International Journal of Electrical and Electronic Engineering & Telecommunications
type: channel
description: IJEETC invites original, previously unpublished, research, survey and tutorial papers, plus case studies and short research notes, on both applied and theoretical aspects of electrical and electronic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.
language: en
publication name: International Journal of Electrical and Electronic Engineering & Telecommunications
issn: 2319-2518
items: Mel-Frequency Cepstral Coefficients (MFCC)-based Acoustic Drone Detection Using Machine Learning and Deep Learning Algorithms
items: Dual-band Modified Trapezoidal Antenna for Wireless Local Area Network (WLAN) and X-Band Communications
items: Hybrid CNN-LSTM Architecture with MFCC-Based Class-Balanced Augmentation for Arabic Speech Emotion Recognition
items: Analysis of FIR Window Length and Phase Lead Effects on Repetitive Control Performance for Hip and Knee Joint Trajectory Tracking
items: A Compact Multiband Antenna for IoT and 5G Applications: Design Optimization and ML-Based Validation
items: A Confidence-Weighted Memory-Augmented Multimodal Voice Agent for Real-Time Emotion-Aware Interaction
items: Enhancing Cybersecurity in Internet of Things Networks Using Advanced Deep Learning Techniques for Intrusion Detection
items: A Poly-flex Microstrip Patch Antenna for 6G Applications: Design and Performance Analysis under Bending Impact
items: Forecasting Generation in Motor Generators Using Neural Networks
items: Lightweight Authenticated Encryption Scheme for Secure RSSI-Based Indoor Localization
items: On the Design of Differential Difference Transconductance Amplifier (DDTA) and Its Applications
items: Deep Learning-Based Channel State Estimation Approach for OFDM System
items: Mobility models based AODV Mobile Ad Hoc Networks Performance Evaluation using NetSim Simulation Program
items: Enhancing Power Quality in Grid-Connected Fuel Cell Systems Using the Gradient-Based Optimizer
items: Integrating Terrestrial and Non-Terrestrial Networks in 6G: A Review of Architectures, AI-Driven Techniques, and Sustainability Strategies
items: Fuzzy Clustering-Based Temperature Control System for Energy Efficiency and Cost Optimization in Smart Homes
items: Timer-Based Capacitance-to-Voltage Converter
items: A Robust IDS System for Intelligent Phishing Website Detection
items: Code Generation by Large Language Models: A Comparative Analysis of ChatGPT, Claude, and DeepSeek
items: Explainable AI-Based Mapping of Public Sentiment on the Impact of Environmental Policies: A Cross-Regional Social Media Analysis
items: AC/AC converter flexibly connects AC microgrid with integrated storage system
items: Evaluating Efficiency and Productivity Trends of Indian Electricity Distribution Utilities: DEA-Malmquist Analysis
items: A Multi-Objective ESTO Framework for Efficient PV Siting and Sizing in Radial Distribution Systems
items: Performance Evaluation and Latency Optimization of Wi‑Fi 7 in High-Density Wireless Environments
items: Evaluation and Optimization of Graphene Nanosheet FET: Analog/RF Perspectives
items: Determination of the Modern Marine Gas Turbine Rotor Forced Vibration Parameters by Numerical and Experimental Methods
items: Multi-Objective Dynamic Self-Healing of Unbalanced and Harmonic-Rich Smart Distribution Networks Using Improved Whale Optimization Algorithm
items: Safety Engineering in Adaptive Multimedia System Design for Emergency Accessibility Based on WCAG
items: Agent-Based Secure Route Switching and QoS Management in VANETs using the Wiedemann Car-Following Prediction Model
items: 5G New Radio LDPC Codes Based Generalized Joint Subcarrier-Time Index Modulation DCSK System

Mel-Frequency Cepstral Coefficients (MFCC)-based Acoustic Drone Detection Using Machine Learning and Deep Learning Algorithms
type: item
description: Drones, or Unmanned Aerial Vehicles (UAVs), have become much cheaper to buy and may become a security risk due to their small size and ability to fly autonomously. Many traditional detection techniques (radar, radio frequency, and visual system) do not work as well when trying to detect small UAVs. Another option has been to use an acoustic method of detection, as drones produce identifiable sounds as a result of their motors and propellers. A low-cost acoustic drone detection system will be developed using machine learning and deep learning. Data were obtained through audio recordings of seven types of drones from publicly available GitHub datasets and by collecting environmental noise through the British Broadcasting Corporation (BBC) Sound Library and YouTube. In order to classify drones versus non-drones, 26 Mel-Frequency Cepstral Coefficients (MFCC) features (13 static and 13 delta) are extracted from the audio signals. Additionally, clustering analysis is performed to confirm the uniqueness of the drone’s acoustic signatures before training the models with the data. Using a convolutional recurrent neural network classifier yielded an accuracy of 97%, F1‒Score of 0.97, and Receiver Operating Characteristic (ROC), Area Under ROC Curve (AUC) of 0.99, outperforming both the Balanced Random Forest (BRF) and Multilayer Perceptron (MLP) classifiers. Furthermore, it demonstrates considerable robustness when tested under noisy conditions.
subject: Articles
  topic: section
subject: drone detection, acoustic signal, MFCC
  topic: keywords
creator: shahad waleed
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-07-31
publication date: 2026-07-31

