International Journal of Electrical and Electronic Engineering & Telecommunications
IJEETC invites original, previously unpublished, research, survey and tutorial papers, plus case studies and short research notes, on both applied and theoretical aspects of electrical and electronic engineering & telecommunications.
A Multi-Objective ESTO Framework for Efficient PV Siting and Sizing in Radial Distribution Systems
There is a clear increase in the growth of renewable energy sources, especially photovoltaics (PV), which requires selecting the appropriate method for connecting them to the distribution network in terms of siting and size to minimize losses as much as possible, improve system efficiency, and enhance voltage stability. This paper presents an Enhanced Search and Tuning Optimization (ESTO) algorithm for addressing the optimal PV siting and size problem. The ESTO algorithm utilizes an adaptive system for dynamically balancing exploration and exploitation stages and thus reducing convergence speed and overcoming local minimum points. The approach is implemented for IEEE 33-bus and IEEE 69-bus systems using backward/forward sweep load flow analyses. The simulation results showed significant improvements in voltage deviation, with the lowest voltage being 0.931pu at bus 18, and it became 0.968 pu, while losses were reduced by 64.7%. The ESTO algorithm was compared with previous algorithms such as PSO, GA and ALO and proved to be superior.
Copyright (c) 2025 International Journal of Electrical and Electronic Engineering & Telecommunications
Performance Evaluation and Latency Optimization of Wi‑Fi 7 in High-Density Wireless Environments
This work studies Wi-Fi 7 (IEEE 802.11be) performance under the context of high-density wireless network situations and particularly focuses on parameters like latency, throughputs, jitter, and packet loss. The intention is to evaluate the performance of Wi-Fi 7 in comparison to Wi-Fi 6 (IEEE 802.11ax) under various network load conditions and assess its ability to support latency-sensitive and bandwidth-intensive applications. A controlled series of experiments was conducted with the help of NS-3 simulations by utilizing 20 to 50 client nodes operating on dual-band access points with support for Multi-Link Operation (MLO). The model of traffic used was VoIP, video conferencing, AR/VR, and background TCP flows to reproduce application behavior as found in real scenarios. The result was such that Wi-Fi 7 achieves up to 40% higher throughputs and approximately 48% lower latency in higher-density situations compared to Wi-Fi 6. Additionally, Wi-Fi 7 exhibited stable jitter and minimal packet loss rates even as the user load increased. These benefits are the result of architectural advancements, including wider channel bandwidths (up to 320 MHz), spatial multiplexing, and parallel use of links. The results suggest that Wi-Fi 7 can meet the growing demands of today's urban, industrial, and intelligent infrastructure installations by providing a solid and scalable quality of service in dense wireless environments.
Multi-Objective Dynamic Self-Healing of Unbalanced and Harmonic-Rich Smart Distribution Networks Using Improved Whale Optimization Algorithm
The increasing penetration of intermittent renewable energy sources (RES), coupled with the decentralized architecture of modern power distribution networks, has introduced substantial challenges in maintaining system stability and ensuring power quality—particularly under fault conditions and nonlinear operating regimes. This paper proposes a dynamic self-healing model for smart distribution systems based on an Improved Whale Optimization Algorithm (IWOA), with the objective of simultaneously optimizing active power loss, voltage deviation, total harmonic distortion (THD), and phase voltage unbalance ratio (PVUR). The proposed IWOA incorporates a nonlinear shrinking mechanism, discrete solution mapping, and a normalized, equally weighted multi-objective function structure. These enhancements significantly improve convergence behavior, solution accuracy, and optimization performance in complex combinatorial search spaces. The proposed framework is validated on a modified IEEE 33-bus distribution system featuring unbalanced topologies, harmonic disturbances, and distributed RES integration. Simulation results demonstrate that IWOA outperforms conventional metaheuristics such as Particle Swarm Optimization (PSO), Differential Evolution (DE), and the original WOA in terms of convergence speed, energy efficiency, and power quality enhancement. This study highlights a promising direction for advanced automated optimization strategies in resilient and sustainable energy distribution infrastructures.
