International Journal of Intelligent Engineering and Systems

11

Authors: Manaswini Pradhan, Alauddin Bhuiyan, Subhankar Mishra, Thanh Thieu, Ioana L. Coman

In the recent decades, the lung cancer is the most dangerous disease that causes 1.6 million deaths per year. Therefore, the early recognition of lung cancer is an effective way to reduce the mortality rate. Compared to other imaging techniques, the histopathology images are effective in identifying the location and level of cancer. In this manuscript, a novel model is implemented for an automatic classification of histopathological images related to lung tissues. Initially, the color normalization technique is applied for improving the contrast of the histopathological images, which are acquired from the LC25000 lung histopathological image dataset. Additionally, the cancer segmentation is accomplished utilizing saliency driven region edge based top down level set (SDREL). Further, the feature descriptors: Alexnet and gray level co-occurrence matrix (GLCM) features were used for extracting the feature vectors from the segmented histopathology images. Lastly, the enhanced grasshopper optimization algorithm (EGOA) and random forest classifier were used for optimal feature selection and lung tissue classification. The simulation result shows that the EGOA-random forest model obtained 98.50 % of accuracy on the LC25000 lung histopathological image dataset.

35

Authors: Valliammal Narayan, Sudhamathy Ganapathisamy

Machine learning methods are widely applied in credit card fraud detection to improve its efficiency and automate the process. The existing methods in credit card fraud detection have limitations of imbalance data problem. In this research, Hybrid Sampling (HS) - Similarity Attention Layer (SAL) – Bidirectional Long Short Term Memory (BiLSTM) is proposed to improve the classification performance of credit card fraud. Two datasets such as European data and Revolutionary analytics are applied to evaluate the SAL-BiLSTM model. Hybrid sampling of SMOTE-ENN model over-sample the minority class and under-sample majority class that helps to reduce difference between original data and generated data. The SAL is introduced to measure similarity of sequence of data to provide weight to unique features and reduces overfitting problem in classification. The proposed method has the advantage of removing the overlapped samples in the majority class and increasing the instances in the minority class. The SAL method in the proposed method helps to focus on unique features in the datasets to improve the classification performance. The BiLSTM model performs forward and backward analysis to find the relevant features for classification. The proposed HS-SAL-BiLSTM has a 99.2 % recall value and the existing RF-SMOTE-Support Vector Machine (SVM) has a 97.7 % recall value.

54

Authors: Dedy Kurnia Setiawan, Widya Cahyadi, Bambang Sri Kaloko

Uninterruptible power supply (UPS) is a form of electrical supply that is frequently used to regulate electrical current and perform as a backup power source when the primary power source fails. Apart from providing backup power, the inverter system proposed in this study can reduce harmonic effects and increase power factor. The control system is based on a synchronized reference frame (SRF) and uses online combined error adaptive fuzzy (OCEAF). The load voltage and current are decomposed into their symmetrical components: positive, negative, and zero. These components are then transformed to a synchronous reference frame (dq), which OCEAF controls. Controlling the inverter is accomplished by converting the controller output to abc coordinates. OCEAF combines the delta error adaptive fuzzy (DEAF) and the absolute error adaptive fuzzy (AEAF) techniques with the NN adaptor. This adaptive fuzzy-PI automatically modifies the Kp and Ki values when the system changes, significantly when the source value alters or is faulty. According to power factor measurements, OCEAF performance is superior, more consistent, and closely unity power factor. As for evaluating network harmonics, systems with OCEAF controllers may reduce network harmonics from 6.24 % to between 1 and 1.5 %. The DEAF and CEAF techniques reduce harmonics by 2-2.5 and 1.5-2 %, respectively.

74

Authors: Yenni Astuti, Risanuri Hidayat, Agus Bejo

Speaker recognition system is intended to recognize a person’s identity. This task can be done by knowing the feature of the contained voice signals. The feature can be extracted using a feature extraction technique. One of the most popular feature extraction techniques is Mel-Frequency Cepstral Coefficient (MFCC). Until this time, the MFCC is still a challenging technique to be developed. In this work, a proposed technique is developed based on the MFCC. The original MFCC is modified in the process of filter bank and by adding spectrogram. The target of this work is to obtain a better recognition accuracy than the original MFCC. This target can be done by applying a technique called Mel-weighted spectrogram. The output of the proposed technique are spectrogram images which contain the feature of the voices. The spectrogram result is then classified using dissimilarity space based on Euclidean distance to identify the person’s identity. For the dataset, this work uses 315 recorded voice signals, consisting of 3 speakers, each pronouncing five words repeatedly 21 times on seven different days. The performance of this system is evaluated by comparing the percentage of accuracy among the proposed technique, the original MFCC, and two other MFCC-based techniques. In this work, the proposed technique is better than the three other techniques with an accuracy up to 88.57%. From these results, the Mel-weighted spectrogram can be considered as a recommendation for obtaining a higher recognition rate in speaker recognition system.

