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5th International Conference on Emerging Electrical Energy, electronics and Computing Technologies, ICE4CT 2024

作者： Rahman, Juwairiyyah Abdul Mugunthan, Sivaguru A.L. Hassan, Hasliza Abu Muslim, Norliana Ab-Rahman, Mohammad Syuhaimi Department of Electrical Engineering Faculty of Engineering and Life Sciences Universiti Selangor Selangor Bestari Jaya45600 Malaysia Department of Computer and Communication Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Perak Kampar31900 Malaysia Department of Electrical Electronics and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Malaysia Electrical and Computer Engineering Program Faculty of Engineering Sohar University PC:311 Sohar Oman

This study aims to increase the number of access users by limiting the sample size to 30 users while ensuring that every Optical Network Unit (ONU) receives data from the Optical Line Terminal (OLT). The proposed solution allows for the determination of power requirements at each ONU based on bus-star topologies, facilitating an increase in user capacity. The power received by each ONU is a critical parameter that system designers must consider when planning the implementation of Fiber to the Home (FTTH), as it impacts the output data in terms of Bit Error Rate (BER) and Signal Quality Factor (QF) for each user. Various parameters were evaluated to ensure that the enhanced network improves scalability while adhering to standard specifications. These parameters included the Q-factor, BER, eye diagram, user count, and received power. The study employed slicing and duplication techniques, utilizing Arrayed Waveguide Grating (AWG) for signal duplication and Wavelength Division Multiplexing for signal slicing. The simulations were conducted using a transmitter operating at a frequency of 1491 nm with a power output of 0 dBm, utilizing the latest version of OptiSystem 20 software. The results highlighted the system's capability to support a total of 73,728 users, meeting stringent performance criteria. Specifically, the system achieved a Q-factor greater than 6, indicating high signal quality, and a bit error rate (BER) lower than 1 × 10-9, ensuring reliable data transmission. For the sampled 30 users, the average Q factor was 8.1527, and the average BER was 5.3648 × 10^-11, with an output power level of -54.278 dBm. Despite an increase in users, the quality of the data supplied to clients remained unchanged. These outcomes underscore the robustness and efficiency of the design in delivering high-capacity, error-resilient optical communication. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Q factor measurement

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Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus

Prediction model using SMOTE, genetic algorithm and decision...

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作者： Azad, Chandrashekhar Bhushan, Bharat Sharma, Rohit Shankar, Achyut Singh, Krishna Kant Khamparia, Aditya Department of Computer Applications National Institute of Technology Jamshedpur India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Electronics & Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology NCR Campus Delhi- NCR Campus Ghaziabad India Department of CSE ASET Amity University Noida201301 India Bengaluru India School of Computer Science and Engineering Lovely Professional University Punjab India

Diabetes mellitus is a well-known chronic disease that diminishes the insulin producing capability of the human body. This results in high blood sugar level which might lead to various complications such as eye damage, nerve damage, cardiovascular damage, kidney damage and stroke. Although diabetes has attracted huge research attention, the overall performance of such medical disease classification using machine learning techniques is relatively low, majorly due to existence of class imbalance and missing values in the data. In this paper, we propose a novel Prediction Model using Synthetic Minority Oversampling Technique, Genetic Algorithm and Decision Tree (PMSGD) for Classification of Diabetes Mellitus on Pima Indians Diabetes Database (PIDD) dataset. The framework of the proposed PMSGD prediction model is composed of four different layers. The first layer is the pre-processing layer which is responsible for handling missing values, detection of outlier and oversampling the minority class. In the second layer, the most significant features are selected using correlation and genetic algorithm. In the third layer, the proposed model is trained, and its effectiveness is evaluated in the fourth layer in terms of classification accuracy (CA), classification error (CE), precision, recall (sensitivity), measure (FM), and Area_Under_ROC (AUROC). The proposed PMSGD algorithm clearly outperforms its counterparts and achieves a remarkable accuracy of 82.1256%. The best outcome achieved by the proposed system in terms of CA, CE, precision, sensitivity, FM and AUROC is 82.1256%, 17.8744%, 0.8070%, 0.8598, 0.8326 and 0.8511, respectively. The obtained simulation results show the effectiveness and superiority of our proposed PMSGD model and their by reduced error rate to help in decision-making process. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision trees

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Design of a Forked Eander Slot Antenna Suitable for LTE 5G Frequency Band 7

Design of a Forked Eander Slot Antenna Suitable for LTE 5G F...