Dual-band Modified Trapezoidal Antenna for Wireless Local Area Network (WLAN) and X-Band Communications
type: item
description: This research introduces a planar trapezoidal-shaped antenna for dual-band wireless communications. An inverted L-Shaped Strip (ILSS) is incorporated into the radiating structure with a 50-Ohm Coplanar Waveguide (CPW) feed on a single-layer FR4 substrate to generate dual resonant modes within a footprint (70 mm × 70 mm). The proposed design achieves impedance bandwidths of 1.86 GHz to 3.57 GHz and 7.12 GHz to 8.97 GHz (|S₁₁| ≤ −10 dB), covering the 2.4 GHz WLAN band and the X-Band for Fixed-Satellite Service (FSS) applications. The dual-band behavior is attributed to the coupling between the modified trapezoidal patch and the ILSS, which introduces an additional current path and enhances impedance matching across both bands. It exhibits maximum gains of 2.72 dBi for the lower band and 3.8 dBi for the higher band, along with radiation efficiencies of up to 94.35% and 71.13%. Antenna performance was optimized using the electromagnetic microwave simulation tool Computer Simulation Technology (CST) and subsequently validated through the fabrication and measurement of a prototype, with good agreement and minor discrepancies between simulated and measured results. These findings confirm the antenna’s suitability for Wireless Local Area Network (WLAN) 2.4 GHz applications and uplink/downlink X-band 7.825 GHz FSS applications. Furthermore, the 7.1 GHz to 8.4 GHz range emerges as a promising candidate for future wireless technologies, particularly 6G, due to its inherent capacity for high data rates, increased capacity, and advanced antenna implementations.
subject: Articles
  topic: section
subject: Dual-band antenna
  topic: keywords
subject: Fixed-satellite service
  topic: keywords
subject: Modified-trapezoidal patch
  topic: keywords
subject: Omnidirectional antenna
  topic: keywords
subject: Patch antenna
  topic: keywords
subject: 2.4 WLAN
  topic: keywords
creator: Suthasinee Lamultree
creator: Punyawat Wiyanat
creator: Kanit Butdaengnoi
creator: Chuwong Phongcharoenpanic
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-07-31
publication date: 2026-07-31

Hybrid CNN-LSTM Architecture with MFCC-Based Class-Balanced Augmentation for Arabic Speech Emotion Recognition
type: item
description: Arabic speech emotion recognition (SER) is also associated with serious challenges, including the variety of dialects, excessive unbalanced classes, and the lack of sufficient data. In order to resolve these concerns, the paper provides a comprehensive methodology, which integrates hybrid CNN-LSTM architecture with strategic data augmentation. Our method is evaluated with the help of two Arabic emotional speech data sets: EYASE (Eastern Youngsters Arabic Speech Emotions) which is the independent validation set and BAVED (Basic Arabic Vocal Emotions Dataset) which is the actual training data. Class-balanced augmentation techniques such as pitch shifting, time stretching, and noise injection are used to enhance the grotesquely unbalanced BAVED dataset with approximately 1,600 samples of each emotion class. Our hybrid architecture integrates both the bidirectional LSTM networks and attention mechanism of modelling a temporal sequence, and the convolutional neural network of extracting spatial features of MFCC representations. The experimental findings demonstrate that the performance of the POL2 is increased substantially, as the general accuracy on BAVED increases to 97.23% as compared to baseline (89%). High arousal emotion recognition is mainly enhanced with almost random performance (50% F1-score) to impeccable classification (100% F1-score). Systematic decomposition according to which architectural sophistication contributes 26 percent of the total performance gains and data augmentation contribute approximately 74 percent to the same. Cross-dataset testing of EYASE indicates that the generalization of EYASE is high across different speakers and recording conditions and that this accuracy is 87%. The findings demonstrate that balanced augmentation of data quality has a greater impact on performance than architectural complexity alone, providing useful guidance for the development of Arabic emotion recognition systems in environments with limited resources.
subject: Articles
  topic: section
subject: Speech Emotion Recognition, Arabic Language, CNN-LSTM, Attention Mechanism,
  topic: keywords
creator: Sarmad Alfarag
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-05-22
publication date: 2026-05-22

Analysis of FIR Window Length and Phase Lead Effects on Repetitive Control Performance for Hip and Knee Joint Trajectory Tracking
type: item
description: This research examines a repetitive-control plug-in for a PID controller used in a two-degree-of-freedom lower-limb model involving the hip and knee joints. All experiments were performed in MATLAB using a periodic reference trajectory and discrete-time simulation of the robotic dynamics. The plug-in uses a finite-impulse-response time frame and a ring buffer to recycle prior control efforts, while a lead term adjusts the phase. Performance was evaluated using the root mean square tracking error during iterations and at steady state. An exhaustive examination of lead compensation values (rcLead 0, 2, 4, and 6) and time window lengths (winLen 3, 5, 7, and an additional steady-state case at winLen 9) was conducted. The controller rapidly reduced errors across several scenarios without observed divergence in simulation. A superior configuration was identified wherein rcLead 2 combined with winLen 5 yielded the lowest steady-state error for both joints, attaining 0.014510 degrees at the hip and 0.004741 degrees at the knee, while also ensuring the most rapid practical convergence. Elevated lead values resulted in increased residual error, while windows that were excessively short or long either amplified noise or caused response delays. The findings indicate that a moderate lead time combined with an intermediate window length provides an efficient and adaptable framework for periodic tracking. Future endeavors will concentrate on validating the methodology on hardware, broadening the analysis to encompass a more extensive array of motion patterns, and executing automated modifications to essential parameters.
subject: Articles
  topic: section
subject: lower‑limb exoskeleton
  topic: keywords
subject: repetitive control
  topic: keywords
subject: Q‑filter
  topic: keywords
subject: FIR window
  topic: keywords
subject: phase compensation
  topic: keywords
subject: PID
  topic: keywords
subject: trajectory tracking
  topic: keywords
creator: Panya Minyong
creator: Assoc. Prof. Dr. Phichitphon Chotikunnan
creator: Rawiphon Chotikunnan
creator: Nuntachai Thongpance
creator: Yutthana Pititheeraphab
creator: Songtham Deewanichsakul
creator: Udomsak Jantontapo
creator: Surachat Chantarachit
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-05-22
publication date: 2026-05-22

