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6th IEEE International Conference on electronics and Communication engineering, ICECE 2023

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Kharel, Rupak Chongqing University School of Microelectronics and Communication Engineering Chongqing China University of Central Lancashire National Yunlin University of Science and Technology Preston United Kingdom School of Psychology and Computer Science Graduate School of Intelligent Data Science Yunlin64002 Taiwan University of Central Lancashire Ton Duc Thang University Preston United Kingdom School of Psychology and Computer Science Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350328721

Vehicle ad-hoc networks (VANETs) have experienced rapid growth due to the advancement of cloud computing, IoT technologies, and intelligent transportation systems (ITS). Vehicles are required to have enhanced storage capacity, onboard computing capabilities, improved sensing power, and communication systems. To address real-world demands like low latency, affordable storage, and mobility in VANET deployments, There have been efforts to integrate fog computing with VANETs in a practical implementation. "An Efficient and Provably Secure Authenticated Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks"was proposed by Ma et al. (IEEE Internet of Things Journal, pp 8065-8075, 10.1109/JIOT.2019.2902840). According to their claims, the use of their secure authentication technique can help prevent security threats. However, after careful investigation, we discovered that their authentication protocol is susceptible to vehicle user impersonation attacks and also does not provide vehicle anonymity. In light of this, we have provided some recommendations to address the current flaws in the protocol developed by Ma et al. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Protecting Vulnerable Road users using IoT-CNN for Safety Measures

Protecting Vulnerable Road users using IoT-CNN for Safety Me...

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electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： R C Karpagalakshmi M Maria Sampoornam Viyyapu Lokeshwari Vinya Monikapreethi S K Yuvaraj S C. Srinivasan Alliance College of Engineering and Design Alliance University Bengaluru Karnataka India Department of Information Technology J.J College of Engineering and Technology Tiruchirappalli Tamil Nadu India Department of Computer Science and Engineering Vardhaman College of Engineering Ranga Reddy Telangana India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The increasing number of road accidents involving Vulnerable Road Users (VRUs) highlights the urgent need for innovative safety solutions. This study proposes a novel approach that leverages IoT and Convolutional Neural Network (CNN) technologies to enhance VRU safety. By deploying IoT sensors on vehicles and infrastructure, the proposed model can accurately track VRU locations and environmental conditions in real-time. A CNN-based model is developed to robustly detect and classify VRUs under various traffic scenarios, with continuous improvement through learning. The integration of IoT and CNN enables real-time communication and timely warnings, facilitating proactive interventions to prevent accidents. Experimental evaluations demonstrate the effectiveness of the proposed system in improving VRU safety. This research offers a promising solution for reducing road accidents and creating safer urban environments.

关键词： Technological innovation Road accidents Urban areas Transportation Road safety Real-time systems Sensors Convolutional neural networks Stakeholders Protection

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Image Acquisition and Deep Convolutional Neural Network Incorporated Cost-Effective Automated Nursery Disease Detection Method for the Tea Industry

Image Acquisition and Deep Convolutional Neural Network Inco...

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Moratuwa engineering Research Conference (MERCon)

作者： Dinithi Bhagya Weerasekara Nipun Shantha Kahatapitiya Nuwan Sanjeewa Vimukthi Madushan Wathudura Nuwan Prasanna Deshan Kalupahana Udaya Wijenayake Bhagya Nathali Silva Akila Wijethunge Ruchire Eranga Wijesinghe Department of Materials and Mechanical Technology Faculty of Technology University of Sri Jayewardenepura Pitipana Sri Lanka Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka IOT Solutions Pvt Ltd Homagama Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics & Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331529048

ISBN: (纸本)9798331529055

Tea cultivation, a globally significant industry across over 60 countries, requires effective management of tea nurseries for robust plant growth and disease control. Implementing automated disease detection in tea nurseries using advanced technologies enhances efficiency, precision, and sustainability compared to manual inspection methods. An image acquisition system incorporating a Deep Convolutional Neural Network (DCNN) was developed as a cost-effective automated solution for nursery disease detection. First, images of Blister Blight (BB) disease in tea plants were collected primarily from various estates. These images were then preprocessed, labeled, and augmented to create a robust dataset. Two models based on the YOLOv5 architecture were trained, with the second model benefiting from dataset augmentation and enhanced labeling. The Improved Model achieved a mean Average Precision (mAP) of 80.6% at a 0.5 threshold, demonstrating its high accuracy in BB detection in tea nurseries. It was integrated with the image acquisition system to enable continuous monitoring of tea leaves in nursery plantations and facilitate the immediate detection of BB diseases. This system holds significant potential for further enhancement in disease management across the tea industry, promoting healthier crops, reducing costs, and minimizing environmental impact.

