearly 100%. Furthermore, at the testing stage using the cross-validation method with a variety of fold 5 to 20 variations showed excellent results, namely 99.89% on the governor election dataset and a range of 98% on the American presidential election dataset. This classification model has been tested using several datasets from several candidate social media accounts. We also created a dataset campaign interaction. This study shows that the proposed model can outperform previous studies. So, that it provides novelty in the form of a campaign interaction guide model for the campaign team.

432

Authors: Eman F. Mohsin, Tariq Tashan, Ekhlas H. Karam

In this paper an optimal Immune-Proportional-Integral-Derivative (Immune-PID) scheme is suggested to control to the heart rate of humans and regulate it, based on Yanagihara, Noma, and Irisawa (YNI) as mathematical model of the heart rate. The parameters of the suggested immune PID controller are optimized by using Biogeography-Based-Optimization (BBO) algorithm. In addition, the proposed controller scheme is implemented using Field Programmable Gate Array (FPGA) due to its great data storage capacity, low consumption of energy, and high speed of operation. The Xilinx system generator blocks and Spartan-6 (XC6SLX45T-3FGG484) board are used due to availability with 1% utilization of hardware platform slices. The performance of the proposed controller is compared to traditional PID and immune controllers. That comparative analysis results show best improvement when using the proposed controller with 0% maximum overshoot, a reduction of steady state error and rising time reaches to 0.1 and 0.0023 second respectively and faster response reaches to 0.01 second.

450

Authors: I Gusti Putu Asto Buditjahjanto

The Fuzzy Multi-attribute Group Decision Making (FMAGDM) has been used to assist people in making decisions for sophisticated problems. In Traditional Chinese Medicine (TCM), decision making is used to select the syndrome differentiation of a patient. If the TCM physicians or practitioners are mistaken to determine the syndrome differentiation of a patient can cause wrong in the medication. Conventionally, the problem in TCM is the difficulty in determining a syndrome differentiation of patients. The difficulty is because the numbers of symptoms are numerous and these symptoms are correlated directly to syndrome differentiation. Even some symptoms in one syndrome can emerge from other syndromes. Therefore, the TCM physicians or practitioners must capable to identify the type of symptoms that correlated to the syndrome. The objective of this research is to use FMAGDM to determine the syndrome differentiation in the lung organ from the TCM perspective. Lung syndrome differentiation has five syndromes and 18 symptoms. This research is composed of three methods such as a Fuzzy Linguistic, FMAGDM, and Simple Additive Weighting. The fuzzy set is utilized to capture the experts’ linguistic preference toward the severity level of symptoms, FMAGDM is utilized to build the weight matrix of the experts and Simple Additive Weighting is utilized to select the syndrome differentiation of the patient through selecting the highest preference value of the expert of lung syndrome differentiation. By using data from 9 lung syndrome patients who had different symptoms, the FMAGDM simulation results showed that FMAGDM can determine the type of lung syndrome suffered by each patient. The FMAGDM simulation results for the 9 patients were the same as the diagnosis result of a physician exactly. This shows that FMAGDM can determine the type of lung syndrome as well.

474

Authors: Hussein Almazini, Ku Ruhana Ku-Mahamud

The performance of different mechanisms utilised to perform anomaly detection depends heavily on the group of features used. Therefore, dealing with a multi-dimensional dataset that typically contains a large number of attributes has caused problems to classification accuracy. Not all attributes in the dataset can be used in the classification process since some features may lead to low performance of classifiers. Feature selection (FS) is a good mechanism that minimises the dimension of high-dimensional datasets. Modified binary grey wolf optimization (MBGWO) is a metaheuristic algorithm that has been successfully used for FS. However, the MBGWO algorithm has a drawback in selecting sub-optimal feature sets from an original set of features. This drawback is related to the linearly decreasing value of a parameter where there is no control between the exploration and exploitation processes. This study proposed an enhanced binary grey wolf optimization (EBGWO) algorithm for FS in anomaly detection by controlling the balancing parameter. The new method focused on obtaining a value for a parameter that controlled the trade-off between exploration and exploitation. Evaluation of the proposed method was on the NLS-KDD dataset with different attack classes and compared with other benchmark algorithms, such as binary bat algorithm, binary particle swarm optimization, and four variants of grey wolf optimiser for FS. The experimental results indicated that EBGWO was superior than other algorithms, where it obtained 19 features only out of a total of 41 features with 87.46% classification accuracy. The proposed algorithm can be applied to detect anomaly in network intrusion and outliers in data that are significant but difficult to find.

