Asian Journal of Electrical and Electronic Engineering
Asian Journal of Electrical and Electronic Engineering (AJoEEE) is a blind, peer-reviewed, open-access biannual journal published in March and September. Its mission is to contribute to original, high-quality research. The journal publishes research papers in the fields of Electrical and Electronic Engineering. Journal site: https://alambiblio.com/ojs/index.php/ajoeee
The Asian Journal of Electrical and Electronic Engineering journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Hybrid Deep Reinforcement Learning for RIS-Assisted 6G IoT Networks: A Comparative Study of DDPG, TD3, and Adaptive Hybrid Policies
Section: Articles Keywords: Keywords: Reconfigurable Intelligent Surface (RIS), Deep Reinforcement Learning (DRL), DDPG, TD3. Hybrid Learning, Beamforming Optimization ,6G Networks, IoT Coverage Enhancement. Reconfigurable Intelligent Surfaces (RIS) are becoming essential for improving coverage and signal reliability in the upcoming 6G wireless networks. In this research, we present a hybrid deep reinforcement learning (DRL) framework to optimize beamforming in RIS-assisted systems. It employs several policy-selection mechanisms to enhance performance stability and reward consistency under dynamic channel conditions. The extensive experimental evaluation over the last 30 testing episodes used trimmed-mean analysis with 95% confidence intervals. With an average return of 14.02 ± 0.62, the Hybrid Best-Action method outperformed the conventional DDPG baseline (11.13 ± 0.87) by 25.97%, which is marked by a significant effect size (Cohen’s d = 1.42, p < 0.0001). Although TD3 achieved a competitive performance (13.34 ± 0.73), the hybrid strategy outperformed it in reward stability and reduced performance variability, which is evidenced by the rolling standard deviation analysis. The results of a one-way ANOVA showed statistically significant differences among all the policies assessed (F(4,145) = 9.8, p < 0.0001), indicating a considerable overall effect size (η² ≈ 0.213). A post hoc power analysis indicates that the sample size we selected (n = 30 per policy) has high statistical power (>0.99) to detect moderate-to-large performance differences. In RIS-assisted IoT applications, the proposed hybrid framework’s reduced reward variability and improved convergence stability are key factors that enhance the reliability of beam alignment and ensure consistent coverage. The findings show that the proposed hybrid DRL method yields statistically significant and practically meaningful improvements over traditional single-policy methods, making it a strong contender for intelligent beamforming optimization in dynamic 6G wireless environments.
Keywords: Reconfigurable Intelligent Surface (RIS), Deep Reinforcement Learning (DRL), DDPG, TD3. Hybrid Learning, Beamforming Optimization ,6G Networks, IoT Coverage Enhancement.
Copyright (c) 2026 AlamBiblilo Publishers https://creativecommons.org/licenses/by-nc/4.0
TD3 vs. DDPG for Coverage-Driven Beamforming Optimization in RIS-Assisted 6G IoT Systems
Section: Articles Keywords: KEYWORDS Reconfigurable Intelligent Surfaces (RIS); Coverage Enhancement; 6G Internet of Things (6G IoT); Deep Deterministic Policy Gradient (DDPG); Deep Reinforcement Learning (DRL); Beamforming Optimization ; Abstract: Reconfigurable intelligent surfaces (RIS) will be necessary in forthcoming 6G mobile networks to ensure flexible coverage. This study presents a simulation model employing Deep Reinforcement Learning to enhance beamforming in RIS-supported systems. We put TD3 and DDPG through 30 different tests, each with a different channel setup. The findings indicate that TD3 exhibits a superior average error rate (13.34 ± 0.73) compared to DDPG (11.13 ± 0.87), reflecting a 19.86% enhancement. This statistical analysis demonstrates that this increase is significant (t ≈ 3.99, p < 0.001) and exhibits a considerable effect size (Cohen's d = 1.03). The average difference is between 1.10 and 3.32 with a 95% confidence level. The variance analysis shows that TD3 has a more stable reward and less variability in performance. The results show that TD3 is a statistically valid way to make smart wireless 6G environments better. This means that it makes IoT connectivity with RIS more reliable and stable.
