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Optimal Machine Learning Enabled Performance Monitoring for Learning Management systems

Computer systems Science & engineering 2023年第3期44卷 2277-2292页

作者： Ashit Kumar Dutta Mazen Mushabab Alqahtani Yasser Albagory Abdul Rahaman Wahab Sait Majed Alsanea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd Diriyah13713RiyadhKingdom of Saudi Arabia Physical Therapy Department Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl Ahsa31982HofufKingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational *** LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning *** recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for *** the different processes involved in ML based LMS,feature selection and classification processesfind *** this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for *** key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in *** pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal ***,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)*** design of GSO and MFO techniques result in reduced computation complexity and improved classification *** presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several *** simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

关键词： Learning management system data mining performance monitoring machine learning feature selection

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Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

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Computer systems Science & engineering 2022年第10期43卷 397-412页

作者： Sunil Dhankhar Mukesh Kumar Gupta Fida Hussain Memon Surbhi Bhatia Pankaj Dadheech Arwa Mashat Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthan302017India Department of Electrical Engineering Sukkur IBA UniversityPakistan Department of Mechatronics Engineering Jeju National UniversityJeju63243Korea Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Faculty of Computing&Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia

In today’s digital era,the text may be in form of *** research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary *** rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords *** primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant *** second goal is to maintain or improve the classification system’s *** experiment included news articles from various genres,such as Bollywood,politics,and *** proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human *** evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.

关键词： Support vector machine(SVM) optimization precision Hindi character recognition optical character recognition(OCR) automatic summarization and compression ratio

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Enhancing Glioma Brain Tumor Detection from MRI using Deep Learning Techniques

Enhancing Glioma Brain Tumor Detection from MRI using Deep L...

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2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Sankara Narayanan, S. Meena, L.C. Thanu, K. Chidambara Chandrasekar, P. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India Rmd Engineering College Chennai India R.M.K Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India Siddartha Institute of Science and Tech Department of Ece Andhra Pradesh Puttur India

ISBN: (纸本)9798350348910

Brain tumor classification from MRI image is one of the important areas in biomedical research. However, the interpretation of these images is subjective, and automated detection methods can improve diagnostic accuracy. This study proposes a hybrid model that combines deep learning, machine learning, and traditional image processing techniques to detect brain tumors from MRI images. The proposed model is based on a convolutional neural network (CNN) algorithm and a machine learning model. The first stage is employing the histogram equalisation approach to enhance the image. In the second stage, features of the grey level co-occurrence matrix are extracted from the improved image. Convolutional neural networks are utilised in the third stage to categorise as normal or abnormal. The abnormal brain tumor is segmented using a morphological segmentation algorithm. This study suggests a number of performance criteria, such as accuracy, sensitivity, and specificity, to evaluate the efficacy of the proposeed method. Our proposes algorithm demonstrates superior performance compared to existing methods regarding accuracy, sensitivity, and specificity. © 2023 IEEE.

关键词： Convolutional neural networks

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Optimizing Healthcare Delivery through CloudBased Clinical Decision Support systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

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2024 OPJU International Technology Conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ebenezar, U. Samson Jenila Vennila, G. Balakrishnan, T. Suresh Krishnan, Prabhakar Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-campus India

ISBN: (纸本)9798350373783

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing. © 2024 IEEE.

关键词： Random forests

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Domain Generalization Study of Empirical Risk Minimization from Causal Perspectives

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IEEE Transactions on Multimedia 2025年

作者： Mo, Zhenling Zhang, Zijun Tsui, Kwok-Leung City University of Hong Kong Department of Data Science College of Computing Kowloon Hong Kong Virginia Tech Grado Department of Industrial and Systems Engineering BlacksburgVA United States

Empirical risk minimization (ERM) is a celebrated induction principle for developing data-driven models. However, ERM has received both pros and cons for its capability on domain generalization (DG). To this end, this paper attempts to study the success and failure of ERM at supervised DG classification tasks, both theoretically and empirically, with causal perspectives. In the theoretical aspect, we first explore different properties of a causal metric termed information flow, followed with discussing relationships between the information flow and the mutual information in the proposed causal graph. Next, we analyze the roles of the transformed causal feature and the transformed spurious feature on modeling performances. It reveals that the interaction between the spurious influencer and the transformed causal feature is the key determining the failure or success of ERM on DG. In the empirical study, we first simulate various DG settings based on the MNIST, Fashion MNIST, and CIFAR10 datasets. Next, we verify developed theories by testing three different neural network configurations in designed experiments. In addition, experiments based on real-world datasets are conducted to further consolidate key points of the proposed theories. To extend application benefits of the theoretical discoveries, a new risk minimization framework with a novel feature intervention for regulating ERM is proposed. It achieves DG improvements over ERM on real-world datasets of image segmentation, image classification, and text classification. © 1999-2012 IEEE.