495

Authors: Adhi Kusmantoro, Ardyono Priyadi, Vita Lystianingrum Budiharto Putri, Mauridhi Hery Purnomo

Microgrid systems are an excellent solution to increasing demand for electricity and are a great way to incorporate renewable energy sources into the electrical energy distribution system. To overcome the fluctuation of renewable energy (PV) based generation, an energy storage system using a battery (BESS) can be used. This paper proposes power management with a modified AC coupling configuration, and a BESS coordinated control strategy based on fuzzy logic. The AC coupling configuration in the microgrid system generally uses batteries to store excess power from the PV. In the proposed AC coupling configuration, two batteries are used, battery 1 (BESS 1) is used to store excess power of the PV array 1 (PVA1) via the inverter battery, while battery 2 (BESS 2) is used to store energy from the PV array 2 (PVA2) ). In this microgrid system, PVA1 is connected to the network, so that the power generated by PVA1 is sent to the AC bus grid using a solar inverter. For two-way power flow on the grid and BESS, inverter batteries are used. The main focus of this article is the analysis of the impact of fuzzy logic (FLC) control strategy and PI control on BESS. When the PV is connected to the grid, control fuzzy logic (FLC) produces an overshoot of 4.73% and a settling time of 14.04 seconds lower than the PI control, overshoot 55.14% and settling time 26.29 seconds. The THD of the voltage and current in this mode is 0.06% and 11.08%. When the microgrid is in island mode, FLC produces an overshoot of 0.54% and a settling time of 13.78, while control PI produces an overshoot of 68.78% and a settling time of 38.11 seconds. The THD of voltage and current in island mode are 0.8% and 11.35%. Therefore, modified AC coupling configuration with fuzzy logic control (FLC) strategy can be used to serve residential loads.

521

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

Social media development makes it possible for everyone to express their opinions and information through text, speech, video, or images. Multiview sentiment analysis in current studies generally combines two modalities, text and image. It seeks to classify social media posts into two or more polarities, such as positive, neutral, or negative. To improve the performance of multiview Sentiment Analysis, we added another modality, which is concepts derived from text and image. Our proposed model integrates three views into a fusion with an ensemble approach by a meta-classifier. We performed text classification with Deep Convolutional Neural Networks. The input feature is Word2Vec for text representation in order to preserve semantic meaning. Additionally, we analyzed concepts from texts with SenticNet 5 as a knowledge base model and extracted concepts from images using the DeepSentiBank model. We obtained 2089 Adjective Noun Pairs and classified it with Multi-Layer Perceptron. Then we combined predicted probabilities from each classifier for Image, Text, and Concept by Ensemble Learning. A meta-classifier was implemented to predict the final sentiment from a fusion of Image-Text-Concept features. The fusion for multiview sentiment analysis works well and could achieve the best accuracy of 70% by applying the ensemble approach with Logistic Regression as the meta-classifier.

547

Authors: Praveen Kumar Devulapalli, Pardha Saradhi Pokkunuri, Maganti Sushanth Babu

Device-to-Device (D2D) communication can be better option for some applications like military and natural disaster places where we need temporary communication network. Selection of relay nodes and routing schemes are key issues in large scale Device-to-Device (D2D) cooperative networks which affects the performance of the network. The performance may be degraded because of wrong selection of relay nodes. In this paper, we proposed a hybrid cluster based D2D cooperative routing scheme by combining the geographic routing and clustering routing. The main idea of our algorithm is to establish communication between similar mobility devices to reduce the mobility effect since the link between the devices moving with the same (approximately) mobility is reliable. All the equal mobility devices are grouped to form a cluster; one of the devices in this is selected as a cluster head. We selected Cluster Head (CH) based on geographic routing and threshold based cooperative communication is provided within the cluster to improve the network performance. We evaluated efficacy of our proposed scheme over conventional routing approaches discussed in the literature. From the results it was observed that the proposed routing scheme improves End-to-End transmission delay by 39%, Energy consumption by 35% (approximately) and Bit Error Rate performance.

1

Authors: Shanigarapu Naresh Kumar, Kalagadda Bikshalu

Cognitive Radio (CR) is generally a wireless communication system that has the ability to improve the network’s system-capacity. Since, the white space or temporally unused spectrum are used to enhance the system-capacity and the important operation involved in the cognition cycle is spectrum sensing. This spectrum sensing supports the Cognitive Radio users to adjust with the environment by identifying the white/vacant spaces without creating any interference to the primary user communication. The traditional filters such as Finite Impulse Response (FIR) filters and median filters used in the spectrum sensing obtains high area utilization in Cognitive Radio. In order to overcome this, an Adaptive Absolute SCORE (AAS) technique is developed based on the FIR for improving the sensing function and radio sensitivity. The area and frequency of the AAS are enhanced by using the Wallace tree multiplier (WTM) and Ladner-Fischer Adder (LFA) in the design of the FIR. The proposed architecture used for the spectrum sensing is named as AAS-WTM-LFA. This AAS-WTM-LFA architecture is developed in the Xilinx tool for different Virtex devices. The performance of AAS-WTM-LFA is analyzed in terms of LUT, slices, flip flops, bonded Input and Output Block (IOB), frequency and power. Additionally, the quality of signal processed through the AAS-WTM-LFA architecture is analyzed as Bit Error Rate (BER) and False Acceptance Rate (FAR). Additionally, the AAS-WTM-LFA architecture is compared with ACS, AAS, AAS-CSLA, AAS-R8-CSA and AAS-R8-CSLA. The number of LUT for AAS-WTM-LFA architecture is 247 for Spartan 6 device, that is less when compared to the remaining architectures.