A Compact Multiband Antenna for IoT and 5G Applications: Design Optimization and ML-Based Validation
type: item
description: This paper presents a new, compact, multiband antenna designed for the IoT and 5G wireless networks to address the growing need for efficient and multi-functional antennas in modern communication systems. The antenna development progresses through four phases to achieve the desired prototype. In the first phase, a single-band antenna utilizing an inverted L-shaped element is designed. The second phase introduces a U-shaped dual-band configuration. The third phase combines a triangular shape with a reversed L structure to attain two distinct resonance frequencies. Finally, the fourth phase culminates in a triple-band antenna resonating at 1.8, 2.4, and 5 GHz, showcasing the design's adaptability. The final prototype is implemented on a cost-effective FR-4 dielectric substrate, making it suitable for Wi-Fi, ISM bands, WLAN, and 5G applications. The design has been fabricated and experimentally validated. To further enhance performance evaluation and reduce the computational burden associated with extensive parametric studies, a data-driven machine learning approach is employed for rapid prediction and optimization, using supervised ML regression models to validate the antenna gain based on simulated design parameters. The models are evaluated using statistical performance metrics, including the variance score, R-squared, mean squared error, root mean square error, and mean absolute error.
subject: Articles
  topic: section
subject: Multiband antenna; IoT; ISM bands; ML; 5G
  topic: keywords
creator: Wazie Abdulkawi
creator: Abdallah Nabil
creator: Md Afzalur Rahman
creator: Samir Al-Bawri
creator: Hassan Ahmed
creator: Yosef Aladadi
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-07-31
publication date: 2026-07-31

A Confidence-Weighted Memory-Augmented Multimodal Voice Agent for Real-Time Emotion-Aware Interaction
type: item
description: Conversational emotion-aware systems are critical in the development of human-centric artificial intelligence. Single-modality systems often fail to identify acoustic patterns and text meaning associated with emotional expression. The study proposes an emotion-aware conversational framework that integrates offline-trained speech emotion recognition with a multimodal chatbot pipeline. In the offline phase, three self-supervised speech models —Wav2Vec2, HuBERT, and Whisper—were fine-tuned on the RAVDESS emotional speech dataset for multi-class emotion classification. Among them, the fine-tuned Whisper model achieved superior performance and was selected as the primary acoustic emotion encoder. The trained Whisper model was then deployed within a real-time chatbot architecture, where speech input is transcribed using automatic speech recognition and combined with acoustic emotion predictions. A confidence-weighted multimodal fusion strategy integrates audio and text-based emotion cues, followed by an Adaptive Confidence-Weighted Temporal Memory (ACWTM) module to model short-term emotional continuity. The model attained 87.3 percent accuracy and outperforms the unimodal baselines. It demonstrates enhanced robustness and conversational coherence with stability, offering a scalable framework for real-time empathetic human-AI interaction.
subject: Articles
  topic: section
subject: Conversational System
  topic: keywords
subject: Multimodal Voice Agent
  topic: keywords
subject: Emotion-Aware Interaction
  topic: keywords
subject: Real-Time
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Memory-Augmented Voice Agent
  topic: keywords
creator: Vijaya Bharathi A
creator: Dr. Prashant Nitnaware
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-05-22
publication date: 2026-05-22

Enhancing Cybersecurity in Internet of Things Networks Using Advanced Deep Learning Techniques for Intrusion Detection
type: item
description: The Internet of Things (IoT) is widely adopted in an increasing number of industries, such as healthcare, industrial automation, and smart infrastructure. They also reveal some critical security vulnerabilities in the systems that use IoT. These IoT networks are heterogeneous, resource-constricted, and faced with dynamic attack vectors, increasingly being targeted by threats such as distributed denial-of-service (DDoS) attacks, malware propagation, and data breaches. Following a static rule-based approach, traditional Intrusion Detection System (IDS) fail to scale appropriately with the diverse attack patterns presented to IoT ecosystems, and existing computational limitations. In this paper, we present a deep-learning-based IDS Framework that integrates Convolutional Neural Networks (CNNs) for spatial anomaly detection, Long Short-Term Memory (LSTM) networks for temporal pattern recognition, and a hybrid Attention-CNN-LSTM architecture for the holistic spatial-temporal dependency analysis. The framework is evaluated in benchmark datasets and achieves state-of-the-art performance attaining 98.9% accuracy, 98.3% F1-score, and 0.994 AUC-ROC on the IoT-23 dataset. It equally demonstrates tremendous effectiveness against stealthy attack detection, with low-and-slow DDoS (99.1%) and botnet (98.4%) attacks; additionally, the framework totally adapts to the heterogeneous environment of the IoT. The hybrid architecture uses significant computational resources (9.7 hours training time, 8.4 GB GPU memory); however, it manages detection accuracy against resource usage, thereby making it a good candidate for real-time implementation.
subject: Articles
  topic: section
subject: Internet of Things Security, Intrusion Detection System, Deep Learning IDS
  topic: keywords
creator: Ashraf Al Sharah
creator: TAREQ A. ALAWNEH
creator: Anas Quteishat
creator: Yazeed Alsarhan
creator: Sara A. Khalil
creator: Mutsam M. Jarajreh
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-05-22
publication date: 2026-05-22

