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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Artificial Intelligence and learning Techniques in Intelligent Fault Diagnosis 4

Artificial Intelligence and Learning Techniques in Intellige...

引用

4th international conference on computer Science and Network Technology (ICCSNT)

作者： Sun Yuanyuan Guo Lili Wang Yongming Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing 100094 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China

ISBN: (纸本)9781467381734

At present, based on computer and information technology, intelligent diagnosis technology is in rapid development. In this paper, the application of artificial intelligence and learning techniques in intelligent fault diagnosis are demonstrated, such as Rule-Based Reasoning, Case-based Reasoning, Network neural, Fuzzy Logic, Genetic algorithm, Rough set theory, Bayesian network theory, Multi-agents, Reinforcement learning, Support Vector machine. Some kinds of applications are introduced. these intelligent fault diagnosis methods are widely used in complex fault diagnosis system. We will try to use them in our future intelligent fault diagnosis system for space station.

关键词： RBR CBR NN FL GA RST Bayesian network Multi-agents RL SVM

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Automatic extraction method of bottom line based on expectation-maximization machine learning algorithm 4

Automatic extraction method of bottom line based on expectat...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Xie, Zhengxuan Li, Gaixiao Song, Zikang Dai, Ke Dalian Naval Academy Dalian China Chart Information Center Tianjin China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

To address the challenge that the automatic extraction of bottom line is prone to interference from external factors, resulting in inaccurate detection and tracking of the bottom line in practical applications, this paper introduces the unsupervised learning expectation maximization algorithm (EM algorithm) into the peak detection and bottom line tracking stages of side-scan sonar images. An automatic extraction method of bottom line based on the EM machine learning algorithm is proposed. In order to evaluate the ability of the proposed method, experiments were conducted in simulated water environment. the results of the experiments show that the application of the EM machine learning method for automatic extraction of bottom line effectively addresses challenges such as occlusion by suspended particles and poor sea conditions. the experimental concludes that the proposed method achieves accurate detection and extraction of bottom line in complex environments, and exhibits promising practical applications. © 2024 SPIE.

关键词： Unsupervised learning

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Classification Prediction of Heart Disease Using machine learning Techniques 4

Classification Prediction of Heart Disease Using Machine Lea...

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Setiawan, Kevis Jonathan Beratha, Putu Satria Anggereainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350318432

Heart disease is also called a common one of global health concerns. A lot of research has been done before to predict someone whether has a heart disease or not by machine learning. In this study, we use five machine learning techniques as comparison which machine learning technique has a most accuracy to recognize heart disease in someone's condition. In this case, we are using UCI Cleveland Dataset as a sample and the result shows that the Support Vector machine and K-Nearest Neighbor gives the most accuracy which is 85% along with many aspects respectively. © 2023 IEEE.

关键词： Support vector machines

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Optimization of machine Vision Dynamic Monitoring System Based on Deep learning Algorithms 24

Optimization of Machine Vision Dynamic Monitoring System Bas...

引用

4th international conference on Internet of things and machine learning

作者： Wang, Yuan Chen, Lei Nie, Jiacheng Ma, Wenkai Song, Pengyu Mei, Di China Automot Parts Technol Tianjin Co Ltd Tianjin Peoples R China

ISBN: (纸本)9798400710353

In order to improve the automation and intelligence level of monitoring systems, an algorithm optimization method based on deep learning was analyzed. the results indicate that the comprehensive application of video preprocessing, object detection, object tracking, and anomaly event detection can effectively improve monitoring accuracy and response speed, demonstrating good adaptability and robustness. this provides practical guidance and broad application prospects for the development of intelligent monitoring technology.

关键词： Deep learning Monitoring system Video preprocessing

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Empowering Healthcare Systems Using machine learning: Working, Classification and application 4

Empowering Healthcare Systems Using Machine Learning: Workin...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： thakur, Keshav Raj Bhushan, Bharat Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350306118

Empowering Healthcare Systems with machine learning: Mechanisms, Classifications, and applications' examines how machine learning (ML) and healthcare are dynamically combining. It explains how ML algorithms manage healthcare data in detail, classifies ML's many healthcare applications, and emphasizes the significant impact of these applications. ML transforms healthcare systems, improving everything from the accuracy of disease diagnosis to the creation of individualized treatment approaches. To ensure appropriate integration, ethical and legal aspects are emphasized. In addition to redefining patient care, this ML and healthcare synergy improves decision-making and improves patient outcomes. the paper's main contribution is its discussion of an overview of machine learning (ML), which includes its needs, features, many applications and challenges and difficulty of (ML) in health sector. this paper highlights how (ML) have the ability to improve healthcare efficiency in general and the diagnosis and personalization of patient care. But it also acknowledges problems like data accessibility, data quality, high dimensionality, efficiency, and privacy issues that demand careful thought. © 2023 IEEE.