22

Authors: Kommalapati Salomi Monica, Dereddy Anuradha, Syed Haroon Rasheed, Barnala Shereesha

Nowadays, most of the application depends on arithmetic designs such as an adder, multiplier, divider, etc. Among that, multipliers are very essential for designing industrial applications such as Finite Impulse Response, Fast Fourier Transform, Discrete cosine transform, etc. In the conventional methods, different kind of multipliers such as array multiplier, booth multiplier, bough Wooley multiplier, etc. are used. These existing multipliers are occupied more area to operate. In this study, Wallace Tree Multiplier (WTM) is implemented to overcome this problem. Two kinds of multipliers have designed in this research work for comparison. At first, existing WTM is designed with normal full adders and half adders. Next, proposed WTM is designed using Ladner Fischer Adder (LFA) to improve the hardware utilization and reduce the power consumption. Field Programmable Gate Array (FPGA) performances such as slice Look Up Table (LUT), Slice Register, Bonded Input-Output Bios (IOB) and power consumption are evaluated. The proposed WTM-LFA architecture occupied 374 slice LUT, 193 slice register, 59 bonded IOB, and 26.31W power. These FPGA performances are improved compared to conventional multipliers such asModified Retiming Serial Multiplier (MRSM), Digit Based Montgomery Multiplier (DBMM), and Fast Parallel Decimal Multiplier (FPDM).

42

Authors: Christy Daniel, Shyamala Loganathan

Multi-class classification of sentiments from text data still remains a challenging task to detect the sentiments hidden behind the sentences because of the probable existence of multiple meanings for some of the texts in the dataset. To overcome this, the proposed rule based modified Convolutional neural network-Global Vectors (RCNN-GloVe) and rule-based modified Support Vector Machine - Global Vectors (RSVM-GloVe) were developed for classifying the twitter complex sentences at twelve different levels focusing on mixed emotions by targeting abstract nouns and adjective emotion words. To execute this, three proposed algorithms were developed such as the optimized abstract noun algorithm (OABNA) to identify the abstract noun emotion words, optimized complex sentences algorithm (OCSA) to extract all the complex sentences in a tweet precisely and adjective searching algorithm (ADJSA) to retrieve all the sentences with adjectives. The results of this study indicates that our proposed RCNN-GloVe method used in the sentiment analysis was able to classify the mixed emotions accurately from the twitter dataset with the highest accuracy level of 92.02% in abstract nouns and 88.93% in adjectives. It is distinctly evident from the research that the proposed deep learning model (RCNN-GloVe) had an edge over the machine learning model (RSVM-GloVe).

63

Authors: Shilpa Kodli, Sujata Terdal

In recent decades, task scheduling and load balancing in the cloud is a growing research area, due to the vast amount of data stored in the server highly increases the load. In order to address this concern, Hybrid Max-Min Genetic Algorithm (HMMGA) is proposed for task scheduling and load balancing in the cloud environment. At first, the load is evaluated for every Virtual Machine (VM), if the load is high, then HMMGA is used for balancing the load. HMMGA selects the best VMs to assign the tasks and migrates the over-loaded VMs tasks to the under-loaded VMs. HMMGA significantly avoids the imbalanced workload performance in the cloud environment. In this research paper, the proposed HMMGA performance is compared to Max-Min algorithm, Low time complexity and low cost binary Particle Swarm Optimizer (IBPSO-LBS) and PSO with Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to examine the efficacy of HMMGA. From the experimental simulation, the result shows that HMMGA averagely delivers 1.63 and 3.88 seconds less make span compared to the Max-Min and TOPSIS-PSO algorithm for five VMs. In addition, HMMGA averagely enhances 10% to 40% of resource utilization than the Max-Min and TOPSIS-PSO algorithm. In another experiment, the HMMGA approximately showed 1.7 to 25.99 seconds less average waiting time compared to the Max-Min and IBPSO-LBS.

