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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 4146 条记录，以下是1-10 订阅

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

Proceedings of the 2023 4th International Conference on Mach...

引用

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

ISBN: (纸本)9798400709449

the proceedings contain 182 papers. the topics discussed include: combinatorial model based on extreme learning machine for network traffic prediction;survey on Chinese word segmentation;research on rock powder coating ratio based on artificial neural network;optimization of maritime intelligence facilities based on big data algorithms;instance discrimination for improving Chinese text classification;failure prediction of bolted composite connections using continuum damage model;prediction study of slope displacement using CGA-BP neural network;research on the knowledge embedding vector method based on TF-IDF and auto-encoder;MCN: magnitude-continuity network for video anomaly detection under weak supervision;and research on network security situation assessment technology based on correlation analysis.

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Social Media Analysis for Criminal Behavior Detection: Methods, application and Challenge 4

Social Media Analysis for Criminal Behavior Detection: Metho...

引用

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

作者： Patel, Prachi Bhushanwar, Kush Patel, Hemlata Piet Parul University Computer Engineering Department Gujarat Vadodara India Piet Parul University Computer Science & Engineering Department Gujarat Vadodara India

ISBN: (纸本)9798331523923

Social media websites have provided rich contextual data but its misuse for criminal activities creates lot of problems. this paper seeks to solve the problem of criminal behavioral analysis on social media platforms using machine learning, deep learning and Natural Language Processing (NLP) methodologies. the goal is to assess the effectiveness of classification algorithms such as SVM, Navier Bayes, Random Forest, Convolutional Neural Network, Recurrent Neural Network, and the Natural Language Processing (NLP) methodology that include sentiment analysis, topic modelling and entity recognition for detecting irregularities. Specifically, this research study discusses about the recent progress in these methods while considering essential problems, including data accessibility, model flexibility, and the topic of ethics. © 2025 IEEE.

关键词： Adversarial machine learning

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

4th International Conference on Sentiment Analysis and Deep ...

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

ISBN: (纸本)9798331523923

the proceedings contain 281 papers. the topics discussed include: deep learning for sentiment analysis;a review on advances in sentiment analysis: a deep learning approach using transformer based models;a grading model for jasmine flowers using deep learning and quantum machine learning;deep learning-based algorithm for feature recognition in the construction process of decorative materials;a review on the applications of machine learning and deep learning algorithms for image recognition;streamlining financial predictions: a web application for stock, cryptocurrency, and market sentiment analysis;sentiment and tone analysis of reddit posts using python;deep learning based diagnosis of gum infection and tooth alignment using dental x-ray;and exploring sentiment and moral dynamics in children's literature using machine learning: a sentiment analysis approach

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application of AI and ML in Wound Healing and Skin Regeneration 4

Application of AI and ML in Wound Healing and Skin Regenerat...

引用

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

作者： Petkar, Taniya G. Kumar, Praveen Sarate, Kirtiksha U. Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331523923

By enabling precise, individualized, and effective treatments, the integration of artificial intelligence (AI) and machine learning (ML) into wound and skin healing is revolutionizing healthcare. Artificial intelligence-controlled tools improve wound assessment, anticipate healing, and collaborate with regular examinations using cutting-edge dressings and imaging technologies. AI computations work on the management of express data to enhance treatment systems, while advancements in nanomaterials like marine collagen and therapies employing young microorganisms further accelerate skin recovery. Despite encouraging findings, there are still problems, such as potential overfitting and dataset limitations. In general, machine learning is changing the way that injuries are treated by providing proactive, tailored solutions that aim to understand outcomes and lower the cost associated with clinical outcomes. © 2025 IEEE.

关键词： Personalized medicine

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the comparative study of microbial classification using machine learning and neural network models based on PCA for feature generation 4

The comparative study of microbial classification using mach...

引用

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

作者： Cheng, Jiangshuai China University of Mining and Technology Xuzhou China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

the classification of microorganisms is an important branch of microbiology and plays a crucial role in understanding microbial diversity. Microorganisms include bacteria, viruses, fungi, protozoa, algae, etc, and they exhibit significant differences in morphology, physiology, and ecological niches. By classifying microorganisms with similar characteristics into groups, it aids in studying both their common properties and differences. In this study, PCA, GBDT, and XGBoost were used to generate new features, and machine learning methods (logistic regression, decision trees, and random forests) were compared with neural networks to determine the best predictive model and feature engineering approach. According to the experimental results, in machine learning models, the random forest model with three new features generated by PCA performed the best. In neural network models, the BP neural network with two new features generated by PCA performed the best. © 2025 SPIE.

关键词： Neural network models

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Revolutionizing Healthcare: the Role of AI and BigData Analytics in Disease Prevention and Management 4

Revolutionizing Healthcare: The Role of AI and BigData Analy...

引用

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

作者： Gatkine, Vaishnavi Khode, Saksham N. Gourshettiwar, Palash Computer Science & Medical Engineering Faculty of Engineering and Technology Maharashtra Wardha440001 India

ISBN: (纸本)9798331523923

Artificial Intelligence (AI) and Big Data Analytics are greatly transforming the healthcare scenario by facilitating the early detection of diseases, personalized medicine, and improved public health interventions. AI, by employing machine learning and deep learning, processes big and complex datasets, whereas Big Data detects patterns and predicts risks, enhancing resource utilization and readiness for health emergencies. Yet, the technologies are faced with a range of challenges that include data security concerns, matters of integrating many systems, and the requirement of real-time processing in ever-changing healthcare environments. Although recent technological advancements such as sophisticated deep learning algorithms and decentralized data processing have been promising solutions to some of these challenges, issues of easy integration and privacy remain. this paper suggests an objective-oriented framework to enhance the application of AI and Big Data solutions in healthcare to overcome current limitations and establish a more efficient, personalized, and predictive healthcare system. © 2025 IEEE.

关键词： Adversarial machine learning

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Advances in sentiment analysis: A comprehensive review of technique and application 4th

Advances in sentiment analysis: A comprehensive review of te...

引用

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

作者： Naki, Ali Ghosh, Gopal School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9781032911571

Sentiment Analysis is a part of NLP that aims to identify the sentiment or opinion from textual data. this review paper gives a comprehensive study of the sentiment analysis method. From traditional methods to modern approaches. here we begin with understanding the foundational concepts of sentiment analysis. We understand various methods including lexicon based, machine learning, Deep learning. Where we are highlighting their strengths, weaknesses, and applications. Apart from this we examine the problems and future work in sentiment analysis. this paper act as a valuable resource for researcher and enthusiasts interested in understanding the sentiment analysis. © 2025 the Author(s).

关键词： Contrastive learning

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

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

引用

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

ISBN: (纸本)9783031812460

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

引用

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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