关键词： machine learning

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application of Deep learning Neural Networks in Sports Shoe Brand Classification Task 4

Application of Deep Learning Neural Networks in Sports Shoe ...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Li, Biao Sun, Guang Sheng, Haoyu Liu, Junjun Hunan University of Finance and Economics School of Information Technology and Management Changsha China Central South University School of Computer Science and Engineering Changsha410083 China

ISBN: (纸本)9798350373233

In recent years, deep learning has gained widespread attention and importance due to its outstanding performance in image feature learning. the rapid development of machine learning and deep learning has provided a powerful tool for handling complex classification problems and has achieved significant results. the development of neural network algorithms has also benefited from the progress in deep learning research. Deep learning is a machine learning method based on multi-layer neural network structures, which improves the modeling capability for complex data and tasks by increasing the depth and complexity of the network. For the task of classifying shoe brands, deep learning can help neural network models better understand features such as appearance, texture, and brand logos of shoes, as well as identify other everyday items to assist in their classification. After identifying the items, using different optimizers can improve the accuracy of classification, enabling more effective classification decisions. © 2023 IEEE.

关键词： Image recognition

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An Empirical Study of Monte Carlo-based Methods in machine learning 4

An Empirical Study of Monte Carlo-based Methods in Machine L...

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4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Lei, Fei Wang, Yan Wang, Lulu Wu, Qiuping School of Finance and Economics Guangdong University of Science and Technology Dongguan China School of Management Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9781510672505

Empirical studies in the field of machine learning based on Monte Carlo methods explore a powerful statistical technique aimed at solving the core problems of uncertainty modeling, model evaluation, and decision making;Monte Carlo methods provide new perspectives and tools for the application of machine learning models through stochastic sampling and simulation;however, Monte Carlo methods are also facing a number of challenges, such as computational efficiency, high-dimensional space problems, and sampling bias, etc.;in order to overcome these problems, this paper explores future research directions that may involve improved sampling methods, innovative applications that incorporate deep learning, etc. therefore, the continuous innovation in this field will provide new opportunities and challenges for the development and application of machine learning models. © 2023 SPIE.

关键词： Monte Carlo methods

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Model Evaluation of Various Supervised machine learning Algorithm for Heart Disease Prediction 7

Model Evaluation of Various Supervised Machine Learning Algo...

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7th international conference on Software Engineering and computer Systems and 4th international conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Hameetha Begum, S. Nisha Rani, S.N. Muscat College Muscat Oman Fatima Michael Engineering College Madurai India

ISBN: (纸本)9781665414074

machine learning (ML) is a type of Artificial Intelligence (Al) that allows the machine to learn from data and become more accurate in predicting outcomes without human intervention. the fundamental idea of machine learning is to mimic the way our brain works. In a variety of application domains, including medicine and healthcare, ML techniques have demonstrated strong prediction abilities. this study evaluated and compared several popular supervised machine learning techniques for predicting heart diseases using medical records from the UCI machine learning repository. the performance of different models, including Support Vector machines (SVM), K-Nearest Neighbor (KNN), and Logistic Regression models, is examined in this paper. the performance of these algorithms was analyzed based on the (Area Under ROC Curve) AUC score. AUC is an evaluation metric that helps us validate our machine learning algorithm how good it is, and the decision is made to implement if the AUC score is more significant than 0.5. the trial result verifies that the Logistic regression algorithm has achieved the highest AUC score of 0.87 compared to other ML algorithms. © 2021 IEEE.

关键词： Support vector machines

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Analysis and Study of Conventional and Advanced Image Segmentation Techniques for machine learning-Based Leaf Disease Detection and Classification 4

Analysis and Study of Conventional and Advanced Image Segmen...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： John, Sreya Rose, P. J. Arul Leena FSH SRMIST KTR Dept of Computer Science Chennai India

ISBN: (纸本)9798350306118

the demand for agricultural crops is constantly rising throughout time. this increment in demand is coupled with many other challenging factors such as climate change, pollution, urbanization etc that affects the quality and quantity of the crop production which can even lead to an economic breakdown. Lately, many studies were done to put an end to the difficulties faced by the agricultural sector. the application of technology in this area has been a pivotal turning point in the conservation of agricultural crops. Consequently, many computer based automated systems were developed to ease the tasks of farmers. Among these innovations, the application of image processing and machine learning has emerged as a breakthrough. By enabling early detection of plant diseases, these tools significantly contribute to maintaining crop health. Segmentation is an important stage in image processing, that is critical in accurately extracting relevant information from images. In this paper, we delve into the significance and advancements of segmentation, highlighting its major role in various applications. Additionally, we have also done a comparative study based on few experiments and observations in the latest improvements of various segmentation techniques. © 2023 IEEE.

关键词： machine learning

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Design and Implementation of a Mobile Teaching-learning Pattern through Atom Publishing Protocol

Design and Implementation of a Mobile Teaching-Learning Patt...

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4th international conference on machine Vision (ICMV) - computer Vision and Image Analysis - Pattern Recognition and Basic Technologies

作者： Wu Yu Tongji Univ Comp Sci & Technol Dept Shanghai 200092 Peoples R China

ISBN: (纸本)9780819490261

the new technologies set the stage for Mobile learning. In this paper, we explored a Mobile Teaching-learning pattern and its advantages. And then we modeled courses with Atom and Atom Publishing Protocol. Grounded on the pattern and modeling, we implemented mobile learning client side with Apple technologies, which could achieve anytime, anywhere learning. And at last, we discussed the application of our system.

关键词： Atompub Mobile Teaching and learning Apple Technology

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