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7th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2024

作者： Xiao, Gengbo Feng, Botao Ding, Xiao Deng, Li Gu, Wenzhe Zeng, Qingsheng Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China Huizhou University School of Computer Science and Engineering Huizhou516007 China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing210016 China

ISBN: (纸本)9798350384437

In this paper, an improved design of a miniaturized, broadband, meander fork-shaped microstrip antenna (MFSMA) with a radiating patch is proposed. By folding the elongated patch, the bandwidth is widened, while also solving the problem of increased board size and higher manufacturing costs caused by an overly long patch. Adding the asymmetric patch to the ground plane causes the lower edge of the bandwidth to decrease. Utilizing the above characteristics, a miniaturized antenna model was designed. The measurement results of this antenna show acceptable radiation characteristics, with a bandwidth of 1.31 GHz - 3.74 GHz (96.24%) in the condition of VSWR © 2024 IEEE.

关键词： Slot antennas

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Traffic Localization in Smart Cities Using Internet of Crowd Sourcing

Traffic Localization in Smart Cities Using Internet of Crowd...

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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Razan A. M Elnour Mamoon M. Saeed Ibtihal Yousif Rania A. Mokhtar Electronics Engineering School Sudan University of Science and Technology (SUST) Khartoum Sudan Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen Dept. of Computer Engineering Taif University Taif KSA

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

This paper aims to examine how IoT localization technologies have impacted on the smart city traffic application including Internet Vehicles (IoV), Internet of Crowed Sourcing (IoCS), Internet of Mobility (IoM), and the Internet of Artificial Intelligence (IoAI). IoV improves car to car and car to infrastructure interfaces by increasing traffic flow and reducing or eliminating risks. IoAI employs big data and machine learning technology to accurately capture traffic information and to timely signal appropriate traffic lights for better and safe navigation of self-driven cars. IoCS extending it to Crowdsourcing infrastructure to real-time data systems to improve traffic control and safety of pedestrians. IoM develops a single unified network of transportation opportunities incorporating both mass transit and personal vehicle modes, as well as micro-mobility solutions. Potential issues like data protection, integration, and system extensibility are also discussed throughout the paper, together with potential advancements in autonomous vehicles and artificial intelligence traffic management. Lastly, the research discusses the prospects of IoT localization for Smart City transportation developments. These systems integrating services like sensors, GPS, RFID and AI based models provide real time navigation, thus resulting in efficient traffic control and enhancing the prospects of developing sustainable urban environments.

关键词： Location awareness Smart cities Transportation Traffic control Real-time systems Internet Safety Internet of Things Automobiles Traffic congestion

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Optimization of Base Station Placement in 4G LTE Broadband Networks Using Adaptive Variable Length Genetic Algorithm

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SN computer Science 2023年第2期4卷 121页

作者： Nwelih, Emmanuel Isabona, Joseph Imoize, Agbotiname Lucky Department of Computer Science University of Benin Benin City Nigeria Department of Physics Federal University Lokoja P.M.B 1154 Lokoja Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum 44801 Germany

The growing subscriptions for cellular communication services have been remarkable globally. This development has placed immense pressure on the network providers to optimally position the radio transmitters to deliver quality services to mobile subscribers. The conventional cellular network planning techniques are prone to errors and incapable of guaranteeing the desired quality of service at user equipment terminals. To address this problem, meta-heuristic evolutionary algorithms have been employed to boost the radio transmitter placement for optimal performance. However, the meta-heuristic evolutionary algorithms require experimental measurements for testing and validation. This paper uses a field measurement-based genetic algorithms approach to optimize base station placement in cellular networks. The proposed method explores the combined impact of strong cellular networks influencing parameters, such as capacity, coverage, and transmit power in the base station placement process. Field signal strength measurement was conducted to examine the propagation loss predictive performance of the referenced COST 231 Hata model. This approach provided the means to determine the cell coverage radius and the minimum base station number needed for model optimization. Higher order sectorization (considering six and 12 sectors per site) on cellular network capacity was employed. Generally, results revealed that the system capacity increases compared to the standard approach with three sectors per site. The adaptive variable-length genetic algorithm approach was employed with a weighted fitness function that combines coverage, capacity, and transmit power parameters. The proposed approach outperforms the conventional analytical method based on the referenced COST 231 Hata model in convergence speed, computational efficiency, and algorithm reliability. Improved performance with the projected approach was demonstrated over the benchmarked particle swarm optimization approach. ©

关键词： Cellular networks COST 231 Hata model Field measurements Genetic algorithm Optimal base station placement Swarm particle optimization

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Optimizing AODV Routing Protocol to Improve Quality of Service Performance for V2V Communication

Optimizing AODV Routing Protocol to Improve Quality of Servi...

