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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3325 条记录，以下是11-20 订阅

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A survey on predictive maintenance for manufacturing using data mining techniques 4th

A survey on predictive maintenance for manufacturing using d...

引用

4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Mathew, Albin Kaur, Savleen Department of Computer Science Lovely Professional University Punjab India

ISBN: (纸本)9781032911571

this paper delves into the significance of predictive maintenance, a technique leveraging intelligent sensors for efficient data collection and evaluation to prevent problems and accidents. A data-focused predictive maintenance system has been established for manufacturing production lines, facilitating the identification of potential issues before they occur. machine learning plays a pivotal role in predicting the precise moment when industrial equipment requires repair, ensuring quick problem-solving and adaptive decisionmaking. the paper’s goal is to conduct a comprehensive literature analysis of data mining and machine learning methods tailored for predictive maintenance, emphasizing those currently under examination in this domain, and showcasing the practical applications of the most advanced machine learning techniques. this review provides a unique and in-depth exploration of the evolving landscape of data mining and machine learning methods within the context of predictive maintenance in manufacturing. Beyond a mere survey, it showcases the potential of cutting-edge machine learning methodologies to revolutionize predictive maintenance practices. Ultimately, this review weaves together the threads of technological innovation and practical utility, providing readers with a comprehensive roadmap to navigate the future of predictive maintenance in manufacturing. © 2025 the Author(s).

关键词： Predictive maintenance

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Supervised ML based Model to Predict Satisfaction of Student Performance During the Pandemic 4

Supervised ML based Model to Predict Satisfaction of Student...

引用

4th international conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Chapke, Praline P. Raut, Anjali B. HVPMs Dept Comp Sci & Engn COET Amravati India

ISBN: (纸本)9798350386356;9798350386349

the coronavirus, COVID-19 has spread around the globe, causing unprecedented changes in major areas including education and the economy. Amid a pandemic, the educational sector is relying heavily on performance prediction models to aid in the study of student status and the subsequent improvement of performance. the term "Educational data mining" describes the practice of using data mining principles and methods in the classroom. the explosion of educational data in recent years has made educational data mining a hot issue. Methods for discovering latent patterns in educational data might be developed within this area. the data can be learned more about student's, their learning habits, and how well they will do in school by applying the techniques drawn from Educational data mining. this study aims to predict how universities would modify e-learning in the aftermath of the outbreak by investigating student's actual experiences utilizing e-learning facilities during this time. A formal questionnaire was distributed and an online survey was created for this purpose. Following an exhaustive survey, a good outcome was achieved in terms of the dataset. In addition, the selected supervised machine learning algorithms such as Multi-Layer Perceptron (MLP), Random Forest (RF), Support Vector machine (SVM), and XG Boost (XGB) are utilized to train the prediction model to determine how likely students are to adopt the e-learning approach in the future. MLP produced the best results compared to other approaches, with an accuracy of 84.1%. the unexpected emergence of the epidemic caused tremendous disruptions to the academic pursuits of people.

关键词： Educational data mining Covid-19 machine learning Supervised Classification Students Performance

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Privacy-Preserving Deep learning Models for Human Activity recognition in Edge Computing Environments 4

Privacy-Preserving Deep Learning Models for Human Activity R...

引用

4th international conference on Artificial Intelligence and Signal Processing

作者： Kumar, Kambala Vijaya Pranitha, Challari Tanuja, Annam PriyaDarsini, Pagolu Indira Patnala, Eswar Dasari, Vinay Kumar Koneru Lakshmaiah Educ Fdn Dept CSE Guntur Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

the increase of wearable devices and smart sensors led to increase of human activity recognition system;this plays a vital role in various domains like healthcare and mainly we need to take further modifications on education, sports and security. Human activity recognition system raises privacy concerns, it often involves the collection of sensitive data of users, there is an approach to modernizing human activity recognition system by using machine learning techniques and data identification and lattice based methods to ensure privacy while restructuring and managing unwanted data or activities in the recognition process. Human activity recognition system involves the continuous monitoring and analyzing the individual activities. To address this, we can use machine learning approaches like deep learning models which improve the accuracy of activity recognition while minimizing the intrusion on the user privacy. Here we use machine learning for accurate the activity recognition and deep-deep learning techniques for preserving privacy and restructuring of unwanted activities. Combining these techniques we can safeguard user privacy in an increasingly data centric world. In this paper we are seeing as the negligible of human exercises from the pictures or recordings taken from the different public where the high goal can be distinguished and getting the exceptionally low goal and protection can be stowed away from such pictures or recordings.