91

Authors: Preethi Srinivasalu, Aishwarya Palaniappan

In recent times, brain tumor detection become an important task in medical image processing applications. The early detection of brain tumor improves the treatment process and increases the survival rate of the patients. However, the manual segmentation and classification of brain tumor is a complex and time consuming process. Therefore, a new automatic brain tumor detection model is implemented in this manuscript for effective brain tumor detection. After collecting the scans from the cloud, the normalization and adaptive histogram equalization techniques are used to enhance the acquired brain scan quality. Further, the tumor regions are segmented by integrating the Adaptive Kernel Fuzzy C Means (AKFCM) clustering algorithm with the Otsu thresholding technique. Next, the deep and textual feature values are extracted from the segmented regions using the Gray Level Co-occurrence Matrix (GLCM), Local Ternary Pattern (LTP), and LeNet-5. The dimension of the extracted feature vectors is optimized by using the Modified Particle Swarm Optimization (MPSO) algorithm, which are given as the input to the Multi-Support Vector Machine (MSVM) for tumor classification. The experimental outcomes confirmed that the MPSO-MSVM model obtained high accuracy of 98.89%, which is superior related to the existing machine learning techniques.

111

Authors: Thuan Thanh Nguyen, Huu Truong Trinh, Hoai Phong Nguyen, Tan Loc Vo, Thanh Long Duong

This paper demonstrates an artificial ecosystem optimization (AEO) for the multi-goal electric network reconfiguration (ENR) problem (ENRP). The membership goal functions of the ENRP comprise power loss reduction, voltage deviation reduction, reduction of load unbalance index among branches and reduction of number of switch operations. To show the advantages of AEO for the multi-goal ENRP, the multi-goal ENR approaches relied on particle swarm optimization (PSO), genetic algorithm (GA) and cuckoo search algorithm (CSA) are applied to contrast with the AEO method. The result comparisons among methods on the 33-node distribution network show that the AEO method can archive the optimal solution with the higher success rate and the lower average value and standard deviation of the fitness values than GA and PSO. In addition, the result comparisons with other previous approaches also show the reliability of the proposed AEO method for the single and multi-goal function ENRP. Therefore, the AEO can be an effective and useful approach for the ENRP to optimize the single and the multi-goal functions.

132

Authors: Anjani Parvathi K, Kotaiah N.C, Radha Rani K

One of the challenging tasks in an active distribution network (AND) embedded with intermittent renewable energy sources (RES) under islanding conditions is the maintenance of frequency and voltage profiles within tolerable limits. Failing to maintain these operational requirements may lead to voltage collapse or complete blackout of the network. In order to avoid this scenario, ADNs may function with contemporary load shedding schemes but these schemes may result in an inadvertent and excessive amount of load shedding, thereby consequently causing unsatisfactory and low reliability at the consumer level. Thus, this paper used Pelican optimization algorithm (POA) approach for minimising the amount of load to be shed by determining the optimal amount of load to be shed within the consumers’ specified limits. Simulations are performed on IEEE 33-bus by assuming different kinds of renewable and distribution generation (DG) units. The computational efficiency of POA is compared with literature works and its performance is also characterised based on 25-indipendent runs of each case. The results obtained by POA are observed to be superior in terms of global optima and less run time. Also, the ADN is observed as satisfactory operation with fewer distribution losses, an improved voltage profile, and enhanced stability margins by imposing optimal load controls to the consumer.

152

Authors: Sumaya A. Ibraheem, Omar Y. Shaaban

Nowadays, latency factor in network virtualization takes a serious attention in 5G networks upgrading. Flex-grid/elastic optical network (EON) virtualization recently become known as the fifth-generation (5G) enabling technology for the optical network slicing efficiency. As a consequence, the solution of the virtual optical network embedding (VONE) problem over EON must be solved with the latency targets guarantee. In this work, a new loading factor was suggested after adding the latency effect to the existing EON constraints by identifying its contributing factors in the network. Then, a virtual network mapping algorithm was presented to solve the VONE problem including latency constraint. Network topologies (NSFNET, USNET) were used under the dynamic scenario and four optimization methods which are; alignment and consecutiveness-aware virtual network embedding (ACT-VNE), the local resource capacity and the shortest path first fit (LRC-SP-FF), greedy algorithm and the shortest path first fit (Greedy-SP-FF), load and resource-aware based on ant colony optimization (LRA-ACO) were used and results have been compared to get the optimal solution. The blocking probability on average was increased by approximately 50 % in both topologies affected by adding latency (between 20-50 ms) as compared to the non-latency cases of previous works. Also, during all simulations, the LRA-ACO gave the best embedding performance.

