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A systematic review of deep learning and machine learning tools for predicting and classifying pediatric genetic disorders 4th

A systematic review of deep learning and machine learning to...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Ishdeep Gagandeep Research Scholar Computer Science and Engineering IK Gujral Punjab Technical University Punjab Kapurthala India Punjab India

ISBN: (纸本)9781032911571

There are various hurdles in the field of Genetic ailments in children, particularly in the realms of timely identification and management. Conventional diagnostic techniques often encounter obstacles such as protracted methods and resource-demanding procedures, hampering timely diagnosis and treatment. This paper investigates the Capability of advanced computing, specifically machine learning and deep learning techniques, to address these challenges. By harnessing available AI (Artificial Intelligence) technologies, scholars and scientists aim to enhance the effectiveness and precision of paediatric genetic healthcare, ultimately enhancing outcomes for children with genetic disorders. Through a comprehensive review of existing literature, this paper review investigates the state-of-the-art AI techniques employed in the prediction and classification of pediatric genetic disorders. Despite challenges such as data availability and algorithm validation, AI presents promising opportunities for revolutionizing paediatric genetic healthcare, paving the way for prompt detection, personalized therapy, and improved recipients outcomes. © 2025 the Author(s).

关键词： Pediatrics

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AI-Powered Cloud computing for Performance Optimization and Scalability in Distributed Systems

AI-Powered Cloud Computing for Performance Optimization and ...

引用

2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Selvam, Muthu Kishan, B.S. College of Computing and Informatics University of North Carolina at Charlotte CharlotreNC United States Bms Institute of Technology and Management Department of Mechanical Engineering Bangalore India

ISBN: (纸本)9798331538538

The IT profession has observed the ingenious application of Artificial Intelligence in the field of cloud computing as a solution to increase performance along with scalability within the distributed computing environment. Conventional cloud computing context has issues with management of resources, responsiveness, and finally security. The following paper will discuss an AI based architecture which will incorporate ML algorithms to optimize the usage of the resources available in the computer cloud to balance the load between different users and ensure security. Thus, the proposed model incorporates reinforcement learning for the dynamic load distribution, predictive analytics for workload forecasting, and deep learning based on anomaly detection to improve the security features of the system. The performance comparison with the baseline and the experiments conducted show satisfactory results in more than one performance indicators. For CPU usage, it rose to 26.4%, which was achieved alongside the provision of the 48% higher request throughput and the 40% better response time under the scenarios of the heavy workload. Moreover, the security framework using CNNs enhanced the detection accuracy of intrusion to 96.7% and a reduction of the false positives by sixty-six percent. These are some of the implications suggesting that the application of artificial intelligence can bring positive changes in the goals regarding cloud performance, resource utilization and overall security features. In this way, using AI-driven automation and prediction integrated in this work is offered a powerful, adaptive, and safe cloud model for expanded usage. With the proposed approach that has been developed the businesses and the service providers will be in a position to attain efficient business operational costs together with strong system reliability that will assist in developing the next generation of intelligent cloud computing. © 2025 IEEE.

关键词： Distributed cloud

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Hybrid Deep learning Model for Prostate Cancer Detection Using LeNet and MobileNet V2 Algorithms

Hybrid Deep Learning Model for Prostate Cancer Detection Usi...

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2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Fatima, Sabiya Ramya, I.M. Shivani R, Ridhi Mayura Rajesh Kumar, K. Jenitha, T. Amulya, M.P. Axis Institute of Technology and Management Department of Computer Science & Engineering Uttar Pradesh Kanpur India East West Institute of Technology Department of Cse Bangalore India Sathyabama Institute Of Science And Technology Department Of Computer Science And Engineering Tamil Nadu Chennai India Chennai India St. Joseph's College of Engineering Department of Cse Chennai India Don Bosco Institute of Technology Department of Ise Karnataka Bangalore560074 India

ISBN: (纸本)9798331538538

Cancer of the prostate is among the most common neoplasms worldwide and in men especially;therefore, early diagnosis enhances the chances of the patients. In this work, we propose a proposed deep-learning framework by integrating LeNet and MobileNet V2 architectures for early diagnosis of prostate cancer from medical images. The proposed model had an accuracy of 96.2% and precision of 97.9%, a recall of 96.9% and F1-Score of 97.4% and surpassed LeNet, MobileNet V2 and ResNet-50 and DenseNet-121 models. The particularity of the test was confirmed with a confusion matrix that revealed 15 false negatives and 10 false positives. From the computational perspective, the model has an inference time of roughly 48 ms for per image and has a memory consumption of 60 MB, it is thus fast for real time applications in clinics. It adds more explainability techniques such as Grad-CAM, which improves interpretability, which is essential for clinician trust. This work shows that PFCLA can be used in early diagnosis of prostate cancer using enhanced deep learning hybrid model and future works that can be done include, Exploratory Study of More real world independent data set on PFCLA along with, Expanding the applicability of PFCLA to other medical imaging tasks. © 2025 IEEE.

关键词： Deep learning

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A comprehensive survey on client selections in federated learning

A comprehensive survey on client selections in federated lea...

引用

international conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Gouissem, A. Chkirbene, Z. Hamila, R. College of Computing and Information Technology University of Doha for Science and Technology Qatar Electrical Engineering Qatar University Qatar

ISBN: (纸本)9781032803722

Federated learning (FL) is a rapidly growing field in machine learning that allows data to be trained across multiple decentralized devices. The selection of clients to participate in the training process is a critical factor for the performance of the overall system. In this survey, we provide a comprehensive overview of the state-of-the-art client selection techniques in FL, including their strengths and limitations, as well as the challenges and open issues that need to be addressed. We cover conventional selection techniques such as random selection where all or partial random of clients is used for the trained. We also cover performance-aware selections and as well as resource-aware selections for resource constrained networks and heterogeneous networks. We also discuss the usage of client selection in model security enhancement. Lastly, we discuss open issues and challenges related to clients selection in dynamic constrained, and heterogeneous networks. © 2025 The Author(s).