关键词： Active learning

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A Review on the Applications of machine learning and Deep learning Algorithms for Image recognition 4

A Review on the Applications of Machine Learning and Deep Le...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of machine learning and Deep learning algorithms together. this study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. this paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. the latest image processing techniques include ViTs alongside GANs and Few-Shot learning but developers need to achieve better results in future improvements. the main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. this paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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Securing Personal Identity in Facial recognition: the Shift to machine Unlearning 4

Securing Personal Identity in Facial Recognition: The Shift ...

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Uday Kiran, G. Srilakshmi, V. Moulika, B. Mahitha, G.S. Laukya, G. Ruthick, M. Telangana Narsapur India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

machine Unlearning, which enables the successful removal of specific data from machine learning models, is a crucial approach for ensuring data privacy and compliance. Most existing unlearning techniques have been evaluated on conventional benchmark datasets, which often fail to replicate the complexity of real-world settings, especially when dealing with sensitive data such as private personal information. the majority of machine learning research has been conducted on traditional datasets, which may not fully capture the challenges encountered in real-world scenarios, particularly concerning sensitive and private data. To address this gap, the proposed method utilizes two datasets, MUFAC and MUCAC, specifically designed to evaluate the effectiveness of machine Unlearning in facial recognition contexts. Following the training of the model using facial data, a machine learning framework is developed. © 2024 IEEE.

关键词： Adversarial machine learning

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Weather prediction using data mining techniques 4th

Weather prediction using data mining techniques

引用

4th Annual international conference on data Science, machine learning and Blockchain Technology, AICDMB 2023

作者： Swathi, B.H. Nidhi, G.D. Sahana, L.J. Prathibha, M.N. Vidyavardhaka College of Engineering Mysuru India

ISBN: (纸本)9781032426853

Even with the recent century’s enormous advancements in science and technology, forecasting the weather remains a very difficult undertaking. In the past ten years, data mining techniques and applications have advanced significantly. Several scholars have investigated the effective use of data mining technologies in predicting climate and weather change. the important characteristics that are essential for data mining methods to be integrated into forecasting weather models are discussed in the paper. this paper’s major goal is to present a thorough comparative examination of the benefits, drawbacks, and outcomes of existing data mining approaches used to weather forecasting model. And also the comparative analysis is done based on the obtained results using different algorithms. © 2024 the Author(s).

关键词： data mining

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Breast Cancer Prediction Using Artificial Intelligence Technology 4

Breast Cancer Prediction Using Artificial Intelligence Techn...

引用

4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Akila, V. Christaline, J. Anita Srm Institute of Science and Technology Vadapalani Dept. of Electronics and Communication Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

the aim of the project is to compare the performance of four different machine learning algorithms for breast cancer prediction such as decision tree, logistic regression, XG boost, and CAT boost. We used a dataset of patient medical records containing various clinical factors to train and test the algorithms. Accurate diagnosis and early detection are essential for enhancing patient outcomes. data mining is a prominent tool in the healthcare industry for processing massive amounts of data. To examine massive amounts of complicated medical data, researchers use a variety of data mining and machine learning approaches. the use of these strategies can help medical practitioners forecast the onset of breast cancer. © 2024 IEEE.

关键词： Algorithms Breast cancer data mining machine learning

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A machine learning-Based Method for Assisted Analysis and Decision Making of Wafer Yield 4

A Machine Learning-Based Method for Assisted Analysis and De...