179

Authors: Purba Daru Kusuma, Anggunmeka Luhur Prasasti

This paper presented a new metaheuristic technique, namely the guided pelican algorithm (GPA). GPA has the improvements for a shortcoming algorithm, namely the pelican optimization algorithm (POA), that mimics the behaviour of pelican birds during hunting prey. It improves the original POA in three ways. First, GPA replaces the randomized target with the global best solution as a deterministic target in phase one. Second, GPA replaces the pelican's current location with the search space size in determining the local search space size in phase two. Third, GPA implements multiple candidates in both phases rather than a single candidate as it is used in the original POA. Simulation is implemented to observe GPA’s performance in optimizing both theoretical and real-world problems. GPA is compared with four algorithms: marine predator algorithm (MPA), particle swarm optimization (PSO), komodo mlipir algorithm (KMA), and POA. The result shows that GPA outperforms all sparing algorithms in optimizing most benchmark functions. GPA is also implemented to optimize the portfolio problem. The result shows that GPA outperforms all sparing algorithms in optimizing the portfolio problem. It outperforms three sparing algorithms in optimizing the portfolio problem. Its performance is 9%, 11%, and 13%, better than PSO, MPA, and KMA consecutively. Meanwhile, its less than 1% worse than POA.

201

Authors: Periya Nayaki Anbarasu, Thanabal Muniyandi Suruli

Nowadays, clinical decision support system (CDSS) is emerged broadly due to the growth of smart applications. Yet, labeling a massive amount of clinical data is expensive and needs domain expertise. Also, how to design a robust CDSS model in a healthcare platform with a minimal number of annotated clinical data was an essential problem. To tackle this issue, a generative adversarial network (GAN)-based semi-supervised training model was developed, which increases the quantity of annotated data and solves the unbalanced annotated labels. As well, support vector machine (SVM) and K-nearest neighbor (KNN) classifiers were applied as the base learners to predict the label for unannotated data. But, the result from both base learners was not often similar, which impacts the prediction efficiency. So, this article introduces a deep ensemble learner model (DELM) for predicting labels to the unannotated data by stacking multiple machine learning classifiers as base learners. In this model, two levels of prediction: the initial level and the second level. During the initial level, multiple base learners including SVM, KNN, naive bayes (NB) and random forest (RF) learn the dataset with both the annotated and unannotated samples independently to predict the class of the unannotated instances. Then, those prediction outcomes are merged during the second level, which applies the deep neural network (DNN) as an ensemble classifier to get the final predicted class of the unannotated instance. Finally, the experimental results exhibit that the DELM with GAN achieves 86.54 %, 84.83 % and 86.72 % accuracies on SPECT, WDBC and Hallmarks databases, correspondingly, compared to the Fuzzy-AHP+ANN, HTM+LSTM, DBSCAN+SMOTE-ENN+XGBoost and GAN-based semi-supervised models.

224

Authors: K. Soumya, Vijay Kumar Garg

Detecting suicide ideation from social media texts is challenging as it necessitates analyzing the content and context of the text utterances. Modeling the temporal trajectory of suicide through various stages like stress, depression, thought and strengthening of the thought becomes difficult with content features alone and this needs context cues too. This work proposes a hybrid feature representation incorporating both content and context features. The richness of features is enhanced using adversarial learning. Neural machine translation is done on hybrid feature representation to provide named entities with emotional intensity tagging. The sequence of emotional intensity tagging is mapped by Bi-directional Long short term memory (LSTM) to suicidal ideation label. The proposed integration of emotion tagging to SI detection with Bi-directional LSTM provided an SI detection accuracy of 95.54% which is atleast 3% higher compared to existing works.

247

Authors: Saif K. Jarallah, Sawsen Abdulhadi Mahmood

Video summarization technologies strive to provide a succinct and thorough description by selecting the most informative frames from the original video. The essential concept of query-based video summarization methods is represented by constructing a video summary related to user query in term of user interest. This paper aims to address the problem of query-based video summarization construction task by adopting a lightweight deep learning model for object detection task in order to evident how concerning a frame or shot is to a given query. Both YOLOv3 and Tiny YOLOv3 deep learning models are used separately in the proposed system to train the networks using images and videos dataset including diverse types of objects such as; (motorcycles, bicycles, cars, buses, and trucks). Subsequence, the most relative frames to a given query are selected and assembled as keyframes using a modified K-mean clustering scheme to provide different interesting summaries from the original video. Based on the experimental results of object detection phase, we have obtained an average object detection accuracy around 93%, 83% based on YOLOv3 and Tiny YOLOv3 deep learning models respectively. Comprehensive experiments were performed to evaluate the proposed video summarization system, which exhibited an efficient summarization rate close to 33% of the original video. Further experiments were conducted using standard UTE dataset to exhibit the competitive performance of the proposed method compared to state of art query-based video summarization methods.

