International Journal of Intelligent Engineering and Systems

12

Authors: Umar Umar

This research discusses the optimization of capacity and locations for multi single-phase distributed generation (DG) in unbalanced distribution systems to reduce power losses, improve bus voltage while maintaining voltage imbalances and harmonics within the acceptable limits. The optimization of single-phase DG uses new enhanced symbiotic organism search (NeSOS) based genetic algorithm (GA) operator. NeSOS is developed by modifying the mutualism and commensalism phases using the random weighted inverse vector. The parasitism phase is modified by introducing sub-phase in parasitism phase. NeSOS based GA operator is validated using the IEEE 33-bus system and tested using an unbalanced 25-bus distribution system. Validation using the IEEE 33-bus distribution system shows that NeSOS based GA operator produces the lowest power loss compared to, chaotic moth-flame optimization (CMFO) based power loss sensitivity factor (PLSF), particle swarm optimization (PSO), improved analytical (IA), loss sensitivity factor (LSF), and hybrid PSO method. Simulations using an unbalanced 25-bus distribution system indicate that NeSOS and SOS successfully reduces active and reactive power loss by 68.24% and 64.93% respectively, however in terms of convergence rate, NeSOS is, on average, 52.39% faster than SOS.

41

Authors: Inas Jawad Kadhim, Prashan Premaratne, Zena Ahmed Alwan

Watermarking digital media content could undoubtedly discourage and prevent such illegal sharing activities, safeguarding the copyrights of the original creators. Such content could make use of invisible watermarking, not just for robustness and security, but also for imperceptibility. The proposed research presents a frame selection strategy based on the difference between the absolute histograms of consecutive frames. To increase security and robustness and avoid embedding the watermark frame by frame, ten frames with the maximum absolute histogram value are selected for watermark embedding. The size of the watermark would determine the number of non-overlapping blocks that a key frame is divided into in order to embed the watermark. The watermarks are embedded into the LSB of the frame-blocks, which have the most matched location where the embedding error will be minimum with high imperceptibility. Security can be further enhanced by generating a secret key based on non-sequential video frames, block location, and watermark size. The proposed method surpasses other comparable methods in terms of imperceptibility and robustness to image processing and geometrical attacks, and it is also computationally inexpensive. The results of the proposed method outperformed the best state-of-the-art methods in MPSNR and MNCC by 16 dB and 0.018, respectively.

65

Authors: Mohammed Jabbar Mohammed Ameen, Alaa Imran Al-Muttairi, Hussam Jawad Kadhim

This paper proposes a novel medical image watermarking scheme for a multiuser wireless system based on data normalization and polar code. The watermark image is normalized, converted into a set of coefficients using Discrete Cosine Transform (DCT), and then embedded with host medical image coefficients. The watermark image is encrypted using a Baker map and then normalized. The normalized value is converted to binary data, then XORed with a logistic map sequence, and employed as frozen bits in polar code to achieve security, robustness, and imperceptibility over the Non-Orthogonal Multiple Access-Universal Filtered Multicarrier (NOMA-UFMC) transmission scheme. A collection of medical images was employed to evaluate the proposed technique for two users, and the findings are Peak Signal to Noise Ratio (PSNR=69.5761), structural similarity index (SSIM=0.9966), mean square error (MSE=0.4399) and Normalized Cross-Correlation (NCC=0.9997) which demonstrate that it is capable of achieving a high level of imperceptibility and robustness against attacks. In addition, the proposed scheme produced good Bit Error Rate (BER) performance by exploiting the properties of polar code. Compared with the traditional techniques, the proposed approach has higher image quality, a secure watermark image, performs high robustness against various attacks, and has low execution time (about 0.3284 seconds). The suggested method is, therefore, a promising one for protecting medical images in healthcare settings.

92

Authors: E. Suganthi, F. Kurus Malai Selvi

The resilient concept of cloud computing (CC) enables individuals and businesses to access the necessary services following their requirements. The methodology presents various functionalities like storage solutions, deployment platforms, convenient admittance to online services, and more. A significant issue in CC is load balancing (LB), which reduces the performance and effectiveness of the resources. The LB can be attained through (Task / Virtual Machine) scheduling and allocation. This research considers virtual machine (VM) allocation, which allocates VM to a suitable physical machine (PM). Implementing an efficient VM allocation approach is essential for mitigating energy consumption (EC) and service level agreement (SLA) breaches. This study presents an effectual LB model for CC based on state and randomization. Based on host utilization, the proposed approach first identifies the PM’s current state (high, medium, and low). Next, select the suitable PM for VM allocation using a randomization approach. The Cloudsim toolkit is employed for simulating the suggested technique, and the PlanetLab workload is used to evaluate the performance regarding EC and SLA violations. The proposed approach is compared with MFPED (Medium-Fit Power Efficient Decreasing) and HVMAP (Hybrid VM Allocation and Placement). The experimental outcome shows that the proposed technique significantly lowers EC by 40.81% and 39.76% and SLA violation by 96.81% and 95.58% for MFPED and HVMAP methods.