关键词： Industries YOLO Costs Accuracy Plantations Crops Real-time systems Convolutional neural networks Monitoring Diseases

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A new design of a digital filter for an efficient field programmable gate array using quantum dot technology

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Materials Science and engineering: B 2024年 300卷

作者： Taghavirashidizadeh, Ali Ahmadpour, Seyed-Sajad Ahmed, Suhaib Jafari Navimipour, Nima Ramkrishna Kassa, Sankit Yalcin, Senay Tehran Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Chitkara University Institute of Engineering and Technology Punjab India Department of Electronics and Communication Engineering Model Institute of Engineering and Technology Jammu India Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Pune India Department of Energy System Engineering School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

Digital filtering algorithms are most frequently used to implement generic-based Field-programmable gate arrays (FPGAs) chips, which are used for higher sampling rates. In the filtering structure, delay and occupied areas play a vital role. Since the existing structures suffered from shortcomings such as high delay and high occupied area, implementing a high-performance digital filter circuit with high speed and low occupied area based on unique technology can significantly improve the performance of whole FPGA structures. One of the best technologies to implement this vital structure to solve these shortcomings is quantum-dot cellular automata (QCA) technology. This paper presents several new efficient full adders for digital filter applications based on quantum technology, including a multiplier, AND gate, and accumulator. The QCADesigner 2.0.3 tool is used to create and validate the suggested designs. According to the results, all designed circuits have simple structures with few quantum cells, low area, and low latency. © 2023

关键词： Nanocrystals

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Air Pollutant (CO) Forecasting: A Hybrid Approach Using Gooseneck Barnacle Optimization and Artificial Neural Networks

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Goos...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Marzia Ahmed Ahmad Johari Mohamad Md Maruf Hassan Touhid Bhuiyan Department of Software Engineering Daffodil International University (DIU) Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Malaysia Department of Computer Science and Engineering Southeast University Dhaka Bangladesh School of IT Washington University of Science and Technology (WUST) Virginia USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN’s hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model’s potential for accurate real-time air quality forecasting, offering a robust, sustainable solution.

关键词： Accuracy Atmospheric modeling Artificial neural networks Predictive models Air quality Data models Real-time systems Environmental monitoring Forecasting Public healthcare

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Enhancing Student Performance Prediction with Greylag Goose Optimization Algorithm

Enhancing Student Performance Prediction with Greylag Goose ...

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Telecommunications Conference (ITC-Egypt), International

作者： Faris H. Rizk Mahmoud Elshabrawy Mohamed Basant Sameh Ahmed Mohamed Zaki Marwa M. Eid El-Sayed M. El-kenawy Computer Science and Intelligent Systems Research Center Blacksburg Virginia USA Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

The current paper addresses the central role of hyperparameter optimization in improving the predictive power of the MLP Regressor for forecasting student performance in Portuguese secondary schools. The uniqueness of this research lies in its exploration of metaheuristic optimization algorithms, specifically highlighting GGO (Greylag Goose Optimization) for enhancement. The study utilized a dataset crucial for understanding and predicting student performance, with a special focus on its distinctive features. By comprehensively tuning the MLP Regressor, the paper demonstrates remarkable improvements in various performance measures, as evident in the enclosed tables. Specifically, the MSE values calculated for the MLP Regressor both before and after GGO optimization are compared. Without optimization, the MLP Regressor had an MSE of 0.0103. After GGO optimization, the MSE significantly improved to 0.0060, indicating enhanced accuracy with GGO in the model. These findings emphasize that hyperparameter optimization, particularly the GGO technique, is crucial for refining the MLP Regressor in predicting student performance. The paper not only delves into the technical aspects but also concludes by highlighting the broader consequences of these outcomes. The potential educational applications are substantial, as improved models can provide more accurate predictions, empowering educators and policymakers to make informed decisions in education. This paper establishes a foundation for future research directions, contributing to the pool of ideas for educational predictive modeling.

关键词： Accuracy Education Refining Metaheuristics Predictive models Hyperparameter optimization Prediction algorithms

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A Dual-Band Frequency Selective Surface with Angular Stability at X-band

A Dual-Band Frequency Selective Surface with Angular Stabili...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Lin Du Botao Feng Xiao Ding Li Deng Wenzhe Gu Qingsheng Zeng College of Electronics and Information Engineering Shenzhen University Shenzhen China Faculty of Humanities and Arts Macau University of Science and Technology Macau China College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering Huizhou University Huizhou China Department of Computer and Engineering University of Quebec in Outaouais Quebec Canada

ISBN: (数字)9798331512620

ISBN: (纸本)9798331512637

This paper proposes a dual-band frequency selective surface (FSS) design. To achieve dual-band functionality, two similarly shaped elements with different sizes are staggered on the substrate. The filtering response is created through strategically designed slots on the middle layer. Three different FSS configurations are designed and analyzed. The inclusion of arms and stubs enhances isolation between the staggered elements, reducing magnetic coupling and strengthening electric coupling, which improves angular stability. The proposed design shows transmission zeros at 7.75 GHz, 9.6 GHz, and 11.75 GHz, with a suppression level of -20 dB, demonstrating its effectiveness in frequency-selective applications.