273

Authors: Neny Sulistianingsih, Edi Winarko, Anny Kartika Sari

The Girvan-Newman (GN) method is one of the most popular methods for detecting communities. However, the method is applied to graphs with only positive weights, while graphs with positive and negative weights are in the real world. This paper proposes improving the GN method to work on graphs with positive and negative weights. The method is called Girvan-Newman Parallel Positive Negative (GN-PPN). Other than the benefit related to the use of negative weights, GN-PPN has a parallelization process that accelerates processing time. This method also uses edge betweenness and pair dependencies as calculations. The GN-PPN method is evaluated using modularity score, Normalized Mutual Information (NMI), and processing time. Our experiments show that the modularity score of the GN-PPN is similar to GN, modified GN (mGN), and Parallel GN (PGN). The accuracy of the GN-PPN method in detecting communities evaluated by NMI shows that the GN-PPN is better than similar methods such as Infomap, which ranges from 0.651 to 0.937. The processing time of the GN-PPN method shows a significant acceleration, which is a decrease of around 45% to 95% compared to other methods such as GN, mGN, and PGN.

293

Authors: De Rosal Ignatius Moses Setiadi, Rahmawati Zulfiningrum, Eko Hari Rachmawanto, Pulung Nurtantio Andono

Telemedicine is a communication in the medical field that requires privacy and confidentiality, so encryption is needed as a security method. The development of encryption methods must accompany technological developments. This research proposes a cryptography method developed and tested on medical images. This method consists of bit plane slicing, system chaos, and hash function. Bit plane slicing to break up the image bits so that they can be scrambled independently and dynamically with the chaos method. The hash function encrypts key 1, which is user input. The chaos method is run with dynamic parameters generated with a hash function to perform the confusion process on each bit plane. Then bit-plane join is performed, followed by the diffusion process using a logistic map with the second key for the parameter. The method is sensitive to slightly modified plain images or keys. Several measurement tools use to prove the method's performance. The result is a mean value are 7.999 for entropy information, 99.6027 for the number of pixels change rate (NPCR), 33.0925 for unified average changing intensity (UACI), 0.0021 for correlation coefficient, 231.8679 for chi-square and histogram, 50.0550 for avalanche effect, and perceptual analysis have proven the proposed method has satisfactory performance. The decryption is also done perfectly, as verified by the structural similarity index (SSIM)=1, peak signal-to-noise ratio (PSNR) is ∞, and bit error ratio (BER) is 0. Finally, this method can also work very fast in less than one second, so it can be implemented in real-time encryption.

315

Authors: Nova Rijati, De Rosal Ignatius Moses Setiadi, Pulung Nurtantio Andono

Tamper detection and localization are essential things in fragile image watermarking to carry out the authentication process. The tampering method continues to evolve from general and complex tampering. This study proposes a tamper detection and localization technique that can withstand a variety of standard and complex attacks with a combination of bidiagonal singular value decomposition, blockwise and group block for authentication. The least significant bit method is used in the embedding process to increase imperceptibility. By combining these methods, fragile image watermarking is produced that is robust against various common tampers such as type I and type II copy-paste, rotate, text addition, and noise addition, as well as various complex attacks such as vector quantization, collage, content only and constant feature. The tamper detection rate is more than 0.99, and the average PSNR value is 51.85 dB. This shows that the proposed method is robust against various tampers and has excellent imperceptibility.

337

Authors: Abousserhane Zakaria, Abbou Ahmed, Bouzakri Hicham

The aim of this paper is developing and validating the sliding mode control application (SMC) for hybrid renewable energy system (HRES) featuring stand-alone and on-grid operations modes. The proposed system composed by a photovoltaic (PV) and wind turbine (WT) as principal sources. The PV is connected to the overall system via boost converter which controlled using MPPT algorithm depending to Adaptive Perturb and Observe (APO) method managed by SMC to extract maximum of power of PV modules. The WT depends to a Permanent Magnet Synchronous Generator (PMSG) and a controlled rectifier to maximize the operating point of the WT. Therefore, the battery and supercapacitors are connected to the system via a bidirectional converter to ensure the charging modes operations. The voltage source converter (VSC) is controlled by SMC had an aim to control the frequency and amplitude of the system outputs injected into the grid and load utility. To expose the performance of the proposed sliding mode control approach, we propose a different comparatives tests include a conventional PI control and hysteresis current control under two operating grid modes depending to different climate scenarios. The developed control strategy for HRES have shown great results such as the control performance, effectiveness and delivers low harmonic distortion values (Max of THD are 3,43% and 4,88%) compared with hysteresis current controller (Max of THD are 4,63% and 10,98%) and PI control (Max of THD are 4,92% and 12,86%) featuring OFF and ON grid, respectively. Finally, the proposed control enhances the HRES quality and stability.