Improved Whale Optimization Algorithm (IWOA);
Safety Engineering in Adaptive Multimedia System Design for Emergency Accessibility Based on WCAG
People with disabilities often face critical challenges in accessing timely and comprehensive emergency alerts. This study presents an adaptive multimedia system that uses safety engineering principles using Web Content Accessibility Guidelines (WCAG). Based on WCAG, the system was created to meet visual, auditory, and tactile output requirements based on user profiles and environmental conditions. The system inputs are fed through the Internet of Things (IoT) and processed by a rule-based adaptation engine. The emergency adaptive system combines environmental data, user health, and manual input to generate appropriate multimedia alerts (audio, visual, or vibration). A prototype was built using React/Tailwind for the user interface and integrated with sensors for environmental monitoring. The system was evaluated in an emergency scenario for 30 participants across visual, auditory, and cognitive impairment groups. The evaluation results showed a high average System Usability Scale (SUS) score of 86.2, indicating excellent usability. The average Accessibility Coverage Metric (ACM) score was also obtained at 0.89, indicating broad and inclusive modality support. Based on the experiments, the Adaptive Decision Function (ADF) score exceeded 0.90 for combinations such as visual with vibration and audio output. This shows strong adaptive performance under various conditions. These results demonstrate the potential of the system to improve the inclusiveness of emergency response for people with disabilities.
Agent-Based Secure Route Switching and QoS Management in VANETs using the Wiedemann Car-Following Prediction Model
In Vehicular Ad-hoc Networks (VANETs), ensuring robust security alongside high Quality of Service (QoS) is essential due to the network’s dynamic topology and limited infrastructure, which can impact safety and comfort applications. This paper presents an agent-based, security-aware multi-path route switching protocol designed to enhance QoS in VANETs. The protocol integrates a secure route-connectivity prediction agent, leveraging the Wiedemann car-following model, with a flexible QoS agent that intelligently selects and switches among secure multi-path routes from source to destination. Unlike traditional methods, the QoS agent employs multiple adaptive switching strategies—including Least Path Switching (LPS), Least Path Switching with Minimum Delay (LPSMD), and Least Disconnection Delay (LDD)—to maintain route stability and optimize performance. To secure communication, vehicular nodes utilize a hybrid key agreement mechanism combining Diffie-Hellman and Short Authentication String (SAS) protocols executed over Wi-Fi Direct out-of-band channels. Extensive simulations in a heterogeneous VANET environment assess the protocol under varying conditions such as node density, average vehicle speed, data collection intervals, and the prevalence of man-in-the-middle attacks (MITMAs). The results show that secure protocol variants significantly improve packet delivery ratios, especially under adversarial and high-mobility conditions, while incurring only modest increases in end-to-end delay due to security overhead. Performance trends remain consistent across scenarios, with LDD-based strategies achieving the best results in both secure and non-secure modes. These outcomes confirm the proposed protocol’s effectiveness in enhancing both security and QoS in real-time VANET environments.
5G New Radio LDPC Codes Based Generalized Joint Subcarrier-Time Index Modulation DCSK System
LDPC (Low-Density Parity-Check) codes are essential error-correcting codes for 5G New Radio wireless communication, significantly improving the reliability of data transmission across noisy and unpredictable wireless channels. Since an evaluation and discussion of the performance with channel coding is significantly absent in two dimensional index modulation DCSK system. Therefore, in this paper, the 5G New Radio LDPC Codes Based Generalized Joint Subcarrier-Time Index Modulation DCSK System (NR-LDPC-GJSTIM-DCSK) is proposed, where the 5G-NR-LDPC code is used as channel coding. The aim is to improve the system's performance over AWGN and multipath Rayleigh fading channels compared to the uncoded GJSTIM-DCSK system. In the suggested system, the detected index bits at the receiver are transformed into soft bits and combined with the soft demodulated bits before the decoding process. The results indicate that the proposed coding techniques enhance the NR-LDPC-GJSTIM-DCSK system. Additionally, the performance of the 5G NR-LDPC-GJSTIM-DCSK system surpasses that of the NR-LDPC-GSIM-DCSK-II sys-tem and is nearly equivalent to the performance of the NR-LDPC-GFTIM-DCSK-II system at high SNR.
5G new radio LDPC, joint subcarrier-time index modulation, differential chaos shift keying, multipath Rayleigh fading channel Introduction
SD-LLN: a novel architecture using SDN and NFV to adapt RPL in dynamic Low-Power and Lossy network (LLN)
In low-power and lossy networks (LLNs), significant challenges arise due to the severe constraints on energy, memory, and processing capacities. These limitations hinder the efficiency of routing protocols like RPL (Routing Protocol for Low-Power and Lossy Networks), particularly in dynamic network topologies where frequent changes occur. We propose an innovative architecture integrating Software-Defined Networking (SDN) and Network Function Virtualization (NFV) with RPL protocol to address this issue. SDN and NFV technologies introduce greater flexibility, programmability, and centralized control, enabling the network to adapt more effectively to dynamic changes. Our proposed SD-LLN architecture leverages these technologies to enhance RPL's adaptability and resilience in varying network conditions. We improve network performance by integrating SDN/NFV with RPL, particularly in routing efficiency, stability, and resource optimization. Compared to traditional RPL implementations, our approach demonstrates significant advantages. The results highlight the potential of this method in managing LLNs, offering a robust framework to effectively overcome dynamic, resource-constrained environments' challenges.