116

Authors: Nour Zawawi, Nermin Negied

The race to halt Alzheimer's disease (AD) in its tracks demands an early warning system. By predicting which mild cognitive impairment (MCI) patients are likely to decline into AD, clinicians can intervene while the window of opportunity remains open. But how to separate the MCI patients bound for AD from those with more benign forms of impairment? The key lies in examining the factors that influence disease progression. While prior studies have scratched the surface, a comprehensive analysis has proven elusive. Enter the Alzheimer's Disease Neuroimaging Initiative database, which tracks AD progression through a wealth of patient characteristics. Leveraging these rich data, our hybrid approach combines survival analysis with machine learning to generate dynamic predictions of time to AD onset. Rather than merely detecting AD early or diagnosing its current state, our model gazes into the future, forecasting progression from MCI to AD before the disease fully erupts. Among similar efforts, the proposed approach stands apart in scale and accuracy, validated on more patients and with higher predictive power than earlier attempts. Even cognitive tests or brain scans alone can foretell decline, with the proposed work achieving a remarkable C-index of 0.85 when evaluated using the whole ADNI dataset not only a sample from it. By revealing who is likely to convert to AD and when, this work enables clinicians to intervene at the critical junction where MCI transitions to inevitable decline. The future of AD treatment may hinge on such early warnings.

139

Authors: V. Sai Geetha Lakshmi, M. Vanithasri, M. Venu Gopal Rao

The multi-objective economic load dispatch problem (ELDP) with non-smooth cost functions and ramp-rate limits presents a challenging optimization task in power systems. This paper proposes the use of a modified cheetah optimizer (MCO) that incorporates opposition-based learning (OBL) and a dynamic adaptive weighting factor to efficiently solve this problem. The simulations are conducted on standard test systems using MATLAB programming. A comparative study is performed, evaluating the performance of the MCO against basic CO and other similar heuristics. The results demonstrate the effectiveness of the MCO in achieving optimal solutions for the multi-objective ELDP with non-smooth cost functions and ramp-rate limits. The proposed approach offers a promising solution for addressing the complex optimization requirements in power system operation and planning. The optimal cost is determined using multi-criterion optimization (MCO) in 3-bus system as $6,838.6434/h, $7,738.789/h, and $8,252.033/h for 700 MW, 800 MW and 850 MW demand levels respectively. The optimal cost is evaluated as $17,988.96/h for the 13-bus test system considering a total demand of 1800 MW. The optimal cost is evaluated as 121960.30 $/hr, for the 40-bus test system considering a total demand of 10500 MW. These outcomes demonstrate the efficacy of MCO in resolving the ELDP with generator and valve controls.

159

Authors: Anand M, Babu S

Research on real-time Facial Expression Recognition (FER) and human emotion prediction has been ongoing. The desire of humans to enable cohabitation between humans as well as machines can only be realized if a machine is able to comprehend, display, and respond to a human’s emotional state. If an Artificial Intelligence (AI) makes judgements on the basis of emotions rather than just algorithms and logic, it will be able to really breathe life without air. Although there have been several attempts to date to do this, it is still believed to be in the early stages of development. Hence, this paper plans to perform the FER prediction with the help of novel intelligent deep learning technology. Initially, the data is gathered from two standard benchmark sources such as FER 2013 and EMOTIC dataset. Next, the pre-processing of the gathered images is accomplished by face detection, rotation rectification, and Local Binary Pattern (LBP). From the pre-processed images, the feature extraction is performed by the Centralized Binary Pattern (CBP) method. These extracted features undergo the final prediction phase that is done by the novel Enhanced Gated Recurrent Unit (EGRU), in which the parameters of GRU are tuned by the nature inspired optimization algorithm referred as Kookaburra Optimization Algorithm (KOA) with the consideration of error minimization as the major objective function. This novel EGRU-KOA predicts the final outcome with consideration of various facial expressions such as sad, surprise, disgust, contempt, happy, and fear respectively. The novelty of the proposed model is demonstrated by comparing it with various conventional models in terms of distinct analysis respectively. The proposed EGRU-KOA in terms of accuracy is 2.78%, 5.60%, 1.63%, and 4.28% better than DBN+QPSO, GDP, CNN, and WKELM respectively. Similarly, the proposed EGRU-KOA with respect to MSE is 84.82%, 87.41%, 88.51%, and 86.82% better than DBN+QPSO, GDP, CNN, and WKELM respectively.

189

Authors: Visalaxi G, Muthukumaravel A

Resource allocation plays a pivotal role in Cloud Computing (CC), significantly impacting its overall performance. In order to fairly spread workload among servers, network ports, and hard drives, task scheduling is a critical module of CC. Cloud computing experiences request overloading as a result of dynamic computing over the internet. To address this challenge, a novel Cloud based Load balancing using Quantum artificial bee colony Optimization Algorithm has been proposed for task scheduling that can significantly enhance the effectiveness of cloud computing operations. The technique focuses on two important aspects of cloud computing: resource allocation and task scheduling. By achieving load balancing between servers, the proposed CLUQOA (Cloud based Load balancing Using Quantum artificial bee colony Optimization Algorithm) improves reliability and lowers expenses, latency, and response times. To show how effective it is, key performance parameters including makespan, resource usage, task migration, task execution time, and response time are assessed. The results of comparative comparisons with other approaches, such as HUNTER (Holistic resoUrce maNagemenT technique for Energy-efficient cloud computing using aRtificial intelligence), FPNSO (Flower Pollination based Non-dominated Sorting Optimization), and ProHPA (Proactive Hybrid Pod Autoscaling), demonstrate the superiority of CLUQOA in terms of improving resource utilization (35.6%, 26.4%, and 13.9%, respectively) and reducing makespan (11.02%, 9.6%, and 10.4%, respectively).