364

Authors: Eman K. Ibraheem, Ekhlas K. Hamza

The increasing number of mobile devices challenges the current radio frequency (RF) networks. A hybrid RF/VLC network (HLRN) is proposed to mitigate the spatial fluctuation of data rate, offering a system throughput greater than that of standalone visible light communications (VLC) or radio frequency (RF) networks. In hybrid networks, the main problem is load balancing, which reduces network performance. Therefore, to solve this problem, the load balancing (LB) schemes in HLRNs are studied and focus on the AP assignment for the users. In that, efficient spectrum sensing is found essential to prevent the wrong holes detection in the band using five technologies divided into three stages, namely: Received signal strength (RSS), particle swarm optimization (PSO) and deep learning methods: Feed forward neural network (FFNN), convolutional neural network (CNN) and cascade back propagation neural network (CBPNN). An event such as high energy noise availability or primary user fluctuation (mobility) impact on the throughput is studied, which was carried out in three stages depending on MATLAB. In the first stage, the received signal strength (RSS) algorithm is proposed, which is used the SNR to calculate throughput for fixed and mobility user and user satisfaction. In stage two, the particle swarm optimization (PSO) algorithm is proposed to achieve a better performance than the RSS algorithm. At last, in stage three, the DL algorithms (FFNN, CNN, CBPNN) are used to enhance the performance's accuracy and compare them. The results in stage one show that throughput using RSS technique is decreasing when the number of users increases by achieving 390 Mbps approx. is detected with single user existence while 180 Mbps approx. is detected with 10 users' existence. And it can be noted that most of the users are connected to wireless fidelity (Wi-Fi) AP. Hence the Wi-Fi is overloaded. Further, only 34 % of the users in RSS based scheme will achieve the desired performance if a user satisfaction threshold of 0.5 is considered for the system. The results in stage two shown that throughput using the PSO technique is outperformed over the others by achieving high average throughput of 960 Mbps approx. Also, it has offered acceptable load balancing for the access points with good user satisfaction reach to 80 % of the users will achieve the desired performance when the user satisfaction threshold is considered to be 0.9 for the system. In stage three, deep learning methods for system performance enhancement are used (as compared with the standard RSS method). throughput is improved, and this improvement is lesser than that in the case of the PSO algorithm. 800 Mbps approx. is achieved using the CNN method, and it is realized that 70 % of the users will achieve full user satisfaction when the user satisfaction threshold is considered 1.2 for the system.

392

Authors: Mina Ibrahim, Muhammad Mahmoud, Reda M. Albadawy, Hatem Abdulkader

Liver cancer is a common type of cancer that causes death, because there are no noticeable symptoms at an early stage, as this disease is not detected in most patients until cancer has reached the advanced stage only. Researchers are developing algorithms that doctors can use to detect liver tumours early by examining images of tissue from a biopsy or an abdominal medical image. The tissue expert must put in the time and effort required at this stage to determine whether or not this tumour is cancerous and in need of treatment. This model can then be used by a histology expert to make an initial diagnosis. Convolutional neural networks (CNNs) are employed in this paper to propose a novel combination of deep learning models that can transfer information from previously trained global models. After that, this information was decanted into a solo model to improve our model approaches to increase the performance in time and accuracy with tuning to an encoder, decoder, shortcuts, and skip connections to custom convolution layers for three classes such as background, origin, and Tumour shape. Regarding semantic segmentation, our model has proved to be a highly effective way to make results more accurate and valuable to assist in the diagnosis of the liver Tumour using CT scans. As a result, we were able to develop a hybrid model that is capable of recognizing CT images of a liver tumour. Our research yielded the greatest possible results, which we got, reaching 99.50% accuracy, a 86.40% precision and a recall of 97.90%. This accuracy with multi-class is higher than that obtained using other previous models that obtained the best accuracy of 0.991 during the annual periodic examination campaigns for liver cancer detection. Using this model, experts in this field can save time and effort while becoming more informed choices. It also keeps the time and effort that would otherwise be required to administer this treatment, especially during the annual examination campaigns.