KEYWORDS Reconfigurable Intelligent Surfaces (RIS); Coverage Enhancement; 6G Internet of Things (6G IoT); Deep Deterministic Policy Gradient (DDPG); Deep Reinforcement Learning (DRL); Beamforming Optimization ;
Analysis of Variable Frequency Drive for Electric Vehicles
Section: Articles Keywords: Conduction topologies, , electric vehicle, variable frequency drive Efficient power electronics are critical in electric vehicle powertrains to optimize motor controls. Traditional traction topologies suffer from high switching losses and design complexities due to intermediate DC-DC boost stages. This paper evaluates the operational boundaries of a three-phase Variable Frequency Drive. Developed within the PSpice, the simulation maps the structural trade-offs between 180° and 120° conduction topologies under variable resistance-inductance load lines. Results show that while the 180° mode maintains line voltage stability across all loads, it poses a severe risk of phase-leg shoot-through short circuits during switching intervals. Conversely, the 120° mode prevents shoot-through by providing an inherent 60° non-conducting safety window. The parametric sweeps show that under a heavy inductive load line, the 120° voltage waveform collapses into an asymmetric triangular profile. Furthermore, transient testing indicates that open-loop Piecewise Linear modulations result in severe gate-pulse overlap. These findings establish critical boundary constraints vital for deploying secure vehicle inverter drive control loops.
Mohamad Izzat Za’im bin Abdul Khalid Abdul Khalid
Section: Editorial Keywords: Editorial, Table of Contents CHIEF EDITOR Prof. Dr. AHM Zahirul Alam, IIUM, Malaysia EXECUTIVE EDITOR Assoc. Prof. Dr. Muhammad Mahbubur Rashid, IIUM, Malaysia EDITORIAL BOARD MEMBERS Prof. Dr. Sheroz Khan Onaizah College of Engineering and Information Technology Saudi Arab Prof. Dr. AHM Asadul Huq Department of Electrical and Electronic Engineering Dhaka University, Bangladesh Prof. Dr. Pran Kanai Shaha Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology, Bangladesh Assoc. Prof. Dr. SMA Motakabber Faculty of Engineering International Islamic University Malaysia, Malaysia Prof. Dr. ABM Harun Ur Rashid Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology, Bangladesh Prof. Dr. Joarder Kamruzzaman Engineering and Information Technology Federal University, Australia Dr. Md Arafatur Rahman Reader in Cyber Security University of Wolverhampton, UK Aims & Scope of Asian Journal of Electrical and Electronic Engineering The Asian Journal of Electrical and Electronic Engineering (AJoEEE), published biannually (June and December), is a peer-reviewed open-access journal of the AlamBiblio Press. The Asian Journal of Electrical and Electronic Engineering (AJoEEE) publishes original research findings as regular papers and review papers (by invitation). The Journal provides a platform for Engineers, Researchers, Academicians, and Practitioners who are highly motivated to contribute to the fields of Electrical and Electronics Engineering. It also welcomes contributions that address the developing world's specific challenges and address science and technology issues from a multidisciplinary perspective. Referees' Network All papers submitted to the AJoEEE Journal will undergo a rigorous review process by a worldwide network of specialised and competent referees. Each accepted paper should have at least two positive referees' assessments. Submission of a Manuscript A manuscript should be submitted online to the Asian Journal of Electrical and Electronic Engineering (AJoEEE) website https://alambiblio.com/ojs/index.php/ajoeee. Further correspondence regarding the status of the paper can be handled through the journal website.
