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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"

共 4138 条记录，以下是51-60 订阅

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Loan Default Forecasting Using machine learning Algorithm in Indonesian Regional Bank 4

Loan Default Forecasting Using Machine Learning Algorithm in...

引用

4th international conference on computer Communication and Artificial Intelligence (CCAI)

作者： Alexander, Jimmy Jan Tjahyadi, Hendra Univ Pelita Harapan Fac Comp Sci Grad Informat Dept Jakarta Indonesia

ISBN: (纸本)9798350362770;9798350362763

In an effort to address the issues of growing credit risk, this research analyzes the application of machine learning algorithms for loan default prediction in regional banks in Indonesia. this study evaluates the efficacy of four primary algorithms - Decision Tree, Random Forest, Gradient Boosting, and Naive Bayes - by analyzing a two-year historical loan dataset. In-depth study indicated that Naive Bayes is the best algorithm, attaining 99.96% accuracy, 98.45% precision, and 99.63% recall, with a perfect Area Under the Curve (AUC) score of 1,000. these results represent the improved capability of Naive Bayes in identifying probable defaults with high precision and accuracy, greatly minimizing the probability of false positives. the findings recommend the application of Naive Bayes as a significant method in local bank credit risk management, providing an effective solution for early default identification and increased financial stability. this study adds to existing literature by presenting actual proof of the efficacy of machine learning technology in predicting credit risk. the suggestions offered can aid regional banks in enhancing their risk management approaches.

关键词： loan default Naive Bayes decision tree random forest gradient boosting

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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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Privacy Preserving machine learning in Energy Services: A Survey 4

Privacy Preserving Machine Learning in Energy Services: A Su...

引用

4th international conference on computer Vision and Data Mining, ICCVDM 2023

作者： Tan, Xuning The Australian National University College of Computer Science Canberra Australia

ISBN: (纸本)9781510674448

Privacy preserving machine learning (PPML), which focuses on training machine learning models while preserving privacy, has attracted many research interests in the recent past. this paper investigates recent research on privacy preserving machine learning (PPML) techniques and their energy applications. PPML allows training models while protecting sensitive data. We categorize approaches into federated learning, homomorphic encryption, differential privacy, and secure multi-party computation. After explaining each method’s principles, we summarize research applying them to energy tasks like forecasting and anomaly detection. Comparative analysis identifies trade-offs between privacy guarantees, costs, and accuracy. Encryption-based techniques provide strong privacy but impact efficiency. Federated learning is more efficient but has weaker protections. Ongoing challenges include the performance of cryptography, calibrating noise, and developing robust aggregation rules. the survey offers a comprehensive reference on the state-of-the-art in PPML for energy, guiding further advancement of practical systems. © 2024 SPIE.

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 ...

引用

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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A Multifaceted Approach to Lung Cancer Diagnosis Using machine learning Techniques 4

A Multifaceted Approach to Lung Cancer Diagnosis Using Machi...

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Sai, Ongolu Bindu Sri Vighnesh, Dosapati Varshitha, Nalliboina Naga Nandhini, Panidapu Iwin thanakumar Joseph, S. Koneru Lakshmaiah Education Foundation Department of Computer science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798331540364

this research investigates the application of machine learning techniques for lung cancer prediction. A comprehensive dataset encompassing patient features and risk variables is analyzed to identify significant factors influencing lung cancer prognosis. Multivariate analysis techniques, including pair plots, violin plots, and scatter plots, are employed to explore the complex relationships between variables and their impact on lung cancer development. three machine learning algorithms-support vector machine (SVM), decision tree, and K-nearest neighbors (KNN)-are evaluated for their predictive power using confusion matrices and ROC curves. Principal component analysis (PCA) is utilized to reduce the dimensionality of the dataset and improve model performance. the study addresses the limitations of existing lung cancer diagnosis systems, focusing on improving early detection accuracy. By leveraging advanced machine learning algorithms, this research aims to contribute to the development of more effective and accurate lung cancer prediction models. © 2024 IEEE.

关键词： Lung cancer

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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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Post-quantum secure and efficient outsourced machine learning 4

Post-quantum secure and efficient outsourced machine learnin...

引用

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

作者： Shen, Bo Yang, Jun Yang, Fei Xu, Yongyong North China Institute of Computing Technology Beijing China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Cloud-assisted machine learning and analysis rely on cloud computing for various data processing services. Usually, data providers protect their data’s privacy using encryption before outsourcing, but the resulting encrypted data are known to be of poor utility. To this end, existing works combine differential privacy with public key cryptography to support multiple user analyses and queries of outsourced data. However, with the emergence of quantum computing, the schemes mentioned above based on the traditional hardness assumption will be threatened. this paper proposes quantum-secure outsourcing of privacy-preserving data publishing schemes. In particular, we develop a highly efficient proxy re-encryption scheme based on the ring learning with errors problem. this scheme allows the cloud service providers to return only the encrypted data that satisfies the user’s query without decrypting it, and the encrypted results can be decrypted using the user’s key. Meanwhile, a novel noise addition mechanism is integrated into the proxy re-encryption scheme, enabling cloud service providers to achieve a dataset sanitization procedure that supports the requests from multiple data users and allows providers to go offline after uploading their datasets. Finally, we present a detailed theoretical analysis and report an experimental evaluation of the real dataset. © 2024 SPIE.

关键词： Outsourcing

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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...

引用

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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Deep learning based garbage recognition system 4

Deep learning based garbage recognition system

引用

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

作者： Song, Zelong School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

the rapid increase in the amount of rubbish and the diversity of the substances in people's daily rubbish are two major problems of rubbish disposal at present, and the rubbish problem brings a serious burden to the environment as well as a waste of resources. At present, the main approach to deal with the waste problem is through waste separation. However, today’s traditional manual classification method is still used to screen the rubbish, which is labour-intensive with low sorting efficiency. this paper proposes a deep learning-based intelligent rubbish classification system, which can quickly achieve the positioning and recognition of rubbish in complex backgrounds for classification by incoming pictures and effectively improve the efficiency of rubbish classification. the system uses TensorFlow as the deep learning platform, and the combination of a convolutional neural network and a fully connected neural network is used to achieve the construction of the deep learning model. Meanwhile, Some nodes of the fully connected layer are discarded with a given probability in the training phase to avoid the overfitting of the system to optimise the system, and the system is also optimised by collecting various types of rubbish pictures and building inspection datasets, using data augmentation to improve the robustness of the model, the fast and accurate identification of different types of rubbish is achieved. the experimental results demonstrate that the intelligent rubbish classification system proposed in this paper has a high performance in terms of both accuracy and efficiency. © 2024 SPIE.

关键词： Efficiency

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Research on Equipment Fault Analysis based on machine learning Algorithm 4

Research on Equipment Fault Analysis based on Machine Learni...

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2023 4th international conference on computer Engineering and Intelligent Control, ICCEIC 2023

作者： Qin, Hua Wang, Pengfei Xu, Linping Unit 92145 of Pla Hongkou Shanghai China Navy Petty Officer Academy Bengbu Anhui China

ISBN: (纸本)9798350308877

this paper builds an intelligent operation and maintenance platform based on machine learning algorithm through using the equipment level and system level fault monitoring data generated in the operation and maintenance process and combining with machine learning data analysis technology. It focuses on solving the difficult problems such as fault feature selection, fault diagnosis model classification, time series trend prediction, etc. the above research provides a decision-making reference for operating the communication links and making equipment maintenance scientifically, which has certain engineering application value and practical significance. © 2023 IEEE.

关键词： Support vector machines

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