418

Authors: B. Uma Rani, A. R. Naseer

Underwater Acoustic Wireless Sensor Networks (UAWSN) is a collection of sensor nodes that are strategically placed at different depths under water to monitor the activities of interest. In contrast to Terrestrial Wireless Sensor Networks (TWSN), a UAWSN is constrained by several challenges like limited bandwidth, multipath propagation, path-loss, noise, Doppler spread, etc. Also, other parameters like salinity, depth and pressure influence the propagation speed and in turn the achieved throughput. Most of the existing works in this direction focus on designing new medium access control protocols to minimize collisions and maximize bandwidth utilization. Other existing routing and data forwarding protocols use different metrics to maximize network objectives like network lifetime, increase network throughput, minimize energy drain, etc. In this paper, we propose a routing and data forwarding mechanism using link bandwidth by considering path-loss and multipath propagation effects. These cross-layer metrics allow us to select high-capacity links increasing overall network throughput. In addition, we propose different optimizations to the data forwarding and routing mechanisms to save energy and increase lifetime of the network. The simulation results show that the proposed mechanism improves 6% packet delivery ratio, 10% throughput, and minimizes energy consumption in comparison to state-of- the-art protocol CLOR.

443

Authors: Mohammed Kh. Al-Nussairi, Sadeq D. Al-Majidi, Ali Jber Mshkil, Adel Manaa Dakhil, Maysam F. Abbodm, Hamed S. Al-Raweshidy

An Automatic Generation Control (AGC) is considered a substantial stage in power systems to ensure that the area-frequency response and the tie-line power of an interchanging steady state errors are acceptable values especially at the transient state. Whiles, several techniques have been designed for the AGC, Fuzzy logic control is commonly used. However, it is faced a longer processing time issues to adjust the accurate single in the transient state. In this paper, a Single Input Fuzzy Logic Gain Scheduling PID Controller (SIFL-PID) is designed as a supplementary loop for AGC of two area interconnected power system to reduce the processing time. The lower number of rules and the simple parameters of the SIFL-PID will contribute to minimise the design time required for the Fuzzy Logic Gain Scheduling FL-PID. The SIFL-PID also reduced the signed distance technique when it is generated one input variable simple mathematical model of the system. In comparison with the FL-PID, the number of rules controlling system can be significantly reduced. The main outcome of this work is that it is assessed the effectiveness of the SIFL-PID for two power systems that are interconnected when it is compared with the FL-PID using the MATLAB/SIMULINK environment. The results prove that, the simple design of the SIFL-PID control is validated under various condition tests. Performance analysis standards are also performed. The results of all simulations demonstrate that the suggested controller provides better performances such as like as minimum Performance analysis standards, frequency deviation power deviations and low overshoot and undershoot.

465

Authors: Nguyen Dinh Hung, Le Cao Quyen, Le Van Dai

The primary goal of this paper is to develop and apply an adaptive fuzzy (AF) based on the proportional integral derivative (PID) controller for the automatic voltage regular (AVR) system in the synchronous generator (SG) to improve output voltage stability. This proposed AF-PID is applied to ameliorate the shortcomings of the traditional PID controller by determining the optimal coefficients for the controller. Firstly, the initial coefficients of the traditional PID controller are calculated by using the Ziegler-Nichols method. After that, it develops the AF algorithm based on the fuzzification, rules of the fuzzy controller, and defuzzification of the voltage signals for self-tuning the coefficients to add the traditional PID controller. The transient response of the system is simulated and analyzed by considering the different step changes in reference voltage. The efficacy of the proposed AF-PID controller for the AVR system in the SG is illustrated by comparing it with traditional PID and without the controller based on the obtained results from the case studies. It can be concluded that when applying the proposed method, the transient response characteristics of the system from the perspective of simulating via the time domain under any sudden load changes in system operation, such as the rise time, steady-state error, max peak overshoot, and settling time are greatly improved. The simulation has been performed using the MATLAB/Simulink program.

492

Authors: Dwi Sunaryono, Riyanarto Sarno, Joko Siswantoro, Diana Purwitasari, Shoffi Izza Sabilla, Rahadian Indarto Susilo, Naufal Rafi Akbar

Epilepsy is a common chronic brain disease caused by abnormal neuronal activity and the occurrence of sudden or transient seizures. Electroencephalogram (EEG) is a non-invasive technique commonly used to identify epileptic brain activity. However, visual detection of the EEG is subjective, time consuming, and labour intensive for the neurologist. Therefore, we propose an automatic seizure detection using a combination of one-dimension convolution neural network (1D-CNN) with majority voting and deep neural network (DNN). EEG signals features are extracted using discrete Fourier transform (DFT) and discrete wavelet transform (DWT) which then these features will be selected with XGBoost to minimize features classified with CNN. The proposed method experimental results show that it can detect epilepsy from EEG signals perfectly with an accuracy of 100%. However, the proposed method only yielded classified EEG signals from the University of Bonn Dataset as its results. The performance of the suggested approach might not be similar to other EEG datasets.