Integrating Deep Learning Techniques for Automated Retinal Disease Detection Using Fundus Images
Section: Articles Keywords: Fundus Image, Retinal Diseases Diagnosis, Deep Learning, Multi-Class Classification, CNN. RetinalFormer was compared with eight state-of-the-art models, including pure CNN architectures (VGG-19, ResNet-50, DenseNet-121, EfficientNetV2-M), pure Transformer models (ViT-Base/16, Swin-Transformer-B), and existing hybrid CNN-Global attention models. Globally, nearly 2.2 billion people have visual impairments, with at least one billion due to avoidable or treatable causes. Retinal disorders, such as Diabetic Retinopathy (DR), Glaucoma, and Age-related Macular Degeneration (AMD), contribute significantly to avoidable blindness but tend to go unnoticed due to their asymptomatic nature until irreversible stages occur. Fundus photography offers a simple, inexpensive approach to screening patients’ retinas; however, it has limitations due to a shortage of ophthalmologists and the need for subjective interpretation. This paper proposes an effective deep learning architecture for automated and multi-class classification of retinal diseases from fundus images. Our proposed model includes a transfer-learning-based Image Quality Evaluation Tool (QET) to filter out low-quality images. Contrast enhancement using the CLAHE method and class-specific data augmentation are considered to address class imbalance. We compare three state-of-the-art deep learning architectures: ResNet-152, EfficientNetV2, and YOLOv11. The multi-Scale Attention Transformer (MSAT) and the Hybrid DenseNet-VGG16 model are used for specific tasks. Feature optimization is performed using the Bitterling Fish Optimization (BFO) algorithm, while hyperparameter tuning is performed using the Honey Badger Optimization (HBO). Explainability is ensured through Grad-CAM heatmaps and t-SNE visualizations. The proposed model is evaluated on standard benchmarking datasets, including RFMiD, ODIR-5K, Drishti-GS1, RIM-ONE, and ORIGA-Light, achieving classification accuracies above 90% compared to existing state-of-the-art models.
Fundus Image, Retinal Diseases Diagnosis, Deep Learning, Multi-Class Classification, CNN.
Blockchain Based Email Security to Mitigate Phishing Attack
Section: Articles Keywords: Blockchain, Smart Contract, Email Security, Phishing Attack, Authentication, Decentralized System Due to the rapid development of research in blockchain technology and cryptocurrencies, all sectors of an economy rely on their security essentials to mitigate various patterns of attack on the Internet. The smart contract is a transaction protocol that strengthens, verifies, and automatically enforces agreements after negotiation between multiple untrustworthy blockchain parties. Despite the positive aspects of smart contracts, issues of security risks, weaknesses, and legal challenges continue to undermine their implementation. This paper proposes an enhanced email verification system using blockchain-enabled smart contracts. In this framework, blockchain email enables swift verification of all emails being transmitted by introducing a challenging framework that prevents an internet attacker or cybercriminal from altering the authentication process. An acknowledgement email will be transmitted to the sender upon successful delivery, and the receiver can automatically receive the email with unique credentials. The findings reveal that the proposed system significantly mitigates phishing attacks by ensuring email authenticity and transaction integrity through blockchain hashing techniques, thereby enhancing email security in both online and offline environments.
Tengku Hanis Sofea Binti Tengku Nor Effendy
Copyright (c) 2024 AlamBiblilo Publishers https://creativecommons.org/licenses/by-nc/4.0
Comprehensive Review of Energy Efficiency Optimization for UAV-Assisted Cognitive Radio Networks with RIS and MEC Integration
Section: Articles Keywords: Unmanned aerial vehicle (UAV), Cognitive radio networks (CRNs) and Mobile Edge Computing (MEC) systems, Reconfigurable Intelligent Surface (RIS). This extensive analysis examines how Cognitive Radio Networks (CRNs) can optimise energy efficiency by integrating UAVs, RIS, and MEC systems. Synergistic applications in 6G networks and IoT infrastructure offer unprecedented opportunities to establish sustainable, intelligent wireless communication systems. UAVs enable flexible network deployment in varied contexts by providing mobile base stations with dynamic spectrum access. Amplification-free programmable passive beamforming and phase-shift control improve wireless communication with RIS. By localising data processing and offloading tasks, MEC reduces latency and communication energy consumption. The survey addresses system complexity, dynamic channel variability, interference control, resource scheduling, scalability, security risks, and privacy protection with a single framework. We note that the collaborative optimisation of UAV trajectories, RIS phase shifts, MEC resource allocation, and CRN spectrum sensing improves energy efficiency by 60-75%, exceeding the gains from decoupled component-level optimisation. Real-time machine learning algorithms for dynamic adaptation, hardware miniaturisation for aerial RIS deployment, blockchain-based security protocols, heterogeneous system interoperability standardisation, and field validation through real-world testbeds to prove theoretical energy efficiency gains are future research priorities.