A Poly-flex Microstrip Patch Antenna for 6G Applications: Design and Performance Analysis under Bending Impact
type: item
description: Flexible antennas are considered a key enabling technology for next-generation wireless systems, particularly for emerging Sixth-Generation (6G) applications that demand compact, conformal, and mechanically deformable structures. The study presents the design and comparative analysis of a microstrip patch antenna operating at 3 THz, implemented on two polymer substrates: Polyimide (PI) and Polytetrafluoroethylene (PTFE). The antenna performance is investigated using Computer Simulation Technology (CST) Microwave Studio under flat and bent configurations to evaluate its electromagnetic stability under mechanical deformation. The evaluation considers key performance parameters, including return loss (S11), Bandwidth (BW), Voltage Standing Wave Ratio (VSWR), realized gain (Gr), radiation efficiency (nrad), radiation characteristics, and surface current distribution. Bending conditions of 5°, 10°, and 15° are applied across the patch width to simulate realistic deformation scenarios for flexible 6G applications. Simulation results demonstrate that both designs maintain stable electromagnetic performance under bending conditions, with negligible variations in resonance behavior. The PI-based antenna exhibits superior impedance matching, wider bandwidth stability, and improved VSWR performance under deformation, whereas the Polytetrafluoroethylene (PTFE)-based design achieves higher realized gain due to its lower dielectric loss. Radiation efficiency remains consistently stable for both substrates, confirming their suitability for flexible THz applications. The findings highlight the suitability of polymer-based substrates for flexible THz antenna applications and provide practical design insights for selecting appropriate materials in future 6G conformal systems.
subject: Articles
  topic: section
subject: 6G, THz band, Microstrip antenna, Flexible antenna, Polymer substrate
  topic: keywords
creator: Salem Almazok
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-07-31
publication date: 2026-07-31

Forecasting Generation in Motor Generators Using Neural Networks
type: item
description: https://docs.google.com/document/d/1jz1mGDze9wEUskiHELUctMnlWnsuihs8/edit?usp=sharing&ouid=106513372135229847230&rtpof=true&sd=true
subject: Articles
  topic: section
subject: optimization, energy planning, generation, fuel consumption, model validation
  topic: keywords
creator: Secundino Marrero Ramírez
creator: Stiven Ismael Veintimilla Herrera
creator: Damian Alban Andrade
creator: Enrique Torres Tamayo
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-05-22
publication date: 2026-05-22

Lightweight Authenticated Encryption Scheme for Secure RSSI-Based Indoor Localization
type: item
description: Indoor localization is a key application of Wireless Sensor Networks (WSNs) for tracking objects, where secure communication is essential to protect sensitive location data. This paper proposes an authenticated encryption scheme optimized for lightweight WSN devices with low processing time. The scheme integrates multiple cryptographic algorithms: ASCON-AEAD for authenticated encryption, X25519 for key exchange, ED25519 for digital signatures, SHA-256 for data integrity, and timestamp-based replay protection. Raspberry Pi devices serve as anchor (AN) and unknown nodes (UN), where AN transmit encrypted data containing estimated position parameters, including RSSI and pathloss exponent (PLE) values. UN then decrypt data from the three nearest AN. Experimental results show the system requires only 17.82 ms processing time at AN and 6.48 ms at UN, with Wi-Fi FTP transmission averaging 3.12 ms per packet. This approach provides an efficient, privacy-aware, and low-power localization environment, outperforming traditional methods such as RSA, AES, and MD5 combination.
subject: Articles
  topic: section
subject: lightweight cryptography
  topic: keywords
subject: authenticated encryption
  topic: keywords
subject: indoor localization
  topic: keywords
subject: ASCON-AEAD
  topic: keywords
subject: Raspberry Pi
  topic: keywords
subject: RSSI
  topic: keywords
subject: Wi-Fi
  topic: keywords
creator: Rafina Destiarti Ainul
creator: Djuwari Djuwari
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-03-01
publication date: 2026-03-01

On the Design of Differential Difference Transconductance Amplifier (DDTA) and Its Applications
type: item
description: This paper presents the design and implementation of a novel active building block termed the differential difference transconductance amplifier (DDTA). The proposed DDTA integrates the characteristics of the differential difference amplifier (DDA) and the transconductance amplifier (TA) to enhance flexibility in analog signal processing. It comprises three TA stages—two forming a DDA structure and one functioning as a TA—allowing independent electronic tuning of transconductance gain (gm). The DDTA features a simple architecture, wide input capability, extremely high input impedance, and high output resistance at both intermediate and output current terminals, enabling accurate current-mode signal processing with minimal loading effects. To demonstrate its versatility, a voltage-mode universal biquadratic filter and dual-mode quadrature oscillator, both based on a single DDTA and utilizing only two capacitors, have been designed and simulated. PSPICE simulation results confirm wide linear voltage-to-current conversion, wideband transconductance performance, and robust impedance characteristics, all in excellent agreement with theoretical predictions. These features validate the effectiveness and practicability of the proposed approach for modern analog circuit designs.
subject: Articles
  topic: section
subject: Differential Difference Transconductance Amplifier (DDTA)
  topic: keywords
subject: transconductance amplifier
  topic: keywords
subject: analog integrated circuit (IC)
  topic: keywords
subject: electronic tunability
  topic: keywords
subject: multifunction filter
  topic: keywords
creator: Orapin Channumsin
creator: Panurut Yaruan
creator: Prof. Worapong Tangsrirat
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-03-01
publication date: 2026-03-01

Deep Learning-Based Channel State Estimation Approach for OFDM System
type: item
description: This paper presents a channel estimation method that employs pilot symbols, which contain no data, and machine learning (ML) to estimate channel coefficients at data symbol locations. In the proposed method, pilot symbols are inserted among data symbols. Channel coefficients are first estimated at pilot symbol positions using the linear minimum mean squared error (LMMSE) and then fed to a feed-forward neural network (FFNN) to predict channel coefficients at data symbol positions. In some conventional pilot-aided channel estimation schemes, the cross-correlation matrix for channel coefficients at data and pilot positions is an essential parameter for estimating channel coefficients at data positions; however, this statistical information may not be available. In the proposed scheme, channel coefficients at data positions can be predicted without any statistical information related to channel coefficients at data positions. Also, the proposed model avoids the high computational complexity in statistical-based channel estimation methods. Computer simulations show that the proposed scheme can achieve performance comparable to classical systems, especially for low signal-to-noise ratio (SNR).
subject: Articles
  topic: section
subject: Dataset, Neural network , Deep Learning, FFNN, Pilot Symbols, Training
  topic: keywords
creator: Ali Alqatawneh
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-03-01
publication date: 2026-03-01