Hidden Weapon Detection and Tracking in Thermal Video Using Auto-Labeling and Deep Learning Approaches
This study proposes an automated framework for detecting and tracking concealed weapons in thermal video, aimed at real-time surveillance in high-security public areas like airports and stadiums. Due to the lack of relevant public datasets, thermal videos were recorded using an infrared camera in scenarios simulating concealed weapons. Frames extracted from the videos were automatically annotated using the GroundedSAM model, which aligns textual prompts (" gun"," knife") with image content, eliminating manual labeling. A YOLOv11n model was trained on over 6,200 labeled thermal images, achieving 92% precision, 82% recall, and 88% mAP\@50. For tracking across frames, a Graph Neural Network (GNN) connected detected objects over time, yielding 0 ID switches and 0.83 consistency for guns, and 1 ID switch with 0.46 consistency for knives. The integration of smart annotation, thermal-aware detection, and GNN tracking demonstrates strong potential for real-time, robust weapon detection in crowded, security-sensitive environments.
GroundedSAM, Autodistill, YOLOv11n, thermal video dataset, GNN Tracking algorithm, and special evaluation metrics for tracking algorithm.
A Robust and Explainable Deepfake Detection Image Framework Using Transfer Learning with Attention Mechanism
The widespread emergence of deepfake technology has raised major concerns regarding the authenticity of digital media. This paper presents a robust and interpretable deepfake image detection framework that combines enhanced image preprocessing with advanced deep learning techniques. A dual-stage preprocessing pipeline, integrating Error Level Analysis (ELA) and Structured Forest Edge Detection (SFED), is proposed to amplify manipulation artifacts and edge inconsistencies. The refined images are processed through five convolutional neural networks (CNNs), each embedded with a Convolutional Block Attention Module (CBAM) to highlight tampering-sensitive features. To enhance explainability, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to visualize model decisions. Furthermore, a stacking ensemble strategy using Extreme Gradient Boosting (XGBoost) is applied to combine predictions from all models for improved accuracy and generalization. Experimental results on a balanced dataset of 140,000 real and fake facial images demonstrate superior performance, with ensemble accuracy reaching 97.25%. The proposed approach offers a reliable and explainable solution to address the challenges posed by synthetic facial image generation.
Deepfake detection, convolutional neural networks (CNN), Error Level Analysis (ELA), Structured Forest Edge Detection (SFED), Convolutional Block Attention Module (CBAM) attention, Grad-CAM, ensemble learning, and image forensics.
Detection and Classification of Rice Plant Diseases Using Fusion Deep and Texture Features
This thesis presents the development of an intelligent system for the early detection of plant diseases using deep learning and advanced image processing techniques. The primary objective is to support food security and enhance agricultural productivity. The proposed system applies Contrast-Limited Adaptive Histogram Equalization (CLAH) and U-shaped convolutional neural Network (U-Net) for image segmentation to improve image quality and accurately localize diseased regions. Feature extraction combines deep features from Convolutional Neural Networks (CNNs) and GramNet, a model based on the Residual Network-18 architecture, with texture features obtained using the gray-level co-occurrence matrix. All features are concatenated to create a comprehensive feature representation. A stacking ensemble approach is adopted for classification using support vector machine, k-nearest neighbors, and extreme gradient boosting as base classifiers. In contrast, the light gradient boosting machine is the final meta-classifier. Experimental evaluation on a balanced dataset shows the model achieves 99.99% accuracy on training data and 96.90% on testing data. This work demonstrates the effectiveness of integrating preprocessing, hybrid feature extraction, and ensemble learning in early plant disease detection.
Deep learning, ensemble classification, feature fusion, rice disease detection, smart agriculture, U-Net segmentation
Real Time Drift Detection and Adaptation using Hybrid ADWIN in Agricultural Environmental Monitoring System
Real-time analysis of streaming data is crucial in agricultural environmental monitoring to address quickly changing conditions like seasonal weather changes. Concept drift, where the statistical characteristics of input data evolve, poses a significant problem for static machine learning models. This research presents a drift-aware framework based on a hybrid adaptive windowing method combined with an online sequential extreme learning machine (OS-ELM). The strategy involves a multi-dimensional extension of ADWIN that is supplemented by the Kolmogorov–Smirnov statistical test and Hoeffding's bound to identify and respond to real-time drift. An experimental IoT platform was constructed to gather environmental parameters such as temperature, humidity, soil moisture, light, pH, and rainfall. Empirical tests on real and synthetic datasets show that the new framework greatly enhances predictive performance, from 85.86 percent to 97.29 percent when drift handling is activated. The findings emphasize the significance of combining adaptive learning with drift detection for accurate and dependable prediction in precision agriculture.