Unmanned aerial vehicle (UAV), Cognitive radio networks (CRNs) and Mobile Edge Computing (MEC) systems, Reconfigurable Intelligent Surface (RIS).
Copyright (c) 2025 AlamBiblilo Publishers https://creativecommons.org/licenses/by-nc/4.0
Section: Editorial Keywords: Editorial, TOC CHIEF EDITOR Prof. Dr. AHM Zahirul Alam, IIUM, Malaysia EXECUTIVE EDITOR Assoc. Prof. Dr. Muhammad Mahbubur Rashid, IIUM, Malaysia EDITORIAL BOARD MEMBERS Prof. Dr. Sheroz Khan Onaizah College of Engineering and Information Technology Saudi Arab Prof. Dr. AHM Asadul Huq Department of Electrical and Electronic Engineering Dhaka University, Bangladesh Prof. Dr. Pran Kanai Shaha Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology, Bangladesh Assoc. Prof. Dr. SMA Motakabber Faculty of Engineering International Islamic University Malaysia, Malaysia Prof. Dr. ABM Harun Ur Rashid Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology, Bangladesh Prof. Dr. Joarder Kamruzzaman Engineering and Information Technology Federal University, Australia Dr. Md Arafatur Rahman Reader in Cyber Security University of Wolverhampton, UK Aims & Scope of Asian Journal of Electrical and Electronic Engineering The Asian Journal of Electrical and Electronic Engineering (AJoEEE), published biannually (March and September), is a peer-reviewed open-access journal of the AlamBiblio Press. The Asian Journal of Electrical and Electronic Engineering (AJoEEE) publishes original research findings as regular papers, review papers (by invitation). The Journal provides a platform for Engineers, Researchers, Academicians, and Practitioners who are highly motivated to contribute to the Electrical and Electronics Engineering disciplines. It also welcomes contributions that address the developing world's specific challenges and address science and technology issues from a multidisciplinary perspective. Referees' Network All papers submitted to AJoEEE Journal will be subjected to a rigorous reviewing process through a worldwide network of specialised and competent referees. Each accepted paper should have at least two positive referees' assessments. Submission of a Manuscript A manuscript should be submitted online to the Asian Journal of Electrical and Electronic Engineering (AJoEEE) website https://alambiblio.com/ojs/index.php/ajoeee. Further correspondence on the status of the paper could be done through the journal website.
Design and Analysis of Digital Control Buck Converter for optimum performance
Section: Articles Keywords: Buck Converter, Pulse Width Modulation (PWM), PI controller This paper aims to design and analyze a digitally controlled buck converter meant for applications requiring DC voltage under varying input voltages and load conditions. The buck converter is designed with a switching frequency of 40 kHz, an inductor value of 200 µH, and a capacitor of 100 µF with Pulse Width Modulation (PWM). The Proportional-Integral (PI) controller is used to maintain the output voltage steady. For simulation and performance evaluation, PSpice and MATLAB/Simulink software are selected. This paper aims to create a digital control system that is better than the traditional analog controllers, offering precise and adaptive regulation. The simulation results highlight how effective the digital PI controller is at reducing voltage ripple and maintaining the output voltage, even when the inputs and loads vary. This study covers detailed parameter calculations, controller design, circuit simulation, and performance analysis, providing a thorough overview of the digitally controlled buck converter system.
