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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Shrikant Khadse Prateek Verma Vivek Lande Faculty of Engineering and Technology Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardha India Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardha India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The concept of telemedicine developed before the upsurge of popularisation of telehealth services, particularly with the support of artificial intelligence and machine learning. This review aims to deeply analyse how these technologies integrate and change telemedicine, improve the diagnostics of diseases, help in the treatment process, and provide more accessible healthcare. Predictive analytics, natural language processing, and tele-therapy monitoring are examples of the ML and A.I. technologies used in patient care management. This is the time of more intelligent and more effective remote care services with the help of several innovative technologies and phenomena like A.I. health chatbots and intelligent treatment algorithms. However, several factors limit its development, including data privacy, legal issues, and users' access to technologies. Directions for further development and continuous healthcare improvement include integrating advanced 5G communication systems and wearables and exploiting blockchain technology for data interchange. These developments create prospects to eliminate current impediments and enhance the provision of healthcare services worldwide. This scope consists of mechanisms for the best present data, emphasizing growth areas of new technologies, especially artificial intelligence and machine learning, that transform healthcare systems.

关键词： Data privacy Shape Reviews Telemedicine Medical services machine learning Transforms Prediction algorithms Predictive analytics Wearable devices

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artificial intelligence and machine learning in Telemedicine: Transforming Remote Healthcare 2

Artificial Intelligence and Machine Learning in Telemedicine...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Khadse, Shrikant Verma, Prateek Lande, Vivek Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Deemed to be University Faculty of Engineering and Technology Wardha India Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Deemed to be University Wardha India

ISBN: (纸本)9798331505745

关键词： Surgery

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A Review on the Diabetes Treatment Using artificial intelligence and machine learning

A Review on the Diabetes Treatment Using Artificial Intellig...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Prathmesh Waghmare Prateek Verma Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Chronic Diabetes Mellitus (DM) is typified by glucoregulatory system dysregulation, which results in hyperglycemia and long-term consequences that impact several organs. With over 425 million people affected globally, early diagnosis and effective management of diabetes are crucial. Recent advancements in artificial intelligence (AI) and machine learning (ML) provide encouraging ways to enhance the treatment of diabetes. The goal of AI is to develop systems that can handle complicated data, which is particularly useful for managing diabetes. As a subset off AI, ML enables the creation of tailored treatment plans and predictive algorithms that improve patient outcomes and diagnostic accuracy. AI and ML integration in the treatment of diabetes has led to significant innovations, such as automated insulin delivery systems and AI-driven clinical decision support tools. These technologies improve glucose management and patient empowerment by providing real-time feedback and personalized recommendations. Additionally, AI applications in retinal screening and genomics have advanced the diagnosis and understanding of diabetic complications and genetic predispositions. Despite these advancements, challenges remain, including data privacy concerns, technical barriers, and limitations in AI model design. Robust data management, device compatibility, and continual AI algorithm validation are necessary to overcome these challenges. All things considered, AI and ML have enormous potential to change the way diabetes is treated by facilitating early diagnosis, individualized therapy, and better patient self-management, all of which will eventually improve the quality of life for those who have the disease.

关键词： Technological innovation machine learning algorithms Reviews Medical treatment machine learning Retina Prediction algorithms Real-time systems Diabetes Insulin

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The Smart Factory of Tomorrow: artificial intelligence and machine learning Reshaping Manufacturing Processes 2

The Smart Factory of Tomorrow: Artificial Intelligence and M...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Priyanga, P. Ashwini, K. Deepa, S.R. Sridevi, S. Department of Artificial Intelligence and Machine Learning RNS Institute of Technology Karnataka Bangalore India Department of Computer Science and Design K.S. Institute of Technology Karnataka Bangalore India Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

The smart factory of the future would not be possible without the development of AI and ML technologies, which have ushered in a new era of production. Traditional industrial processes are being revolutionized by AI and ML due to their capacity to evaluate large quantities of data and make autonomous choices, which is leading to greater efficiency, productivity, and profitability. Predictive maintenance is one area where AI and ML are making important contributions. These systems may prevent unexpected and expensive failures by constantly monitoring equipment performance and analyzing real-time data. Taking preventative measures like these results in less downtime, lower maintenance expenses, and more efficient machinery. Synergies between AI and ML are improving factory quality assurance. These technologies can identify even the smallest flaws or deviations from product standards using sophisticated vision systems and pattern recognition algorithms. Manufacturing companies may reduce waste and customer complaints by maintaining a constant quality standard via the use of automated inspection methods. Optimization of production planning and scheduling is another important use of AI and ML in manufacturing. © 2023 IEEE.

关键词： artificial intelligence Collaborative Robots (Cobots) Industrial Internet of Things (IIoT) Lean Manufacturing machine learning Manufacturing Processes Predictive Maintenance Production Planning Quality Control Smart Factory

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Exploring the Tangible Impact of artificial intelligence and machine learning: Bridging the Gap between Hype and Reality 1

Exploring the Tangible Impact of Artificial Intelligence and...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dattangire, Rahul Vaidya, Ruchika Biradar, Divya Joon, Ashish Publicis Sapient Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India University of Texas at Arlington Computer Science ArlingtonTX76019 United States Andhus Technologies Inc Software Developer PensacolaFL United States

ISBN: (纸本)9798350367720

Beyond being merely trendy terms in technology, machine learning (ML) and artificial intelligence (AI) have directly influenced every industry. This article thus demystifies the true meaning of these technologies by sifting through the sultry descriptions. Here, we explore how AI and ML enhance efficiency, decision-making, and problem-solving in various sectors including health, Agriculture, finance, and production as we discover real-world applications. Continuing with the discussion of implementation challenges, the aspects of bias, ethical pitfalls, and report cards are examined. In addition, the article also covered how the integration of AI as well as ML remains a way of improving processes, creating better user experiences, and spurring innovation. This paper, which is also based on case studies, discusses the relationship between social welfare and AI and the importance of AI's development and use being done responsibly. Considering all the discussed aspects, this volume provides a rather nuanced perspective on AI and ML, unmasking the myths around these concepts to reveal the fact that they have a highly significant impact on the current reality and its projected evolutions. © 2024 IEEE.