214

Authors: Muhammad Arif Faizin, Dias Tri Kurniasari, Nazhifah Elqolby, Muhammad Aidiel Rachman Putra, Tohari Ahmad

Information and communication technology is growing rapidly, making it the target of various attacks. The attacks can be in the form of data theft, phishing, and Denial of Service (DoS). There are many ways to handle attacks on communication networks, including developing an Intrusion Detection System (IDS) model. Research on IDS has developed a lot and focuses on certain things such as feature selection, dealing with data imbalance problems. Feature selection is essential to the IDS model because of the dataset’s characteristics, which have many features. Besides, the number of features included in the classification can affect the detection performance of the IDS model. This research proposes an IDS combining mutual information with thresholding feature selection and XGBoost classification algorithm. Mutual information is used to measure the dependency between every input feature and the target features. After the amount of information is obtained with mutual information, thresholding is used to decide the best number of features in the classification process. Then, the data are classified using XGBoost selected features. The proposed method was tested using four metrics: accuracy, precision, recall, and f1-score. This study used UNSW-NB15 as the primary dataset to analyze the best combinations of feature selection method and thresholding value. In addition, the proposed method has also been tested using NSL-KDD and CIC-IDS2017 datasets to evaluate the performance compared with previous research. The proposed method performs best using the CIC-IDS2017 dataset with 99.89 % accuracy and 99.68 % F1 score. Furthermore, it can reduce computational training time compared with other IDS methods that only use feature selection or tree-model-based algorithms without thresholds.

239

Authors: Nallasivan. G, Karpagam. T, Geetha. M, Sankarasubramanian. R. S, Kannan. R, Bhuvanesh. A, Poojitha. G

The management of various multimedia assets, such as photos, videos, audio files, and other rich media content, within a cloud computing environment is referred to as managing multimedia resources in the cloud. To suit the needs of applications and users, this entails the effective storage, retrieval, processing, and distribution of multimedia resources. Given the significance of work planning and managing resources in the cloud computing environment, we present a unique hybrid algorithm in this research. Many cloud-based computing systems have made extensive use of traditional scheduling techniques like ant colony optimization (ACO), first come first serve, etc. The cloud gets client tasks at a high rate, so it is important to handle resource allocation for these tasks carefully. Using the improved pelican optimization algorithm, we efficiently distribute the tasks to the virtual machines in this proposed work. The proposed hybrid algorithm (Improved POA + Improved GJO) is then used to distribute and manage the resources (Memory and CPU) as needed by the tasks. According to experimental findings, the accuracy of the proposed technique increases by 1.12%, 2.11%, and 14.2%, respectively. It shows that the proposed method has good accuracy compared with the existing HUNTER, FT-ERM, and RU-VMM approaches.

262

Authors: Abdulaziz Alashaikh

Conditional Preference Networks (CP-nets) have emerged as a prominent and intuitive model for representing condi-tional preferences within the AI community. However, the literature lacks a concrete investigation on their applica-bility within Multiobjective Evolutionary Algorithms (MOEAs). Many optimization problems that are tackled by MOEA have a room for conditional preferences on the decision space and thus reasoning about CP-nets in MOEA is of great importance. In this work, we motivate the use of CP-nets within the MOEA framework and provide a sim-ple yet powerful approach that can be augmented during the search to return solutions that are not only optimized for the objectives but also preferred by the decision maker’s CPnet. Furthermore, we consider the case where the de-cision maker has no clear idea about her preferences at the beginning of the search but willing to engage during the search to answer simple comparison questions. Our experimental results show that it is possible to return solutions that are both optimal and preferred even when the search starts with empty preference information. The obtained solution space achieved at least 23% improvement in the average penalty score.

290

Authors: Gunawan, Ivan Hosea 1 Esther Irawati Setiawan, Kimiya Fujisawa

This research evaluates the performance of End-to-End Neural Coreference Resolution models in English and Indonesian linguistic contexts, drawing particular attention to the model by K. Lee, recognized for its simplified preprocessing methodology. In this research, we use raw text to find and link mentions in documents, and handle different languages. For the evaluation metrics, the English model achieved F1-Scores of 67.94% and 67.14% on the OntoNotes-5.0 development and training sets. The Indonesian model, prepared using a CoNLL-2012 formatted dataset, attained an F1-Score of 68.88% on a 25% segment of the Book of Markus. We also analyzed the additional features integrated into the model to assess their contributions to performance improvement. The findings indicate that while the English model demonstrates generalizability across various coreference challenges, the Indonesian model’s performance is more domain-specific, being particularly effective within the confines of the Book of Mark.

329

Authors: Weerachai Khamwiangsa, Sakon Klongboonjit

All vehicles are exported globally through sea freight, which involves extended storage periods and exposure to high temperatures. Consequently, issues with the brake system have been identified, arising from the presence of air and contaminants within the system. The aim is to minimize the number of Air in the Vehicle Brake System, targeting zero defects while eliminating repair and labor costs. Research integrating TOPSIS and EDAS with FAHP to address design and transportation issues in vehicles has not yet been identified. Through a systematic literature review, specialist questionnaires, and the Fuzzy Analytic Hierarchy Process (FAHP), the study identified the main contributing factors to the brake system issues and established their importance ranking. Based on this analysis, a multi-criteria framework for weights was determined using the FAHP comprehensive evaluation method. Subsequently, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method and The Evaluation based on Distance from Average Solution (EDAS) were employed to evaluate and classify the alternatives based on the obtained weights. By specifying a scoring guideline, the attributes of the brake system could be quantified, ensuring objectivity. Consequently, the brake system issue is implemented, verified, and analysed using TOPSIS and EDAS supported by FAHP. the priority ranking of 4 criteria levels and alternatives are calculated with Pi = 0.995, 0.690, and 0.574 respectively in the TOPSIS technique and ASi = 0.796, 0.500, and 0.478 respectively in the EDAS technique. The total Rejection per thousand of Air in a Vehicle Brake System is 0.99 for CA1, 0.12 for CA2, and 0.03 for CA3. The results highlight the effectiveness of integrating TOPSIS and EDAS with FAHP in reducing the presence of Air in the Vehicle Brake System and minimizing repair costs, with a primary focus on enhancing vehicle safety control as the most critical concern. Additionally, the research prioritizes assessing the risk associated with Air in the Vehicle Brake System.

