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检索条件"任意字段=International Conference on Advances in Machine Learning and Systems Engineering"

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Introduction to advances in Information, Intelligence, systems and Applications 14th

Introduction to Advances in Information, Intelligence, Syste...

引用

14th international conference on Information, Intelligence, systems, and Applications, IISA 2023

作者： Tsihrintzis, George A. Virvou, Maria Bourbakis, Nikolaos G. Jain, Lakhmi C. Department of Informatics University of Piraeus Piraeus185 34 Greece Department of Electrical and Computer Engineering Wright State University DaytonOH45435-0001 United States KES International Shoreham-by-Sea United Kingdom

ISBN: (纸本)9783031674259

Information is widely available and accessible, but frequently leads to information overload and overexposure and the effort for coding, storing, hiding, securing, transmitting, and retrieving it may be excessive. Intelligence, inspired by biological and other paradigms, is required to manage information and extract knowledge from it. Information and Multimedia systems, with an increasing level of Intelligence, are being developed that incorporate these advances. As a result, new Technologies, Protocols, and novel Applications are emerging. In turn, the novel applications lead to the collection of new information and the "Information → Intelligence → systems → Applications" circle is repeated at ever higher levels. This book includes extended versions of selected research works that were presented at the 14th international conference on Information, Intelligence, systems, and Applications (IISA2023), Volos, Greece, July 10–12, 2024 (https://***/iisa2023) along with additional relevant papers by invited authors. These works cover a broad spectrum of topics from both novelties in theory and advanced methodologies as well as applications in the Energy, Agriculture, Medicine, Forensics, Healthcare, Education, Tourism, Inspection, Traffic and Software engineering. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Enhancing Academic Feedback systems with Deep learning and Ensemble Methods 1

Enhancing Academic Feedback Systems with Deep Learning and E...

引用

1st international conference on advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sharma, Hemant Joshi, Hiren Gujarat University Dept. Computer Science Ahmedabad India

ISBN: (纸本)9798331518578

This paper attempts to improve the academic feedback systems via complex deep learning and ensemble methods. Leveraging the NIRF Rankings 2022 dataset that provides radical performance metrics of Indian academic institutions, the project would attempt to develop trustworthy models to monitor and facilitate improvement in the academic feedback system. The techniques used in data preparation prevent missing values, outliers, and categorical data in using advanced machine learning models. In order to construct the model and capture fine-grained patterns linked to academic achievement, 1D Convolutional Neural Networks (CNNs) along with Multi-Layer Perceptrons (MLPs), both individually and in combination, are employed extrapolated from the National Institutional Ranking Framework to obtain data. Precision, accuracy, recall, along with the F1-score will be used to evaluate these models' performance. By the time these findings are finished, they should be able to provide practical advice on how to improve academic evaluations' effectiveness and advance educational institutions' more flexible feedback systems. © 2025 IEEE.

关键词： Contrastive learning

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machine learning-Based Wine Quality Prediction 2

Machine Learning-Based Wine Quality Prediction

引用

2nd IEEE international conference on advances in Information Technology, ICAIT 2024

作者： Dhondiyal, Shiv Ashish Kumar, Rohit Verma, Himanshu Dhyani, Ashish Singh, Sumeshwar Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India GNIOT Institute of Professional Studies Greater Noida India Dept. of Hospitality Management Graphic Era Deemed to be University India Dept. of Computer Science and Engineering Jaypee Institute of information technology sector 62 India

ISBN: (纸本)9798350383867

The main goal of this attempt is to assess the quality of wine and determine if it is excellent or terrible. Humans have been engaging in the act of tasting for generations and have consistently made predictions using their sensory organs. A number of industries have recently embraced new technology and used it to a variety of uses. However, human knowledge is required in a number of businesses, such as product quality assurance. With the increasing popularity of the product, modern innovations have increased the cost of the treatment. To ensure the quality of the output, this study looks at a number of machines learning algorithms, including Random Forest Classification, Decision Tree classifiers, and Support Vector machines (SVM). These methods make use of the product's current attributes to simplify the quality control procedure. © 2024 IEEE.

关键词： Adversarial machine learning

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Application of Artificial Intelligence in Healthcare systems: A Scientometric Analysis

Application of Artificial Intelligence in Healthcare Systems...

