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IJEETC invites original, previously unpublished, research, survey and tutorial papers, plus case studies and short research notes, on both applied and theoretical aspects of electrical and electronic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.
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
Copyright (c) 2025 International Journal of Electrical and Electronic Engineering & Telecommunications
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.
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
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.
Rehabilitation of Bamberg Kit in the High-Voltage Lab at University ‎of Technology- Iraq
High-voltage (HV) labs are crucial for the power and energy sector, as they provide ‎necessary testing for power equipment and devices at voltages significantly higher ‎than their operating levels. The Department of Electrical Engineering at the ‎University of Technology has one of the biggest HV labs in Iraq, and it was well-‎equipped at some point. Unfortunately, the lab has been out of operation and ‎neglected for decades including the Bamberg kit. No catalog nor a manual existed ‎when the rehabilitation process started. In addition, no internet-based ‎documentation could be found regarding the Bamberg kit due to closing the ‎production line by the manufacturer. Thus, over the recent years, the extensive ‎repair works were carried out and guided by practical experience, detailed analysis, ‎and innovative problem-solving. Remarkably, these efforts were achieved with ‎minimal costs, far below initial departmental estimations. Currently, all the HV ‎generators in the Bamberg kit are back to its normal operation. By the end of the ‎project, most of the kit generator circuits and measurement systems were ‎successfully restored, reaching 80% of a full rehabilitation with less than 1% of the ‎estimated cost.‎ ‏ ‏Besides listing and detailing the variety of rehabilitation works that ‎have been done, the paper serves the documentation process of the lab, and this ‎contributes to similar labs that still utilizing Bamberg HV kit‎.
Deep Deterministic Policy Gradient-based Spectral Efficiency for Massive MIMO Communication System
Massive MIMO is a new configuration of MIMO technology that uses many antennas up to the order of hundreds to serve tens or hundreds of user equipment (UE) at the same time and frequency. Massive MIMO technology is one of the spatial diversity techniques used to increase the spectral efficiency (SE) of current fifth-generation (5G) communication systems. Massive MIMO has a high complexity in signal processing because it serves a large amount of user traffic at the same time. Therefore, this paper proposes to use one of the deep learning methods, deep deterministic policy gradient (DDPG), to analyze the SE performance in massive MIMO communication systems. In the simulation, we model a massive MIMO system with multiple access points (APs) and multiple UEs. We train the channel using the proposed DDPG model. We then analyze the signal-to-interference plus noise ratio (SINR) for each end-user path, comparing it with conventional massive MIMO (without deep learning). In addition, the complexity of the proposed DDPG model in terms of runtime is analyzed and compared with the convex optimization algorithm (CVX). The simulation results show that the performance of the massive MIMO system with the proposed DDPG model produces optimal and higher SE than without using the DRL method, i.e. 85%. Furthermore, the proposed DDPG model has a lower complexity with a runtime ratio of 1:8000 compared to the CVX algorithm for the same number of epochs.
deep deterministic policy gradient (DDPG)
Development of an Adapted Theoretical Model for Increasing the Energy Efficiency of Induction Motor Drives
The paper presents a developed adapted theoretical model for increasing the energy efficiency of induction motor drives. The model uses input/output parameters that provide practical opportunities for a wide scope of applications and improved conditions for implementation of energy efficiency measures. The study performed confirms the applicability of characteristics of induction motors, contained in a selected software product, for contactless measurement of the active power. The analytical description and mathematical modelling, supported by 36 simulation scenarios, justify the development of the target-adapted model. The plotted regression relationships demonstrate high accuracy with an average coefficient of determination of 0.999. The study recommends measures for increasing the load on the drives and implementing energy-efficient motors that demonstrate significant energy savings. The simulation model proposes an algorithm that provides a basis for creating specialized software products for automation of scientific research and conducting of energy audits.
IoT based RPL Routing Protocol improvement against DDoS cyber attack using NetSim v14.1 Simulation Program
The IoT is a developing technology that can enhance the communication capabilities of wireless sensor networks (WSN) when integrated together. The nature of IoT networks is characterized by self-organization and decentralization, leading to dynamic changes in the position of nodes. Therefore, routing in the IoT is essential for the successful transmission of data. RPL Routing protocol for LLNs (low power and lossy networks), was evaluated for the IoT devices. The aim of this paper is to examine the IoT network performance based on RPL as a routing protocol when bit and piece DDoS cyber attack mitigate the traffic into the network. This simulation is done in number of NetSim verified scenarios in terms of throughput, delay, Jitter, packet delivery rate and bit error rate Quality of Service (QoS) parameters. The results show that RPL when applied to the IoT network investigated a better QoS improvement than any ad-hoc routing protocol like (AODV) which is taken in this study.
