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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sumathi, V. Ramesh, S. Basavaraddi, Chethan Chandra S. Visumathi, J. Ishwarya, M.V. Srinivasan, S. Sri Sairam Engineering College Department of Mathematics Tamil Nadu Chennai India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India R&d Don Bosco Institute of Technology Vtu Department of Artificial Intelligence and Machine Learning Karnataka Belagavi India Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Optimization of industrial activities is significantly helped by Advanced Process Control (APC), which increases efficiency, decreases costs, and improves product quality. Artificial Neural Networks (ANNs) and the Internet of Things (IoT) have greatly improved APC in manufacturing. It examines how APC systems may include ANNs and IoT devices in industrial processes. By connecting to a wide range of sensors placed strategically throughout a production facility, IoT devices may gather data in real time, allowing for managing and monitoring crucial parameters. To APC systems, ANNs are invaluable for predictive modeling and control due to their adaptability to changing process conditions and capacity to represent complicated nonlinear interactions. Manufacturers may gain better control over their operations, improving productivity and product quality, by using data from the IoT to train and update ANN models. Recent advances in APC using IoT devices and ANNs are reviewed in this study. Their applications in various industrial industries are discussed, and case examples are shown to demonstrate their usefulness. Further improvements in APC for manufacturing using IoT devices and ANNs may be guided by highlighting the field's problems and future research goals. Challenges with data security, integration challenges, and reliability impact the accuracy of predictive modeling, scalability, and performance of IoT systems. © 2024 IEEE.

关键词： Adaptive control systems

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Parkinson's Disease Detection using Hand Drawn Spirals and Waves

Parkinson's Disease Detection using Hand Drawn Spirals and W...

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2023 International Conference on Recent Advances in Science and Engineering technology, ICRASET 2023

作者： Shashidhara, H.S. Kalbag, Saish K Mathias, Tanya Srinivasa, K.G. Niranjanamurthy, M. Affiliated to Visvesvaraya Technological University M S Ramaiah Institute of Techology and Engineering Department of Informaition Science and Engineering Belgaum Bangalore India International Institute of Information Technology Department of Data Science and Artififial Intelligece Chhattisgarh Naya Raipur India Affiliated to Visvesvaraya Technological University B M S Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Belgaum Bangalore India

ISBN: (纸本)9798350306927

Parkinson's disease, a neurodegenerative disorder, affects millions of people worldwide. Early detection is critical for effective treatment and management. Early detection of Parkinson's disease is challenging due to the lack of accessible and non-invasive screening methods. Current diagnostic techniques often require specialized equipment and invasive procedures, limiting their feasibility for widespread screening. There is a need for cost-effective and accessible approaches that can accurately detect early stages of Parkinson's disease. The model requires a high degree of precision in distinguishing between these patterns, which is a significant challenge to overcome. In this research work, we proposed a novel approach for detecting early stages of Parkinson's disease by analyzing hand-drawn spirals and waves in photographs. © 2023 IEEE.

关键词： Diagnosis

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Regularity and positivity of solutions of the Consensus-Based Optimization equation: unconditional global convergence

arXiv

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arXiv 2025年

作者： Fornasier, Massimo Sun, Lukang Technical University of Munich School of Computation Information and Technology Department of Mathematics Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Germany

Introduced in 2017 [28], Consensus-Based Optimization (CBO) has rapidly emerged as a significant breakthrough in global optimization. This straightforward yet powerful multi-particle, zero-order optimization method draws inspiration from Simulated Annealing and Particle Swarm Optimization. Using a quantitative mean-field approximation, CBO dynamics can be described by a nonlinear Fokker-Planck equation with degenerate diffusion, which does not follow a gradient flow structure. In this paper, we demonstrate that solutions to the CBO equation remain positive and maintain full support. Building on this foundation, we establish the unconditional global convergence of CBO methods to global minimizers. Our results are derived through an analysis of solution regularity and the proof of existence for smooth, classical solutions to a broader class of drift-diffusion equations, despite the challenges posed by degenerate diffusion. © 2025, CC BY.