引用

2024 international conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ajibade, Samuel-Soma M. Lee, Angela Siew Hoong Jasser, Muhammed Basheer Akinola, Tomiwa Faith Sunway University Dept of Computing and Information Systems Selangor Malaysia Istanbul Ticaret Univ Dept of Comp. Engineering Turkey Sunway University School of Engineering and Technology Department of Computing and Information Systems Selangor Malaysia University of Ibadan Faculty of Public Health Department of Health Promotion Nigeria

ISBN: (纸本)9798350358155

This bibliometric study examines the worldwide progression of machine learning applications in medical and healthcare research between 2010 and 2023. A large dataset of published articles pertaining to machine learning applications in the medical and healthcare sectors is mined for useful information in this study. The data extraction procedure involves retrieving pertinent information from primary sources such as journals, books, and conference proceedings. Subsequently, the retrieved data is subjected to analysis to discern the patterns and tendencies in the use of machine learning in medical and healthcare research. A total of 1,220 publications were found in the Scopus database over the past 14 years. In addition, the study demonstrated that most AIHS research has concentrated on artificial intelligence applications to address a wide range of problems, including patient data security and chronic medical difficulties (such as cardiovascular disorders). Policymakers, healthcare practitioners, and researchers around the world may find this study's conclusions helpful. Emerging ethical concerns, integration, and real-world uses in smart healthcare systems, and the Internet of Things (IoT), could be the focus of future research. © 2024 IEEE.

关键词： artificial intelligence bibliometric analysis healthcare systems machine learning medical research

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Emotion Speech Recognition in the Cloud 2

Emotion Speech Recognition in the Cloud

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2nd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Ganesh, Devalla Bhaskar Prasanth, Balisetty Chowdary, Manne Teena Avala, Varsha Sree Gangashetty, Suryakanth V Koneru Lakshmaiah Education Foundation Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350327533

Emotion speech recognition in the cloud has emerged as a transformative technology with the potential to revolutionize various sectors, induding customer service, mental health, and human-computer interaction. This abstract provides an overview of the key concepts and approaches involved in the development and deployment of emotion speech recognition systems in doud-based environments. Emotion speech recognition leverages advances in machine learning, natural language processing, and cloud computing to detect and analyze emotional cues in spoken language. By identifying emotional states, such as happiness, sadness, anger, and more, these systems offer valuable insights for businesses and healthcare providers, enabling them to improve customer experiences and mental health services. © 2024 IEEE.

关键词： Cloud computing Deep learning models Emotion recognition Natural language processing Speech analysis

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A machine learning-Based Methodology for IoT Security 2

A Machine Learning-Based Methodology for IoT Security

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2nd IEEE international conference on advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Pousia, S. Kowsick, N. Rinishvanth, M.S. Rahul, D. Shri Hari, S. Pravin Kumar, S. Bannari Amman Institute of Technology Sathyamangalam India

ISBN: (纸本)9798350315905

The popularity of IoT and gadget connectivity is rapidly growing in the present world. Cyber risk has become a significant issue for IoT devices, particularly edge devices, as D2D communication and Internet traffic have grown. The use case of machine learning for IoT security is presented in this proposal. Recent scientific advancements often lead to the development of new technologies. In order to help with this proposal, machine learning is now used by our society. It is because maintaining and managing the data is extremely difficult for humans. As new technologies are developed, cybersecurity measures can be enhanced through innovative methods. The most recent and promising strategy in cyber-physical security is machine learning (ML), which can help address a number of burgeoning issues. Explore potential problems with IoT security controls when applying machine learning to IoT systems and the design of IoT systems. Because smart gadgets are so accessible and in such high demand, IoT systems are exposed to new cyber-physical security and privacy assaults. IoT systems need to be secured with strong, adaptable, and contemporary security techniques. © 2023 IEEE.

关键词： Cyber Physical System

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MediGuide AI using machine learning 2

MediGuide AI using Machine Learning

引用

2nd international conference on machine learning and Autonomous systems, ICMLAS 2025

作者： Pooja, R. Elugubanti, Nikitha Mary Shiba, C. Kalai priya, V. Sasirekha, K. Sabapathy, S Rathana R.M.K Engineering College Computer Science and Business Systems Tiruvallur District Kavaraipettai India R.M.K Engineering College Department Of Computer Science and Business Systems Kavaraipettai India R.M.K College of Engineering and Technology Department Of Computer Science and Engineering Tiruvallur District Puduvoyal India R.M.D Engineering College Department Of Computer Science and Business Systems Kavaraipettai India Dhaanish Ahmed College of Engineering Department Of Computer Science and Engineering Vanchuvanchery Chennai India

