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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 4173 条记录，以下是181-190 订阅

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proceedings of 2023 4th international conference on Neural Networks and Neurotechnologies, NeuroNT 2023

Proceedings of 2023 4th International Conference on Neural N...

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4th international conference on Neural Networks and Neurotechnologies, NeuroNT 2023

ISBN: (纸本)9798350301526

the proceedings contain 16 papers. the topics discussed include: explainable artificial intelligence in clinical decision support systems;gradient-based explainable artificial intelligence methods for eye disease classification;sliding control algorithm with a neural network for exoskeleton electric drive system;deep learning for early diagnosis of diabetic retinopathy: a study using convolutional neural network;neural network control of environmental parameters in neonatal incubators;federation subjects' import dependence level hierarchical clustering model;assessment of the applicability of machine learning methods for the detection of pathology of internal human organs based on the results of bioelectrography;the possibility of three-dimensional sound visualization to assess its localization;analysis of messages in social networks using artificial intelligence methods;applying neural network to assess application user experience;drying installations intelligent information system and knowledge base;and classification and prediction of lung diseases according to chest radiography.

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Malware feature selection and adversarial sample generation method based on reinforcement learning 4

Malware feature selection and adversarial sample generation ...

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4th international conference on Telecommunications, Optics, and computer Science, TOCS 2023

作者： Li, Xiaolong Yan, Zhenhua Zhang, Shuang Li, Xinghua Wang, Feng Power Science Research Institute of State Grid Ningxia Electric Power Co. LTD Ningxia Yinchuan China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

Amidst the intricate battleground where attackers and defenders are perpetually locked in a sophisticated digital cat-and-mouse game, the task of discerning malware within an adversarial crafted environment manifests escalating complexity. the current research delineates the utilization of an enhanced reinforcement learning technique, orchestrated to engender adversarial malware specimens, thereby strategically navigating through machine learning detectors. this endeavor not only bolsters the robustness of malware identification systems but also adeptly navigates the perpetually evolving machinations of malware creators. Within this research, an environmental model is meticulously constructed, emulating detection engines and feature extractors, with malware samples assimilated as input. By integrating an autonomously generated reward function, we ascertain the model’s agility and the concomitant generation of manifold adversarial malevolent samples. the empirical evaluations underscore that, in contrast to conventional machine learning approaches, our methodology exudes superior flexibility and efficacy, furnishing a more formidable challenge to malware detection mechanisms. © 2024 SPIE.

关键词： Reinforcement learning

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

4th International Conference on Smart Systems: Innovations i...

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

ISBN: (纸本)9789819736898

the proceedings contain 60 papers. the special focus in this conference is on Smart Systems: Innovations in Computing. the topics include: An Algorithmic Framework for Clustering Cab Pickup Geo-points for Cab Recommender System (CRS);simulation and Analysis of Routing Protocols Using Real-Time Data in Vehicular Ad Hoc Networks;research Trends and Key themes in the Intersection of Renewable Energy and Smart Homes;smart Attendance System Using Face Recognition;Data Augmentation to Improve Fake Account Detection Using ANN;end-to-End Portfolio Optimization Using Transformer Encoder Model for Long Positions Strategy;phonetic Redundancy Avoidance Technique;towards Sustainable Power: A Quantitative Analysis of Environmental Factors and Energy Demand in Different Indian States;customer Segmentation Using K-means Clustering;web Miner: Automated Web Crawling and Database System with Puppeteer and ***;machine learning-Based Cardiovascular Disease Prediction;Efficient Identification of Waste in Water Bodies with YOLO V7-Object Detection;bumbleBee: the Multi-purpose Voice Assistant;efficientNet Deep learning Model for computer-Aided Polyps Classification from Colonoscopy Images;improved Gastric Cancer Diagnosis with machine learning Technique: Addressing Imbalanced Data Distribution;an Effective and Secure Privacy Diagnosis Made Online System for the E-Healthcare Ecosystem Based on Federated learning;efficientViT: An Efficient Vision Transformer for Fire and Smoke Image Classification;Tomato Disease Classification Using CNN;artificial Intelligence application for Electronic Governance to Achieve Sustainability: A Case of Selected Segments of Indian Economy;analyzing Sentiments of Global Workforce Towards Hybrid Work Culture in the New Normal;deep learning Techniques for Real-Time Object Detection and Classification;Task Scheduling Cost Evaluation over Cloud with Using GA, ACO, PSO.

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A Deep learning Based System to Predict the Plant Disease Using Streamlit 4

A Deep Learning Based System to Predict the Plant Disease Us...

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

作者： Warbhe, Mohan K. Bore, Joy Jordan Chaudari, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering MaharashtraSawangi Wardha442001 India

ISBN: (纸本)9798331523923

the proposed web application for tomato leaf disease detection exemplifies the transformative power of Artificial Intelligence and computer Vision in modern agriculture. Addressing the critical issue of early and accurate disease detection, this solution bridges the gap between advanced technological innovations and the practical needs of farming. the application is designed using Streamlit and Python to combine the usability of a friendly interface with the power of robust analytical capabilities in CNN, offering real-time reliable diagnosis and actionable insights to farmers. this application provides the highest possible accuracy and adaptability through the thorough training and evaluation of models and is a good asset for disease management. Deployment on scalable cloud platforms ensures access, reliability, and global reach. Future improvements, including extending disease detection to other crops, multilingual support, real-time recommendations, and integration with IoT devices, promise to expand its reach and utility. this initiative is a shining example of how AI-based solutions can transform agricultural practices by enhancing productivity, reducing losses, and ensuring sustainability. the application empowers farmers and contributes to a resilient agricultural ecosystem, addressing the pressing need for innovative and sustainable solutions in a rapidly evolving world. © 2025 IEEE.

关键词： Smart agriculture

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N-gram Features and machine learning Models for Recognizing Human Enzyme Protein Sequences 4

N-gram Features and Machine Learning Models for Recognizing ...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mehta, