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检索条件"任意字段=4th International Conference on Machine Learning and Computer Application, ICMLCA 2023"

共 2341 条记录，以下是11-20 订阅

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machine and deep learning approaches for heart disease risk assessment 4th

Machine and deep learning approaches for heart disease risk ...

引用

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

作者： Sharma, Rahul Deepika Khan, Reshma Department of Computer Science and Engineering Chandigarh University Mohali Gharaun India

ISBN: (纸本)9781032911571

Heart disease is a major public health problem the world over, and accurate measures of risk assessment and appropriate classification of persons are very necessary in the early identification for treatment. this is one reason that the techniques of machine learning and deep learning find great application in the healthcare business, as they can analyze patterns of data and make predictions very effectively. the following research paper provides deep research on the techniques of machine learning (ML) and deep learning (DL) used to evaluate and categorize heart disease illness risk. It describes the different problems of the heart, the factors of risk, and models of ML and DL approaches. It also outlines what the current data sources in use are: digital health records, wearable technology, imaging, genetic data, and patient–reported outcomes. the review observes that, though challenges continue to be present in data collection, processing, and feature selection, it further has the ability to make the model free from bias and reliable. Finally, the authors state that such models might play a role in better patient outcomes and appropriate use of health resources, therefore allowing for focused medical strategies for the treatment of cardiac diseases. © 2025 the Author(s).

关键词： Electronic health record

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Breast cancer diagnosis using machine learning model 4th

Breast cancer diagnosis using machine learning model

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

作者： Arthi, T.S. Partheeban, N. Singh, Bhoomika Singh, Aparajitha Tripathi, Nikhil Department of Computer Science and Engineering Galgotias College of Engineering & Technology Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032911571

Early and accurate detection of breast cancer, particularly Invasive Ductal Carcinoma (IDC), is critical for improving patient outcomes. Traditional diagnostic methods like histopathology and mammography have limitations in terms of time, cost, and potential subjectivity. this study proposes a lightweight machine learning (ML) model for IDC classification using digital whole-mount breast cancer slide images. the model aims to achieve high accuracy while being suitable for deployment in resource-constrained environments. We evaluate our model on a publicly available breast histopathology dataset and compare its performance with other Deep learning (DL) models, focusing on both accuracy and computational efficiency. the results demonstrate that our model achieves a high classification accuracy (around 89.3 percent) with minimal processing power and memory requirements. Furthermore, successful deployment on a Raspberry Pi embedded system validates its potential for real-world application in resource-limited settings, potentially leading to improved breast cancer diagnosis accessibility. Index Terms—Bi-directional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), Deep learning (DL), Invasive Ductal Carcinoma (IDC), Multilayer Perceptron (MLP), Raspberry Pi, Tiny machine learning (TinyML). © 2025 the Author(s).

关键词： Convolutional neural networks

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Interactive Sign Language learning System-A Comprehensive application for Sign Language Alphabet and Word Recognition 4

Interactive Sign Language Learning System-A Comprehensive Ap...

引用

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

作者： Chandrasekaran, R. Vijayaraj, S. Balakumaran, M. Arul Stephen, C. Abhijjit, D. Soundarya, M.K. Vels Institute of Science Technology & Advanced Studies Department of Biomedical Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Electrical and Electronics Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Electronics and Communication Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Civil Engineering Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

the Interactive Sign Language learning System is a ground breaking application designed to provide a comprehensive and interactive learning experience for sign language alphabet and word recognition. Built on advanced technologies, this system aims to bridge the gap between individuals with hearing impairments and non-sign language users, enabling effective communication and inclusion. the application offers a user-friendly interface, where learners can easily navigate through various sign language alphabets and individual words, receiving real-time visual feedback and guidance. With a vast library of sign language gestures and clear video demonstrations, learners can practice their signing skills with confidence and accuracy. the system utilizes computer vision and machine learning algorithms to recognize and interpret users' sign language gestures, providing immediate feedback to ensure proper hand movements and positioning. Additionally, the system offers interactive exercises and quizzes, allowing learners to test their knowledge and track their progress over time. the application also incorporates gaming elements, making the learning process engaging and enjoyable. Whether used by individuals wanting to learn sign language or organizations looking to enhance accessibility, this comprehensive learning system is an invaluable tool that empowers users to communicate effectively and inclusively with the deaf community. © 2025 IEEE.

关键词： Adversarial machine learning

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Research on identification of domestic commercial block cipher algorithms based on deep learning 4

Research on identification of domestic commercial block ciph...

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Li, Li Chen, Jun School of Computer Science Chengdu University of Information Technology 24 Block 1 Xuefu Road Chengdu610225 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

With the widespread application of domestic commercial cryptographic algorithms and the advancement of commercial cryptographic application evaluation, the compliance of these algorithms has garnered significant attention. Various security agencies and research institutions in China have initiated studies on the identification of commercial block cipher algorithms and have explored their application in cryptographic evaluation work. this paper focuses on extracting features from ciphertext using the NIST randomness test method, followed by training and testing these features through various machine learning and deep learning methods. the paper consolidates relevant domestic research on this topic. In the final part of the study, encrypted data from the COCO2014 dataset using the domestic commercial cryptographic algorithm SM4 and the AES128 (CBC, ECB) algorithms are used for algorithm identification, employing MLP, CNN, LSTM, and Attention mechanisms. the experimental results demonstrate that CNN exhibits higher accuracy and stability compared to existing solutions, while the Attention mechanism shows advantages in subsequent AES128-ECB identification, albeit with highly sensitive to variations in the key-dimension selection. © 2025 SPIE.

关键词： Encryption algorithms

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Ensemble learning-based approach for water quality prediction 4

Ensemble learning-based approach for water quality predictio...

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Zhang, Jinchuan Sichuan Agricultural University Ya'an China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Water quality prediction is crucial for environmental protection and water resource management, particularly in accurately predicting pH values of water bodies, which can help identify potential water quality issues and take appropriate measures. this study utilizes historical water quality data provided by the United States Geological Survey to predict the pH values of multiple water stations in Georgia for the following day. Various feature engineering methods, such as Principal Component Analysis and Lasso Regression, along with machine learning models, were applied. We combined the strengths of different models using the Stacking ensemble learning technique, which significantly improved predictive performance. the experimental results show that the combination of feature engineering—specifically Polynomial Feature Generation—and the Stacking ensemble model effectively enhanced prediction accuracy. this research provides a new approach to improving water quality monitoring methods and contributes to more precise water resource management and environmental protection. © 2025 SPIE.

关键词： Prediction models

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Fusion of data dimensionality reduction algorithms baced on category representation theory 4

Fusion of data dimensionality reduction algorithms baced on ...

引用

4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Xu, Xiaoxiang Li, Fanzhang Zhang, Li School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9781510687615

For a long time, people have believed that representation problems are one of the bottlenecks in the field of machine learning. therefore, it is a long-term and exploratory work to study machine learning representation methods. Due to this, we use category theory to study the fusion representation of data dimensionality reduction. We propose the basic concept of category representation for data dimensionality reduction and provide a data dimensionality reduction fusion representation framework. We have conducted research and analysis on algorithms such as PCA, KPCA, and LDA, identified the essential connection between PCA, KPCA, and LDA and proposed a data dimensionality reduction fusion representation algorithm based on a data dimensionality reduction fusion representation framework. Finally, we demonstrated the feasibility of the proposed method through experiments. © 2025 SPIE.

关键词： Dimensionality reduction

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Saliency detection method for Panoramic Images based on GCN-ELM 4

Saliency detection method for Panoramic Images based on GCN-...

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4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： An, Nan Yu, Haiyang Zhang, Ripei Hu, Xiaojuan Li, Yanfeng School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In the field of saliency detection for panoramic images, traditional equirectangular and cube projection methods in panoramic image saliency detection often face issues like distortion and discontinuities, impacting detection accuracy. this study introduces an innovative image resampling technique and a GCN-ELM joint model. By evenly distributing spherical pixels onto a 2D plane, the method reduces pixel redundancy from equirectangular projection. Experimental results show that this approach significantly enhances saliency detection performance compared to existing methods. © 2025 SPIE.

关键词： Adversarial machine learning

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machine learning Techniques for Branch-and-Cut Methods: the Selection of Cutting Planes 4th

Machine Learning Techniques for Branch-and-Cut Methods: The...

引用

4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Giallombardo, Giovanni Miglionico, Giovanna Sammarra, Marcello Arcavacata Italy Cosenza Italy

ISBN: (纸本)9783031812408

the selection of cuts to be added to the current LP relaxation is one of the most critical task in Branch-and-Cut methods, since it strongly affects the performances of the algorithm. Recently, machine learning techniques have become popular to define effective cut selection strategies. In this paper we explore the possibility of selecting cuts by ranking them via support vector regression. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Integer linear programming

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4th EAI international conference on Data Information in Online Environments, DIONE 2023

4th EAI International Conference on Data Information in Onli...

引用

4th EAI international conference on Data Information in Online Environments, DIONE 2023

ISBN: (纸本)9783031807121

the proceedings contain 21 papers. the special focus in this conference is on Data Information in Online Environments. the topics include: Deep Image Inpainting Incorporating Texture Prior Based on Gabor Filter;the Improved XdeepFM Algorithm Based on Attention Mechanism and Factorization machine;deep learning-Based Glaucoma Diagnostic Assistance System on Mobile Devices;using Alignment Chain to Boost Genetic Sequence Alignment Process;analysis of the Industrial Internet Industry Chain and Supply Capability Analysis of Key Links Within Jiangxi Province;a Lithium-Ion Battery Cathode Material Literature Entity Recognition Method Based on Deep learning;design and Implementation of Improved Multi-objective Genetic Algorithm Based on Uniform Distribution;Wavelet and Kalman Filter-Empowered Traffic Detection for Secure QUIC Network Communication;hyFed: A Hybrid Blockchain Empowered Federated learning Privacy Fair Framework;research on the Capability Status of Industrial Internet Security Supervision Platform;solar System Dynamics with Jet Propulsion Laboratory Ephemeris;elderly Health Care Data Integration Framework: Design and Implementation;challenges and Prospects of Power Network Security Protection in the Context of a New Power System: A Case Study of Jiangxi;enhancing Sequence Alignment Efficiency through Concurrent Utilization of Multiple Arm Processors in a Sequential Processing Framework;Fusion of Multiscale Convolution and LSTM for Stock Price Prediction;development of a Web application for the Sociocultural Diffusion of the Municipality of Lamas, Peru;A Solution Against Selective Jamming Attack in IEEE 802.15.4e Wireless Networks;TDFM and TAFM: Time-Aware and Feature Fusion-Based Deep Recommendation Models for Short Videos;blockchain Based Access Control: A Review and Future Perspectives.

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Federated learning: Concepts, application and Future Scope 4th

Federated Learning: Concepts, Application and Future Scope

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Jain, Pallav Patidar, Sanjay Delhi Technological University New Delhi India

ISBN: (纸本)9789819751990

Artificial is a constantly evolving and progressing technology. In today’s society, artificial intelligence models are frequently employed. With today’s data and the capabilities of artificial intelligence, we can attain human intellect and, in some situations, considerably more. But also, the rapid increase of data and how it is managed is a cause for concern. machine learning is an artificial intelligence sub-area that allows a machine to learn from prior data. Nowadays, machine learning is utilized in a broad range of applications. However, it is a tough task to apply machine learning on decentralized data. In the recent years, a new technique known as Federated learning has garnered considerable attention. It concentrates to train distributed and decentralized data while maintaining its privacy. In our paper we have explained what Federated learning is, the characteristics of Federated learning, what are the hardships we face in applying Federated learning, in which field Federated learning is currently used and what are the future scope of Federated learning. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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