关键词： Self-supervised learning

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Automated Heterogeneous Network Learning with Non-Recursive Message Passing

arXiv

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arXiv 2025年

作者： Li, Zhaoqing Jiang, Maiqi Chen, Shengyuan Li, Bo Chen, Guorong Huang, Xiao Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Hong Kong Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong School of Intelligent Technology and Engineering Chongqing University of Science and Technology China

Heterogeneous information networks (HINs) can be used to model various real-world systems. As HINs consist of multiple types of nodes, edges, and node features, it is nontrivial to directly apply graph neural network (GNN) techniques in heterogeneous cases. There are two remaining major challenges. First, homogeneous message passing in a recursive manner neglects the distinct types of nodes and edges in different hops, leading to unnecessary information mixing. This often results in the incorporation of "noise" from uncorrelated intermediate neighbors, thereby degrading performance. Second, feature learning should be handled differently for different types, which is challenging especially when the type sizes are large. To bridge this gap, we develop a novel framework - AutoGNR, to directly utilize and automatically extract effective heterogeneous information. Instead of recursive homogeneous message passing, we introduce a non-recursive message passing mechanism for GNN to mitigate noise from uncorrelated node types in HINs. Furthermore, under the non-recursive framework, we manage to efficiently perform neural architecture search for an optimal GNN structure in a differentiable way, which can automatically define the heterogeneous paths for aggregation. Our tailored search space encompasses more effective candidates while maintaining a tractable size. Experiments show that AutoGNR consistently outperforms state-of-the-art methods on both normal and large scale real-world HIN datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Teeth and Jaw Segmentation from CBCT images Using 3D Deep Learning Models 18

Teeth and Jaw Segmentation from CBCT images Using 3D Deep Le...

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18th International Computer engineering Conference, ICENCO 2022

作者： Abdo, Yassmina Mohamed, Nader Alsawaf, Maryam Elsaeed, Mohamed Ain Shams University Faculty of Computers and Information Sciences Scientific Computing Department Cairo Egypt Higher Institute of Engineering and Technology Mechatronics Engineering Department Cairo Egypt Kafr Elshiekh University Faculty of Engineering Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9781665458955

The outcome of oral surgery is dependent on precise preoperative planning. In order to properly plan for dental surgery or suitable implant placement, it is necessary for accurate segmentation of the jaw tissues: the teeth, the cortical bone, the trabecular core, and overall, the inferior alveolar nerve. Achieving 3D virtual surgical planning is very hard and is a major drawback because most of the existing jaw segmentation methods require a large amount of expert knowledge for manual or partially automatic segmentation. What is more, due to the lack of experienced doctors and experts, high-quality expert knowledge is hard to achieve in practice. Furthermore, metal artifacts and large variations in their shape and size among individuals influence the segmentation of the jaw in CBCT scans seriously. Therefore, this paper presents a new automatic jaw segmentation in CBCT scans dataset. This approach is divided into two main phases;the first phase adopts the use of image processing techniques to extract the region of interest (ROI) which is the area containing the jaws. It is an important phase as it prepares the 3D image data before using it in the second phase as it makes the training more efficient rather than training the model overall 3D head of CBCT scans. In the second phase, we use several models to segment each jaw separately and then gets a 3D segmentation view of both jaws. We implement such a segmentation approach on head CBCT scans given as Dicom Numpy files datasets and then evaluate the performance. Our proposed approach has been tested in a validation set that has been evaluated using Dice's coefficient and the Attention Unet model achieved promising results with a 91 © 2022 IEEE.

关键词： Deep learning

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Artificial Intelligent based Models for Event Extraction using Customer Support Applications 2

Artificial Intelligent based Models for Event Extraction usi...