关键词： Particle swarm optimization (PSO)

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Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing 24

Octopus: Experiences with a Hybrid Event-Driven Architecture...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis

作者： Haochen Pan Ryan Chard Sicheng Zhou Alok Kamatar Rafael Vescovi Valérie Hayot-Sasson André Bauer Maxime Gonthier Kyle Chard Ian Foster Department of Computer Science University of Chicago Chicago IL USA Data Science and Learning Division Argonne National Laboratory Lemont IL USA Department of Computer Science Southern University of Science and Technology Guangdong China Department of Computer Science Illinois Institute of Technology Chicago IL USA Department of Computer Science University of Chicago Chicago IL USA and Data Science and Learning Division Argonne National Laboratory Lemont IL USA

ISBN: (纸本)9798350355543

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus' ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients.

关键词： Event-driven architecture

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm 5

An Enhanced Traffic Incident Detection using Factor Analysis...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Vanithamani, S. Karunakaran, P. Pandiaraja, P. Tamilarasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector Machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks. © 2024 IEEE.

关键词： Dimensionality reduction

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Stroke Home Rehabilitation Approach Using Mobile Application Based on PostNet Machine learning Model 23

Stroke Home Rehabilitation Approach Using Mobile Application...

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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Das, Utpal Chandra Le, Ngoc Thien Benjapolakul, Watit Vitoonpong, Timporn Pluempitiwiriyawej, Charnchai Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

ISBN: (纸本)9798400700712

Stroke is a significant cause of mortality and disability globally, with its occurrence in the human brain and motor function being linked to various parts of the human body. Stroke victims often experience disabilities or mobility problems in affected body parts, either on one or both sides of the body. Physiotherapy exercises are the primary treatment and medication for stroke patients, necessitating daily monitoring by a physiotherapist. However, this approach is expensive, and rehabilitation centers and physicians are scarce. Numerous research studies have been conducted to address this issue from various perspectives. This study proposes a Convolutional Neural Network (CNN) based pose net machine learning (ML) model for stroke home rehabilitation using pose detection and classification with a skeleton base model and human pose estimation drawing. We trained our ML model using different human pose images for this model. The tested accuracy of our CNN model is 100% for our exercise pose image test case. Later we build and mobile application for remote rehabilitation. Tested the application with ten different subjects, achieving a 98% for elbow extension, and in the case of elbow flexion and Normal position of balancing both sides, all the persons achieved a 100 % accuracy rate under a laboratory environment. © 2023 ACM.

关键词： Patient treatment

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HealthGuardian: Artificial Intelligence System for Early Diagnosis and Clinical Support

HealthGuardian: Artificial Intelligence System for Early Dia...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： V Radhamani D. Sudha Devi E Chandra Blessie P J Isabella Priya R Tanushree Department of Computing (Decision and Computing Sciences) Coimbatore Institute of Technology Coimbatore Tamil Nadu India Department of Computing (Data Science) Coimbatore Institute of Technology Coimbatore Tamil Nadu India Department of Computing (Artificial Intelligence and Machine Learning) Coimbatore Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

In today's fast-paced world, access to timely and accurate healthcare information is critical, yet many individuals face challenges in assessing their symptoms, finding appropriate care, and understanding potential treatments. To address these issues, we propose HealthGuardian, an AI-powered clinical decision support system designed to assist users with symptom checking, personalized treatment recommendations, healthcare provider suggestions, online consultations, and early-stage diabetes prediction. HealthGuardian allows users to input their symptoms through natural language processing (NLP) and a recur-rent neural network (RNN) model, providing possible diagnoses and advising when to seek medical attention. The system offers personalized treatment plans based on medical history, suggests nearby healthcare providers using the Overpass API, and facilitates online consultations with doctors. Additionally, it features a predictive tool for early-stage diabetes risk assessment using various machine learning algorithms. The system leverages machine learning algorithms such as logistic regression, decision tree classifier, Naive Bayes, and random forest classifier to enhance diagnostic accuracy and prediction reliability. Our results demonstrate that HealthGuardian improves early diagnosis accuracy, provides actionable treatment guidance, and increases healthcare accessibility, helping users make informed decisions about their health.