Energy Efﬁciency Optimization in Algorithm for RIS-Assisted UAV-Enabled MEC-IoT Networks
Section: Articles Keywords: RIs, UaVs, MEC The combination of drones and smart reconfigurable surfaces (RISs) is becoming increasingly important for improving energy efficiency and wireless communication performance in Internet of Things (IoT) networks. This research focuses on developing an iterative optimization algorithm based on the fmincon algorithm in MATLAB. Sensing and transmission parameters are updated simultaneously at each iteration to achieve maximum energy efficiency. The algorithm starts with initial values. To optimize the energy efficiency of a system integrating a drone that provides mobile edge computing (MEC) services to IoT devices, the proposed system takes into account several critical factors, including drone trajectory optimization, optimal bit allocation between local and drone processing, and phase shift optimization in smart reconfigurable surfaces. The goal is to maximize overall energy efficiency by jointly optimizing these elements through a novel algorithm that alternates between optimizing the smart reconfigurable surfaces' phase shifts, the drone trajectory, and bit allocation. Simulation results demonstrate that the proposed solution significantly outperforms other measurement approaches in terms of energy efficiency, while examining the impact of variables such as the number of users, the reflectance elements of reconfigurable smart surfaces, and base station antennas on system performance. In conclusion, this research presents a novel approach to enhancing energy efficiency in RIS-enabled and drone-enabled MEC systems for IoT networks, achieving significant improvements over existing methods.
Impact Analysis of Energy-Efficient Demand-Side Management in Six-Bus Systems: Replacing Lighting Loads Based on Consumer Preferences
Section: Articles Keywords: opf, THD, Power Quality, CFL One of the most important strategies for improving power utilization and lowering total energy usage in distribution systems is energy-efficient Demand Side Management (DSM). Within a substation's load profile, this study assesses the effects of swapping out incandescent lights for consumer-preferred, energy efficient substitutes like LEDs and Compact Fluorescent Lamps (CFLs). Despite certain harmonic distortion issues related to non-linear lighting loads like CFLs, DSM techniques in particular, demand response strategies have been demonstrated to lower power costs, improve power factor, but increase Total Harmonic Distortion (THD). The study evaluates system metrics, including power, THD, power factor, and energy consumption, under different load combinations using MATLAB simulations. The findings of analyzing optimal power flow (OPF) on a six-bus network and modeling a balanced three-phase system show decreases in real power demand, operational losses, and system expenses. Additionally, the economic advantages of DSM interventions are highlighted by differences in Locational Marginal Pricing (LMP) among bus nodes. Results indicate that DSM techniques greatly improve system sustainability and efficiency in power distribution networks by implementing targeted load reduction and energy-efficient lighting.
Performance Analysis of a Microcontroller-Based Overvoltage and Overcurrent Protection System
Section: Articles Keywords: Microcontroller, Static Relay, Fault protection A microcontroller-based over-voltage and over-current monitoring and protection system has been developed to enhance the safety and reliability of electrical systems by automatically disconnecting the circuit under fault conditions. The system integrates a PIC18F2550 microcontroller, relay switches, transistors, current transformers (CT), potential transformers (PT), and other discrete components, with a Windows-based graphical user interface (GUI) programmed in Visual Basic and USB communication for real-time monitoring and control. The microcontroller processes voltage and current signals from the CT and PT, compares them against predefined threshold values, and activates the relay driver through a transistor when an over-voltage or over-current condition is detected, disconnecting the circuit after a brief delay to prevent damage. Experimental results validate the system's effectiveness; for example, it successfully triggered protection at 4.7 A against a 4.5 A current threshold and at 214 V against a 210 V voltage threshold. Various test scenarios confirmed the system’s reliability in identifying and responding to fault conditions. Compared to recent research, the proposed design demonstrates improved performance in detection speed, operational accuracy, and system simplicity, offering a robust and cost-effective solution for modern electrical protection needs.