Mobility models based AODV Mobile Ad Hoc Networks Performance Evaluation using NetSim Simulation Program
type: item
description: Mobile Ad Hoc Networks (MANETs) are self-organizing, wireless networks devoid of any infrastructure in which mobile nodes can form connections and transmit messages cooperatively. Their adaptive nature and ability to self-configure makes them a viable solution in an Internet of Things (IoT) environment, while node mobility does impose some challenges in terms of route stability, packet loss, and energy wastage. This study examines how two different mobility models affect the performance of the Ad-hoc On-Demand Distance Vector (AODV) routing protocol in the NetSim environment version 14.2. The effect of using two different speeds (10 m/s and 50 m/s) for both models and the AODV routing protocol were put to the test by observing five quality of service (QoS) metrics: Throughput, End-to-End Delay, Packet Delivery Ratio (PDR), Routing Overhead, and Energy Consumption. From the simulation results, it is concluded that node mobility will have a major affect routing behavior and energy use. Overall, the Random Waypoint mobility model provides the highest throughput and PDR along with moderate delay and energy consumption, as compared to Random Walk and No-Mobility solutions. All mobility models (including No-Mobility) exhibited degraded performance at 50 m/s due to excessive link breakage, however Random Waypoint provided the most overall balance in performance. In summary, these findings show that moderate, structured node mobility will enhance route stability, and the overall transport protocol performance for MANETs. These findings add value to potential designs for mobile network optimization in the future IoT.
subject: Articles
  topic: section
subject: mobility
  topic: keywords
subject: energy efficient
  topic: keywords
subject: routing overhead
  topic: keywords
subject: velocity
  topic: keywords
subject: MANET
  topic: keywords
creator: Shayma Wail Nourildean
creator: Yousra
creator: Nadia
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-03-01
publication date: 2026-03-01

Enhancing Power Quality in Grid-Connected Fuel Cell Systems Using the Gradient-Based Optimizer
type: item
description: Abstract—Recent studies on Energy Management Systems (EMS) have focused on enhancing fuel cell-integrated grid systems. Effective power electronics control strategies for such systems should be easy to implement, capable of minimizing harmonic distortion, and robust under weak grid conditions. However, weak grids present significant challenges, including elevated total harmonic distortion (THD), voltage fluctuations, harmonics from nonlinear loads, and frequency deviations. To address these issues, this paper proposes advanced EMS control strategies optimized using the Gradient-Based Optimizer (GBO) for fuel cell-integrated microgrid systems. The performance of the proposed GBO-PI controller is assessed through two analytical frameworks: THD, convergence analyses, and transient response of DC link voltage under both fixed and variable load conditions. This algorithm has optimized advanced control strategies for EMS in fuel cell-integrated grid systems to enhance the DC link voltage, improving transient response and power quality. The conventional method yields a THD of 6.61%, which is reduced to 0.60% using the proposed controller. The GBO-PI surpassed the settling time of PSO-PI, GJO-PI, and the no-controller scheme by margins of 61.4%, 60.0%, and 26.7%, respectively. Additionally, the proposed GBO-PI outperforms the compared methods—no controller, GJO-PI, and HHO-PI by 34.21%, 14.46%, and 11.67% in terms of overshoot, respectively. The findings demonstrate that Advanced control strategies enhance fuel cell microgrid integration by improving robustness and sustainability, while effectively managing dynamic load variations and ensuring stable power injection into the electrical grid.
subject: Articles
  topic: section
subject: DC-link Voltage control
  topic: keywords
subject: THD
  topic: keywords
subject: Fuel cell
  topic: keywords
subject: hydrogen
  topic: keywords
subject: optimization algorithm
  topic: keywords
subject: gradient-based optimizer
  topic: keywords
subject: grid
  topic: keywords
creator: Mohammad Golam Mostafa
creator: Nor Zaihar B. Yahaya
creator: Ramani Kannan
creator: Nursyarizal Bin Mohd Nor
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-03-01
publication date: 2026-03-01

Integrating Terrestrial and Non-Terrestrial Networks in 6G: A Review of Architectures, AI-Driven Techniques, and Sustainability Strategies
type: item
description: The integration of terrestrial networks (TNs) and non-terrestrial networks (NTNs) has become a cornerstone of sixth-generation (6G) communications, promising ubiquitous connectivity, global coverage, and resilient service delivery. While several existing reviews have examined this integration, they largely focus on isolated aspects such as architecture, control, virtualization, or artificial intelligence (AI), leaving sustainability underexplored. This paper provides a comprehensive review that systematically synthesizes the state-of-the-art across three interdependent dimensions: architectural approaches, AI-driven optimization, and sustainability strategies. We first classify architectural frameworks shaping TN–NTN integration, then examine how AI techniques drive intelligent resource management and orchestration, and finally position sustainability as a core design pillar through energy- and carbon-aware strategies. Unlike prior reviews, this work consolidates fragmented perspectives into a triadic taxonomy that illustrates how architectural design choices influence AI-driven orchestration and sustainability outcomes. The review also identifies open challenges, including interoperability across domains, standardization gaps, and balancing energy efficiency with performance guarantees. By linking architecture, intelligence, and sustainability, the paper offers a forward-looking roadmap for building resilient, intelligent, and green TN–NTN systems, thereby contributing to the realization of 6G as an enabler of equitable global connectivity and alignment with United Nation Sustainable Development Goals (UN SDGs).
subject: Reviews
  topic: section
subject: Non-Terrestrial Networks
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Energy-efficiency
  topic: keywords
creator: Thavavel Vaiyapuri
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-01-15
publication date: 2026-01-15

