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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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Green Cloud Computing: A Sustainable Energy-Efficiency Approach for Business Rapidity and the Environment 6th

Green Cloud Computing: A Sustainable Energy-Efficiency Appro...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Bristy, Sadia Sultana Azam, Tooba Islam, Mukta Marion Rahman, Raisur Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (纸本)9783031501500

Cloud computing is significantly assisting in addressing IT issues. To lessen the environmental impact, a thorough investigation of cloud computing and energy-efficient algorithms is required. A green cloud computing strategy aims to build an environment for cloud computing that is more energy-efficient, maximizes resource consumption without overprovisioning, leaves fewer carbon footprints, reduces Greenhouse Gas emissions, and minimizes E-wastages. Some features and techniques that Green Cloud Computing focuses on bringing to the cloud environment include containerization, designing green data center facility infrastructure, server consolidation, virtual machine placement, designing green networks and protocols, green data management, sustainable software development, and safe disposal techniques with the three R’s (Reuse, Reduce and Recycle). This paper offers a thorough examination of green cloud computing and its traits. The main objective of this study is to investigate green cloud computing, including its various components and conversion processes, as well as to compare energy-efficient VM placement techniques for green cloud computing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy efficiency

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An Intelligent Optimization Technique Of Automatic Speech Recognition For Smart Homes 13

An Intelligent Optimization Technique Of Automatic Speech Re...

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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. The Higher Institute for Management and Information Technology Minya Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

The creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine Learning (ML) algorithms, particularly optimization techniques. The widespread use of electronic devices has sparked a strong desire to incorporate the Internet of Things (IoT) into houses, leading to the development of smart homes. As networked gadgets proliferate rapidly, this phenomenon is characterized by rapid proliferation. In smart buildings, smart cities, smart grids, and smart homes, interconnected electronic devices are becoming more popular. The objective of this paper is to enhance the functionality of home automation systems through the performance of speech recognition using the Bat-Salp Swarm Optimization (BSSO). This paper investigates the notion of (BSSO), a data analysis methodology that facilitates the automated construction of analytical models. The implementation of BSSO provides an enhancement to the feature selection process in speech recognition, providing an approximation solution that improves the accuracy of system decisions. The use of the BSSO technique improves the precision of the voice recognition system and also incorporates an Artificial Neural Network (ANN) for the classification part. The findings substantiated the efficacy of the employed methodology. © 2024 Copyright for this paper by its authors.

关键词： Bat Optimization Algorithm Salp Optimization Algorithm Smart homes Speech recognition

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Blockchain based EHR system architecture and the need of blockchain inhealthcare

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Materials Today: Proceedings 2023年 80卷 2064-2070页

作者： Singh Chouhan, Arun Sanaullah Qaseem, Mohammad Mohammed Abdul Basheer, Qazi Asma Mehdia, Ms. Department of Computer Science School of Engineering Malla Reddy University India Nawab Shah Alam Khan College of Engineering and Technology India CSE Department Nawab Shah Alam Khan College of Engineering and Technology India

Purchases are concurrently safe and secure as well as credible because of making use of cryptographic guidelines. Recently, blockchain technology has become extremely cool and trendy and infiltrated various domain names, usually as a result of the appeal of cryptocurrencies. One field where blockchain technology possesses significant ability is medical care, because of the requirement for an extra patient-centric method to medical care systems and to link inconsonant devices and also boost the reliability of digital medical care files (EHRs). In this organized review, an analysis of cutting edge blockchain research study in the field of health care is carried out. It's own' prospective in the multi-stakeholder operated market like health and wellness has been responsible for several expenditures, researches, and executions. Electronic Health Records (EHR) devices typically made use of for the swap of wellness details amongst healthcare stakeholders have been criticised for centralising energy, failures and attack-points along with exchange records protectors. EHRs have battled when faced with multi-stakeholder as well as unit criteria while adhering to safety and security, privacy, reliable as well as other regulative constraints. Blockchain is promising among others to attend to the many EHR challenges, largely trustless and also protected swap of health-relevant information among stakeholders. This paper provides the demand for blockchain in medical care and also blockchain-based EHR device architecture. © 2021

关键词： Blockchain

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LIME-RAY: What Does a Neural Network See from X-Rays? 2

LIME-RAY: What Does a Neural Network See from X-Rays?