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2nd International Conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Mayuranathan, M. Akilandasowmya, G. Jayaram, B. Velrani, K. Sundara Kumar, M Jogendra Vidhya, R.G. Srm Valliammai Engineering College Department of Computer Science Engineering India Sri Sairam Institute of Technology Department of Computer Communication and Engineering India Rmk Engineering College Department: Computer Science and Business Systems Tamilnadu Gummidipoondi Taluk Tiruvallur District Kavaraipettai India School of Computing Sathyabama Institute of Technology Information Technology India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350325799

Recently, AI and NLP have been used to improve customer care. Customer support relies on event extraction and relevant data. In customer support applications, AI-based algorithms extract events. Event extraction identifies and extracts specific events or activities from textual data like customer service chats, emails, and social media interactions. Customer support teams may discover issues, trends, and improve the customer experience by automatically extracting events. Customer support event extraction can be done using AI-based models. Event extraction relies on named entity recognition (NER), sentiment analysis, and text classification. NER classifies customer names, product names, and problem keywords. Text classification classifies client issues into preset categories like billing, technical support, or product feedback, while sentiment analysis assesses the customer's emotional tone. Event extraction AI models use machine learning algorithms. Support vector machines (SVM) and random forests can be trained on annotated customer support data to reliably classify events. Recurrent neural networks (RNNs) and transformers can capture complicated patterns in textual data, making them promising event extraction methods. Customer support applications benefit from AI-based event extraction algorithms. It frees up support teams to focus on client issues by automating evaluation and categorization. It also helps firms identify emerging trends and enhance their products and services to answer client complaints. AI-based customer support event extraction models face hurdles. Labeled training data is costly and time-consuming to obtain. Ensuring model generalizability across multiple domains or languages is ongoing research. In conclusion, AI-based event extraction in customer support systems can improve customer experiences and support team efficiency. © 2023 IEEE.

关键词： Support vector machines

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ScaDLES: Scalable Deep Learning over Streaming data at the Edge

arXiv

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arXiv 2023年

作者： Tyagi, Sahil Swany, Martin Department of Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

Distributed deep learning (DDL) training systems are designed for cloud and data-center environments that assumes homogeneous compute resources, high network bandwidth, sufficient memory and storage, as well as independent and identically distributed (IID) data across all nodes. However, these assumptions don't necessarily apply on the edge, especially when training neural networks on streaming data in an online manner. computing on the edge suffers from both systems and statistical heterogeneity. systems heterogeneity is attributed to differences in compute resources and bandwidth specific to each device, while statistical heterogeneity comes from unbalanced and skewed data on the edge. Different streaming-rates among devices can be another source of heterogeneity when dealing with streaming data. If the streaming rate is lower than training batch-size, device needs to wait until enough samples have streamed in before performing a single iteration of stochastic gradient descent (SGD). Thus, low-volume streams act like stragglers slowing down devices with high-volume streams in synchronous training. On the other hand, data can accumulate quickly in the buffer if the streaming rate is too high and the devices can't train at line-rate. In this paper, we introduce ScaDLES to efficiently train on streaming data at the edge in an online fashion, while also addressing the challenges of limited bandwidth and training with non-IID data. We empirically show that ScaDLES converges up to 3.29× faster compared to conventional distributed SGD. © 2023, CC BY.

关键词： Stochastic systems

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Sustainable Development Goal-17 and Nigeria Data Protection Regulation Adoption among Organizations in Nigeria

Sustainable Development Goal-17 and Nigeria Data Protection ...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Sumayya Babangida-Sabo Samuel C. Avemaria Utulu Abel Ajibesin Department of Informaton Systems School of Informtaon Technology and Computing American University of Nigeria Yola Nigeria Department of Software Engineering School of Information Technology and Computing Amerucan University of Nigeria Yola Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Adoption of computerized systems by organizations in Nigeria poses some data security challenges to citizens and people from other nations. The challenges require organizations to deploy personal data security that are in line with global standards, hence, Nigeria Data Protection Regulation (NDPR) was introduced. NDPR is to promote ethical personal data security and Nigeria’s attainment of the sustainable development goal-17. However, only small number of organizations in Nigeria are able to adopt the NDPR because its adoption is complex and necessitate the implementation of organizational change management (OCM) project. Experience shows that OCM projects usually fails because many OCM frameworks used for OCM projects do not align with both scientifically proven ideas and organizational realities. Our study addresses the benefits of collaborative practice research studies to the production of OCM frameworks that incorporates both theoretical ideas and organizational realities. Our study provides viable solutions to the problem of lack of practice oriented OCM frameworks that organizations in Nigeria can use to drive the adoption the NDPR.

关键词： Ethics Data security Standards organizations Data protection Collaboration Organizations Production

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