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20th International Joint Conference on computer science and Software engineering, JCSSE 2023

作者： Gidvarowart, Supagid Suchato, Atiwong Wanvarie, Dittaya Pratanwanich, Naruemon Tuaycharoen, Nuengwong Chulalongkorn University Faculty of Engineering Department of Computer Engineering Bangkok Thailand Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand

ISBN: (纸本)9798350300505

This case study uses the Karate Framework to reduce the time and resources required for testing an application programming interface (API) for an Online Assessment Web Application, which is part of a continuous development process involving numerous complex features and test cases challenging to perform tests manually. The study aims to verify the correctness of the API's operation and facilitate regression testing during the development process to promptly detect errors. The study involved the testing of 162 test cases using the Karate Framework. Additionally, software quality assurance reports were created to confirm the quality and effectiveness of the Karate Framework in reducing manual testing time by over several hours compared with manual testing. Also, it is more effective than Robot Framework due to Karate Framework's capability of parallel threads. The findings suggest that using the Karate Framework can potentially optimize API testing processes of the case study. © 2023 IEEE.

关键词： Software testing

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A Deep Learning Approach for Detecting Diabetic Retinopathy

A Deep Learning Approach for Detecting Diabetic Retinopathy

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2024 International Conference on computer and Applications, ICCA 2024

作者： Nabil, Mohamed Hossam Hassan Hammam, Laila Bastawrous, Hany Ayad Ebrahim, Gamal A. The British University in Egypt Electrical Engineering Department Cairo Egypt Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Ain Shams University Faculty of Engineering Computer and Systems Engineerig Department Cairo Egypt

ISBN: (纸本)9798350367560

The World Health Organization (WHO) reports that diabetic retinopathy affects one-third of diabetics, regardless of their stage of the disease. Several research efforts are focused on its automated detection and diagnosis. Identifying diabetic retinopathy is crucial due to the damage that occurs to the blood vessels of the eye retina, leading to vision blur or even complete blindness. Thus, an annual checkup is needed for people with diabetes. Moreover, uncontrolled sugar levels for diabetes patients could worsen the current stage of diabetic retinopathy. Consequently, automated detection can greatly contribute to the treatment of disease. This can be carried out through several algorithms, including deep learning models and support vector machines, in addition to transfer learning. This contribution proposes a new approach for diabetic retinopathy automated detection based on convolutional neural network (CNN) models. The proposed model provides both binary and multi-class detection. Both scenarios have shown promising results, where the training accuracies of both the binary classification and the multi-class are 92% and 94%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Pager Explosion:Cybersecurity Insights and Afterthoughts

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2359-2362页

作者： Chuan Sheng Wanlun Ma Qing-Long Han Wei Zhou Xiaogang Zhu Sheng Wen Yang Xiang Fei-Yue Wang Swinburne University of Technology Hawthorn VIC 3122Australia IEEE the School of Computer and Mathematical Sciences the University of AdelaideAdelaideSouth Australia 5005Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

PAGER EXPLOSION:THE *** 2024 Lebanon pager explosions represent one of the most unexpected and devastating technological incidents in recent *** September 17 and 18,2024,thousands of pagers and walkie-talkies exploded simultaneously across Lebanon and parts of Syria,resulting in 42 deaths and more than 3500 *** handheld communication devices,previously regarded as secure and low-profile,were rigged with concealed explosives and remotely triggered by attackers.