82

Authors: M. Rajendra Prasad, D. Krishna Reddy

In Internet of Things (IoT), secure communication is a prime concern since the open internet source and vast heterogeneity offers several challenges to the network. To achieve an enhanced security, an effective trust evaluation model is required through which the abnormal nodes can be detected and isolated. Towards this objective we have proposed a Light Weight Trust Sensing (LWTS) mechanism for IoT routing. Several factors like Packet Forwarding Factor, Packet Consistency Factor and Packet Repetition Factor are employed to analyze the behaviour of IoT nodes. Along with these factors, the proposed model also checks for energy efficiency to achieve an improved network lifetime. Trust Calculation process is accomplished in two phases; they are direct and indirect fashion. Finally based on obtained total trust, each neighbour node are categorized as No Trust, Average Trust, Fair Trust and Good Trust and the node with good trust is selected as next-hop forwarding node. For the proposed approach extensive simulations are carried out and the performance is measured through Packet Delivery Ratio, Malicious Detection Rate and Average Energy Consumption. The obtained results prove the effectiveness when compared to existing approaches.

103

Authors: Ramachandira Moorthy Buvanesvari, Kanagaraj Suresh Joseph

Named Data Networking (NDN) is a developing Internet design that utilizes a new network communication model dependent on the identity of Internet content. Its core component, the Pending Interest Table (PIT) serves an important role of recording Interest packet information. In managing PIT, the issue of flow PIT measuring has been very challenging because of the huge use of long Interest lifetime especially when there is no adaptable replacement strategy, subsequently affecting PIT performance. Named Data Networking (NDN) might experience some emerging threats such as Interest Flooding Attacks (IFA). In this paper, we focus on the IFA that can seriously devour the memory resource for the Pending Interest Table (PIT) of each included NDN router by flooding a huge amount of malicious Interests with spoofed names. To extricate the pressure of PIT attacked by IFA, we propose a methodology of efficient Secured PIT management and attack detection strategy by using a cuckoo search optimization algorithm- Deep convolutional neural network (CSOA-DCNN) algorithm in Named Data Network. The CSO algorithm initially utilizes a learning technique and afterward considers improved search operators and deep convolutional neural network architecture (DCNN) for classification. The network simulation tool is utilized to design and calculate PIT management. The results of the study on a 20 Gbps gateway trace shows that the corresponding PIT contains 1.5 M entries, and the lookup, insert and delete frequencies are 1.4 M/s, 0.9 M/s and 0.9 M/s. The contribution of this study is significant for Interest packet management in NDN routing and forwarding systems.

124

Authors: Boonlert Suechoey, Somsak Siriporananon, Pana Chupun, Churirat Boonkhun, Chai Chompooinwai

This research paper presents an analysis of electric motors performance by the assessment of vibration characteristics caused by bearing faults, housing bearing looseness and rotor unbalance. In this study, an analysis of fault that occurred in electric motor has followed ISO 10816-3 Standard. Bring the used electric motors for testing and measuring the vibration of the electric motors by using the Spectrum Analysis & Field Balancing Analyzer model ADASH A440-VA4 Pro. The vibration results of 22kW, 55kW and 75kW motor imply the malfunction of bearing faults, housing bearing looseness, and rotor unbalance. The results obtained from the analysis will be used as references and guidelines for predictions of faults in the electric motors, including maintenance scheduling of electric motors.

147

Authors: Abdellatif Haj, Youssef Balouki, Taoufiq Gadi

Business Rules (BR) are usually written by different stakeholders, which makes them vulnerable to contain different designations for a same concept. Such problem can be the source of a not well orchestrated behaviors. Whereas identification of synonyms is manual or totally neglected in most approaches dealing with natural language Business Rules. In this paper, we present an automated approach to identify semantic similarity between terms in textual BR using Natural Language Processing and knowledge-based algorithm refined using heuristics. Our method is unique in that it also identifies abbreviations/expansions (as a special case of synonym) which is not possible using a dictionary. Then, results are saved in a standard format (SBVR) for reusability purposes. Our approach was applied on more than 160 BR statements divided on three cases with an accuracy between 69% and 87% which suggests it to be an indispensable enhancement for other methods dealing with textual BR.

169

Authors: Krishna Priya Gurumanapalli, Nagendra Muthuluru

Internet-of-Things (IoT) is growing network paradigm which enables mutual communication between the user and smart devices using the internet. The IoT devices are susceptible to the security threats, due to placement of restricted computational capabilities of the computing devices in IoT. The conventional encryption algorithm utilizes the high amount of resource block in it which increases the area and power. Moreover, Two Factor Authentication (TFA) scheme based authentication protocols does not have the efficiency to secure the data. Because the random number generated by the TFA is ideal for all IoT devices which are easy to hack by the unauthorized persons. In this Research paper, the Linear Feedback Shift Register (LFSR) based Reconfigurable Physical Unclonable Function (RPUF) is proposed to overcome the security issues caused in the IoT communication. The RPUF is designed based on the LFSR to generate the random number for every clock cycle. Normally, reconfigurable process helps to generate the different output values for every clock cycle. But, it failed to generate different outputs for same input values. Here, LFSR based RPUF helps to generated the different response values even the same challenge is given to the input side. The Lightweight TFA scheme is presented for IoT, where PUF has been considered as one of the major authentication factors. At last, Spartan 6 and Virtex 6 Field Programmable Gate Array (FPGA) performances are calculated for proposed TFA-RPUF-IoT and existing TFA-PUF-IoT protocols. In Spartan 6, TFA-RPUF-IoT protocol occupied 11 slices, 31 LUTs, 42 flip flops which are less compared to conventional TFA-PUF-IoT.

