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An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

computers, Materials & Continua 2022年第2期70卷 3311-3319页

作者： Khattab Alheeti Abdulkareem Alaloosy Haitham Khalaf Abdulkareem Alzahrani Duaa Al_Dosary Department of Computer Networking System College of Computer Sciences and Information TechnologyUniversity of AnbarRamadiIraq College of Medicine University of AnbarRamadiIraq Department of Computer Science Faculty of Computer Science and Information TechnologyAlbaha UniversityAlbahaSaudi Arabia College of Computer Sciences and Information Technology University of AnbarRamadiIraq

Self-driving and semi-self-driving cars play an important role in our daily *** effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital ***,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution *** this case,roadside units are considered one of themost important communication *** distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage *** this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these ***,this approach is based primarily on k-means to find the optimal location of each roadside *** addition,this approach supports dynamicmobility with a long period of connectivity for each ***,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and ***,the system can achieve a high connectivity rate with a low error rate while reducing costs.

关键词： Self-driving cars roadside unit autonomous vehicles distributed systems connectivity

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Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

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computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

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computer systems science & Engineering 2023年第9期46卷 3103-3119页

作者： Khaled M.Alalayah Fatma S.Alrayes Mohamed K.Nour Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsNajran UniversitySharurahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and *** malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,*** is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of *** prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)*** study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in computer *** presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the *** accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality ***,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is *** FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier *** experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct *** comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.

关键词： computer networks machine learning security malware detection feature selection ransomware

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Revolutionizing Healthcare: AI-Powered Solutions, Technological Integration for Accessible and Effective Medical Service

Revolutionizing Healthcare: AI-Powered Solutions, Technologi...

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2024 International Conference on Advances in Computing Research on science Engineering and Technology, ACROSET 2024

作者： Shaikh, Almas Jafar Takbhate, Sakshi Bharat Gawai, Shailesh Limbore, Altaf Mainoddin Patil, Vaidehi Avinash JSPM's Rajarshi Shahu College of Engineering Department of Computer Science and Business System Tathawade India

ISBN: (纸本)9798350388800

The main characteristics of the healthcare platform that this study suggests for rural areas are User ID or image-based recognition, Options for consultation, Disease Prediction, Integration with Aasha Workers, Technical considerations and Project Objectives. User ID or image-based recognition are used for a secure login process. Options for Consultation is to select from medical chatbots or video consultations with physicians. Disease Prediction is to make recommendations for possible disease diagnosis, real-time image analysis (such as reviewing uploaded photos) and machine learning are used. Risk assessment: Machine learning algorithms evaluate risks for different health disorders based on user-provided data (age, medical history, etc.). Integration with Aasha Workers is a platform establishes a connection with Aasha workers, who can provide medication as required following consultations. Technical considerations are a platform can be built using web development frameworks such as Flask or Django. Strong privacy and data security protocols are necessary. Project Objectives: Expand access to healthcare in remote locations. Give patients knowledge and tools for self-evaluation to empower them. Improve interaction and cooperation between medical professionals, patients, and Asha employees. © 2024 IEEE.

关键词： Adversarial machine learning

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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer science 2024年第5期5卷 564页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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Comments on 'VERSA: Verifiable Secure Aggregation for Cross-Device Federated Learning'

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IEEE Transactions on Dependable and Secure Computing 2024年第1期21卷 499-500页

作者： Luo, Fucai Wang, Haiyan Yan, Xingfu Zhejiang Gongshang University College of Computer and Information Engineering Hangzhou310000 China Peng Cheng Laboratory Department of New Networks Shenzhen518066 China South China Normal University School of Computer Science Guangzhou510631 China

Federated learning (FL) allows a large number of users to collaboratively train machine learning (ML) models by sending only their local gradients to a central server for aggregation in each training iteration, without sending their raw training data. The main security issues of FL, that is, the privacy of the gradient vector and the correctness verification of the aggregated gradient, are gaining increasing attention from industry and academia. To protect the privacy of the gradient, a secure aggregation was proposed;to verify the correctness of the aggregated gradient, a verifiable secure aggregation that requires the server to provide a verifiable aggregated gradient was proposed. In 2021, Hahn et al. proposed VERSA, a verifiable secure aggregation. However, in this article, we will point out a flaw in VERSA, which indicates that VERSA does not work. To address the flaw, we present several approaches with different advantages and disadvantages. We hope that by identifying the flaw, similar errors can be avoided in future designs of verifiable secure aggregation. © 2004-2012 IEEE.