关键词： injuries thoughts explosion

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Collision avoidance and routing based on location access (CARLA) of mobile robots

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Neural Computing and Applications 2025年第9期37卷 6401-6430页

作者： ElSayyad, Shimaa Ezzat Saleh, Ahmed I. Ali, Hesham A. Saraya, M.S. Rabie, Asmaa H. Abdelsalam, Mohamed M. Computers Engineering Department MISR Higher Institute for Engineering and Technology Mansoura Round Road Mansoura City35516 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Dean of Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa Mansoura Egypt Faculty of Engineering Mansoura National University Gamasa Mansoura Egypt

The paper introduces a new path-planning robotic system methodology called Collision Avoidance and Routing based on Location Access (CARLA) for use in critical environments such as hospitals and crises where quick action and saving human lives are vital. The main focus of our framework is on accuracy and fast responses, such as delivering tools or items in a specific area while avoiding collisions with other robots and obstacles. CARLA is designed to provide quick responses during emergencies, unlike most existing algorithms that are integrated into site control units or distributed among mobile robots on-site. By being loaded onto a remote server node rather than individual robots, CARLA helps to conserve the robots' capabilities, hardware resources, and power consumption. Additionally, our system utilizes cloud computing and Fog servers technology to improve data transmission times between the cloud and smart devices, especially for applications with strict timing requirements like emergency response. The Fog platform is also leveraged to enhance on-site access to real-time interaction and location-based services by bringing processing power closer to the robots from far-off Cloud servers. CARLA has various applications, such as in factories and warehouses, where mobile robots need to be selected and directed by a central control system remotely. The proposed framework consists of three main modules: Robot Knowledge Module, Robot Selection Module, and Route Reservation Module, which will all be discussed in detail in this paper. The results of simulations using this framework show that the robots have improved flexibility and efficiency in terms of computing paths and successfully fulfiling requests without colliding, compared to traditional methods used in similar scenarios. © The Author(s) 2025.

关键词： Mobile robots

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Polarization Insensitive Broadband Zero Indexed Nano-Meta Absorber for Optical Region Applications

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computers, Materials & Continua 2022年第4期71卷 993-1009页

作者： Ismail Hossain Md Samsuzzaman Ahasanul Hoque Mohd Hafiz Baharuddin Norsuzlin Binti Mohd Sahar Mohammad Tariqul Islam Space Science Center(ANGKASA) Universiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityDumki8602PatuakhaliBangladesh Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia

Broadband response metamaterial absorber(MMA)remains a challenge among researchers.A nanostructured new zero-indexed metamaterial(ZIM)absorber is presented in this study,constructed with a hexagonal shape resonator for optical region *** design consists of a resonator and dielectric layers made with tungsten and quartz(Fused).The proposed absorbent exhibits average absorption of more than 0.8972(89.72%)within the visible wavelength of 450–600 nm and nearly perfect absorption of 0.99(99%)at 461.61 *** on computational analysis,the proposed absorber can be characterized as *** developments of ZIM absorbers have demonstrated plasmonic resonance characteristics and a perfect impedance *** incidence obliquity in typically the range of 0◦–90◦both in TE and TM mode with maximum absorbance is more than 0.8972(∼89.72%),and up to 45◦angular stability is suitable for solar cell applications,like exploiting solar *** proposed structure prototype is designed and simulated by studying microwave technology numerical computer simulation(CST)*** finite integration technique(FIT)based simulator CST and finite element method(FEM)based simulator HFSS also helps validate the numerical data of the proposed ZIM *** proposed MMA design is appropriate for substantial absorption,wide-angle stability,absolute invisible layers,magnetic resonance imaging(MRI),color images,and thermal imaging applications.

关键词： Hexagonal resonator loaded star shaped polarization insensitive compact broadband metamaterial absorber optical region applications

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Revolutionizing Road Maintenance: YOLO Based Pothole Detection System 10

Revolutionizing Road Maintenance: YOLO Based Pothole Detecti...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Saranya, E. Nivetha, R. Abirami, S. Mohaideen Arsath, M. Dharaneesh, S. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Potholes on roadways pose a significant risk to public safety, leading to numerous accidents and fatalities annually. In this research, we present a custom-trained machine learning model for real-time pothole detection using the You Only Look Once (YOLO) version 7 object detection algorithm. Our study aims to address the critical issue of pothole-related accidents by evaluating the effectiveness of YOLO V7 in identifying potholes under various road conditions and lighting scenarios. We compare YOLO V7 with a sequential Convolutional Neural Network (CNN) algorithm and demonstrate that YOLO V7 outperforms in real-time pothole detection. The system incorporates a camera that captures live footage and highlights potholes in real time, enhancing road safety. Our research showcases the versatility of YOLO V7 in detecting potholes in both daylight and low-light conditions. Furthermore, we explore the feasibility of deploying YOLO V7 on edge devices for real-time pothole detection from moving vehicles. The results affirm YOLO V7's high accuracy and fast processing capabilities, making it a suitable solution for improving road safety by alerting drivers to road hazards. This research contributes to enhancing road safety and reducing accidents caused by potholes, ultimately saving lives and minimizing road infrastructure damage. © 2024 IEEE.