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Smart Computing, IoT and Machine Learning (SIML), International Conference on

作者： Ketut Bayu Yogha Bintoro Tri Kuntoro Priyambodo Muhammad Nurul Musthofa Dept.of Computer Science and Electronics Faculty of Mathematics and Computer Sciences Gadjah Mada University Yogyakarta Indonesia Dept. of Informatic Engineering Faculty of Sciences Technology and Design Universitas Trilogi Jakarta Indonesia

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

The study focuses on Quality of Service (QoS) issues in learning automata-based AODV (LA-AODV) for V2V communication protocols. It analyzes and compares the LAAODV protocol with the original AODV protocol in dynamic traffic situations. The study evaluates critical metrics such as the number of vehicles, average speed, congestion spots, and vehicle time loss across various traffic scenarios, including smooth traffic, steady flow traffic, and heavy traffic. NS3 and SUMO are used as essential tools for this evaluation. The study finds that the LA-AODV protocol performs better in smooth traffic conditions than AODV. It demonstrates a $38.27 \%$ higher packet delivery ratio (PDR), an $\mathbf{8 5 . 2 6 \%}$ lower packet loss ratio (PLR), and a remarkable $56.62 \%$ increase in average throughput compared to AODV. Both protocols exhibit similar performance in steady flow traffic with high PDRs $(97.60 \%$ and $96.87 \%$, respectively) and low PLRs ($2 \%$ and $3 \%$, respectively). However, AODV outshines LA-AODV with a higher PDR $\mathbf{(} \mathbf{9 7 . 6 0 \% )}$) than LA-AODV $(\mathbf{9 6 . 8 7 \%}$) in heavy traffic. LA-AODV, on the other hand, excels in average throughput by $17.24 \%$ in heavy traffic. The study offers valuable insights for improving V2V communication protocols and enhancing communication efficiency to handle dynamic and traffic situations.

关键词： Protocols Mobility models Urban areas Packet loss Quality of service Machine learning Throughput

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Impact of Aging on Dielectric Properties of Oil-Paper Insulation System using Gas-to-Liquid Oil and Mineral Oil 23

Impact of Aging on Dielectric Properties of Oil-Paper Insula...

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23rd International Middle East Power Systems Conference, MEPCON 2022

作者： Emara, Manal M. Atiya, Eman G. Mansour, Diaa-Eldin A. Kafrelsheikh University Faculty of Engineering Department of Electrical Engineering Kafrelsheikh Egypt Tanta University Faculty of Engineering Department of Electrical Power and Machines Engineering Tanta Egypt School of Electronics Communications and Computer Engineering Electrical Power Engineering Department Egypt-Japan University of Science and Technology Alexandria Egypt Tanta University Faculty of Engineering Electrical Power and Machines Engineering Department Tanta Egypt

ISBN: (纸本)9781665463638

Lately, gas-to-liquid (GTL) oil usage has a great trend as cooling and dielectric medium in power transformers due to their good properties, represented by its purity, chemical uniformity, and better performance than conventional oils. By the way, the study of GTL oil effect in comparison to conventional mineral oil (MO) on the electrical properties of the insulation paper under thermal aging is presented in this paper. Thermal aging samples were obtained based on an accelerated thermal aging test in the laboratory, that simulates the thermal stress in the service. Two different aging durations (i.e., 3 and 6 days) are applied to the two different oil types. Pure oils and aged oil samples were tested for dielectric losses and dielectric constant for a range of frequencies from 20 Hz to 2 MHz. In addition, insulation papers were impregnated in the same samples to check their effect by thermal aging. The results indicated that aging has a lower impact on the properties of GTL oil and GTL oil-paper insulation under aging conditions compared to that of MO. © 2022 IEEE.

关键词： Thermal aging

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Compact Physical and Electrical Patch Antenna Engineered for 5G Mobile Devices and Multiband Systems

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Progress in Electromagnetics Research B 2025年 111卷 71-81页

作者： Jemaludin, Nazrin Haziq Al-Gburi, Ahmed Jamal Abdullah Abdulhameed, Muhannad K. Hashim, Sarah R. Mohammed, Dunya Zeki Saeidi, Tale Gupta, Anupma Zakaria, Zahriladha Nurhayati, Nurhayati Elabd, Rania Hamdy Jalan Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia Computer Scince Department University of Kerbala Karbala Iraq Air Conditioning Engineering Department Faculty of Engineering Warith Al-Anbiyaa University Iraq Department of Cyber Security Al-Zahraa University for Women Karbala Iraq Department of Electronic and Communications Engineering Gilgamesh University Baghdad Iraq Co. Donegal LetterkennyF92 FC93 Ireland Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamilnadu Chennai602105 India Department of Electrical Engineering Universitas Negeri Surabaya Surabaya60231 Indonesia Department Electronic and Communication Higher Institute of Engineering and Technology in New Damietta Damietta Egypt