关键词： Logistic regression Accuracy Machine learning algorithms Neural networks Medical services Diabetes Reliability Risk management Medical diagnostic imaging Random forests

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Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Wu, Yicheng Xia, Yong Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Xi’an710072 China The University of Adelaide Australian Institute for Machine Learning AdelaideSA5000 Australia Monash University Faculty of Information Technology Department of Data Science and AI Australia

The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, capturing cross-dataset semantics. Particularly, we create a synthetic task with a CutMix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint that aligns aggregated predictions from various tasks with those from the synthetic task, further guiding the model to accurately segment foreground regions during training. We evaluated our VerSemi framework against seven established SSL methods on four public benchmarking datasets. Our results suggest that VerSemi consistently outperforms all competing methods, beating the second-best method with a 2.69% average Dice gain on four datasets and setting a new state of the art for semi-supervised medical image segmentation. Code is available at https://***/maxwell0027/VerSemi. © 1982-2012 IEEE.

关键词： Self-supervised learning

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The Impact of AI on English Language Teaching Environments: A Comprehensive Analysis

The Impact of AI on English Language Teaching Environments: ...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Nimma, Divya Arunadevi, V. Gupta, Santoshi Sen Kavitha, P. Mishra, Abhishek Kumar Muthuperumal, S. Data Analyst in UMMC University of Southern Mississippi United States Dr.Mahalingam College of Engineering and Technology Department of Science and Humanities Pollachi India Graphic Era Hill University School of Management Bhimtal India Graphic Era Deemed to Be University Uttarakhand Dehradun India Department of English Erode India CSJM University Department of Lifelong Learning and Extension Kanpur India K.Ramakrishnan College of Engineering Department of Mathematics Trichy India

ISBN: (纸本)9798331517953

The integration of Artificial Intelligence (AI) into English language teaching (ELT) has transformed academic practices by means of enhancing personalization, interactivity, and engagement. Traditional teaching techniques often conflict to provide individualized mastering experiences and timely feedback, main to varied pupil results and engagement degrees. Despite the provision of digital equipment, many current techniques lack the sophistication wished for adaptive learning and real-time interplay, ensuing in limited personalization and inconsistent academic help. This study introduces a comprehensive evaluation of AI's impact on ELT environments, focusing on personalized mastering paths, interactive remarks, and gamification. The novelty of the proposed technique lies in its mixed-techniques design, combining quantitative surveys, qualitative interviews, and case studies to assess AI equipment' effectiveness comprehensively. The study assesses adaptive gaining knowledge of systems, intelligent tutoring systems, and gamified structures to perceive their benefits and obstacles. The typical technique includes a literature assessment to contextualize AI advancements in schooling, followed with the aid of surveys to collect quantitative information on AI tool utilization and effectiveness amongst educators and college students. Semi-established interviews offer in-intensity qualitative insights, whilst case studies provide realistic examples of AI implementation in real-international settings. data evaluation employs statistical strategies for quantitative outcomes and thematic analysis for qualitative records, integrating findings to give a holistic view of AI's impact. Results indicate that AI notably complements personalized getting to know and engagement but also famous challenges such as data privacy concerns and ranging effectiveness throughout exceptional tools. © 2024 IEEE.

关键词： Contrastive learning

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Improved Approach for Mapping Anthropological Facial Features Based on A Convolutional Neural Network 2nd

Improved Approach for Mapping Anthropological Facial Feature...

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2nd International Conference on Advances in Information and Communication technology, ICTA 2023

作者： Huong, Nguyen Thu Long, Nguyen The Lien, Pham Thi Department of Informatics Institute of Cybersecurity and Digital Technologies MIREA – Russian Technological University Moscow119454 Russia Laboratory of Artificial Intelligence and Machine Learning Institute of Information Technology and Data Science Irkutsk National Research Technical University Irkutsk664074 Russia University of Information and Communication Technology Thai Nguyen University Thai Nguyen70000 Viet Nam

ISBN: (纸本)9783031495281

Our facial anthropological and actions are non-verbal communication tools that account for 93% of emotions in human communication, 55% of which are facial expressions and gestures. Facial emotions can be easily analyzed from facial images, and computers can interact with people the way people interact with each other. For this reason, automatic classification and analysis of facial emotions is currently an interesting and challenging problem that has great implications for society. In this work, the random forest method combined with a neural network was proposed to improve the classification of emotions by building feature maps. Training and testing models were built to perform the experiments, which achieved very good accuracy in the range of 98.5% to 99.7% for the two datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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