引用

4th international conference on machine learning and Intelligent Systems Engineering (MLISE)

作者： Qin, Jieli Xiamen Inst Technol Higher Educ Key Lab Flexible Mfg Equipment Integr Xiamen 361021 Peoples R China Xiamen Inst Technol Sch Mech Elect & Informat Engn Xiamen 361021 Fujian Peoples R China

ISBN: (纸本)9798350375084;9798350375077

With the rapid development of the semiconductor industry, the process technology has become more complex, and it is increasingly important to maximize the control of defects in production and improve wafer yield. Defects shown in Wafer Bin Maps (WBMS) are often strongly correlated with local system failures in production. In view of this, this paper proposes the convolutional neural networks (CNNs) architecture based on machine learning, and realizes the classification of hybrid defects by designing a separate classification model of minimum defects and using the probability analysis of recognition results. the purpose is to classify and statistics the faults of the wafer diagram fault diagram, perform yield analysis and calculation, identify defect types according to the wafer (fault pattern recognition), and take corresponding decisions to adjust the process in view of the systematic fault chip. the effectiveness of the method is proved by experiments, and the identification accuracy of single defects is higher, and the detection performance of mixed defects is also improved, and the overall performance is better.

关键词： Wafer Bin Maps(WBMs) yield Convolutional neural networks defect identification

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Self-attention Siamese Network for Unsupervised Few-Shot learning Tasks 4th

Self-attention Siamese Network for Unsupervised Few-Shot Lea...

引用

4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Bhattarai, Binit Luitel, Pratik Kartheek, Mukku Nisanth Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

ISBN: (纸本)9783031821523;9783031821530

In the real world, it is hard to extract large data with the characteristics that are labeled. this becomes a problem when trying to implement artificial intelligence to real-life problems. In this work, we propose a self-attention siamese network guided by the principles of unsupervised learning and few-shot learning which can learn robust representation without requiring the large number of labeled data. Our approach introduces a self-attention module to capture rich contextual information and a projection head that embeds features into a discriminative space. We have performed a comprehensive experiment evaluation to show the effectiveness of our approach compared to other supervised training methods and other few-shot learning techniques. Our results highlight the potential of adding self-attention to the siamese network for enabling unsupervised few-shot learning tasks, paving the way for more efficient machine learning systems capable of learning from limited unlabeled data.

关键词： Self Attention Unsupervised learning Few-shot learning Siamese Network SASN

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A Novel Model for Recognising Handwritten Devanagari Numerals using machine learning 4

A Novel Model for Recognising Handwritten Devanagari Numeral...

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4th international conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Prathima, Ch Arava, Ramprakash Reddy Sevitha, K. Manikanth, G. Vinay, D. Surya, C. Mohan Babu Univ Sch Comp Tirupati Andhra Pradesh India KSRM Coll Engn Dept CSE Kadapa Andhra Pradesh India Sree Vidyanikethan Engn Coll Dept CSSE Tirupati Andhra Pradesh India

ISBN: (纸本)9798350386356;9798350386349

this research study performs a comprehensive comparative analysis aimed at developing effective machine learning models for classifying handwritten Devanagari numerals. the research focuses on evaluating the performance of various models to determine the most accurate classification approach. Initiating with data pre-processing, including feature extraction and normalization, the data is prepared for model training and assessment. A diverse range of machine learning models, from traditional methods like Support Vector machines (SVM) to advanced techniques such as Random Forests, K-Nearest Neighbors, and Convolutional Neural Networks, are considered for the comparative analysis, ensuring a thorough assessment of classification capabilities. Cross-validation techniques are employed during model training and testing to enhance reliability. Statistical tests are utilized to assess the performance variations among models, enhancing the robustness of the analysis. Visual representations of performance metrics and comparison results offer clear insights. this research study aims to identify the most suitable machine learning model for handwritten Devanagari numeral classification, potentially advancing character recognition systems and linguistic applications.

关键词： Handwritten character recognition machine learning Support Vector machine(SVM) data augmentation Transfer learning Digital document processing

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