192

Authors: Karrar Mohsin Alwan, Ahmed H. AbuEl-Atta, Hala Helmy Zayed

Accurate intrusion detection is necessary to preserve network security. However, developing efficient intrusion detection system is a complex problem due to the nonlinear nature of the intrusion attempts, the unpredictable behaviour of network traffic, and the large number features in the problem space. Hence, selecting the most effective and discriminating feature is highly important. Additionally, eliminating irrelevant features can improve the detection accuracy as well as reduce the learning time of machine learning algorithms. However, feature reduction is an NP-hard problem. Therefore, several metaheuristics have been employed to determine the most effective feature subset within reasonable time. In this paper, two intrusion detection models are built based on a modified version of the firefly algorithm to achieve the feature selection task. The first and, the second models have been used for binary and multi-class classification, respectively. The modified firefly algorithm employed a mutation operation to avoid trapping into local optima through enhancing the exploration capabilities of the original firefly. The significance of the selected features is evaluated using a Naïve Bayes classifier over a benchmark standard dataset, which contains different types of attacks. The obtained results revealed the superiority of the modified firefly algorithm against the original firefly algorithm in terms of the classification accuracy and the number of selected features under different scenarios. Additionally, the results assured the superiority of the proposed intrusion detection system against other recently proposed systems in both binary classification and multi-classification scenarios. The proposed system has 96.51% and 96.942% detection accuracy in binary classification and multi-classification, respectively. Moreover, the proposed system reduced the number of attributes from 41 to 9 for binary classification and to 10 for multi-classification.

212

Authors: Norah Bakhashwain, Alaa Sagheer

Deep learning is one of the most remarkable artificial intelligence trends. It stands behind numerous recent achievements in several domains, such as speech processing, and computer vision, to mention a few. Accordingly, these achievements have sparked great attention to employing deep learning in time series modelling and forecasting. It is known that the deep learning algorithms built on neural networks contain multiple hidden layers, which make the computation of deep neural network challenging and, sometimes, complex. The reason for this complexity is that obtaining an outstanding and consistent result from such deep architecture requires optimizing many parameters known as hyperparameters. Doubtless, hyperparameter tuning plays a critical role in improving the performance of deep learning. This paper proposes an online tuning approach for the hyperparameters of deep long short-term memory (DLSTM) model in a dynamic fashion. The proposed approach adapts to learn any time series based application, particularly the applications that contain streams of data. The experimental results show that the dynamic tuning of the DLSTM hyperparameters performs better than the original static tuning fashion.

232

Authors: Vincent Michael Sutanto, Zaki Indra Sukma, Afiahayati Afiahayati

Protein secondary structure prediction is one of the problems in the Bioinformatics field, which conducted to find the function of proteins. Protein secondary structure prediction is done by classifying each sequence of protein primary structure into the sequence of protein secondary structure, which fall in sequence labelling problems and can be solved with the machine learning. Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are 2 methods that often used to solve classification problems. In this research, we proposed a hybrid of 1-Dimensional CNN and SVM to predict the secondary structure of the protein. In this research, we used a novel hybrid 1-Dimensional CNN and SVM for sequence labelling, specifically to predict the secondary structure of the protein. Our hybrid model managed to outperform previous studies in term of Q3 and Q8 accuracy on CB513 dataset.

257

Authors: Shahbaa I. Khaleel

As a result of the development in multimedia technology and direct dealing with it in social media, it has led to interest in the techniques of compacting color images because of their importance at present. Since image compression enables the representation of color image data with the fewest number of bits, which reduces transmission time in the network and increases transmission speed. To ensure the compression process is performed without loss of data, the lossless compression methods are used because no data is lost during the compression process. In this research, a new system was presented to compress the color images with efficiency and high quality. Where the swarm intelligent methods were used, as well as hybridizing it with fuzzy using the Gustafson kessel fuzzy method to improve the clustering process and create new clustering methods with fuzzy swarm intelligence to obtain the best results. Swarm algorithms were used to perform the process of clustering the image data to be compressed and then obtaining a clustered data for this image data. In contrast, a lossless compression method was used to perform the encoding of this clustered data where the huffman method was used for encoding. Four methods were applied in this research to different color and lighting images. The PSO swarm intelligent was used, which in turn was hybridized with the Gustafson kessel fuzzy method to produce a new method for fuzzy particle swarm (FPSO), as well as the grey wolf optimization method GWO, which was hybridized with Gustafson kessel and obtained a new method, which is the fuzzy grey wolf optimizer FGWO, and the results were graded efficiently from the first to the fourth method, where the FGWO method with the huffman was the most efficient depending on the standards measurement that were calculated for all methods, the compression ratio was high in this new method, in addition to the standards of MSE, RMSE, PSNR, etc. among the important measurements of the compressing process.