Section: Editorial Keywords: Editorial, Table of Contents CHIEF EDITOR Prof. Dr. AHM Zahirul Alam, IIUM, Malaysia EXECUTIVE EDITOR Assoc. Prof. Dr. Muhammad Mahbubur Rashid, IIUM, Malaysia EDITORIAL BOARD MEMBERS Prof. Dr. Sheroz Khan Onaizah College of Engineering and Information Technology Saudi Arab Prof. Dr. AHM Asadul Huq Department of Electrical and Electronic Engineering Dhaka University, Bangladesh Prof. Dr. Pran Kanai Shaha Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology, Bangladesh Assoc. Prof. Dr. SMA Motakabber Faculty of Engineering International Islamic University Malaysia, Malaysia Prof. Dr. ABM Harun Ur Rashid Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology, Bangladesh Prof. Dr. Joarder Kamruzzaman Engineering and Information Technology Federal University, Australia Dr. Md Arafatur Rahman Reader in Cyber Security University of Wolverhampton, UK Aims & Scope of Asian Journal of Electrical and Electronic Engineering The Asian Journal of Electrical and Electronic Engineering (AJoEEE), published biannually (March and September), is a peer-reviewed open-access journal of the AlamBiblio Press. The Asian Journal of Electrical and Electronic Engineering (AJoEEE) publishes original research findings as regular papers, review papers (by invitation). The Journal provides a platform for Engineers, Researchers, Academicians, and Practitioners who are highly motivated to contribute to the Electrical and Electronics Engineering disciplines. It also welcomes contributions that address the developing world's specific challenges and address science and technology issues from a multidisciplinary perspective. Referees' Network All papers submitted to AJoEEE Journal will be subjected to a rigorous reviewing process through a worldwide network of specialised and competent referees. Each accepted paper should have at least two positive referees' assessments. Submission of a Manuscript A manuscript should be submitted online to the Asian Journal of Electrical and Electronic Engineering (AJoEEE) website https://alambiblio.com/ojs/index.php/ajoeee. Further correspondence on the status of the paper could be done through the journal website.
A Comparative Study Between Transformer Coupled Un-Tuned and Tuned Power Derivative Circuits for Ultrasonic Cleaning System
Section: Articles Keywords: Ultrasonic, Piezoelectric Effect, Ultrasonic Transducer , Megasonic Transducer This paper discusses the challenges in developing a high-efficiency driver circuit for an ultrasonic cleaning system. The main issue lies in finding the suitable driver circuit and drive signal to ensure the correct frequency level for the ultrasonic transducer. This difficulty affects the removal of contaminants from inaccessible areas due to the inability to control cavitation bubbles. To address these problems, the authors studied the behaviour of piezoelectric transducers and designed driver and output power circuits. The LTspice XVII software was used to design and simulate various circuits, and it was found that trapezoidal pulses were the most suitable drive signals for avoiding power loss and achieving reasonable efficiency. The generated output frequency of 38.6 kHz provides sufficient energy for cleaning various small metallic items like jewellery, wristwatches, glasses, etc. The findings of this study will be helpful for those conducting research in this area in the future.
A Systematic Review on Facial Detection and Recognition: Limitations and Opportunities
Section: Articles Keywords: Detection, Recognition, Masked, Facial, Artificial Intelligence, Algorithms. Face recognition technology is a biometric tool that identifies people by facial characteristics. Individuals collect the facial photography, which is then automatically processed by picture recognition software. Face detection and recognition have several potential applications in various departments like security, education, healthcare, etc. Therefore, the fundamentals and methods of broad facial detection and recognition have been discussed in this article. Owing to the outbreak of the pandemic, people are now required to wear masks so that the spreading of the coronavirus is prevented, which makes it challenging to monitor sizable crowds of mask-wearing individuals. Face masks have higher interclass similarities and interclass variability because they cover a significant portion of the face, fooling face recognition systems' facial verification process. Thus, this paper has also discussed various aspects of masked face recognition.
Detection, Recognition, Masked, Facial, Artificial Intelligence, Algorithms.
Securing the IoT Edge Devices Using Advanced Digital Technologies
Section: Articles Keywords: IoT, Edge computing, Cyberattacks, Artificial intelligence, Machine learning As the IoT ecosystem continues to grow, edge computing is becoming essential for handling and analyzing the vast amount of data generated by connected devices. Unlike traditional centralized data models, where information is sent to remote centers for processing, edge computing processes data closer to where it is generated. This decentralized approach helps reduce latency, optimizes bandwidth usage, and improves both privacy and security. However, the rise in IoT devices and the spread of edge computing also increase the potential for cyberattacks, demanding more robust security measures. With AI and machine learning being utilized to analyze IoT data, edge computing facilitates this analysis directly at the data source, pointing to a future where AI and ML applications are more prevalent on edge devices.