352

Authors: Venkata Vidyalakshmi Guggilam, Gopikrishnan Sundaram

In the relam of the Internet of Things (IoT), prevalence of missing data due to continuous data collection by smart devices necessitates the essential preliminary step of data imputation before engaging in information mining activities. IoT data exhibit robust interconnections in both spatial and temporal dimensions, surpassing the limitations of Euclidean space. Yet, prevailing machine learning and deep learning approaches often focus solely on temporal attributes or capture spatial features exclusively within a Euclidean framework. To address these challenges, this paper introduces a novel network named ST-Bi-LSTM-VAE (Spatio-Temporal Bidirectional Long Short-Term Memory based Variational Auto-Encoder). The architecture of ST-Bi-LSTM-VAE is primarily grounded in the Variational Auto-Encoder (VAE) framework. This innovative approach incorporates two distinct types of VAEs. The first type is dedicated to computing the adjacent matrix of the device network, a crucial input for the Graph Convolutional Network (GCN) essential in capturing intricate spatial relationships among devices. The second type of VAE is specifically tailored for data imputation, leveraging both global spatial and temporal dependencies. Empirical experiments conducted on diverse publicly available datasets substantiate the efficacy of ST-Bi-LSTM-VAE. The results obtained consistently demonstrate that proposed method surpasses baseline techniques in maintaining pattern, structure, and trend across datasets even at 50% missing gap for imputation task with 4.91% performance improvement in case of Intel Berkley Research Laboratory (IBRL) dataset and 3.5% on PRSA dataset.

380

Authors: Sarmad Ibrahim, Akram Jaddoa Khalaf, Shamam Alwash

This paper proposes an integrated method to simultaneously determine the optimal placement and sizing of battery energy storage systems (BESSs) in power distribution networks using the hunger games search algorithm (HGSA). The objective of the proposed method focuses on concurrently reducing power losses and improving the voltage deviation index to improve the distribution system performance while keeping all constraints within permissible limits. The HGSA is a nature-inspired algorithm that simulates the behavior of prey and predators in finding food. In this paper, the HGSA is used to search for the optimal solution in a multi-dimensional search space consisting of the candidate BESS locations and sizes. The proposed method is applied to modified IEEE 69-bus and IEEE 85-bus test distribution systems with five different scenarios, and the results indicate that the HGSA can efficiently determine the optimal placement and sizing of BESSs, resulting in significant power loss reduction (i.e., 69.13-98.08% for the first test system and 52.97-95.03% for the second test system) and voltage deviation improvement (i.e., 94.75-99.89% for the first test system and 12.12-93.37% for the second test system) compared to the base case. Furthermore, the performance of the HGSA is compared with other well-known metaheuristic optimization algorithms (i.e., whale optimization algorithm, chaotic neural network algorithm, genetic algorithm, grey wolf optimizer, and water cycle algorithm), and the results show that the HGSA outperforms these optimization algorithms in attaining the best optimal solution with faster convergence and less number of iterations.

401

Authors: Karrar Sahib Nassrullah, Ivan Viktorovich Stepanyan, Haider Sahib Nasrallah, Neder Jair Mendez Florez, Abdelrrahmane Mohamed Zidoun, Shahad Raouf Mohammed

The focus of this study is a relatively new development in the field of control: machine learning control. This study offers a mathematical framework for machine learning control and explores three symbolic regression-based techniques for supervised and unsupervised learning. One of the challenges associated with machine learning control pertains to the control general synthesis. This entails figuring out a control function contingent upon the object’s state, guaranteeing the attainment of the control objective while optimizing the quality criterion value across all possible initial states within a permissible zone where finding a satisfactory solution occurs within the space of codes. The implementation of the small variations principle within the basic solution is suggested as a viable technique for developing the algorithms of search. This paper extensively discusses three symbolic regression techniques, including Cartesian genetic programming (CGP), synthesized genetic programming (SGP) and parse-matrix evolution (PME). Notably, synthesized genetic programming, being a novel technique, and PME get utilized for the first time to address the general synthesis of control problems. The mathematical expression’s SGP code is a six-row integer matrix; the first row of the matrix represents the functions that take two arguments, while the second and fourth rows represent the functions that take one argument. The third and fifth rows represent the arguments of the mathematical expression, and the sixth row represents the priority. The computational example demonstrates the potential of symbolic regression approaches as unsupervised machine learning control techniques for addressing the machine learning control challenge of general synthesis of control in order to achieve the stability of a mobile robot system. Likewise, practical experience shows that synthesized genetic programming has faster efficiency than Cartesian genetic programming and parse-matrix evolution in discovering solutions, about 2.33 and 2.11 times on average, respectively.