281

Authors: Irene RHT Tangkawarow, Riyanarto Sarno, Daniel Siahaan

The Semantics of Business Vocabulary and Rules (SBVR) standard was developed by the Object Management Group (OMG) for business purposes. SBVR is used for transformation of business vocabulary and business rules into business processes. Gateways are used for regulating the divergence and convergence of flow objects in the business process. The existing business rules in SVBR do not support all gateways in BPMN, whereas there are conditions where branching situations in business rules occur. This article introduces parallelism rules (OR rules) and complex rules to increase 50.6% usage of the existing AND rules and XOR rules in SBVR. The main contribution of this research is to introduce new formal model of inclusive gateway (OR) and complex gateway that allow parallelism and branching to be modeled using SBVR. Thus, this study increases coverage of the usage gateway in SBVR achieved 66.7%. The authors provide branching cases with various levels of complexity, i.e. nested conditions and non-free choice conditions, using the formal description of SBVR.

306

Authors: Fadoua Oudouar, El Miloud Zaoui

In this work, we present a new approach to solve the Capacitated Location-Routing Problem (CLRP). The aim of this method is to determine the depot locations, to assign customers to facilities and to define routes for each depot to serve the associated clients. The proposed approach contains two phases, which are the constructive phase and the improvement phase. In the first phase, we select the depots to be opened, allocate the customers to open depots using a Hierarchical Ascendant (HA) method and we solve the vehicle routing problem for each depot using Sweep algorithm. In the second phase, we apply a Variable Neighborhood Descent (VND) with three structures in order to optimize the cost obtained by the first phase. Two sets of well-known instances from the literature are used to evaluate the performance of this method, and the numerical results obtained are compared with the experimental results of other methods. Results show that our method is competitive with respect to the best-known solutions (BKSs) and demonstrate its efficiency in comparison with other approaches.

326

Authors: Rihab Salah Khairy, Ameer Saleh Hussein, Haider TH. Salim ALRikabi

The movement of cash flow transactions by either electronic channels or physically created openings for the influx of counterfeit banknotes in financial markets. Aided by global economic integration and expanding international trade, attention must be geared at robust techniques for the recognition and detection of counterfeit banknotes. This paper presents ensemble learning algorithms for banknotes detection. The AdaBoost and voting ensemble are deployed in combination with machine learning algorithms. Improved detection accuracies are produced by the ensemble methods. Simulation results certify that the ensemble models of AdaBoost and voting provided accuracies of up to 100% for counterfeit banknotes.

356

Authors: Akram Jaddoa Khalaf, Samir Jasim Mohammed

The QRS detection algorithm is substantial for healthcare monitoring and diagnostic applications. A low error detection without adding more computation is a big challenge for researchers. The proposed QRS detection algorithm is a simple, real-time, and high-performance hybrid technique based on decision tree and artificial neural networks (ANN). In this study, the five stages algorithm is designed, implemented, and evaluated for wearable healthcare applications. The first stage is filtering the original ECG signal to reduce the noise and baseline wandering. After that, a maximum or minimum moving-window for positive or negative peaks respectively is searching R-peaks for any expected value and finding the Q and S corresponding to this R-peak. Only these values from all ECG samples are passed to the next stage for feature extraction to reduce the algorithm computation. Stage four is excluded any unlikely points using the mean of the slope and level based on a simple decision tree. Finally, artificial neural networks are designed to classify the rest point for QRS detection using ANNs for each peak polarity to improve the network’s performance by separating the data as a positive or negative peak. The algorithm is evaluated based on MATLAB using the MIT-BIH Arrhythmia Database, and the results show a low error rate detection of 0.25%, high sensitivity of 99.86%, and high predictivity of 99.89%. We develop a new approach for real-time QRS detection with low resources and high efficiency compared with other approaches.

376

Authors: Eko Hadiyono Riyadi, Tri Kuntoro Priyambodo, Agfianto Eko Putra

Most of the communication protocols in the Industrial Control System (ICS) are vulnerable to cyber-attacks. Initially, the network protocol was designed for reliable performance, and thus did not incorporate data transmission security features. Therefore, ICS requires adequate data transmission security. This paper suggests improving the security of data transmission through a dynamic symmetric four-key-generators system, wherein the system anticipates cyberattacks by generating four keys before encryption. It involves four generators: a random initial key generator, a keystream generator, a key scheduling algorithm generator, and a pseudo-random number algorithm generator. In the receiver section, the system generates three keys before decryption to ensure data confidentiality and to avoid cyber-attacks. The test results show that the proposed system keyspace is ≈22048 bits, meaning that the key is more secure from brute force attacks. As a result, the cipher data have a correlation value of 0.00007. The entropy value is 7.99, indicating that the cipher data is more secure. Also, speed tests show that the processing time still qualifies as real-time.