Concept drift, adaptive windowing, ELM, IoT, sensor data, drift detection, data stream mining, precision agriculture
Optimizing Myocardial Infarction Prediction Performance Using Advanced Machine Learning Techniques
Recent research in the medical field focuses on artificial intelligence-based prediction of heart disease like Myocardial Infarction (MI) due to its increasing rate, which has adverse effects on human health. Specified the increasing prevalence of cardiovascular disorders and challenges posed by limited medical resources, the study seeks to deliver a reliable, computer-aided diagnostic tool for timely and accurate patient assessments. Machine Learning (ML) plays a crucial role in MI prediction with high accuracy and timely response. The study focuses on diverse Machine Learning algorithms to investigate the risk of heart disease with a comparative analysis of algorithms including Ridge Classifier, Radius Neighbors Classifier, Linear SVC, and Extra Trees Classifier for two different datasets of heart disease recognition. The comparison of their performance highlights the highest accuracy (1.00) of prediction improving other evaluation parameters. Two datasets collected from the hospitals are utilized in this study. ECG plays a crucial part in observing heart fitness, making the development of computer-aided diagnostic tools for the automatic ECG signal classification. Thus, the study makes use of the ECG Heartbeat Categorization Dataset and incorporates Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to enhance diagnostic accuracy in addition to its efficiency in categorizing ECG signals into five classes including Normal, Fusion of Paced and Normal, Fusion of Ventricular and Normal, Atrial Premature, and Premature Ventricular Contraction. The testing and validation reach the highest accuracy of 0.98. The advancements in proposed techniques enable more effective automated classification, enhancing cardiovascular diagnostics and improving patient outcomes while supporting better management of heart health.
Performance Evaluation of Si, SiC, and GaN based Boost Converters for Photovoltaic Applications using MPC-CNN Duty Cycle Optimization
This paper proposes an intelligent control method for DC-DC Boost converters in photovoltaic (PV) systems by integrating Model Predictive Control (MPC) with Convolutional Neural Networks (CNN) to optimise the duty cycle in real time. The hybrid controller leverages the high accuracy of MPC and the rapid response capability of CNN to maintain output voltage stability, enhance energy conversion efficiency, and adapt effectively to dynamic solar irradiance conditions. Simulation results demonstrate that while MPC achieves near-zero error with excellent stability, CNN provides quicker inference but exhibits higher voltage fluctuations. The hybrid MPC–CNN approach successfully balances both precision and speed. Additionally, the study evaluates the performance of three semiconductor technologies—Silicon (Si), Silicon Carbide (SiC), and Gallium Nitride (GaN)—under identical control conditions. Results indicate that GaN outperforms the others with the highest efficiency (97.6%), lowest voltage ripple (2.8 V), minimal switching loss, and the fastest transient response (0.17 ms), while SiC offers a cost-performance trade-off and Si presents significant limitations. The paper concludes that combining intelligent MPC–CNN control with GaN-based devices provides an optimal solution for high-performance PV systems and recommends future work on real-time embedded implementation, adaptive learning, and integration into smart grid platforms.
Automatic Fish Classification Using Lanczos Resampling and Deep Learning
The development of automation in the fish industry, a sector of the food industry, is an interesting topic and is very much needed. It is to increase productivity and anticipate the threat of food scarcity in the future due to the continued growth of the human population as well as the impact of climate change and global warming. Automatic fish classification using computer vision has been widely developed in fish industry automation, and a lot of research on that topic has been published. However, many research approaches are proposed with good results but with complicated methods, while others are proposed with simple methods whose results are not satisfactory. In our work, we suggest an easy while effective approach. We focus on the main distinguishing features, namely body shape and scale patterns, to differentiate fish species. We apply the Lanczos resampling technique, which can properly support it, so that our proposed simple deep learning model can effectively learn and recognize fish species from those features. Finally, the experimental results using the Fish-Pak dataset demonstrate that the proposed method can effectively recognize fish with an accuracy rate of 97.16%.