428

Authors: Mays Kareem Jabbar, Thaar A. Kareem

Wireless Sensor Networks (WSNs) play a pivotal role in remote monitoring, surveillance, and Internet of Things (IoT) applications. The efficient utilization of battery-powered sensor nodes in WSNs, given their limited power capacity, is crucial for successful data transmission. Conventional clustering algorithms while efficient in clustering, often lacks the efficient management of data generated by sensor nodes, leading to redundant data in applications like IoT leading to reduced network lifetime. To overcome this issue, this paper introduces a novel approach, named CCOA-DC (Improved Coati Optimization with Cognitive Factor (CCOA) through Data Compression (DC)), in clustering heterogeneous aggregated WSN data. The research unfolds in two novel phases. Initially, a non-negative matrix factorization (NMF) model is introduced data compression for clustering, addressing the challenge of data transmission and energy efficiency. Subsequently, the performance is enhanced through load balancing, featuring dynamic cluster head selection via Improved Coati Optimization (COA) with cognitive factor (C), denoted as CCOA. A distinctive aspect is the incorporation of the NMF data compression technique in both clustering and cluster head selection processes, introducing an energy-efficient, load-balanced, and compressed data aggregation mechanism. The proposed CCOA-DC undergoes rigorous testing, comparing its performance against existing models to validate its superiority. Comparative analyses with renowned models such as TCBDGA, HEED, and FEEC-IIR underscore the distinct advantages of CCOA-DC. Notably, it achieves a reduction of 78.57% of packet loss ratio compared to FEEC-IIR model. The model achieves high packet delivery ratio which is 98.67%, and shows optimized energy consumption of 68.01% Joules. This novel compression-based metaheuristic data aggregation algorithm showcases its effectiveness in addressing the energy conservation challenge, affirming its prominence in the area of WSNs based IoT applications.

460

Authors: Suhas Kamshetty Chinnababu, Anandababu Jayachandra

Globally, diabetes mellitus is the most dangerous disease and it is important to predict disease at an early stage to treat the disease. The learning-based algorithms play a significant part in supporting decision-making in diabetes prediction as well as diagnosis. Machine Learning (ML) based diabetes classification suffers from poor performance due to constraints such as limited labeled data and the challenge of data imbalance. Therefore, this research proposes the Self-adaptive Evolutionary Algorithm (SAEA) with Convolutional Neural Network (CNN) for the prediction and classification of diabetes. For validating the proposed method’s effectiveness, the data is collected from benchmark datasets such as the Pima Indian Diabetes dataset (PIDD), Frankfurt Hospital, Germany, and North California State University (NCSU). Then, the min-max normalization is used for normalizing the data in the pre-processing step. The Chi-square-based feature selection technique is used for selecting the best feature for categorial features and finally, the SAEA with CNN is used for classification and it classifies the disease into diabetes and non-diabetes. The proposed method’s effectiveness is estimated by using various matrices and it attains the accuracy of 99.87% and 99.99% by using PIDD and German datasets when compared to existing approaches like Deep Neural network (DNN), CNN, and Deep CNN (DCNN).

485

Authors: Dandy Pramana Hostiadi, Tohari Ahmad, Muhammad Aidiel Rachman Putra, Gede Angga Pradipta, Putu Desiana Wulaning Ayu, Made Liandana

The number of cases in the cyber era has increased significantly, which are caused by malicious software known as malware. This malicious software penetrates the network, infects several computers, and forms a collection of zombie computer networks commonly known as Botnets. These botnet threats can gravely impact valuable system resources and stored data and cause severe financial losses if not handled appropriately. Several previous studies introduced a botnet detection model using algorithms from machine learning by optimizing the feature selection process and having high detection results. However, feature selection is carried out without determining the role of features in the mandatory and non-mandatory categories. In fact, not all features can be selected because they have an important role and influence detection performance. This paper proposes a detection model by optimizing feature selection techniques. The initial process is to categorize features into mandatory and non-mandatory features. The feature selection process is carried out on non-mandatory features using two approaches: Univariate and ANOVA. Then, the best features from the feature selection results are aggregated with the Mandatory features and processed in a classification model for detecting malware attacks. The aim is to obtain the best features used in the classification model to improve detection performance by measuring accuracy, precision, and recall. The classification model used is a Decision tree and was tested on three different datasets, namely CTU-13, NCC, and NCC-2. The experiment result obtained an accuracy of 99.27% on the CTU-13 dataset, 98.96% on the NCC dataset, and 98.87% on the NCC-2 dataset. The resulting average precision value is 98.68% in the CTU-13 dataset, 98.26% in the NCC dataset, and 97.90% in the NCC-2 dataset. Finally, the resulting average recall value was 99.27% on the CTU-13 dataset, 98.96% on the NCC dataset, and 98.87% on the NCC-2 dataset. The detection results showed better results than previous research. This model can make analyzing attacks easier and determine treatment when a malware attack occurs.

515

Authors: Shivashanker K, Janaki M

The process of designing and controlling modular multilevel converter (MMC) poses numerous challenges. So, hardware-in-the-loop (HIL) simulation and transient analysis are necessary to test the efficacy and reliability of MMC in different operational conditions. This paper presents transient analysis of MMC using MATLAB/Simulink, and results are verified using HIL simulation to validate the performance of decoupled current and circulating current controllers in real-time (RT). The decoupled current control enables the independent control of real and reactive power in AC system, and circulating current control minimizes the circulating currents in MMC phase-legs. The transient analysis is performed on the study system with a rating of 1000 MVA, 220 kV in MATLAB/Simulink. The analysis is carried out for a step perturbation of 100 MW in the real power and 2.0 kA in reactive current references, and results show the satisfactory responses with MMC controllers. The simulation results are validated with HIL simulation of MMC using two RT simulators. The HIL simulation results show the satisfactory responses with closely matching responses to MATLAB/Simulink results.