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2nd International Conference on Information and Communication technology, ICICT 2024

作者： Datta, Ayon Al Wazed Wasi, Wasif Muntasir, Fahim Nafis, Mahdee Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Department of Electrical and Electronic Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (纸本)9798331508227

In the field of medical imaging, neural networks have significantly enhanced the analysis and interpretation of X-ray images, providing advanced capabilities in detecting patterns and anomalies. Despite their potential, challenges remain, particularly concerning the interpretability and reliability of these models. The LIME-RAY approach addresses these challenges by integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks. This hybrid technique leverages CNNs for spatial feature extraction and LSTMs for temporal pattern recognition, improving the accuracy and interpretability of the model’s predictions. Furthermore, Local Interpretable Model-agnostic Explanations (LIME) are used to provide transparent, interpretable results, crucial for gaining the trust of healthcare professionals. Our approach is validated using a diverse dataset of chest X-rays, including cases of Covid-19, Pneumonia, and Tuberculosis. The proposed method demonstrates superior performance in multi-class disease classification and offers valuable insights into the decision-making process, bridging the gap between complex neural network models and practical healthcare applications. ©2024 IEEE.

关键词： Convolutional neural networks

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Market Segmentation Using Machine Learning and Data Analysis: A Comprehensive Study 15

Market Segmentation Using Machine Learning and Data Analysis...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Ajay Dhanda, Namrata Tiwari, Sharda Verma, Rajat Amity University Uttar Pradesh Amity School of Engineering and Technology Lucknow India Babu Banarasi Das Northern India Institute of Technology Department of Computer Science and Engineering Lucknow India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350370249

This research explores the integration of machine learning into market segmentation, a strategy traditionally reliant on demographic, geographical, and psychographic traits. With the rise of big data, machine learning offers enhanced precision by uncovering intricate consumer behavior patterns often missed by conventional methods. The study involves reviewing relevant literature, preprocessing datasets, and developing machine learning models to evaluate their effectiveness in segmenting markets. Findings indicate that machine learning significantly improves segmentation accuracy, enabling businesses to predict behaviors and tailor marketing strategies more effectively. This research contributes by highlighting machine learning's potential to transform market segmentation and drive business growth. Future work should focus on real-world implementation of these models to validate their practicality and impact. © 2024 IEEE.

关键词： consumer behavior customer insights machine learning market segmentation predictive analytics targeted marketing

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Prediction of material property using optimized augmented graph-attention layer in GNN

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Materials Today: Proceedings 2022年 69卷 1419-1424页

作者： Sathana, V. Mathumathi, M. Makanyadevi, K. Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur India

Research in materials science continues to focus on the development of ML models capable of accurately predicting material properties (MPP). Graph neural networks (GNNs) have also proven to be quite actual in extracting physicochemical properties from MPP, despite the fact that many other techniques have been offered in the past. Despite this, existing GNN models fail to distinguish between the contributions of various atoms adequately. To forecast the properties of inorganic materials, we have developed an upgraded graph neural network model dubbed IGAT-GNN. OAGAT and a global attention layer, where a meta-heuristic method is used to achieve optimal weight in OAGAT, are the distinguishing features of IGAT-GNN. Each of the two methods, OAGAT layers and global attention layers, is used to better understand the local relationships among nearby atoms and their overall influence to the material's stuff. This results in much improved prediction performance for various qualities that have been examined. As we have demonstrated through numerous experiments, the IGAT-GNN outperforms other currently available GNN models while also revealing new information about the relationship between atoms and their respective material properties. The proposed model alewives the accuracy of 99.95%, which is the high quality accuracy than other algorithm. © 2022

关键词： Machine learning

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An Empirical Literature Review on Multi Class Data Stream Learning with Class Imbalance Problem in Real World Data Sources 5

An Empirical Literature Review on Multi Class Data Stream Le...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Muley, Rajesh Reddy Battula, Bhanu Prakash Sanaboina, Chandra Sekhar Jawaharlal Nehru Technological University Department of Computer Science and Engineering Andhra Pradesh Kakinada India Kkr & Ksr Institute of Technology & Sciences Department of Computer Science and Engineering Andhra Pradesh Guntur India University College of Engineering Kakinada Jawaharlal Nehru Technological University Department of Computer Science and Engineering Andhra Pradesh Kakinada India