关键词： Learning systems

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Sem-AI: A Unique Framework for Sentiment Analysis and Opinion Mining Using Social Network Data

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SN computer science 2025年第2期6卷 1-23页

作者： Maruthupandi, J. Sivakumar, S. Kumar, V. Senthil Srikaanth, P. Balaji Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bangalore India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Trichy India Department of Networks and Communication SRM Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

Social media has become an essential forum for people to share their thoughts and sentiments owing to the quick rise in mobile technology. Business and political organizations might benefit from understanding public sentiment when making strategic decisions. Challenges in the area include handling noisy and incomplete data, variation in language use across domains, and integration into existing systems of models developed for analysis. In these ways, they can seriously hamper the accuracy and generalizability of sentiment analysis models. In view of the challenges, this work has developed a new and innovative framework of AI-based semantic analysis for assessing and classifying user views from social data. In spite of this, sentiment analysis is crucial for determining the opposing perspectives of the general public's sentiments. This work constructed a new and innovative framework called Semantic analysis based on AI (SemAI) for assessing and categorizing user views from social data. With high efficiency, a collection of intelligence algorithms in the stages of feature extraction, optimization, and sentiment prediction are applied here. The Degree of Correlation Network Model (DCNM) is used in this work to extract the subset of dependable and most-required features from social network data. The most recent meta-heuristics model, Heap based Optimization (HbO), is used to reduce the dimensionality of retrieved features. Furthermore, the unique classification algorithm, Self-Attention based Deep Analyzing Network (SA-DAN), is used in this work to precisely identify and categorize attitudes into positive, neutral, and negative classifications. This present study used a variety of most recent social corpus data to investigate and evaluate the Sem-AI model's outputs. The SemAI model achieved impressive results, with accuracy, precision, and F1-scores of 99%, 98.9%, and 99%, respectively, across various social corpus datasets. © The Author(s), under exclusive licence to S

关键词： Classification Degree of correlation network model (DCNM) Heap based optimization (HbO) Opinion mining Self-attention based deep analyzing network (SA-DAN) Semantic analysis Social data

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Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

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computers, Materials & Continua 2023年第3期74卷 6587-6604页

作者： Mohammed Maray Haya Mesfer Alshahrani Khalid A.Alissa Najm Alotaibi Abdulbaset Gaddah AliMeree Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Warwick UniversityCovetryUK Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In recent years,wireless networks are widely used in different *** phenomenon has increased the number of Internet of Things(IoT)devices and their *** IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks *** scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic *** Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT *** Intrusion Detection system(IDS)models can be employed to secure the SDN-enabled IoT environment in this *** current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT *** presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature ***,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT ***,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the *** process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s *** proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model.

关键词： Internet of things SDN controller feature selection hyperparameter tuning autoencoder

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Indian Supreme Court Judgement Dataset for Prediction Models

Indian Supreme Court Judgement Dataset for Prediction Models

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2023 Intelligent Computing and Control for Engineering and Business systems, ICCEBS 2023

作者： Sivaranjani, N. Karthik, R. Sancith, Jaya Siva, P. Sri Sairam Engineering College Department of Computer Science and Business System India

ISBN: (纸本)9798350394580

The development of legal records requires extra time and their absurd length raises the need for programmed legal record handling frameworks. One of the handling steps is to recognize the essence of the reports expressions for legitimate case records. A computerized framework that could help a legal professional in anticipating the result of a case would help facilitate the legal interaction. For such a framework to be essentially valuable, expectations by the framework ought to be reasonable. To advance exploration in growing such a framework, this paper presents the Indian Legal Judgment Dataset (ILJD). ILJD is a huge corpus of 35k Indian Supreme Legal disputes commented on with unique court choices. A part of the corpus (a different test set) is commented on with best quality level clarifications by lawful specialists. © 2023 IEEE.

关键词： chi2 Legal Dataset Legal judgement Prediction Machine Learning Supreme Court of India

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