Fuzzy Clustering-Based Temperature Control System for Energy Efficiency and Cost Optimization in Smart Homes
type: item
description: Temperature control systems are among the most demanding and critical uses of server rooms, industrial plants, and smart homes because they are nonlinear and dynamic. Although PID controllers are ubiquitous in climate control, their fixed linear gain parameters are not very adaptable to different operating conditions. Fuzzy control systems, especially soft clustering-based systems, offer more versatile methods by facilitating smooth and continuous adjustment of temperature and thus more precise and adaptive control. On the basis of both Fuzzy K-Means and Fuzzy C-Means (FCM) algorithms, in this research, a clustering-based PID system is developed and evaluated. The system performance is verified via simulation with emphasis placed on temperature tracking accuracy, precision, and cost of energy. The results illustrated that the FCM-PID controller achieves the highest stable as well as precise temperature response compared to conventional PID and Fuzzy K-Means PID controllers. In addition, it achieves around 10% of energy cost reduction and accuracy of 97.6% with a lower MAE of 0.48°C, reflecting its real-time adaptive control efficiency. These results suggest that the integration of FCM-PID has the potential to be a smart energy-saving technology for future generations of HVAC systems and other temperature-sensitive applications
subject: Articles
  topic: section
subject: Fuzzy Clustering
  topic: keywords
subject: Temperature Control System
  topic: keywords
subject: Fuzzy C-Means
  topic: keywords
subject: Cost Optimization
  topic: keywords
subject: Fuzzy K-Means
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: PID
  topic: keywords
creator: Vaishali Salve
creator: Varsha Yelmar
creator: Dr. Magan Ghatule
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-05-22
publication date: 2026-05-22

Timer-Based Capacitance-to-Voltage Converter
type: item
description: This article presents the design and analysis of a capacitance-to-voltage converter based on 555-timer ICs as the core design element. The circuit structure consists of two primary active components: two 555-timer ICs connected in conjunction with a low-pass filter circuit. The first 555-timer IC operates in astable mode to generate a signal that controls the timing of the second IC. The target capacitor or capacitive sensor to be measured is connected to the second 555-timer IC, which operates in monostable mode. The output signal from the second 555-timer IC is then converted into a DC voltage output using a low-pass filter circuit. The circuit was tested using laboratory capacitors in the range of 52.72 pF to 807.61 pF, and the data were analyzed using Microsoft Excel. The results showed excellent agreement with the measurements obtained from the GW INSTEK LCR-819. The maximum uncalibrated error was found to be −3.32% of full scale. After applying linear curve fitting in Excel, the maximum error was reduced to 0.92% of full scale, confirming the effectiveness of the calibration approach. Furthermore, the circuit was applied to measure the moisture content of paddy rice using a cylindrical capacitive sensor within the moisture range of 12.1% to 23.2%. The experimental data were fitted to a derived equation, which was then used to predict rice moisture content, producing results that corresponded closely with those obtained from the KETT PM-450 grain moisture meter.
subject: Articles
  topic: section
subject: Capacitance-to-voltage converter (CVC), 555-timer, Capacitive sensor, Capacitive readout circuit
  topic: keywords
creator: Anucha Kaewpoonsuk
creator: Apinai Rerkratn
creator: Sudarat Tokampang
creator: Noppadon Sisuk
creator: Wandee Petchmaneelumka
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-03-01
publication date: 2026-03-01

A Robust IDS System for Intelligent Phishing Website Detection
type: item
description: Phishing websites continue to pose significant threats to cybersecurity by deceiving users into revealing sensitive information. To address this, this paper proposes a machine learning-based phishing detection model using a hybrid feature selection method that combines the Dragonfly Algorithm (DA) and Whale Optimization Algorithm (WOA), referred to as DAuWOA. The model leverages the ISCX-URL2016 dataset for evaluation. The proposed feature selection technique improves detection accuracy by removing irrelevant attributes and enhancing model efficiency. Adaptive Boosting (AdaBoost) is used as the classifier, and its hyperparameters are fine-tuned using Grid Search. Experimental results demonstrate that the proposed DAuWOA-AdaBoost model achieves superior performance compared to existing methods, attaining 97.07% accuracy, 96.89% recall, 97.15% precision, 97.02% F1-score, and 94.14% MCC. The proposed model not only boosts accuracy but also enhances computational efficiency, showing promise for real-time phishing detection systems.
subject: Articles
  topic: section
subject: Machine learning
  topic: keywords
subject: Feature selection
  topic: keywords
subject: Dragonfly Algorithm
  topic: keywords
subject: Whale Optimization Algorithm
  topic: keywords
subject: Phishing
  topic: keywords
creator: Mosleh Abualhaj
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-03-01
publication date: 2026-03-01

Code Generation by Large Language Models: A Comparative Analysis of ChatGPT, Claude, and DeepSeek
type: item
description: As generative AI models become increasingly integrated into software development workflows, understanding their efficiency and code quality is critical. This study offers a comprehensive comparison of three leading AI models—ChatGPT GPT-4-turbo, Claude Sonnet, and DeepSeek-V3—for automated code generation, focusing specifically on sorting algorithms. The models are evaluated across multiple metrics including execution time, memory usage, peak memory consumption, logical and physical file sizes, and code readability. Python implementations of Insertion Sort, Merge Sort, Quick Sort, and Heap Sort are generated by each model and benchmarked in a consistent Linux Docker environment. Results reveal that ChatGPT leads in overall efficiency, with the fastest average execution time, the lowest peak memory usage, and the highest readability scores. DeepSeek demonstrated competitive performance, especially in producing readable code, while Claude showed higher memory consumption and lower readability. This analysis provides practical insight into the trade-offs between code quality and system performance in AI-generated programming, offering valuable guidance for researchers and developers alike
subject: Articles
  topic: section
subject: Large language model
  topic: keywords
subject: ChatGPT
  topic: keywords
subject: Claude sonnet
  topic: keywords
subject: DeepSeek
  topic: keywords
subject: Code generation
  topic: keywords
creator: Yousef Alraba'nah
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-01-15
publication date: 2026-01-15