IoT, sensors, RPL, NetSim, QoS, cyber attack
Reliable and Efficient Cognitive Radio Communication based on QoS-Driven HCARA Routing Approach
Cognitive radio networks are characterized by their ability to adapt to dynamic spectrum variables and network environment conditions. Reliable communication in cognitive radio networks is essential to achieve the most efficient use of spectrum and flexible communication between the different network components. In this paper a novel Hybrid Cognitive Adaptive Routing Algorithms (HCARA) for the routing process from the cognitive radio network, which is based on achieving the highest quality of service and the least delay in the process of sending and receiving data between secondary nodes in the network. The communication path between the nodes is chosen dynamically in a reliable and efficient manner depending on the channel conditions, traffic load and network topology, as the proposed protocol is based on multiple routing metrics during the decision-making process, including throughput, packet delivery ratio and delay through round trip of the data transmission. The proposed HCARA system aims to enhance the reliability of the connection by combining outperforms existing Clustering-Based Routing Protocol (CRP) and Multi-metric Routing Protocol with Service Differentiation (MRSED) routing protocols. HCARA achieved an average throughput of 14.64 Mbps, surpassing CRP and MRSED, which recorded 11.86 Mbps and 12.92 Mbps respectively. PDR for HCARA was measured at 98.51 %, significantly higher than CRP's 89.24 % and MRSED's 94.81%, indicating better reliability in delivering packets to their intended destinations.
Multi-metric Routing Protocol with Service Differentiation (MRSED)
Hybrid Cognitive Adaptive Routing Algorithms (HCARA)
Compact Triple-Band Monopole Antenna with ACS-Fed for IoT Devices on WLAN/WiMAX/5G/V2X Networks
This research investigates a compact ACS-fed monopole antenna designed to generate three frequency bands in wireless communication applications. The antenna design combines the ACS-fed technique with C-shaped, inverted L and U-shaped radiating strip branches to downsize the antenna size and generate multi-band radiation. The antenna is implemented on an inexpensive FR-4 substrate measuring 20×9×1.6 mm3. A systematic parametric assessment that includes variations in the length and width of the radiating strip has been carefully carried out, identifying three optimal frequency operational modes for the proposed antenna using an EM simulator Ansoft HFSS. The impedance bandwidths of the lower, middle, and higher frequency modes are 320 MHz (2.31-2.63 GHz), 470 MHz (3.3-3.77 GHz), and 2820 MHz (5.12-7.94 GHz), respectively. At 2.5 GHz, 3.55 GHz, and 5.45 GHz resonant frequencies, the measured antenna gain parameters are 5.67 dBi, 5.21 dBi, and 4.56 dBi. Due to its compact form factor and stable characteristics, the proposed antenna design emerges as a promising solution for internal antenna implementation in Internet of Things (IoT) devices, especially in the context of WLAN, WiMAX, 5G sub-6GHz, and V2X wireless applications. Manuscript received Month date, 2024; revised Month date, 2024; accepted Month date, 2024. *yusnita.rahayu@lecturer.unri.ac.id
Beyond 5G (B5G) networks will deploy hybrid multi-radio access technologies for improved spectrum efficiency (SE) and energy efficiency (EE). For further capacity enhancement, code and power domain multiplexing are applied on hybrid power domain sparse code non-orthogonal multiple access (PD-SCMA) schemes. In order to realize optimal performance, robust resource allocation (RA) policies are required with the best candidate for the complex overloaded B5G systems based on Artificial Intelligence (AI) techniques. This work develops a deep neural network (DNN) aided resource allocation scheme for an uplink PD-SCMA network with near-user (NU) and far-user (FU) groups, multiplexed in the power and code domain. The RA problem is formulated as a non-convex joint optimization problem then decomposed into three sub-problems namely codebook assignment (CA), user clustering (UC) and power allocation (PA). For the three sub-problems, a three-stage generic fully-connected deep neural network (DNN) is trained to approximate the PA, CA and UC resource allocation solution by the proposed modified primal-dual interior point method (mPD-IPM). The proposed mPD-IPM generates the near-optimal RA solutions that form the DNN input labels. The DNN-mPD-IPM not only greatly enhances the computational efficiency but also achieves good convergence rates and guarantees both the ergodic and non-ergodic sum rates of the system compared to generic algorithms. Simulation results show that the DNN aided resource allocation closely learns the system capacity and computational performance of the proposed mPD-IPM and further outperforms generic RA algorithms.
A CNPVaR-NPV Trade-off Model for Resource-Constrained Project Scheduling Problem Under Random Environment
Projects face various risks and opportunities throughout their lifecycle. Integrating risk management into the project progression is a crucial factor for project success and an essential component of project management. This paper aims to evaluate the trade-off between conditional net present value at risk (CNPVaR) and net present value (NPV) in a stochastic environment. Assuming activity duration and cash flow as random variables and activity start and execution methods as decision variables. Under the constraints of priority relationships, execution mode, resource supply, and deadlines, a CNPVaR-NPV trade-off model with the objectives of minimizing CNPVaR and maximizing NPV is built. Based on the characteristics of the model, an algorithm combining non-dominated sorting genetic algorithm II and Monte Carlo simulation is designed. The HVAC system project of Dongguan Jinyun Digital Park 5# Building is used as a case study to illustrate the effectiveness of the model and the algorithm. The results show that considering the CNPVaR-NPV trade-off model can effectively mitigate potential risks and reduce project losses.
resource constrained project scheduling problem
Copyright (c) 2024 International Journal of Electrical and Electronic Engineering & Telecommunications