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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

Computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia faculty of computing Riphah International UniversityIslamabad46000Pakistan faculty of computing and informatics(FCI)Multimedia University Cyberjaya63100Malaysia faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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Intelligent Machine Learning Based Brain Tumor Segmentation through Multi-Layer Hybrid U-Net with CNN Feature Integration

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Computers, Materials & Continua 2024年第4期79卷 1301-1317页

作者： Sharaf J.Malebary Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityP.O.Box 344Rabigh21911Saudia Arabia

Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity ***, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training ***, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation *** experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinician

关键词： Brain tumor Hybrid U-Net CLAHE transfer learning MRI images

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A Practical Study of Intelligent Image-Based Mobile Robot for Tracking Colored Objects

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Computers, Materials & Continua 2024年第8期80卷 2181-2197页

作者： Mofadal Alymani Mohamed Esmail Karar Hazem Ibrahim Shehata College of computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia faculty of Electronic Engineering Menoufia UniversityMenouf32952Egypt faculty of Engineering Zagazig UniversityZagazig44519Egypt

Object tracking is one of the major tasks for mobile robots in many real-world ***,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot *** contrast to previous simulation studies,this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue(RGB)colored objects in a real experimental ***,a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative(PID)schemes to achieve accurate tracking results,considering robot command delay and tolerance *** design of developed controllers implies some motion rules to mimic the knowledge of experienced *** scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot *** PID control failed to handle some tracking scenarios in this *** proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets,while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm,*** promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads.

关键词： Mobile robot autonomous systems fuzzy logic control real-time image processing

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Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

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Computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

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Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

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Computers, Materials & Continua 2024年第2期78卷 2441-2468页

作者： Vaishnawi Priyadarshni Sanjay Kumar Sharma Mohammad Khalid Imam Rahmani Baijnath Kaushik Rania Almajalid Department of Computer Science Gautam Buddha UniversityGreater NoidaUttar PradeshIndia College of computing and informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia School of Computer Science Shri Vaishno Devi UniversityKatraIndia

Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in *** detection and effective treatment of BC can help save women’s *** an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection *** paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data *** novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated *** analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.

关键词： Autoencoder breast cancer deep neural network convolutional neural network image processing machine learning deep learning

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Two-Stage IoT Computational Task Offloading Decision-Making in MEC with Request Holding and Dynamic Eviction

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Computers, Materials & Continua 2024年第8期80卷 2065-2080页

作者： Dayong Wang Kamalrulnizam Bin Abu Bakar Babangida Isyaku Department of Computer Science Faculty of ComputingUniversiti Teknologi MalaysiaJohor BahruJohor81310Malaysia Department of Computer Science Faculty of Information Communication TechnologySule Lamido UniversityJigawa741103Nigeria

The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are *** addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time *** is resulting in TD missing potential offloading opportunities in the *** fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic *** Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time *** framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning *** results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.

关键词： Decision making internet of things load prediction task offloading multi-access edge computing

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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge computing

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour faculty of computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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Joint Active and Passive Beamforming Design in Intelligent Reflecting Surface (IRS)-Assisted Covert Communications:A Multi-Agent DRL Approach

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China Communications 2024年第9期 11-26页

作者： Gao Ang Ren Xiaoyu Deng Bin Sun Xinshun Zhang Jiankang School of Electronic Information Northwestern Polytechnical University Key Laboratory of Near Ground Detection and Perception Technology Department of computing and informatics Bournemouth University

Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals ***,when multiple IRSs are involved,accurate channel estimation is still a challenge due to the extra hardware complexity and communication *** the crossinterference caused by massive reflecting paths,it is hard to obtain the close-formed solution for the optimization of covert *** this basis,the paper improves a heterogeneous multi-agent deep deterministic policy gradient(MADDPG) approach for the joint active and passive beamforming(Joint A&P BF) optimization without the channel estimation,where the base station(BS) and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency(CSE) *** to the 'centralized training and distributed execution' feature of MADDPG,each agent can execute the active or passive beamforming inde pendently based on its partial observation without referring to *** results demonstrate that the proposed deep reinforcement learning(DRL) approach could not only obtain a preferable CSE of legitimate users and a low detection of probability(LPD)of warden,but also alleviate the communication overhead and simplify the IRSs deployment.

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A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 1-40页

作者： Xianwei Jiang Yanqiong Zhang Juan Lei Yudong Zhang School of Mathematics and Information Science Nanjing Normal University of Special EducationNanjing210038China School of computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into *** article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 *** Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional *** from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung *** Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence *** addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification *** language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition ***,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among *** to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition ***,suitable datasets and evaluation criteria are also worth pursuing.

关键词： Chinese Sign Language Recognition deep neural networks artificial intelligence transfer learning hybrid network models

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A survey on federated learning:a perspective from multi-party computation

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Frontiers of Computer Science 2024年第1期18卷 93-103页

作者： Fengxia LIU Zhiming ZHENG Yexuan SHI Yongxin TONG Yi ZHANG Institute of Artificial Intelligence and Key Laboratory of Mathematics informatics Behavioral Semantics Beihang UniversityBeijing 100191China State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy computing Beihang UniversityBeijing 100191China Pengcheng Laboratory Shenzhen 518055China Zhongguancun Laboratory Beijing 100190China Institute for Mathematical Sciences and Engineering Research Center of Financial computing and Digital Engineering Renmin University of ChinaBeijing 100872China

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw *** enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model *** survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training *** also pinpoint future directions to deploy federated learning to a wider range of applications.

关键词： sfederated learning multi-party ycomputation privacy-preserving data mining distributed learning

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