Explainable AI-Based Mapping of Public Sentiment on the Impact of Environmental Policies: A Cross-Regional Social Media Analysis
type: item
description: It is essential to comprehend public opinion regarding environmental sustainability programs to develop successful policies and interventions. Conventional survey techniques, however, are inadequate to obtain current, large-scale opinion trends. This research uses machine learning and deep learning techniques, such as BERT and LSTM, to investigate tweets and social media conversations on environmental policy. Unstructured text is converted to structured sentiment scores, identifying opinions as positive, negative, or neutral. In addition, an Extended Policy Analytical Framework was utilized, which included region- and policy-resolved assessment, event-aligned sentiment effect analysis, emotion profiling using the NRC Emotion Lexicon, cross-region generalization, and Explainable AI methods like SHAP-based keyword interpretation. BERT proved to be the most accurate classifier with an accuracy of 0.97, and this was followed by Random Forest with an accuracy of 0.94. Results show substantial sentiment changes surrounding key policy events like COP26 in India (+0.14) and US carbon tax discussions (−0.15) and identify characteristic emotional patterns by policy topic, with hope and excitement leading clean energy debates, anger and fear dominating carbon pricing discussions, and sadness and anger defining deforestation control. Cross-region comparison revealed 5–7% decreases in macro-F1, pointing to the importance of region-based adjustment, and SHAP-based explanations detected framing effects impacting public support or opposition. Overall, this research illustrates the viability of explainable and AI-driven sentiment analysis to track and decode public debate on environmental policy. The results offer actionable, evidence-based information for policymakers, allowing more effective communication strategies, greater public engagement, and assistance in the implementation of effective sustainability practices in different regions.
subject: Articles
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Impact on Public Sentiments
  topic: keywords
subject: Environmental Policies
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Dr. H. E. Khodke
creator: Dr. Priti S. Lahane
creator: Dr. Shivaji R. Lahane
creator: Dr. V. Srinadh
creator: Dr. R. Juliana
creator: Dr. Vishal Naranje
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-01-15
publication date: 2026-01-15

AC/AC converter flexibly connects AC microgrid with integrated storage system
type: item
description: A more stable grid is achieved by storage systems, with AC microgrids providing more continuous grid operation by supplying energy to electrical loads from distributed energy sources and energy sources from the old distribution grid. Energy conversion between components in the microgrid system such as power sources, storage systems and AC microgrids is a requirement to contribute significantly to energy management. AC/AC converters use basic DC/DC conversion forms to convert energy such as Cuk, Sepic. The article provides specific application content of renewable energy sources supplied to microgrids integrated with storage systems using AC/AC converters with 3 ports that can continuously convert energy for loads in the grid and storage systems. The results of the work are performed by simulation software and experiments with power fluctuations in the range of 2-5.5kW. The converter has an average efficiency of each case together close to 97% when the proposed converter is switched with 3 main switches to connect to three different independent ports in a power system connected to an AC microgrid. The converter is simulated and tested in the laboratory with the results compared with the reference literature.
subject: Reviews
  topic: section
subject: AC/AC converter
  topic: keywords
subject: AC/DC converter
  topic: keywords
subject: energy storage
  topic: keywords
subject: bi-directional converter
  topic: keywords
subject: microgrid
  topic: keywords
creator: Vinh Nguyen
creator: Van Dung Nguyen
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-01-15
publication date: 2026-01-15

Evaluating Efficiency and Productivity Trends of Indian Electricity Distribution Utilities: DEA-Malmquist Analysis
type: item
description: This study evaluates the technical efficiency and total factor productivity (TFP) of 54 Indian electricity distribution utilities (DMUs) from 2020 to 2023 using Data Envelopment Analysis (DEA) and the Malmquist Productivity Index (MPI). The Charnes-Cooper-Rhodes (CCR) model revealed that only 35.18% of DMUs (19 out of 54) were technically efficient, with average efficiency improving marginally from 0.84 to 0.88. Slack-based inefficiencies resulted in an estimated input overutilization of Rs. 63,270.12 crore, highlighting significant operational inefficiencies. While pure efficiency improved by 9%, technological regress (TECHCH = 0.932) due to outdated infrastructure and regulatory delays led to a 6.76% decline in productivity. Persistent Aggregate Technical and Commercial (AT&C) losses (19.24%) further contributed to a 3.83% drop in TFP. Top-performing utilities like DMU46 (DGVCL) and DMU44 (Puducherry PD) demonstrated cost efficiency (Rs. 0.776 crores/MU) and reduced ACS-ARR gaps, whereas lagging DMUs such as DMU05 (NESCO) and DMU43 (TANGEDCO) suffered from high losses and technological stagnation. The findings underscore the urgent need for AI-driven smart grid interventions to enhance efficiency, reduce losses, and ensure sustainable electricity distribution in India by 2047
subject: Articles
  topic: section
subject: Electricity distribution utilities, Data Envelopment Analysis (DEA), Total Factor Productivity (TFP)
  topic: keywords
creator: Ramaiah Veerlapati
creator: P. Chandrasekar
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-01-15
publication date: 2026-01-15

A Multi-Objective ESTO Framework for Efficient PV Siting and Sizing in Radial Distribution Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: ESTO
  topic: keywords
subject: Power system losses
  topic: keywords
subject: Voltage deviation
  topic: keywords
subject: Photovoltaics
  topic: keywords
subject: Renewable energy
  topic: keywords
creator: zaid alhadrawi
creator: Ali Almousawi
creator: Ali Majeed
rights: Copyright (c) 2025 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2025-11-10
publication date: 2025-11-10

Performance Evaluation and Latency Optimization of Wi‑Fi 7 in High-Density Wireless Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: Wi-Fi 7
  topic: keywords
subject: IEEE 802.11 be
  topic: keywords
subject: Multi-Link Operation
  topic: keywords
subject: Latency Optimization
  topic: keywords
subject: Throughput Analysis
  topic: keywords
subject: NS-3 Simulation
  topic: keywords
subject: Wireless Network
  topic: keywords
subject: high-density deployments
  topic: keywords
subject: quality of service
  topic: keywords
subject: real-time applications
  topic: keywords
creator: Almamoon Alauthman
creator: Tamer Shraa
rights: Copyright (c) 2025 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2025-11-10
publication date: 2025-11-10