This article presents a compact multi-band microstrip patch antenna designed for 5G, Ku, and K-band applications. The antenna operates at 3.5 GHz and 15.6 GHz, supporting 5G communications (3.3–3.6 GHz) and satellite applications (15.6–20 GHz). Fabricated on an FR4 substrate (Εr = 4.3, tan δ = 0.025) with dimensions of 13 × 10 × 1.6 mm3 (0.15λo × 0.12λo × 0.02λo), where λo represents the wavelength at the lower frequency of 3.5 GHz, the antenna achieves return losses of −19 dB and −39 dB at the respective frequencies, with peak gains of −2.8 dBi and 3.7 dBi. The design’s consistency is validated through a comparative analysis with recent studies. The antenna was placed near the ear and mouth area of a human head phantom model to perform a comprehensive SAR analysis. SAR analysis confirms compliance with safety standards, maintaining SAR levels below 2 W/kg. The proposed design demonstrates promising performance for modern communication systems. © (2024), (Electromagnetics Academy). All Rights Reserved.

关键词： Slot antennas

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An Efficient Wireless Sensor Network based Intrusion Detection System 2

An Efficient Wireless Sensor Network based Intrusion Detecti...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Rajassekharan, Dinesh Ramachandran, V. Sharma, Dilip Kumar Palanisamy, Thirumoorthy Myilsam, Y. Varatharaj Poonia, Ramesh Chandra Peninsula College Faculty of Computer Science Malaysia Gitam School of Computing Gitam Deemed to Be University Department of Computer Science and Engineering Bengaluru561203 India Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India Nandha Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India V.S.B College of Engineering Department of Electrical and Electronics Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Computer Science Delhi-NCR Uttar Pradesh Ghaziabad201003 India

ISBN: (纸本)9798350369960

Wireless Sensor Networks (WSNs) are widely used in diverse applications due to their cost-effectiveness and versatility. However, they face substantial difficulties because of their innate resource constraints and susceptibility to security attacks. A possible method to improve the security of WSNs is clustering-based intrusion detection and responding mechanisms. An in-depth analysis of the clustering-based intrusion detection and response method for WSNs is presented in this study. The suggested method efficiently uses data mining and machine learning techniques to identify unusual behaviour and probable intrusions. The system effectively analyses data inside clusters by grouping Sensor Nodes (SN) into clusters, allowing it to differentiate between legitimate patterns and insecure activity. The network may respond promptly to identified breaches and react to the responsive mechanism, which reduces their impact and protects network integrity. The proposed Mathematically Modified Gene Populated Spectral Clustering Based Intrusion Detection System and Responsive Mechanism (MMMMGPSC-IDS-RM) is compared with existing state-of-art techniques, and MMMMGPSC-IDS-RM outperforms with the highest detection rate of 96%. © 2023 IEEE.

关键词： IDS machine learning responsive mechanism Spectral clustering WSN

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CFOL: A Superficial Machine Learning Methodology to Identify Malwares by Using Convolutional Fuzzy Optimization Logic

CFOL: A Superficial Machine Learning Methodology to Identify...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Suriya, N. Sowmya, M. Priyanka, P. Rajesh, K. Edlabadkar, A.P. Abbas, N. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Qis College of Engineering and Technology Department of Mba Pondur Road Vengamukkapalem Ongole Andhra Pradesh Prakasam District523272 India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole AndhraPradesh Prakasam District523272 India Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Economics and Administrative Sciences Department of economic Zarqa Jordan College of Business Administration University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Malware poses a significant threat to the security and integrity of modern computer systems. Traditional malware detection methods, such as signature-based approaches, struggle to keep pace with the rapid evolution of malware. This paper presents Convolutional Fuzzy Optimization Logic (CFOL), a novel machine learning-based methodology for malware detection that leverages Convolutional Neural Networks (CNNs) enhanced with an Attention Mechanism. Data was collected from Kaggle's malware dataset, which was preprocessed to extract key features like file headers, API calls, and metadata. The CNN with Attention Mechanism demonstrated an accuracy of 97.80%, outperforming traditional models such as Random Forest and Support Vector Machines (SVM), which achieved accuracies of 91.3% and 89.6%, respectively. The Attention Mechanism allowed the model to prioritize the most critical malware features, significantly reducing the false positive rate (FPR) to 2.2%, one of the lowest among the tested models. Additionally, the system achieved high precision (98.2%), recall (97.1%), and F1 score (97.35%). The integration of fuzzy logic optimization further enhanced the decision-making process in cases of ambiguous classifications. The results validate the effectiveness of CFOL in detecting complex and evolving malware with high precision and minimal false positives. © 2024 IEEE.

关键词： Convolutional neural networks

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