关键词： Federated learning

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Harnessing Dimensional Contrast and Information Compensation for Sentence Embedding Enhancement

Harnessing Dimensional Contrast and Information Compensation...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： He, Kang Ding, Yuzhe Li, Bobo Wang, Haining Li, Fei Teng, Chong Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (纸本)9798350368741

Unsupervised sentence embedding learning excels through positive sample construction and instance-level contrastive learning (ICL). However, this approach can lead to over-compression and dimensional contamination from noisy data augmentation and unconstrained ICL processes. To mitigate these issues, we propose a novel enhancement method, MSSE, which incorporates an Information Compensation Mechanism (ICM) and a Dimensional-Level Contrastive learning Mechanism (DCM). ICM, inspired by the information bottleneck principle, prevents excessive compression in representation learning. DCM constrains ICL and minimizes information leakage across dimensions. Experimental results show that MSSE surpasses existing competitive baselines across seven STS tasks, including unsupervised and few-shot scenarios. The source code is available at https://***/Hekang001/MSSE. © 2025 IEEE.

关键词： contrastive learning natural language processing Sentence embedding unsupervised learning

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Artificial Intelligence in Health Care computing and Assessments in Oman: an Effectual Study 2

Artificial Intelligence in Health Care Computing and Assessm...

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2nd international conference on Machine learning and Autonomous Systems, ICMLAS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Jabri, Jaber Saleh Salim Al Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Health care plays a vital responsibility in a human life span. World countries targets to provide better care for their citizens. Digital transformations and innovations in Artificial Intelligence (AI) technologies helps in deep for the novel solutions to speed-up the decision making process in health management and patient consulting. AI branches including Machine learning (ML), Deep learning (DL), Neural Networks (NN), Natural Language Processing (NLP) and so forth can support innovations in heal care domain. This paper presents the comprehensive and noble review on recent existing tools and techniques and also recommends advanced techniques for health care industry that are to be used in Sultanate of Oman. Furthermore, overcoming AI challenges are resulted and presented through the study. This work mainly aimed to produce the multiple dimensions and challenges that helps to develop AI powered real-life applications in health care support system. © 2025 IEEE.

关键词： Nutrition

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Charting a New Course: Encouraging Programming Class Participation and Interest Through Multimedia and Flipped Classroom Activities 14th

Charting a New Course: Encouraging Programming Class Partic...

引用

14th international conference on Methodologies and Intelligent Systems for Technology Enhanced learning, mis4TEL 2024

作者： Nikolov, Ivan Department of Architecture Design and Media Technology Aalborg University Aalborg9000 Denmark

ISBN: (纸本)9783031855603

The integration of new technologies and evolving learning preferences necessitates a reevaluation of teaching methods in higher education. Beyond traditional lectures and textbooks, students now seek increased engagement and interactive learning experiences. This shift is particularly crucial in engineering and computer programming courses. In this paper, we showcase the development of three project-based learning courses across different semesters in Aalborg University’s Medialogy education. These courses were strategically redesigned to leverage multimedia materials and embrace flipped classroom learning, aiming to enhance class participation and overall student engagement. We provide an overview of the advantages and challenges associated with these teaching designs, present initial results indicating improved student engagement, and discuss potential pitfalls and future directions to fortify this approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770807

The proceedings contain 102 papers. The special focus in this conference is on Cognitive computing and Cyber Physical Systems. The topics include: Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language Processing (NLP) and Machine learning Models;defense Strategy Security Mechanism for Sensor Networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using Machine learning and Image Processing;Leveraging the Power of MRMR in Machine learning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct Artificial Feed Forward Neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep learning-Models for Kidney Tumor Segmentation using CT Images;investigation of Quantum Machine learning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep learning;Exploring Prominent Convolutional Neural Network Frameworks to Identify COVID-19 Deceases by Using Medical Images;non-invasive Technique for Detecting Glycosuria Through Image Processing and Deep learning Approaches;deep learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures;THz Wideband Metamaterial Absorber for Different Applications.

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Predicting Online learning Performance and Designing Interventions Using learning Analytics 14

Predicting Online Learning Performance and Designing Interve...

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14th international conference on Educational and Information Technology, ICEIT 2025

作者： Li, Yanjing Wang, Xiaowei Fu, Qiang Institute of Education Science Research Heilongjiang University Harbin China Shandong University of Political Science and Law Faculty of Cybersecurity Jinan China School of Electronic and Information Engineering Guangxi Normal University Guilin China

ISBN: (纸本)9798331540883

With the widespread adoption of online learning, educators face the challenge of effectively improving student performance. This study aims to establish a grade prediction model and design corresponding learning intervention strategies by analyzing online learning behaviors. We utilized learning data from the edX online learning platform, including study duration, engagement, and quiz scores. Spiking neural network was employed to analyze the data and identify key behavioral features that influence student performance. The results indicate a significant correlation between learning behaviors and grades, with specific study habits and engagement levels playing a crucial role in grade prediction. Furthermore, based on the analysis results, we designed and implemented a series of learning intervention measures. This research provides empirical evidence for personalized learning and teaching interventions, emphasizing the importance of data-driven decision-making in education. © 2025 IEEE.

关键词： Students

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770777

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