关键词： Couplings Frequency selective surfaces Program processors Filtering Magnetic separation Dual band Stability analysis Filtering theory Substrates Magnetic properties

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Exploring Students' Attitudes Towards Robotics technology in STEM Education: A Machine Learning-Based Prediction Analysis

Exploring Students' Attitudes Towards Robotics Technology in...

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Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Mahmoud I. Abd El Aziz Ahmed M. Elshewey Zahraa Tarek El-Sayed M. El-kenawy Ahmed M. Ghazy Roheet Bhatnagar Mahmoud Y. Shams Department of Curriculum Instruction and Educational Technology Faculty of Education Kafrelsheikh University Kafrelsheikh Egypt Computer Science Department Faculty of Computers and Information Suez University Suez Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura Egypt Dept. of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Department of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheiku University Kafrelsheikh University Egypt

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

The aim of this paper is to predict the extent to which students will accept robotics technology in future teaching plans. The study involves students engaging in a two-week project-based learning program on robotics technology, after which they will decide whether it is suitable for them. To achieve this, the paper uses several prediction algorithms based on Machine Learning (ML), including Linear Regression (LR), Ridge Regression (RR), Elastic Net (EN) regression, and K-Nearest Neighbor (KNN) models. The sample group consists of 35 students from STEM high schools in Egypt. The research findings indicate that the students have gained confidence and satisfaction in robotics technology and believe it will have significant value in the future. The prediction results obtained from the ML regression models support these findings. The dataset used in the study includes seven input features and one target output representing the total prediction values of accepting and rejecting robotics technology in STEM schools. The regression models LR, RR, EN, and KNN achieve determination coefficients (R2) of 99.99%, 98.80%,99.20%, and 85.60%, respectively.

关键词： Machine learning algorithms Computational modeling Linear regression Education Machine learning Nearest neighbor methods Predictive models Prediction algorithms Robots STEM

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Object Acquizition of Stationary and Moving Targets

Object Acquizition of Stationary and Moving Targets

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International Conference on Modern Problems of Radio engineering, Telecommunications and computer Science

作者： Myroslav Riabyi Sergey Edward Lyshevski Roman Odarchenko Faculty of Computer Sciences and Technologies National Aviation University Kyiv Ukraine Department of Electrical and Microelectronic Engineering Rochester Institute of Technology Rochester NY USA Faculty of Air Navigation Electronics and Telecommunications National Aviation University Kyiv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

This paper studies near real time obj ect acquisition by means of detection, identification and engagement on stationary, moving and maneuvering targets in the air and land domains. Using the streamed videos from monochrome and color cameras, a machine vision concept accomplishes object detection by recognizing the object-specific edges and shapes which yield unique geometric features. Object recognition with descriptive identification at reasonable accuracy is performed using the Jaccard index. The region proposal detectors with the intersection over union is implemented by a convolutional neural network. The machine learning models, implemented by the low power system-on-module, were trained. These machine learning algorithms and models support feature detection and matching, localization, and, three-dimensional spatiotemporal reconstruction. The two-and three-dimensional geometry with object and background contrast and color mappings are considered. Subsequent tracking and locking on maneuverable targets is performed using spatio-temporal three-dimensional analytics. The airborne machine acquisition module with trained models is demonstrated. A concept suits monochrome, color and thermal analog and digital cameras. Experiments in typified operational environments demonstrate acceptable accuracy, convergence, low latency and near real-time capabilities for different object categories and classes within developed custom datasets.

关键词： Accuracy Target tracking Atmospheric modeling Color Machine learning Real-time systems Thermal analysis Telecommunications Object recognition Videos

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Energy Consumption for Cognitive Radio Network Enabled Multi-Access Edge Computing

Energy Consumption for Cognitive Radio Network Enabled Multi...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Areeg Ali Elnaim Amin Babeker Mohammed Barakat Abdulguddoos S.A. Gaid Mamoon M. Saeed Zainab. E. Ahmed Rashid A. Saeed College of Engineering and Communication Alneelain University Khartoum Sudan School of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Dept. of Communication & Computer Engineering Taiz University Taiz Yemen Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen Department of Electrical and Computer Engineering Gezira University Khartoum Sudan Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi

This article discusses optimizing energy Consumption in CRN-enabled Multi-access Edge Computing (MEC) systems, presenting a novel approach to optimize the energy Consumption of a cognitive radio network-enabled MEC system using iterative optimization techniques. The proposed model aims to minimize the system's energy consumption while maintaining its performance and quality of service requirements. The iterative optimization algorithm is used to find the optimal allocation of resources, such as computing, communication, and power, among different nodes in the MEC system. As a result, the cognitive radio network enables efficient spectrum utilization by dynamically allocating available spectrum to different users based on their requirements. The performance evaluation approach is evaluated through simulations, and the results show that it can significantly improve the energy consumption of the MEC system while maintaining its performance and QoS requirements. This article contributes to developing sustainable and energy-efficient communication systems for future wireless networks.

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