Smart Grid Data Compression and Reconstruction by Discrete Wavelet Transform
A smart grid offers safe, reliable, and useful electricity. Phasor measuring units, smart meters, and other monitoring and measurement devices in the smart grid keep track of statuses at every grid level. As a result, utilities, control centers, and customers must exchange and retain an enormous amount of data in real time. As a result, data storage and communication require efficient data compression. To accurately reflect system statuses and reconstruct nearly flawlessly at the receiving end, compression should be carried out in a way that preserves all of the data&#39;s critical information. This work uses the Discrete Wavelet Transform to recover compressed voltage sag signals. With reduced data, the disruptions can be communicated more quickly. Because the proposed system uses lesser filters and few decomposition layers, it is simpler than the previous design. The results of the simulation demonstrate improvement in the reconstruction error and compression ratio by minimizing their values. This design is time-saving and simple to use.
Compression, Decomposition, Reconstruction , Noise, Smart Grid, Wavelet Transform
A Novel Method for Detecting High Impedance Single-Line-to-Ground Fault in an Electrical Power Distribution System based on Analysis of Current Waveforms
Successful detection of high-impedance fault (HIF) has remained a challenge for researchers and power system protection engineers because of the low magnitude of the fault current and fluctuating fault current that can become momentarily unstable. This paper presents a new method for detecting HIF in electrical power distribution system based on analysis of alternating current waveforms. Since HIF current can be asymmetrical or symmetrical, the research modeled and analyzed both the positive and negative half-cycles of pre-fault and post-fault current waveforms. Fuzzy logic system is integrated with this new HIF detection system to provide a means for categorizing fluctuating average fault current values in appropriate Fuzzy subsets. The effectiveness of this new detection method was verified through numerical simulations implemented using MATLAB software, Fuzzy logic toolbox and Python integrated development environment. Results obtained show that this new technique can effectively detect high-impedance fault in electrical power distribution system at any point between 00 and 3600 of the current waveforms.
High impedance fault, electrical power distribution, half-cycle, Fuzzy logic
An integrated solution for temperature monitoring in the insulation of medium voltage underground cables
To ensure the safe operation of power systems and minimize serious accidents, understanding the temperature distribution in medium voltage underground cables plays an important role. Initially, a cross-sectional model of the MV underground cable was created. Based on the temperature data collected at the cable sheath surface, the temperature distribution characteristics of the cross-linked polyethylene insulation layer inside the cable were determined from the heat source of the cable core current, using the finite element method. The temperature distribution according to the thickness of the cable insulation layer also affects the electric field distribution in the insulation at different times and in the steady state. Furthermore, the relationship between the cable sheath surface temperature and the maximum insulation layer temperature was established. The results provide a temperature prediction model of the cross-linked polyethylene insulation layer, supporting the setting of temperature parameter warning thresholds for the medium voltage underground cable monitoring system.
A Performance Analysis of ML-Based Intrusion Detection Systems in Cloud Environments
Intrusion Detection Systems (IDS) are important for protecting cloud environments against emerging cyber threats. This paper introduces AI-SCAN, a deep learning IDS that utilizes a CNN architecture to achieve better threat detection with better scalability, flexibility, and low false positives. The proposed system overcomes key challenges of dataset bias, external validation, and class imbalance to provide robust performance in dynamic cloud networks. To reduce dataset bias, we examine model performance on a variety of attack types and assess its efficacy with external validation on separate datasets outside the CSE-CICIDS2018 benchmark. Our solution combines SMOTE-based data augmentation and class weighting strategies to counteract minority attack classes, promoting model generalization. Hyperparameter tuning and feature selection also improve AI-SCAN's efficiency, reducing computational overhead without sacrificing high detection accuracy. Empirical observations indicate 97.5% accuracy, 96.5% precision, and 95.0% recall, higher than conventional ML-based IDS implementations. AI-SCAN's novel cyber threat detection with low false positives supports its applicability in real-time cloud deployment. The current study conducts comparative analysis among conventional ML, ensemble learning, and deep learning-based IDS models and positions AI-SCAN as a robust, scalable, and fault-tolerant cybersecurity measure.
Distributed Blockchain Wireless Sensor Network Architecture for Malicious Node and Sensor Data Detection
Wireless sensor network (WSN) is a private network with many sensors. It has the ability acquire sensor data with reduced energy consumption but considered vulnerable, primarily due to centralization. Therefore, disruptive blockchain technology capable of hashing block-based security was recommended to overcome the problem. The results showed that with 10-500 block node iterations and three distributed Peer servers, the Scrypt hash had the fastest execution time of 469.49 seconds compared to the others. The combination of two consensus mechanism methods, including smart contract (Mac Address and SHA256 on the microcontroller) and Proof of Information (PoI) to the three sensor parameters (Temperature or Temp, Humidity or Humd, and MQ2) led to detection and maintenance of sensor nodes and data reaching 97.37% validity. Moreover, sensor control consisting of two radio frequency identification (RFID) nodes and one quick response (QR) code node applied to 500 block node iterations led to 592.47 seconds execution time longer than the monitoring sensors. This showed that the separation of monitoring and controlling sensors was the best for the architecture model proposed. The major reason was that the monitoring sensors prioritized automation, speed, and data accuracy in real time. Meanwhile, the controlling sensors focused on the interaction between users and devices, specifically when integrated into a transaction system.