关键词： Adversarial machine learning

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Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques

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HELIYON 2024年第10期10卷 e30866页

作者： Khamaj, Abdulrahman Ali, Abdulelah M. Saminathan, Rajasekaran Shanmugasundaram, M. Jazan Univ Coll Engn & Comp Sci Ind Engn Dept Jazan Saudi Arabia Jazan Univ Coll Engn & Comp Sci Mech Engn Dept Jazan Saudi Arabia BMS Inst Technol & Management Dept Artificial Intelligence & Machine Learning Bangalore India

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and nonessential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. The artificial intelligence algorithms demonstrate the correlation between the number of vacant staff jobs and both the frequency of license event reports each year and the ratio of contract employees to regular employees in the administrative domain. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective machine learning neural network models are derived from generative adversarial network (GAN) algorithms and proposed for administrative, operational and maintenance loops of nuclear reactor control unit.

关键词： artificial intelligence machine learning Human factor engineering Nuclear reactor control unit GAN iForest

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Human Factors Engineering Simulated Analysis in Administrative, Operational and Maintenance Loops of Nuclear Reactor Control Unit Using artificial intelligence and machine learning Techniques

SSRN

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SSRN 2024年

作者： Khamaj, Abdulrahman Ali, Abdulelah M. Saminathan, Rajasekaran Shanmugasundaram, M. Industrial Engineering Department College of Engineering Jazan University Jazan Saudi Arabia Mechanical Engineering Department College of Engineering Jazan University Jazan Saudi Arabia Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Bangalore India

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and non-essential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective neural network models for machine learning program are suggested for administrative, operational and maintenance loops of nuclear reactor control unit. © 2024, The Authors. All rights reserved.

关键词： machine learning

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Exploring the Tangible Impact of artificial intelligence and machine learning: Bridging the Gap between Hype and Reality

Exploring the Tangible Impact of Artificial Intelligence and...

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Advanced Computing and Emerging Technologies (ACET), International Conference on

作者： Rahul Dattangire Ruchika Vaidya Divya Biradar Ashish Joon Data Engineering Publicis Sapient Houston Texas USA Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Computer Science University of Texas at Arlington Arlington Texas USA Software Developer Andhus Technologies Inc Pensacola FL

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

Beyond being merely trendy terms in technology, machine learning (ML) and artificial intelligence (AI) have directly influenced every industry. This article thus demystifies the true meaning of these technologies by sifting through the sultry descriptions. Here, we explore how AI and ML enhance efficiency, decision-making, and problem-solving in various sectors including health, Agriculture, finance, and production as we discover real-world applications. Continuing with the discussion of implementation challenges, the aspects of bias, ethical pitfalls, and report cards are examined. In addition, the article also covered how the integration of AI as well as ML remains a way of improving processes, creating better user experiences, and spurring innovation. This paper, which is also based on case studies, discusses the relationship between social welfare and AI and the importance of AI’s development and use being done responsibly. Considering all the discussed aspects, this volume provides a rather nuanced perspective on AI and ML, unmasking the myths around these concepts to reveal the fact that they have a highly significant impact on the current reality and its projected evolutions.

关键词： Technological innovation Ethics Decision making Finance machine learning Production Pressing Medical services Writing Problem-solving

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The Smart Factory of Tomorrow: artificial intelligence and machine learning Reshaping Manufacturing Processes

The Smart Factory of Tomorrow: Artificial Intelligence and M...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Priyanga P S. Sridevi Ashwini K Deepa S R Department of Artificial Intelligence and Machine Learning RNS Institute of Technology Bangalore Karnataka India Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamil Nadu India Department of Computer Science and Design K.S. Institute of Technology Bangalore Karnataka India

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A Special Issue on Recent Progress in Developing artificial intelligence and machine learning Methodologies

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IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE 2021年第2期9卷 7-+页

作者： Mohammed, Sabah Tai-hoon, Kim Ghamisi, Pedram Chang, Ruay-Shiung Lakehead Univ Dept Comp Sci Thunder Bay ON P7B 5E1 Canada Korea Informat Secur Agcy Seoul South Korea Koreas Def Secur Command Seoul South Korea Beijing Jiaotong Univ Beijing 100044 Peoples R China UTAS Hobart Tas Australia Helmholtz Zentrum Dresden Rossendorf Machine Learning Grp Helmholtz Inst Freiberg Resource Technol D-01328 Dresden Germany VasoGnosis Inc San Jose CA USA Inst Adv Res Artificial Intelligence Vienna Austria IEEE Image Anal & Data Fus IADF Comm Vienna Austria Chung Shan Inst Sci & Technol Taoyuan Taiwan Natl Taiwan Univ Sci & Technol Taipei Taiwan Natl Dong Hwa Univ Hualien Taiwan Taiwan Hospitality & Tourism Coll Hualien Taiwan Natl Taipei Univ Business Taipei 100 Taiwan

The articles in this special section focus on recent progress in developing artificial intelligence and machine learning methodologies.

关键词： Special issues and sections artificial intelligence machine learning Geology Deep learning Data models Sensors Soil measurements Robot sensing systems

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