Evaluation and Optimization of Graphene Nanosheet FET: Analog/RF Perspectives
type: item
description: Graphene Nanosheet Field Effect Transistor (GNSFET) is constructed for the first time by using Technology Computer-Aided Design (TCAD), marking advancement in nanosheet FET technology. The device’s analog /RF performance are evaluated with respect to the main geometric parameters of GNSFET, such as the gate-length, the channel-width, and the channel- thickness. Key analog/RF performance metrics have been analyzed targeting the optimization process, including the following: ION/IOFF ratio, subthreshold-swing (SS), transconductance (gm), transconductance efficiency (gm/Id), output conductance (gds), gate capacitance (Cgg), cut-off frequency (FT), and intrinsic gain (Av). Output results reveal that scaling-down (Tch) to 6 nm, (Wch) to 14 nm, and (Lg) to 16 nm enhances the ION/IOFF ratio to 5.2×1011 and reduces SS to 60.05 mV/dec, which creates better switching efficiency. Reducing (Tch) from 8 nm to 6 nm, and (Wch) from 18 nm to 12 nm boosts the ION/IOFF ratio by 34.48% and 33.19%, respectively, highlighting the main role of device’s small geometry in improving electrostatic control. Transconductance (gm) increases by 22.51% when (Lg) is scaled from 16 nm to 12 nm, while Cgg decreases with reductions in (Lg), (Wch), and (Tch), aligning with graphene’s inherent high carrier mobility. Notably, FT improves by 11.71% under (Tch) scaling, underscoring graphene’s potential for high-frequency RF applications. Although, intrinsic-gain (Av) benefits from (Wch) and (Tch) scaling, it exhibits a significant augmentation (38.88%) when (Lg) is increased from 12 nm to 16 nm, reflecting a trade-off between gain optimization and speed. This work could be considered as foundational framework for graphene-based nanosheet FETs in next-generation analog/RF circuits.
subject: Articles
  topic: section
subject: NSFET, graphene, analog/RF, performances optimization
  topic: keywords
creator: Firas Natheer Hassan Agha
creator: Khalid Mohammad
creator: Hussein AL Abdulqader
creator: Billel Smaani
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-01-15
publication date: 2026-01-15

Determination of the Modern Marine Gas Turbine Rotor Forced Vibration Parameters by Numerical and Experimental Methods
type: item
description: In this paper the problem of the gas turbine rotor forced vibration has been studied. The paper outlines a finite elements refined mathematical model of the gas turbine rotor durability, taking into account the influence of forced vibration, high temperature and centrifugal force, constructional damping parameters and can be used for the various types of marine gas turbine engines including even floating power plants. The developed mathematical model is based on the curvilinear finite elements of hexagonal type. Its verification has been done by the usage the modern highly automatized experimental equipment that is based on the holographic interferometry method application in real time. The first impeller cooled blades forced vibration parameters have been determined for several most dangerous forced vibration modes, temperatures up to 10000C and angular velocities of shaft rotation, corresponding with the gas turbine engine normal working regime. The calculated data together with the numerical and experimental investigation of this rotor fluid flow and thermal state will be used for the next stage of studies, concerning the rotor creep and fatigue crack problems.
subject: Articles
  topic: section
subject: gas turbine rotors; forced vibration; gas flow temperature; centrifugal force; constructional damping; durability
  topic: keywords
creator: MAHMOUD AL SUOD
rights: Copyright (c) 2026 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2026-01-15
publication date: 2026-01-15

Multi-Objective Dynamic Self-Healing of Unbalanced and Harmonic-Rich Smart Distribution Networks Using Improved Whale Optimization Algorithm
type: item
description: 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.
subject: Articles
  topic: section
subject: Self-healing distribution networks
  topic: keywords
subject: Improved Whale Optimization Algorithm (IWOA);
  topic: keywords
subject: Power quality optimization
  topic: keywords
subject: Voltage unbalance
  topic: keywords
subject: Harmonic distortion
  topic: keywords
subject: Smart microgrid reconfiguration
  topic: keywords
creator: Nguyen Tung Linh
creator: Anh Nguyen
creator: Long Pham
rights: Copyright (c) 2025 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2025-11-10
publication date: 2025-11-10

Safety Engineering in Adaptive Multimedia System Design for Emergency Accessibility Based on WCAG
type: item
description: 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.
subject: Articles
  topic: section
subject: Emergency Alert System
  topic: keywords
subject: Adaptive Multimedia
  topic: keywords
subject: Safety Engineering
  topic: keywords
subject: WCAG 2.1
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
creator: Marvin Chandra Wijaya
rights: Copyright (c) 2025 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2025-11-10
publication date: 2025-11-10

Agent-Based Secure Route Switching and QoS Management in VANETs using the Wiedemann Car-Following Prediction Model
type: item
description: 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.
subject: Articles
  topic: section
subject: VANETS
  topic: keywords
subject: Security
  topic: keywords
subject: Agents
  topic: keywords
subject: Path Switching
  topic: keywords
subject: Routing
  topic: keywords
subject: Connectivity Prediction
  topic: keywords
creator: Maen S. Saleh
rights: Copyright (c) 2025 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2025-11-10
publication date: 2025-11-10

5G New Radio LDPC Codes Based Generalized Joint Subcarrier-Time Index Modulation DCSK System
type: item
description: 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.
subject: Articles
  topic: section
subject: 5G new radio LDPC, joint subcarrier-time index modulation, differential chaos shift keying, multipath Rayleigh fading channel Introduction
  topic: keywords
creator: Muntaha Kadhum Musa
creator: Fadhil S. Hasan
rights: Copyright (c) 2025 International Journal of Electrical and Electronic Engineering & Telecommunications
date: 2025-09-15
publication date: 2025-09-15