511

Authors: G. Krishna Raju, P. Padmanabham, A. Govardhan

Introduction of the World Wide Web (WWW) and progressions in the field of multimedia and computer technology have enlarged the amount of image collections and databases including art galleries, digital libraries, and medical imageries that extend to millions of images. From these large-scale datasets, the retrieval procedure of images is carried out by classical approaches like chi square distance, colour histogram and text-based image retrieval, which however requires more time for getting the desired images. Thus, there is a need of proposing an effective retrieval system for images by handling these large numbers of images. For this reason, a new Content-Based Image Retrieval (CBIR) system has been implemented in this paper for retrieving the desired images of users from the collected images. Initially, different kinds of images like medical images, texture images, and environmental images etc., are collected from the standard image database. The deep feature extraction using Visual Geometry Group network (VGG-16), Inception v3, and Xception are used to get the features of all the images in the database. The parameters in these three deep learning techniques are tuned with an enhanced optimization algorithm of Modified Bypass-Rider Optimization Algorithm (MB-ROA). The features from VGG16, Inception and Xception are used for optimal weighted fused feature selection using enhanced ROA under training phase. During testing with query images, it undergoes deep feature extraction with the same set of techniques and, then fed into the optimal weighted fused feature selection to get the features of query image. Then, the multi-similarity function considering the cosine, Euclidean and Jaccard similarity are checked between the optimal features of database images and query image. The database images with the minimum similarity with the query image are retrieved from the database. The overall precision and recall to retrieve the top ten images related to the provided query image from the Corel dataset are 77.25% and 76.89%, respectively, while 70.15% and 70.26%, respectively from the VisTex dataset. The experimental findings show that the recommended model is effective at retrieving images from the database.

541

Authors: Yunifa Miftachul Arif, Hani Nurhayati

One of the challenges of learning mathematics is making students like the subject to understand the material being taught more efficiently. Therefore, several studies propose the use of gamification elements in the mathematics learning process. Another important consideration in learning mathematics is the accuracy of the subject matter given to students. This study proposes a metaverse-based mathematics pedagogy media (MMPM) equipped with learning material selection (LMS) to adaptively select subject matter according to the level of student knowledge. We use the multi-criteria recommender system (MCRS) to support LMS in providing recommendations for determining the choice of subject matter. The method used to calculate similarity is cosine-based, while the ranking is based on average, worst-case, and aggregate. We built the MMPM system using the unity game engine in the experimental stage. At the same time, the subject matter that becomes the focus of scenario visualization is about beam, cube, prism, and pyramid. The test results show that the LMS system works well in carrying out its duties by adaptively choosing the appropriate subject matter scenarios for students based on their pre-test results. In this study, MCRS-based LMS produces the highest accuracy of 92% for two to three input items and the lowest 90% for four input items.

563

Authors: Hussein A. Hussein Al-Delfi, Fadhil Sahib Hasan

This paper suggests the transmission of secure speech signals through the MIMO-OFDM system over a doubly selective fading channel. The two-dimensional chaotic map like logistic (TDLM), standard (TDSM), and triangle function combination discrete chaotic map (TD-TFCDM) are used to generate pseudo-random bit generator (PRBG) and index permutation (IP) to encrypt the information based on ciphering and scrambling techniques. Different speech quality and residual intelligibility measures including log-likelihood ratio (LLR), frequency-weighted segmental signal-to-noise ratio (fwSNRseg), signal-to-noise ratio loss (SNRLOSS), correlation coefficient (CC) and spectrogram measures as well as Bit Error Rate (BER) performance are used to test the performance of the proposed security system. The simulation results show that the speech signal over the MIMO-OFDM system behaves well with high security. Also, the speech encryption system using TDSM and TD-TFCDM outperforms TDLM in most residual intelligibility measures.

589

Authors: Mie Mie Oo, Nu War

Acoustic scene classification is a difficult issue among artificial intelligence, signal processing, and machine learning. Scene recognition performance has a robust relation with feature learning using deep convolutional networks. In the following research, end-to-end deep residual network embedded channel attention is explored to learn the discriminative features from the audio scene. Log-Mel spectrogram is obtained from input raw audios. It is forwarded to proposed attention network. An extracted feature layer is concatenated with the SoftMax classifier in the proposed attention network. The experimentation is carried out on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 and 2017 datasets. The proposed channel-attention-based residual network achieves classification results with an average accuracy of 80.27% and 80.82%, respectively.