539

Authors: Annapoorani S, Lakshmi M

Depression is a widespread psychological disorder that significantly impacts individuals. The conventional diagnostic approach through doctor-patient communication lacks objectivity and precision. The development of a more accurate and balanced technique for the detection of depression is critical. Due to its ability to effectively describe brain activity, resting electroencephalography (rEEG) is a valuable technique for investigating brain differences between healthy controls and patients with depression. This study examined the rEEG data from 95 healthy controls and 119 individuals with depression. This study constructed the brain's functional network and extracted linear spectral power density parameters from the rEEG data using correlation. Extracting distinctive features for identifying depression in rEEG data remains challenging due to the absence of crucial multichannel and temporal contexts in most existing methods. This research proposes taking advantage of an attention-based convolutional neural network with Long Short-Time Memory (ACNN-LSTM) to enhance its ability to identify of depressive disorders by extracting more selective features from rEEG signals. The ACNN-LSTM being presented incorporates a channel-wise attention mechanism to dynamically assign weights to different electrodes. A Convolutional Neural Network (CNN) is used to extract the important spatial details from the encoded rEEG data. An LSTM combines extended self-attention to examine the time-frequency trends in rEEG data by evaluating significance based on built-in similarities in the signals. The proposed model demonstrated strong performance with an accuracy of 97.34% in distinguishing between depression and healthy controls using 10-fold cross-validation. According to our research, the front and occipital lobes—particularly the Fp1 and Fz locations and the O1 and O2 positions with the oscillations of beta and alpha—play the largest roles in the detection of depressive disorders.

563

Authors: Ammaiyappan Kavitha, Valavandan Srinivasan Meenakshi

Mobile Adhoc Networks (MANETs) are vulnerable to various attacks such as Black Hole Attack (BHA), Gray Hole Attack (GHA), and Wormhole Attacks (WHA). While researchers have focused on detecting and mitigating individual attacks, protection against collaborative attacks is limited. Therefore, this article introduces a new Tunicate Swarm Optimization Q-learning-based Collaborative Attacker Detection Algorithm (TSOQCADA) to identify and prevent collaborative attackers like BHA, GHA, and WHA, thereby improving routing efficiency. This algorithm utilizes feedback about node properties such as energy, reputation, buffer space, transmission delay, and packet transfer rate from all nodes to determine efficient packet routing. In the TSOQ-learning algorithm, the TSO is adopted to set the Q-table values, resulting in faster convergence of Q-learning. First, a Q-table with prior knowledge is trained to enhance searchability. Additionally, a novel selective search mechanism is adopted to improve exploration efficiency and reduce unwanted explorations by considering the correlation between current and target locations. Furthermore, a nonlinear function is designed to achieve a tradeoff between search and use abilities in Q-learning, dynamically changing ε value in the ε-greedy method according to the number of iterations. Thus, the TSOQ-learning can efficiently obtain a routing path by isolating collaborative attackers with low reputation values. Simulation results show that the TSOQCADA achieves a Packet Delivery Ratio (PDR) of 94.8%, Packet Loss Rate (PLR) of 5.2%, energy consumption of 2.53J energy/packet, throughput of 355Kbps, and End-to-End (E2E) delay of 35ms for a network of 100 nodes with 20 malicious nodes in MANET, outperforming the Efficient Trust-based Routing Scheme (ETRS), Hybrid Trust-based Reputation Mechanism (HTRM) and Deep Neural Learned Projective Pursuit Regression-based Watchdog Malicious Node Detection and Isolation (DNLPPR-WMNDI) algorithms.

586

Authors: Samar Farhan, Mohammed Aal-nouman

The deployment of small cells (SCs) in heterogeneous beyond 5G (B5G) networks holds immense promise in meeting the ever-growing demand for data rates and ensuring the desired Quality-of-Service (QoS) for cell-edge users equipment (CEUEs) in densely populated B5G networks. In our pursuit of jointly maximizing the sum-rate while minimizing interference for CEUEs, we present an optimization-based approach with a specific focus on fulfilling the minimum QoS requirements of CEUEs. Our innovative two-step algorithm begins with a reinforcement learning (RL)-based matching process among UEs and available resources, followed by optimal power allocation to UEs based on the matched pairs. At the first step, by leveraging a Q-learning-based method, our algorithm identifies the optimal UEs-resources pairing. The learning process utilizes the achieved sum-rate of each pairing among UEs and resources at each step and converges to a sub-optimal pairing among them. In the second step, power allocation for the selected pairing of the first step is solved in optimal manner. Achieving optimal power allocation is facilitated by exploiting the difference of concaves form of the objective function and harnessing the majorization-minimization (MaMi) technique considering the minimum required QoS of CEUEs.Our numerical results showcase the effectiveness of the proposed scheme, demonstrating near-optimal performance. The results show the employed RL approach effectively converges to the near-optimal pairing among UEs and resources in a dense environment. Additionally, it is evident that the optimal power allocation not only maximizes the sum-rate but also minimizes interference for CEUEs. Considering different values of macro cell (MC) transmission power and SC radius, the proposed schemes achieve a sum-rate enhancement of at least 10% and 25% compared to other existing matching and power allocation methods, respectively.