关键词： Convolutional neural networks

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Classification of Malignant or Benign Cancer using Neural Networks 7

Classification of Malignant or Benign Cancer using Neural Ne...

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7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Haidar, Gaby Abou Achkar, Roger Semaan, David Owayjan, Michel American University of Science and Technology Beirut Lebanon Faculty of Engineering and Computer Science Department of Computer and Communications Engineering Lebanon

ISBN: (纸本)9798350325850

Cancer detection and predetection are being highly researched and studied recently due to the high density spread of this disease. It occurs when abnormal cells divide continuously without limitation where it can spread randomly to different parts of the body. Different types of cancer are recorded targeting the blood, lungs, bones, liver, and many other organs. People with cancer have similar symptoms such as lumps, abnormal bleeding, eating problems, fatigue, weight loss, and some more severe issues. Breast Cancer, which will be addressed in this work, is a type of cancer that hits women in general. It is dangerous especially when cells around the breast area overgrow and cause lumps on the breasts. In order to pre-detect and try to avoid breast cancer., it's important to perform annual mammography check. This work is aimed to create a classification system that uses neural network algorithms to conclude whether a certain tumor is a malignant or a benign tumor. Back Propagation algorithm as well as supervised learning were used and tested with highly satisfying successful classification. © 2023 IEEE.

关键词： Diseases

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Dynamic UAV Deployment in Multi-UAV Wireless Networks: A Multimodal-Feature-Based Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第12期12卷 18765-18778页

作者： Bai, Yu Xie, Boxuan Liu, Ying Chang, Zheng Jantti, Riku Aalto University Department of Information and Communications Engineering Espoo02150 Finland University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610051 China University of Jyväskylä Faculty of Information Technology Jyväskylä40014 Finland

The use of unmanned aerial vehicles (UAVs) as aerial base stations has attracted increasing research interest in recent years. A key challenge in this field is determining how to deploy multiple UAVs in dynamic environments, particularly where mobile user demands fluctuate. To address this challenge, this article presents an adaptive UAV deployment scheme in a dynamic multi-UAV wireless network, considering the mobility of UAVs and users, state variability, and adjustable UAV transmission power. By jointly optimizing the UAVs’ operational modes, transmission power levels, and movement strategies, our objective is to achieve a tradeoff between minimizing power consumption and maximizing ground user coverage. A deep reinforcement learning (DRL) approach is proposed to address these challenges. To capture the dynamic variations of users and UAVs in the environment, a multimodal feature state space is designed, consisting of both a multichannel image and vectors. The image component integrates real-time data on user distribution and the UAV coverage area, while the vectors represent UAV operational modes, position data, and system temporal information. These multimodal features are processed using a combination of convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) for advanced feature extraction. To enhance training stability and efficiency, the proposed approach updates parameters using the proximal policy optimization (PPO) method. Simulation results demonstrate the effectiveness of the proposed scheme in balancing power consumption and coverage while effectively managing system dynamics. © 2014 IEEE.