ISBN: (纸本)9798331522100

Data mining and knowledge discovery is one of the cutting-edge fields in science and technology for identifying pressing issues in real world. The amount of data collected has increased due to the quick development of technology and the low cost of internet access. One of the difficult problems is analyzing the massive volume of data that comes in from various data streams. Machine learning research has primarily concentrated on binary classification data problems with high unbalanced data from the earliest literatures published on till recently. When it comes to processing data streams effectively, research on highly unbalanced multi-class data is still mostly underdeveloped. This study focuses on reviews of the models or methods for dealing with extremely unbalanced multi-class data, as well as their advantages and disadvantages and related fields. The objectives of this article are to: comprehend the trend of highly imbalanced multi-class data through examination of related literatures;to analyze the traditional and contemporary approaches to handling highly imbalanced multi-class data;and develop a framework for highly imbalanced multi-class data. After doing the chosen highly imbalanced multi-class dataset analysis and adapting it to the most recent machine learning algorithms or approaches, there will be talks on unresolved issues and the way highly imbalanced multi-class data should go in the future. © 2025 IEEE.

关键词： Labeled data

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Modelling and Impact Analysis of Antipode Attack in Bufferless On-Chip Networks

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SN computer science 2023年第3期4卷 1-9页

作者： Kunthara, Rose George Josna, V.R. Neethu, K. James, Rekha K. Jose, John Division of Electronics Engineering School of Engineering Cochin University of Science and Technology Kerala Cochin 682022 India Department of Computer Science Cochin University of Science and Technology Kerala Cochin 682022 India Department of Computer Science and Engineering Indian Institute of Technology Guwahati Assam Guwahati 781039 India

With advancements in VLSI technology, Tiled Chip Multicore Processors (TCMP) with packet switching Network-on-Chip (NoC) have evolved as the backbone of modern data intensive parallel systems. Manufacturers are looking at the prospect of using several third-party Intellectual Property (IP) cores in their TCMP designs due to strict time-to-market restrictions. Outsourcing IP from vendors across the world exposes System-on-Chip (SoC) designs to malicious implants such as Hardware Trojans (HTs). The performance of entire system is adversely affected by the presence of malicious HT in NoC routers, which can negatively disrupt communication between tiles. Generally, in buffered NoC, hardware trojans affect flits when they are in input buffers. Hardware trojans in bufferless NoC is a less explored area. In this paper, we model an HT that leads to antipode attack, which can occur on Permutation Deflection Network (PDN) of a bufferless router. In a bufferless router such as CHIPPER architecture, only the highest priority flit gets productive port, while other flits may or may not get productive port depending on port availability, leading to deflections. The modelled trojan misroutes all flits of HT-infected router to non-productive ports without modifying the flit control field. We investigate the effects of such an intermittent HT and analyse its effects at NoC level in terms of performance metrics such as average flit latency, deflection rate, throughput and router link utilisation. Experimental evaluations conducted on an 8 × 8 bufferless mesh NoC indicate that the modelled HT degrades network performance due to increased flit deflections and traffic across central routers which impacts system reliability. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Antipode attack Bufferless router Deflection routing Hardware trojan NoC security

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Deep Retinal Image Analysis and Classification Using Deer Hunting Optimization-Based Tandem Pulse Coupled Neural Network

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Journal of The Institution of Engineers (India): Series B 2022年第6期103卷 1909-1916页

作者： Vinayaki, V. Desika Kalaiselvi, R. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Kumaracoil India Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Gummidipoondi India

Retinal eye diseases lead to vision loss and visual deficiency. Various kinds of human eye diseases are arteriosclerosis, diabetic retinopathy, hypertension, and glaucoma. Diabetic retinopathy is the form of the injured retina which is occurred by diabetes. The improper treatment without proper observations leads to permanent blindness. Therefore, it is necessary to detect the disease at an earlier stage to protect their vision. The main aim of this research is to detect diabetic retinopathy early from the fundus images. The proposed approach detects diabetic retinopathy through four major stages, namely pre-processing, segmentation, feature extraction, and classification. The proposed classifier uses the deep neural network for classifying the diabetic retinopathy (DR) infected images and normal images. Here, the proposed approach utilizes features such as coherence, edge features, shape features, local binary pattern, and Gray Level Co-occurrence Matrix (GLCM) features from the segmented output. The performance measures like sensitivity, specificity, accuracy, precision, F1-measure and processing time are utilized for the estimation of the proposed classifier and compared to other approaches. From the performance evaluation, it is noted that the proposed diabetic retinopathy detection has obtained an accuracy of 99.42%, a sensitivity of 98.84%, and a specificity of 96.49% that outperforming other state-of-art methods. © 2022, The Institution of Engineers (India).

关键词： Local binary pattern

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