Copyright (c) 2021 AlamBiblilo Publishers https://creativecommons.org/licenses/by-nc/4.0
Ripeness Assessment and Quality Control of Mango Gold Susu using an E-Nose System
Section: Articles Keywords: e-nose, support vector machine, principal component analysis, VOC In this paper, the development and implementation of an electronic nose (e-nose) system utilizing the MQ sensor series from MOS-type gas sensors to classify mango gold susu ripeness is presented. The system's performance was enhanced through machine learning techniques, including Principal Component Analysis (PCA) for data dimensionality reduction and Support Vector Machine (SVM) for classification. The SVM classifier demonstrated high accuracy, particularly in identifying unripe and overripe mangoes, with accuracy scores of 1.00 and 0.99, respectively. A comprehensive database of volatile organic compound (VOC) profiles was established, leading to a precise prediction model for assessing the different stages of ripeness based on the mango’s VOC profile.
Improving Crowd Counting Performance: A Convolutional Neural Network Approach with Transfer Learning
Section: Articles Keywords: Crowd Counting, VGG16, Enhanced Performance, Smart Cities, Public Safety, Convolutional Neural Networks (CNNs). Precise crowd counting is critical to public safety and smart city planning since it solves the problems associated with the time-consuming manual counting of people in photos and videos. Transfer learning has become a key building block for improving crowd counting techniques, especially when used to Convolutional Neural Networks (CNNs). Because pretrained models already know the pertinent weights and architecture, using them in transfer learning minimizes computational demands and shortens training time. This paper presents a crowd counting method with an emphasis on optimizing the VGG16 model with a mall dataset. The results show that using VGG16 for transfer learning leads to higher performance when compared to more modern methods like AdaCrowd and PSSW models. In addition, the paper highlights how adaptable our proposed method is and how well it can transfer knowledge from one dataset to another.
Crowd Counting, VGG16, Enhanced Performance, Smart Cities, Public Safety, Convolutional Neural Networks (CNNs).
Improving the Privacy in Wireless-Enabled 5G Networks: A Lightweight Protocol for IIoT Communications
Section: Articles Keywords: Wireless, Privacy, VID, AI/ML, IIoT, Security threats, 5G networks The vision and major elements of the fifth generation (5G) ecosystem have previously been explored. We examine how security may impact the envisioned 5G wireless systems and the challenges and potential solutions to aid in these efforts and define the security and privacy aspects of 5G networks. 5G networks have provided solutions for quicker machine control, problem identification, performance analysis, and data access. Interaction between Internet of Things (IoT) nodes occur across an unsecured wireless channel, which has positive and negative effects. Despite being physically separated, unauthorized nodes could communicate via an unprotected wireless channel to gather data and take over industrial devices. Secure sessions can mitigate these risks, but it might be challenging to construct a secure session over a weak channel. To address this issue, the Variable Identification (VID) is used. VID offers a simple key exchange platform to authorized Industry Internet of Things (IIoT) nodes while guarding against unauthorized use. The lightweight changeable pseudonyms used by VID for trust-building are selected at random from a pool discovered in the home network and terminal devices. All IDs are chosen at random from a pool and are used to protect data against forgery, replay, alteration, impersonation, and man-in-the-middle attacks, among other things, between the home network and terminal equipment. The ProVerif tool is used to evaluate the suggested system, and the findings demonstrate that it is trustworthy and resistant to prospective attacks.
Evaluation of Classification Algorithms in Tracing Malicious Telephone Numbers
Section: Articles Keywords: Telecommunications, classification, machine learning, network Mobile phones and telecommunications networks have recently played an important role in modern society. They are dispensable parts of our lives as they facilitate the way we communicate. However, apart from their benefit, their proliferation has some drawbacks as telephone networks can be exploited. For example, commercial calls can be made repeatedly to advertise companies’ products. These calls annoy customers because they promote products without considering customers’ interests. These unexpected calls not only cause a negative impact on the networks but also disturb mobile phone users. To confront this problem, the network administrators need some methods to detect the phone numbers that are used to make the harassment. Therefore, we proposed a solution based on machine learning classification models. Then the performance of some models, namely K-Nearest Neighbors, Decision Tree, and Logistic Regression, is compared. By applying the machine learning models, network administrators can identify and restrict malicious telephone numbers.