WSN, Blockchain, Hash, Distributed, Smart Contract, Proof of Information.
Phishing Detection Techniques: Exploring Machine Learning and Deep Learning Models
The number of Internet users has increased significantly in recent years, driven by the growing popularity of online education, e-commerce, and other digital services. E-commerce, in particular, has seen significant growth due to increasing consumer demand for a convenient and secure online shopping experience. The COVID-19 pandemic has significantly sped up the uptake of e-commerce, changing consumer habits and propelling online transactions at an extraordinary rate. Nevertheless, this swift digital shift has heightened vulnerability to cyber threats, resulting in a significant rise in phishing attacks targeting the theft of confidential user data. In this article, we explore the use of machine learning (ML) and deep learning (DL) methods for detecting phishing, emphasizing conventional models, ensemble techniques, and hybrid systems. We analyze the key obstacles in this domain, such as data imbalance, significant computational expenses, and the challenges of real-time applications. Regarding research, this research emphasizes the potential of hybrid models and advanced methods to enhance the accuracy, efficiency, and scalability of fishing systems. This result emphasizes the urgent need for a reliable, adaptive, and flexible detection system in order to express the growing risk of personal and organizational security in the digital development environment and to fight the increase in phishing attack
Relay Coordination in Resilient and Sustainable Power Systems: Review on Optimization Techniques and Future Directions
This article presents a technical review of advanced relay coordination techniques in modern power systems. Focusing on directional overcurrent relays, the study examines optimization-based methods for tuning key relay parameters, which include the pickup current and the time multiplier setting, to minimize the total relay operating times and ensure reliable protection. Both deterministic and metaheuristic approaches, including various hybrid algorithms, are analyzed in terms of their ability to formulate and solve different objective functions subjected to coordination time intervals and other operational constraints. Validation of proposed solutions on various IEEE test systems using simulation tools such as DIgSILENT, MATLAB, and PSCAD/EMTDC are also incorporated. The review also highlights emerging trends in real-time adaptive protection and cybersecurity integration, providing a roadmap for enhancing the robustness of protection schemes in increasingly complex and dynamic power networks.
Hybrid Adaptive Peak Load Threshold Controller for Battery Energy Storage System: An Industrial Case Study
Battery Energy Storage Systems (BESS) provide a flexible solution for peak load reductions in industrial power management. Industrial facilities face challenges in managing peak power demands due to unpredictable load variations and the limitations of traditional BESS control strategies. To address this, a Hybrid Adaptive Peak Load Threshold (HAPLT) controller is introduced, integrating day-ahead forecasting with real-time 30-minute updates to refine thresholds dynamically. This approach integrates advanced predictive modeling techniques to optimize peak load reduction, enhance energy savings, and ensure reliable operation under real-world conditions. Validation using Daikin R&D power network data showed an average Maximum Demand Reduction Factor (KMDR) of 0.89. Real-time analysis demonstrated effective power demand management and optimal state-of-charge (SOC) control. The system successfully reduced peak loads while preventing early battery depletion. The HAPLT controller minimizes forecasting errors, optimizes battery utilization, and enhances energy savings, proving a robust solution for industrial applications.
Battery energy storage system, Peak Load Reduction, Dynamic threshold adjustment
Critical Analysis of Indian Electric Distribution Utilities using DEA with Sensitivity Analysis
India is presently the world's fifth-largest economy and is also one of the fastest-growing. This rapid growth trajectory positions India on track to become the third-largest economy globally by 2030. Electricity is a critical component of the nation's economic and human development, whose growth is directly or indirectly dependent on the growth of other sectors. Currently. India is the third-largest electricity producer in the world after China and the United States. However, its per capita electricity consumption is relatively low, ranking around 106th globally. This indicates that while India generates a significant amount of electricity, the consumption per person is still modest. The development of the electricity sector in India has occurred in multiple stages, with significant legislative changes implemented in 1991, 1998, 2003 and 2020 in which it aimed to modernize and improve various aspects of the sector, including generation, transmission, and distribution. Though there have been notable challenges persisting with poor performance, especially in the distribution segment. Owing to this, in the present manuscript, investigations are focused on fifty-five distribution utilities that provide electricity to a significant portion of India's population across twenty-eight states. In this study, an evaluation of the relative performance efficiencies and classification of fifty-five Indian electric distribution utilities (IEDUs) is performed. For this, Data Envelopment Analysis is employed to analyze and identify the underlying reasons for the deficiencies in sector performance. The Charnes- Cooper-Rhodes and Banker- Charnes-Cooper models have been employed, and overall and pure technical efficiencies have been computed and compared the inefficient with efficient IEDUs for the period of 2016–2019. The outcome specifies performance efficiency scores and classification of IEDUs as Decision-Making Units (DMUs) by sensitivity analysis. The sensitivity analysis reveals that 40 out of 55 DMUs, i.e., 72.72%, come under the category of significantly inefficient and distinctly inefficient with a base technical efficiency score of less than 90%.