622

Authors: Ahmed Sabah Al-Araji, Khulood Eskander Dagher, Mohammed Najm Abdullah

This paper presents an adaptive neural network acting like a proportional-integral-derivative (PID) controller that uses an intelligent meta-heuristic technique to improve the drug infusion rate (propofol) as a manipulated variable in closed-loop control of anesthesia systems using the Bispectral Index (BIS) as the primary controlled variable. The effect of propofol on the human body is modelled using the pharmacokinetic (PK) and pharmacodynamics (PD) models. A physiological dataset of patients, including gender, weight, height, age, and the like, determines the parameters of the PK/PD mathematical model. The proposed controller seeks to provide the optimal propofol control action, which is in charge of swiftly, precisely, and accurately maintaining a triad of hypnosis, analgesia, and neuromuscular blockade by infusing several drugs that are specific to each state. To train this neural network like a PID controller with the radial basis function (RBF) in a neuron, the meta-heuristic method is employed. The first technique is particle swarm optimization (PSO), which has been widely used in both data estimation and training because of its quick computing speed, while the second technique is the chaotic PSO algorithm, and the third technique is the modified CPSO algorithm (MCPSO). The fundamental proposed procedures of the MCPSO algorithm use the chaos method, including the coefficients of acceleration, and remove the two random parameters from the velocity update equation to generate more randomness in the search space to quickly solve the local minima problem. The PSO, CPSO, and MCPSO meta-heuristic algorithms use the mean square error (MSE) performance index to find and optimize the optimal or the nearly ideal gain parameters of the nonlinear neural network to function like a PID controller. The simulation results show that the proposed controller for different physiological dataset patients is characterized by its efficacy and resilience in terms of controlling the depth of the hypnosis state and the infusion rate of the anesthetic drug during surgery in order to avoid under- or over-dosing of the drug for the patient through the desired value of BIS (50) with minimizing the steady-state error, which is equal to zero without any oscillation. Moreover, the comparison results showed that the proposed RBF-NN-PID controller enhanced the time in one minute to reach the depth of anesthesia at the moderate hypnotic state when compared to the fractional-order adaptive high-gain controller, in which the time to reach the depth of anesthesia is two minutes. In contrast, the adaptive neuro-fuzzy controller reached the depth of anesthesia in three minutes. Therefore, the time was improved by 50% and 67%, respectively. In particular, the surgery BIS index was kept at the BIS desired 50 at the moderate hypnotic state without any error and with no oscillation at steady-state.

647

Authors: Preeti Sadanand Topannavar, Dinkar M. Yadav, Varsha Bendre

The early analysis of brain tumors plays a significant role in enhancing treatment and patient survival rates. The accurate diagnosis is critical to creating treatment plans that extend the lifetime of affected individuals. The existing methods have limitations like less accuracy and not able to learn global features. To overcome this issue, Global Self-Attention Module based Convolutional Neural Network (GSAM based CNN) is proposed for brain tumor classification. The Figshare dataset is preprocessed through data augmentation which is utilized to enhance the data size, and the median filter is employed to eliminate noise in an input image. The preprocessed images are given to Adaptive Kernel Fuzzy C Means (AKFCM) with Otsu thresholding for segmentation process. After segmentation, the Sine Cosine Reptile Search Algorithm (SCRSA) is employed for feature selection. Then, the features are provided to GSAM based CNN for brain tumor classification. The proposed model achieves better result on Figshare dataset on the metrics of accuracy, precision, sensitivity, specificity and f1-score, with values of 99.83%, 99.65%, 99.79%, 99.78% and 99.71%, correspondingly, when compared to the existing methods like Comprehensive Learning Elephant Herding Optimization based K-Nearest Neighbor (CLEHO based KNN) and Parallel Deep CNN (PDCNN) with data augmentation.

671

Authors: Sahar K. Hassan, Zaid M. Khudair

The study presents an innovative approach to designing a Circular Microstrip Patch Antenna capable of covering a broad range of frequency bands. The physical measurements of the antenna are 34×22 × 1.57 mm³. Rogers RT5880 (Lossy) substrate has been chosen to build the antenna, which has a dielectric constant (????????) of 2.2. The antenna’s radiating patch layer and the ground plane layer has been structured from copper, each with a thikness of 0.035 mm. The resonance at five distinct frequencies: 5.2 GHz, 6.628 GHz, 9.098 GHz, 12.036 GHz, and 13.076 GHz is the key element of this antenna. These frequencies cover a broad range of applications including WLAN, C-band, X-band, and Ku-band. Simulations has been applied using the CST Software version 2021 to validate the antenna performance. The adaptability of the antenna design, coupled with its successful validation, makes it suitable for a broad range of wireless applications across different frequency bands. This highlights their effect and applicability across a variety of technological fields.

696

Authors: Harikrishna Mulam, Venkata Rambabu Chikati, Bhargav Salugu

The Electrocardiogram (ECG) serves as a crucial indicator of diverse cardiac conditions, emphasizing the importance of precise signal classification for automated arrhythmia detection. ECG is an efficient tool for the diagnosis and detection of arrhythmia. Detecting arrhythmias in extended ECG segments can result in episodes being overlooked. However, since ECG transmits a massive amount of information, it becomes very complex and challenging to extract the relevant information from visual analysis. To overcome this problem, this research proposes Long Short-Term Memory (LSTM) with Luong Attention Mechanism approach for the classification of arrhythmia from ECG into 5 classes. When LSTM is combined with Luong attention, they can learn which parts of the ECG signal are crucial at each time step, and effectively capturing both short-term and long-term dependencies. For evaluating the performance of the proposed method, the data is collected from a benchmark dataset called the MIT-BIH dataset. After the collection of the dataset, the pre-processing is done using Continuous Wavelet Transform (CWT) to reduce the low and high-frequency noise. After that, the pre-processed data is forwarded to the feature extraction process to extract the relevant features by using the statistical (Skewness, Kurtosis, Moment, etc.) and time-frequency domain features. Finally, the LSTM is used to classify the arrhythmia classes. From this analysis, the proposed LSTM with Luong Attention Mechanism achieved better results in overall metrics. The proposed method achieves the accuracy of 99.75% which is comparatively higher than the existing approaches like Deep Residual Convolutional Neural Network (DRCNN) and Depth wise Separable CNN with Focal Loss (DSC-FL-CNN).

