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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 4166 条记录，以下是11-20 订阅

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

4th International Conference on Numerical Computations: Theo...

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

ISBN: (纸本)9783031812439

the proceedings contain 105 papers. the special focus in this conference is on Numerical Computations: theory and Algorithms. the topics include: Resource Allocation via Bayesian Optimization: an Efficient Alternative to Semi-Bandit Feedback;multi-Objective and Multiple Information Source Optimization for Fair & Green machine learning;extended Optimal Control Problem for Practical application;explainable Process Deviance Discovery with Data-Efficient Deep learning;line Search Stochastic Gradient Algorithm with A-priori Rule for Monitoring the Control of the Variance;a machine learning Approach to Speed up the Solution of the Distributor’s Pallet Loading Problem;combined First- and Second-Order Directions for Deep Neural Networks Training;constrained Global Optimization by Smoothing;the Unreasonable Effectiveness of Optimal Transport Distance in the Design of Multi-Objective Evolutionary Optimization Algorithms;an Improved Modified Jaya Optimization Algorithm: application to the Solution of Nonlinear Equation Systems;GPU Acceleration of the Enhanced Jaya Optimization Algorithm for Solving Large Systems of Nonlinear Equations;effective Resistance Based Community Detection in Complex Networks;a Comparison of Formulations for Aircraft Deconfliction;optimal Recombination Problem in Genetic Programming for Boolean Functions;heuristics with Local Improvements for Two-Processor Scheduling Problem with Energy Constraint and Parallelization;numerical Analysis of Optimal Control of Assets and Liabilities by a Bank;optimal Control for Stochastic Multi-agent Systems With the Use of Parallel Hybrid Genetic Algorithm;DC Optimization in Adversarial Sparse Support Vector machine;A First-Order Optimality Condition in Nonsmooth Generalized Semi-infinite Programming (GSIP);miniaturisation of Binary Classifiers through Sparse Neural Networks;Price Forecasting for Bitcoin: Linear Regression and SVM Approaches;inside the Box: 0–1 Linear Programming Under Interval Uncertainty;machine Learn

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machine learning methods and resources: An overview 4th

Machine learning methods and resources: An overview

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

作者： Goyal, S. Kaur, A. Batra, N. Haryana Mullana-Ambala India

ISBN: (纸本)9781032911571

machine learning tasks are made simpler by a variety of tools that provide an extensive selection of features and ease of use. One renowned framework that excels at deep learning is TensorFlow, which makes model creation and execution quick and easy. With its extensive library of conventional methods, Scikit-learn make experimentation and implementation easier. PyTorch is a popular tool for dynamic graph analysis that may be used for both production and research purposes. A high-level API called Keras facilitates quick prototyping by streamlining deep learning workflows. Scalability is a key feature of Apache Spark, enabling networked machine learning on big datasets. Together, these technologies improve accessibility and quicken machine learning innovation across domains and sectors. this paper will expose a variety of methods within this paper that may be applied at various points of creating a robust machine learning model. It will also show you through the tasks the program completes, give case studies of a few applications, and describe which methods from machine learning are used to achieve the main objectives of the application. © 2025 the Author(s).

关键词： Adversarial machine learning

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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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Exploring the diverse landscape of machine learning techniques for skin cancer 4th

Exploring the diverse landscape of machine learning techniqu...

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

作者： Singla, Ishdeep Chaube, Nirajan Keyal, Prince Kumar Mandal, Dipak Kumar Chandigarh University Punjab Mohali India

ISBN: (纸本)9781032911571

the world is suffering from the cancer and the skin cancer is one of the leading cancer that the people are suffering from it. Skin cancer is a significant world wide health concern, necessity for the accurate and timely diagnosis for the effective treatment and managements. this review paper dive into the rapidly increasing field of the machine learning techniques in the field of the skin cancer Detection. By studying all the present literature, we provide an comprehensive review of the diverse machine learning technique used in the this *** main objective is to explain the current landscape of machine learning techniques, including the deep learning, svm machine and ensemble methods and their respective application in the skin cancer diagnosis. through a systematic analysis of studies, we identify key trends, challenges, and advancements in the field. Additionally, we discuss the potential implications of machine learning driven approaches for enhancing early detection rates, improving diagnostic accuracy, and facilitating personalized treatment strategies. this review aims to contribute to a deeper understanding of the evolve role of machine learning in skin cancer research and clinical practice, ultimately fostering advancements in patient care and *** advantages derived from the application of these technologies encompass heightened diagnostic precision, enhanced treatment efficacy, and the facilitation of more streamlined healthcare delivery. © 2025 the Author(s).

关键词： Lung cancer

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Enhancing Pneumonia Treatment in Children by machine learning Techniques 4

Enhancing Pneumonia Treatment in Children by Machine Learnin...

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

作者： Gajbhiye, Ashwini Gundewar, Swapnil Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Pneumonia is a health challenge that affects many people more so the children, thus leading to high death rates across the world. there is a persuading evidential proof which shows that the techniques in the application of the machine learning (ML) have helped in improving the diagnosis and treatment of pneumonia. this paper discusses a number of ML techniques such as CNNs, SVMs and Random Forest that can be used to enhance the reliability and effectiveness of detecting pneumonia from enhanced chest x-ray images. the classification shows how other methods like Min-Max normalization and a specific type of image pre-processing called Contrast-Limited Adaptive Histogram Equalization (CLAHE) can make image quality even better as well as increase the accuracy of the model. Here, by using the sophisticated dalliance of ML models it is possible to dramatically minimize the incidence of diagnostic errors and enhance the results of pneumonia treatments thus witnessing an important evolutionary leap in the specialty. © 2025 IEEE.

关键词： Convolutional neural networks

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machine learning Based Critical Velocity Predictions in Slurry Flows 4

Machine Learning Based Critical Velocity Predictions in Slur...

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4th IEEE international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Mathi, Ravi Chandra Aravelli, Aparna Florida International University Dept. of Computer and Information Sciences MiamiFL United States North Carolina A&T State University Department of Mechanical Engineering GreensboroNC United States

ISBN: (纸本)9798331518882

this paper examines the application of machine learning models for the prediction of critical velocity in Newtonian and non-Newtonian slurry flows. Experimental data and sensor measurements from established literature are used to train and test the models. Data cleaning, preprocessing, descriptive statistics, comparative analysis, and error evaluation have been conducted to make the models reliable. Several multi regression models have been trained and evaluated using original and synthetic data sets to improve generalization. Results look promising in their predictive ability, where different performances are realized for diverse slurry types and experimental conditions. © 2025 IEEE.

关键词： Non Newtonian flow

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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...

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

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

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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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Ensemble learning-based approach for water quality prediction 4

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

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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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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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