Optimized Distributed Gradient Boosting with Explainable Artificial Intelligence for Real-Time Ransomware Detection in Networked Environments
The severity of ransomware threats is increasing significantly, posing substantial risks to individuals and organizations. To address this problem, several techniques have been suggested; however, they are mostly signature-based, capable in identifying conventional ransomware but inadequate in detecting novel variants. This paper introduces a ransomware detection framework using distributed gradient boosting and explainable artificial intelligence. The suggested design utilizes XGBoost algorithm for classification and explainable artificial intelligence for feature importance. The proposed design is deployed and tested using a dataset of 84 features. Weight-based feature selection is used to minimize the number of features. The selected features are used to train the XGBoost classification algorithm. Furthermore, the significance of the chosen features is assessed using SHapley Additive exPlantions (SHAP). The results show better performance in terms of accuracy, precision, recall, and F1-Score. The suggested approach outperforms the baseline machine learning algorithms and comparable results against state-of-the-art models across all performance criteria used.
Ransomware, XGBoost, Explainable, Machine Learning, Security
Optimizing Intrusion Detection with Triple Boost Ensemble for Enhanced Detection of Rare and Evolving Network Attacks
Abstract—In the rapidly evolving cybersecurity landscape, Intrusion Detection Systems (IDS) are essential for identifying and mitigating malicious activities within network environments. This study presents a novel IDS framework designed to address critical challenges in the field, including handling class imbalances, outlier detection, and feature selection inefficiencies. A comprehensive preprocessing pipeline is employed, utilizing the Synthetic Minority Over-sampling Technique (SMOTE) to manage class imbalances, the Z-score method for outlier detection, and Ridge regression for effective feature selection. The core innovation lies in the development of a TripleBoost ensemble model, which integrates AdaBoost, CatBoost, and XGBoost to leverage their complementary strengths. This approach achieves a significant performance boost, evidenced by an accuracy of 97.38%, precision of 95.34%, recall of 99.56%, and an F1-score of 96.40%. The model successfully overcomes limitations faced by traditional IDS models, such as poor detection of rare attack types and scalability issues in dynamic network environments. By improving detection accuracy and generalization capabilities, the framework represents a substantial advancement in IDS technology. Future work will explore real-time detection optimizations and the adaptability of the model in complex network paradigms, further enhancing its potential to secure modern network infrastructures.
Synthetic Minority Over-sampling Technique
A PRISMA-Based Systematic Review of Cloud-Edge Orchestration using the MAPE-K Framework
Cloud computing offers abundant computational capacity but suffers from inherent latency. Edge computing mitigates this by processing data closer to the edge of the infrastructure, reducing latency and improving performance. However, challenges arise due to inadequate edge processing capacity and significant cloud latency. A viable solution to address these challenges is cloud-edge integration, which is a collaborative resource distribution model. Our study examines cloud-edge orchestration, focusing on performance and efficiency improvements through orchestration techniques. We perform a comprehensive systematic literature review using the PRISMA model that compiles 10,389 records from the decade spanning 2015 to 2024, filtered down to 89 studies. Using the Monitor-Analyse-Plan-Execute over Shared Knowledge (MAPE-K) framework, we assess cloud-edge orchestration and categorise performance metrics. Additionally, we evaluate task distribution criteria between cloud and edge computing environments, identify challenges, and outline prospective directions. Our findings provide insights into optimising cloud-edge computing for mainstream applications, ensuring improved resource management and efficiency in cloud-edge computing environments.