720

Authors: Ratnam Dodda, A Suresh Babu

Text document clustering (TDC) is becoming the most famous technique in various real-world applications like information retrieval, data mining, and so on. The TDC poses a significant challenge in these applications as the volume of data collection continues to grow daily. In clustering, the metaheuristics algorithms are used for solving the NP-hard problem and to enhance the model performance. Once the solution space is maximum, traditional approaches are incapable of identifying the solution in minimal time. In this research, the Chaotic Northern Goshawk Optimization (CNGO) with K-means Clustering algorithm is proposed for TDC. Initially, three benchmark datasets such as Reuters-21578, 20-Newsgroup and BBC Sport are used to estimate the performance. Then, the pre-processing techniques such as tokenization, stop word removal, stemming and lemmatization are used for data denoising. The word2vec feature extraction technique is used for converting the document into numerical vectors and finally, the CNGO with K-means is used for TDC. The effectiveness of the proposed method is estimated by utilizing various metrics and it attains the accuracy of 98.36%, 96.26% and 97.36% by using 20-Newsgroup, Reuters-21578 and BBC datasets when compared to competitor approaches like Salp Swarm Algorithm (SSA) and Rider Optimization-based Moth Search Algorithm (Rn-MSA).

744

Authors: Ruaa Abdulkareem Yaseen, Fadhil S. Hasan

This paper introduces a new index modulation method called Grouping Frequency-Time Index Modulation Non-Active for Differential Chaos Shift Keying (GFTIM-DCSK-II). The proposed system exploits the advantages of index modulation (IM) so that both the energy and spectrum efficiency of the system are enhanced. The suggested system effectively employs both frequency and time resources, allowing for efficient transmission at large data rates. For each symbol period, the new approach divides all data bits into equal G groups. For each group, the system includes frequency index bits, denoted as m_1, which are utilized to choose a non-active subcarrier from a total of 2^(m_1 ) subcarriers. Additionally, time index bits, denoted as m_2, are employed to select a single time slot from 2^(m_2 ) total time slots. After that, the modulated bits m_0 are subjected to DCSK modulation and thereafter sent over the active subcarriers. Therefore, G(m_0+m_1+m_2), where m_0=(2^(m_1 ) 2^(m_2 )-1), is the total amount of bits transmitted with the GFTIM-DCSK-II system. To better demonstrate the distinctive characteristics of the proposed scheme the energy efficiency, complexity, and spectral efficiency of the GFTIM-DCSK-II system in comparison to the most advanced alternatives available are thoroughly analyzed. The system with q=8, m_1=1, and m_2=1 saves around 40% more energy than the CTIM-DCSK system also the proposed system enhanced the spectral efficiency around five times more than CTIM-DCSK and GSIMDCSK-II with q=8, m_1=1, and m_2=4. Bit error rate (BER) formulas across multipath Rayleigh fading channels and additive white Gaussian noise (AWGN) are calculated analytically. The proposed system showed an enhancement in BER performance around 5 dB than CI-DCSK2 and GSTIM-DCSKII across the AWGN channel at a BER of 10^(-4) at the same data rate.

771

Authors: Saad M. Alwash, Osama Qasim Jumah Al-Thahab, Shamam F. Alwash

The paper presents an algorithm for harvesting optimal power points (OPP) utilizing the direct power control (DPC) technique of the dual-fed induction generator (DFIG) wind turbine. The conventional Fibonacci search algorithm (FSA) technique cannot find the exact global peak (GP) strategy and may fail. It proposes a new modified Fibonacci search algorithm (MFSA) to extract (OPP) with variable-speed wind energy turbines (VSWET), which is very important for many people and companies. Among the other different algorithms, the MFSA is more efficient and has a simple strategy to track the optimal power point that depends on calculating the effective speed of the Ω_m in an interval [a, b]. The research paper suggests modifying the Fibonacci-based search on the rotor side conversion (RSC) of a 7.5 KW DFIG in the WECS wind energy conversion strategy. The simulation uses the built-in MATLAB/SIMULINK (R2023a) software. It demonstrates the ease of implementation, lower total harmonic distortion THD and efficiency since it uses a step size that is not fixed based on the Fibonacci sequence.

802

Authors: T. Divya, J. Viji Gripsy

This study proposes three contributions focused on lung cancer detection and severity level identification. The absence of non-invasive technologies for predicting lung cancer necessitates faster, more efficient, and more accurate classification procedures due to the absence of non-invasive technologies for predicting lung cancer. Creating an automated and intelligent prediction system is crucial for identifying phases and predicting the possibility of a recurrence. The objective is to create an automated detection system for identifying lung cancer using an optimization-focused deep learning model. We develop an adaptive multi-swarm PSO and combine it with the firefly algorithm to determine the ideal weight values for the Wavelet Neural Network (WNN) model. We use the HAPSO-FFA-WNN method to explore problems with multiple optimal solutions. This study evaluated two lung cancer datasets, and the proposed HAPSO-FFA-WNN model achieved 97.58% accuracy for dataset 1 and 98.54% accuracy for dataset 2. Furthermore, the proposed model achieved better precision, recall, and MCC performance metrics.