400

Authors: Mohamed Labib Borham, Ghada Khoriba, Mostafa-Sami Mostafa

Due to the energy limitation in Wireless Sensor Networks (WSNs), most researches related to data collection in WSNs focus on how to collect the maximum amount of data from the network with minimizing the energy consumption as much as possible. Many types of research that are related to data collection are proposed to overcome this issue by using mobility with path constrained as Maximum Amount Shortest Path routing Protocol (MASP) and zone-based algorithms. Recently, Zone-based Energy-Aware Data Collection Protocol (ZEAL) and Enhanced ZEAL have been presented to reduce energy consumption and provide an acceptable data delivery rate. However, the time spent on data collection operations should be taken into account, especially concerning real-time systems, as time is the most critical factor for these systems' performance. In this paper, a routing protocol is proposed to improve the time needed for the data collection process considering less energy consumption. The presented protocol uses a novel path with a communication time-slot assignment algorithm to reduce the count of cycles that are needed for the data collection process with reduction of 50% of the number of cycles needed for other protocols. Therefore, the time and energy needed for data collection are reduced by approximately 25%and 6% respectively, which prolongs the network lifetime. The proposed protocol is called Energy-Time Aware Data Collection Protocol (ETCL).

420

Authors: Putu Desiana Wulaning Ayu, Sri Hartati

This study analyses the use of a pixel classification model to segment amniotic fluid areas on ultrasound (US) images characterized by noise, blurry edge, artifacts, and low contrast. In contrast with the previous methods, this study constrains a training set of pixels based on neighbourhood information with the rectangle window sampling method used to determine the characteristics of each pixel in its environment specifically. The feature extraction is no longer based on the global characteristics of the object rather by taking the value of each pixel in the object area using the sampling window. This research also combines the local first-order statistical methods and gray level information in the window to obtain the characteristics of each pixel. Furthermore, Random Forest and Decision Tree (C.45) were used to classify each pixel into four classes, namely amniotic fluid, placenta, uterus, and fetal body. The classification performance testing of pixel sampling data showed that the Random forest with 5 × 7 window sizes achieved the highest performance at 99.5% accuracy, precision, and recall, respectively. Furthermore, the proposed model was evaluated using 50 new test US images to segment the amniotic fluid area. According to experimental result, proposed models can produce better segmentation area with an increase in the IoU value by 18.3% or a Jaccard coefficient value rate of 0.183 in the range of 0-1 with the previous state of the art method. Furthermore, the proposed model reduces the error rate and improves accuracy by 6.61% and 84.77%, respectively.

444

Authors: Erwin Sutanto, Fahmi Fahmi, Wervyan Shalannanda, Arga Aridarma

With the current technology trend of IoT and Smart Device, there is a possibility for the improvement of our infant incubator in responding to the real baby’s condition. This work is trying to see that possibility. First is by analyzing of open baby voice database. From there, a procedure to find out baby cry classification will be explained. The approach was starting with an analysis of sound’s power from that WAV files before going further into the 2D pattern, which will have features for the machine learning. From this work, around 85% accuracy could be achieved. Then together with sensors, it would be useful for infant incubator’s innovation by utilizing this proposed configuration.

464

Authors: Talitha Puspita Sari, Maya Prananda Sari, Gabriela Evania Sulistyo, Ardyono Priyadi, Vita Lystianingrum, Mauridhi Hery Purnomo

In the present day, the electrical power demand is increasing and expected to continue growing. The renewable energy sources as the power supply are also increasing but not much as needed. As a result, the power systems operate close to their maximum stability limit. This paper proposed to extend the value of Critical Clearing Time (CCT) to enhance the stability system, especially the transient stability. A Supercapacitor Energy Storage (SCES) is installed at one of the generator bus; however, the sizing of SCES must be optimized due to the economics and power balance constraint. In this paper, the Firefly Algorithm (FA) is used to obtain the optimal sizing of SCES with the objective function is the highest value of CCT. Lagrange interpolation is used to validate the effectiveness of FA, such as computation time and accuracy. According to the simulation result obtained, the proposed method using FA has a faster calculation time than Lagrange interpolation. Moreover, the proposed method is validated on a modified IEEE 9 bus with various cases. By using FA, the best increase of CCT is obtained when the optimal size of SCES is 0.120 p.u. The overall CCT increase is 0.15766s or around 54.61%.