关键词： Deep reinforcement learning

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Adaptive Quantum and Graph-Attentive Security Framework for DoS Attack Mitigation in VANETs 2

Adaptive Quantum and Graph-Attentive Security Framework for ...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Anjum, Asma Rao, P. S. V. Srinivasa Sindhu, M. Bhatt, Pooja Priya, M.R. Maranan, Ramya HKBK College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560045 India Joginpally B R Engineering College Department of Computer Science and Engineering Telangana Hyderabad500075 India R.M.K Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601206 India Parul institute of Engineering and Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology Post Limda Gujarat 391760 India NIE First Grade College Department of Computer Science Karnataka Mysuru570008 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331505745

Vehicular Ad Hoc Networks (VANETs) are considered crucial for real-time vehicle-to-vehicle communication, which in turn enhances the efficiency of traffic and road safety. VANETs are very vulnerable to Denial-of-Service (DoS) attacks, which seriously disrupt communication and degrade network performance. This paper introduces a high-performance neural network-based detection and prevention mechanism that can effectively counter DoS attacks. The proposed method is initiated with the real-time monitoring of the network. Data collection and preprocessing through the Adaptive Self-Guided Loop Filter (ASGLF) are used to filter out noise from the data. The Fast Hybrid Vision Transformer (FHVT) conducts feature extraction for network traffic to effectively preserve the spatial and temporal relationships. Using the Reflecting Equivariant Quantum Neural Network (REQNN) integrated with Heterogeneous Edge-Enhanced Graph Attention Network (HEEGAT) ensures timely anomaly detection accuracy of attacks. Subtraction-Average-Based Optimizer (SAO) optimizes the detection parameters to effectively utilize resources. The experimental results have shown accuracy in the detection of DoS attacks as 99.9% with a PDR of 99.9% and also improved throughput efficiency by 99.8%. These results validate the effectiveness of the proposed model in securing VANETs from DoS attacks, offering uninterrupted network availability and improved vehicular communication security for intelligent transportation systems. © 2025 IEEE.

关键词： Advanced driver assistance systems

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Highly Compact Wideband High-Gain Four-Element MIMO Antenna for 5G New Radio IoT

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IEEE Internet of Things Journal 2025年第12期12卷 22350-22365页

作者： Khan, Shahid Khan, Owais Shah, Syed Ahson Ali Nasir, Jamal Malik, Bilal Tariq Khan, Salahuddin Koziel, Slawomir COMSATS University Islamabad Department of Electrical and Computer Engineering Islamabad45550 Pakistan Gwangju Institute of Science and Technology Department of Electrical Engineering and Computer Science Gwangju61005 Korea Republic of King Saud University College of Engineering Riyadh11421 Saudi Arabia Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gdansk80-233 Poland Reykjavik University Department of Engineering Engineering Optimization and Modeling Center Reykjavik101 Iceland

Wideband millimeter-wave (mm-wave) coverage is essential for the high-speed, low-latency communication required in next-generation 5G New radio (NR) Internet of Things (IoT) systems. This study develops a T-shaped four-element, highly compact wideband multiple-input multiple-output (MIMO) antenna covering the mm-wave n260 (37 40 GHz) and n259 (42 43.5 GHz) bands for 5G NR IoT applications. The antenna is designed on a 0.76 mm-thick Rogers RO4350B substrate with overall dimensions of 28 28 mm2 (3.3 3.3 ?2). For design simplicity, a T-shaped patch backed by a full-ground plane is devised to serve as the radiator of a single-element antenna with the dimensions of (Ls Ws) mm2, optimized through a systematic three-step design process for improved performance. Moreover, the design has evolved into a 4 4 orthogonal MIMO configuration, achieving improved gain, polarization diversity, and higher data rates, with each element exhibiting wideband characteristics across 11.5 GHz (36.5 48 GHz) with high gains of 9.8 dBi and 6.6 dBi at n260 and n259 5G mm-wave bands, respectively. Additionally, arc-shaped complementary split-ring resonators (CSRR) are integrated into the ground plane to significantly enhance the gain while effectively reducing mutual coupling, remarkably achieving a maximum gain of 9.8 dBi. Furthermore, the MIMO antenna exhibits an envelope correlation coefficient of less than 0.10 between any two MIMO elements encountering the required condition of © 2025 IEEE.

关键词： Microwave antennas

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