609

Authors: Saritha Ibakkanavar Guddappa, Rajeshwari Mahabhaleshwar Hegde

Cyber physical system (CPS) is an engineered system that integrates the physical world with the cyber world through enhanced computation, communication and control (3C) abilities. But, the CPS’s sensors are susceptible to malicious threats due to its dynamic topology and open medium of the network. Therefore, an effective secure adaptive cluster-based routing is required to be developed for improving the security of CPS. In this paper, the multi objective-trust based hybrid optimization algorithm (MO-THOA) is proposed to improve CPS security. The MO-THOA is the combination of trust based flower pollination algorithm (TFPA) and trust based ant colony optimization (TACO). In that, the TFPA is used to select a secure adaptive cluster head (ACH) and TACO is used to identify the secure routing path via ACHs. Therefore, the proposed MO-THOA improved the security against malicious attacks while broadcasting the data. The performance of the MO-THOA is evaluated using packet delivery ratio (PDR), average end to end delay (AEED) and normalized routing overhead (NRO). The existing methods namely grey-wolf updated whale optimization algorithm (GU-WOA), moving centroid based routing protocol (MCRP) and multi-objective ant colony optimization (MOACO) are used to evaluate the MO-THOA method. The PDR of the MO-THOA for 20 nodes is 0.981, it is high when compared to the GU-WOA, MOACO and MCRP.

627

Authors: Sara Ahmed, Fatma A. Omara

Cloud computing has gained many attentions worldwide. Workflow systems become a significant method for develop scientific applications. Therefore, workflow scheduling is considered one of the most important issues in cloud computing. It concerns about mapping tasks on cloud resources (i.e., Virtual machines (VMs)), to improve scheduling performance. Because the existing heterogeneous earliest finish time (HEFT) algorithm is considered one of the best and simplest algorithms, many algorithms have been proposed to improve the performance of the HEFT algorithm. According to our previous work, a modification has been done to HEFT algorithm to enhance the performance, called modified heterogeneous earliest finish time (M-HEFT). The goal of M-HEFT algorithm is to reduce make span, maximize resource utilization and increase load balance. According to the work in this paper, an enhancement has been added to our previous M-EFT algorithm to reduce the tradeoff among make span, resource utilization, and load balance, called enhanced modified heterogeneous earliest finish time (EM-HEFT). The enhanced EM-HEFT algorithm consists of two phases; task prioritization and task-VM mapping. In task prioritization phase, a priority will be provided to each task in directed acyclic graph (DAG) by introducing new factors in priority value to be more aware about task requirements. According to task-VM phase, tasks are allocated to resources as in our previous M-HEFT algorithm. To evaluate the performance of the proposed EM-HEFT algorithm, a comparative study has been done among the proposed algorithm and four existed algorithms (HEFT, Efficient scheduling algorithm use critical path and static level attribute, Optimized Min-Min (OMin-Min) and our previous M-HEFT). The experimental results show that the proposed algorithm outperforms other algorithms by minimizing make span by 25%, improving resource utilization by 43% and load balance by 14% in average.

657

Authors: Bakr S. Shihab, Hadeel N. Abdullah, Layth A. Hassnawi

Recently, path planning algorithms have been one of the primary and important functions of unmanned aerial vehicles (UAVs). Path planning algorithms in UAVs focused on path length, average path length, computation time, and standard deviation from the mean path length. In spite of this, it faced many difficulties and problems, such as many obstacles, path segmentation, and the increasing number of obstacles and paths in urban environments. This work proposes polynomial functions for path planning and obstacle avoidance. Since it enables us to plan the path in static internal environments, it enables us to plan the path quickly and with less computing time because it does not require high memory and does not require pre-compute of the path. Instead, the route is plotted in real time, Where the appropriate equation is entered into the program, so that the vehicle follows the curve of the entered equation. An accurate data set and metrics were used to measure the efficiency of the proposed method. The experimental results showed a clear improvement in the work of the polynomial function on A*, PSO and genetic algorithms, as this improvement appears very clearly when compared to the computing time, which was reduced by 15% in the method of polynomial functions where the path calculation took only parts of The second, as well as the path length was halved in the polynomial method as the results showed, which reduces the time of battery and memory consumption, the cost of calculating the path and the time to reach the goal.

682

Authors: Ahmed Wasif Reza, Khairum Islam, Sidratul Muntaha, Omair Bin Abdur Rahman, Rabeya Islam, Mohammad Shamsul Arefin

The emergence of new and improved technological advances created severe problems in the security state of the educational certification system. Throughout this paper, a proposal has been made to improve security. Here, Blockchain technology has been introduced as reliable secure storage for the educational certification system, providing an additional facility to the users. That is the validation and authentication of the student’s academic records. Moreover, for security purposes, Blockchain technology can replace the traditional academic certification system and contribute to a new model for sharing student information. After completion of data inclusion and hashing, the blocks will be inserted into the Blockchain network. This proposed model enhances document security and fraud reduction and additionally reduces a significant amount of authentication time almost up to double the current speed. With this system, we will get a certification process in which all data will be digitalized and secured in an unbreakable database with proper authentication and with a noticeable amount of time efficiency.