ISBN: (纸本)9798331505745

Providing diverse patient groups with their specific medication requirements, using a wide range of drugs, poses meaningful challenges for prescribing. Standardized approaches often lead to large waste, many side effects and suboptimal outcomes. Therefore, we need improved, more personalized healthcare. MediGuide AI's drug recommendation system is powered by machine learning and it offers a thorough solution. Optimized data pre-processing and analysis, leading to the desired outcome, is achieved using advanced machine learning algorithms like Naive Bayes, TF-IDF and the Natural Language Toolkit. Highly personalized prescriptions are generated using thorough patient data, including demographics and meaningful clinical factors to achieve substantially better accuracy. Important enhancements include dual recommendation outputs, which provide many drug recommendations alongside a large number of alternative Ayurvedic treatments presented side by side for effortless comparison. An advanced personalized dosage calculation module precisely determines drug dosages using advanced regression models based on age, weight and metabolism. This system helps patients track and record their medications and it also suggests several nearby Ayurvedic shops based on their location. MediGuide AI employs exceptionally powerful algorithms and its advanced tools deliver remarkably precise and exceptionally user-friendly healthcare solutions. Current systems make use of rule-based methods that cannot learn from new data, whereas MediGuide AI leverages a hybrid Naïve Bayes and PassiveAggressiveClassifier model for higher accuracy and real-time adaptability. It also includes Ayurvedic options to ensure personalized healthcare experience. © 2025 IEEE.

关键词： Drug dosage

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Advanced machine learning Models for Electrical Fault Detection and Classification in Transmission Lines 3

Advanced Machine Learning Models for Electrical Fault Detect...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Deepika, Dugganapalli Charan, Maridinapalli Madhu Nossam, Sri Chakradhar Manitha, P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

With the fast development of power systems, efficient methods for fault detection and classification are needed to maintain the stability, safety and efficiency of the systems. In particular, this paper investigates advanced machine learning techniques for fault detection and classification in transmission lines using supervised learning algorithms, including Support Vector machines (SVM), Decision Trees, Random Forests, Naïve Bayes and Linear Discriminant Analysis (LDA). A dataset consisting of three phase currents and voltages from MATLAB simulations of normal and faulty system operations are used to study the problem. These models classify faults to be single line to ground, line to line and the three phase faults. Performance evaluation is done by metrics such as accuracy, precision, recall, F1 score, and AUC with respect to ROC analysis. Decision Trees are of high reliability and have good computational efficiency for fault detection among the models. The results presented in this research point to the importance of machine learning in reducing fault diagnosis circuit in order to achieve a more intelligent and resilient power transmission systems. © 2025 IEEE.

关键词： Power transmission lines

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advances in Crime Identification: A machine learning Perspective

Advances in Crime Identification: A Machine Learning Perspec...

引用

2024 international conference on Communication, Computer Sciences and engineering, IC3SE 2024

作者： Rachana, N. Norton Stanley, S.A. Sandeep, J. Sreeja, C.S. Department Of Computer Science Bangalore India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366846

Crime profoundly impacts individuals, communities, and families. Technological advancements have provided perpetrators with new opportunities for criminal activities. The primary objective of the police department is to resolve crimes, ensuring justice for the victims. Additionally, preventing such incidents is crucial for creating a safer world. The landscape of criminal justice has undergone a significant shift with the integration of machine learning techniques, unlocking unparalleled potential for accuracy and efficiency. This study thoroughly examines the concept of various applications of machine learning in crime detection, prediction, and prevention. We examine the evolution of these technologies, from early developments to state- of-the-art methodologies, conducting a thorough analysis of their strengths, limitations, and ethical considerations. Moreover, the paper sheds light on crimes discussed in academic circles, serving as a repository for scholars and researchers. This facilitates informed discussions and guides future research endeavours. © 2024 IEEE.

关键词： machine learning

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Automated Power Factor Correction and Predictive Energy Monitoring Using machine learning

Automated Power Factor Correction and Predictive Energy Moni...

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2024 international conference on Science, Technology, engineering and Management, ICSTEM 2024

作者： Govindaraj, V. Palpandian, P. Varun, V. Sankar, Kiran M. Ram, Murali Department of EEE Karpagam College of Engineering Coimbatore India

ISBN: (纸本)9798350376913

The generation of power is important. What is more important is the proper utilization of the power generated. Our project "Automated Power Factor Correction and Predictive Energy Monitoring Using machine learning" aims to reduce power losses by improving the power factor in the power systems across industries that are the major power consumers. In the quest for optimizing energy efficiency and sustainability in today's electrified world, the integration of machine learning(ML) algorithms into power systems is a great way of demonstrating how the energy that is being sent is being utilized can predict what would the energy consumption be in the future which can help in reducing the stress applied on power generation. © 2024 IEEE.

关键词： Electric circuit breakers

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