Intelligent Model for Detecting GAN-Generated Images based on Multi-Classifier and Advanced Data Mining Techniques
The ability of generative adversarial networks (GANs) to produce images that closely resemble real ones has raised concern. This requires the creation of efficient detection techniques because it has significant ramifications for digital media, security, and ethics. In order to demonstrate the growing difficulties of attaining authenticity in the rapidly developing field of artificial intelligence (AI), the study addresses the growing problem of differentiating real human faces from AI-generated images by using the Orange3 data mining framework to evaluate a set of 3,203 images from Kaggle platform and classify them into real human faces and AI-generated faces. Shape attributes, texture descriptors, and color histograms were among the relevant features that were extracted using machine learning techniques. A training set of 70% of the data and a testing set of 30% were used. K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), AdaBoost, and Gradient Boosting (GB) were the four ML techniques that were applied. According to the results, the accuracy of KNN and AdaBoost was 99.4% and 97.07%, respectively, while GB and ANN reached 99.8% and 99.9%. These results demonstrate the efficacy of sophisticated ANN algorithms in achieving high accuracy in predictive modeling tasks. These results show potential in developing a powerful ML-based system that can reliably differentiate between artificial intelligence-generated and actual faces, allaying growing worries about the veracity of visual content.
rtificial intelligence-generated Images, Kaggle, Adaptive Boosting, Decision Trees, Gradient Boosting, and Random Forest
Programmable Load with Real-Time Data Logging and Load Profiling for DC-DC Converters and Renewable Sources
The validation of renewable energy systems and battery technologies is challenging due to the dynamic and non-ideal behaviour of real-world loads. This study presents a programmable load solution designed to emulate such loads using FET switches and passive components, controlled by an STM32 microcontroller for precision and reliability. The system operates in multiple modes, including constant current (CC), constant voltage (CV), constant resistance (CR), constant power (CP), and energy source profiling with a maximum voltage of 100V and current capacity of 50A, incorporating overcurrent and overvoltage protection for safety. It supports the validation of diverse power sources such as switched-mode power supplies (SMPS), battery chargers, and renewable energy systems, while providing real-time data logging and remote monitoring for detailed analysis. This solution reduces testing time and costs by enabling efficient, automated testing and characterization, offering flexibility for various scenarios and ensuring the quality, reliability, and performance of DC-DC converters and renewable energy systems
DC-DC Converter testing, Renewable energy system characterization, battery characterization, STM32-based control system, real-time data logging
Enhancing of features for road crack image usingEEcGANs
Continuously monitoring roads for the purpose of examining their defects is a difficult task for the human element due to the increasing length of roads. Therefore, several types of digital imaging tools are used, for example (drones, surveillance cameras fixed on poles and cameras fixed on a moving car). These imaging tools automatically take pictures under various weather conditions, in addition to the blurriness generated by cameras mounted on moving cars. This paper proposes a method to enhance road surface images captured under different conditions, where the image will be enhanced in frequency domain and spatial domain and then added to the Enhancement with Enlighten of Conditional Generative adversarial networks (EEcGANs) to obtain a generated and enhanced image. The metrics PSNR, SSIM DIV, CC and UIQ were used to evaluate performance of proposed method, where the values were (29.4019, 0.8996, -0.12151, 972758 and 0.970491) respectively.
N-RDD2024 Dataset, image enhancements methods, Conditional Generative adversarial networks (cGANs), evaluation metrics for generated image
DDoS Attack Detection Using Machine Learning and Improved Clustering Algorithm
DDoS attacks have become the most destructive type of attacks in the network system recently, out of all the attacks that are present. The major purpose of this paper is to develop a system that efficiently identifies DDoS attacks appearing in any networked system using an Improved clustering technique and combination one of the best classification algorithms. This technique clusters the available dosage attack dataset using the Improved clustering algorithm Modified Fuzzy C-means (MFCM) and uses the classification algorithm to classify the data and detect assaults generated in the system based on specific network features of the transformed data packet. The clustering algorithm relies on the distance measure and fuzzy coefficients only, so it is unable to detect some attack cases and a few normal cases, so we combined it with classification techniques to overcome the errors that may occur when classifying and obtain the highest accuracy. A set of experiments were conducted on APA-DDoS dataset using two clustering algorithms in addition to the proposed algorithm and another set using classification algorithms. The efficiency of the modified clustering algorithm was tested based on the entropy criterion, which reached 0.99, which is the highest compared to the results of other algorithms. As for the training algorithms, they were tested using standard evaluation criteria: accuracy, detection rate, and false positive rate. The results obtained from the proposed hybrid classification method showed that the accuracy level is 1.0 at a constant rate with all classification algorithms used, and this in turn increases the attack detection rate.
APA-DDoS dataset, standard clustering methods, modified (MFCM), training algorithms and evaluation metrics.