484

Authors: Rania Moutchou, Ahmed Abbou, Salah Eddine Rhaili

This paper presents a modelling study and focuses on an advanced higher order slip mode control strategy (Super Twisting Algorithm) for a variable speed wind turbine based on a permanent magnet synchronous generator to capture the maximum possible wind power from the turbine while simultaneously reducing the effect of mechanical stress, powered by a voltage inverter and controlled by vector PWM technique. This paper presents first and second order sliding mode control schemes. On the other hand, a challenging matter of pure SMC of order one can be summed up in the produced chattering phenomenon. In this work, this issue has been mitigated by implementing a new control. The proposed control, characterized by a precision in the case of a continuation of a significant reduction of the interference phenomenon, successfully addresses the problems of essential non-linearity of wind turbine systems. This type of control strategy presents more advanced performances such as behaviour without chattering (no additional mechanical stress), excellent convergence time, robustness in relation to external disturbances (faults in the network) and to non-modelled dynamics (generator and turbine) which have been widely used in power system applications by first order sliding mode control. In particular, second-order sliding regime control algorithms will be applied to the PMSG to ensure excellent dynamic performance. The suggested control is compared to the proportional-integral controller and sliding mode control of order one. The results of simulations under turbulent wind speed and parameter variations show the efficiency, robustness and significantly improved performance of the proposed control approach to distinguish and track quickly (about 10ms depending on the shading pattern) and at the same time saving the main priorities of the sliding mode of order one by reducing the existing chatter. The systems performances were tested and compared using Matlab/Simulink Software.

506

Authors: Ahmad Ashari, Anny Kartika Sari, Helna Wardhana

The System Modeling Language (SysML) used the Requirement Diagram to model non-functional requirements, such as response time, size, or system functionality, which cannot be accommodated in the Unified Modeling Language (UML). SysML Requirement Diagram, in its implementation, integrates with several diagrams describing the requirements, which are referred to as additional elements. The absence of transformation rules for these additional elements to become OWL ontology causes difficulties in reading, understanding, and tracking the requirements. In this research, an extended rule of the Requirement Diagram transformation is proposed to solve the problems. First, some transformation rules are defined to make requirements easier to trace and realize the ontology generation's automatic transformation. Second, the time required during transformation processing to prepare and generate the OWL file shows the proposed model's performance. The ontology components produced from this research, such as class, subclass, object property, and data property, can be viewed in Protégé.

528

Authors: Vanathi Arunachalam, Nagamalleswara Rao Nallamothu

Data aggregation plays a vital role in the Internet of Things (IoT), and it aggregates the collected sensor data from devices to suppress redundant data transmissions. Many-to-one traffic pattern in the IoT induces hotspot problem and inefficient data aggregation. The Routing protocol for low-power and lossy networks (RPL) in the network layer impacts the hotspot problem due to the frequent usage of forwarding nodes and load imbalance. The processes of network layer protocol, such as trickle algorithm and Objective Functions (OF) for Destination Oriented Directed Acyclic Graph (DODAG) construction, need more attention to avoid hotspot for efficient data aggregation. This work proposes a Load Balanced RPL (LoB-RPL) protocol to avoid hotspot creation using a composite metric based parent selection, DODAG construction, and local topology adaptive decision on trickle parameters. The LoB-RPL improves the Minimum Hop with Hysteresis Objective Function(MRHOF) using the composite metric based parent selection and tunes the parameters of the Trickle algorithm. It ensures efficient maintenance of DODAG structure, hotspot avoidance, and unnecessary DIO transmissions. Beyond the advantages of composite metric based parent selection, consideration of dynamic parameters may induce frequent parent switching in RPL. To avoid frequent changes in the DODAG structure, the LoB-RPL optimally decides the parent switching threshold. Thus, the proposed work ensures a load-balanced and an energy-efficient RPL routing as well as data aggregation in the IoT environment. The LoB-RPL delivers outperforming results compared to the base RPL under various inter-packet interval time over 50 node topologies.

552

Authors: Kei Eguchi, Wanglok Do, Akira Shibata

This paper presents a novel high step-down dc/dc converter topology with a single inductor for 48V data center applications. Distinguished from conventional dc/dc converter topologies with a single inductor, the proposed converter topology is designed by series connecting a step-down cross-connected Fibonacci converter with a buck converter. By combination of these converter modules, the proposed converter topology offers a high voltage gain, viz. 1/48×, without cascade connection. Hence, high power efficiency is provided by the proposed single inductor topology. The characteristics of the proposed converter topology are investigated by theoretical analysis, computer simulations, and experiments. In the given theoretical analysis and the performed simulations, the proposed converter topology demonstrates higher power efficiency than the conventional converter topologies with a single inductor. Furthermore, the validity of the proposed topology is confirmed by the experiments.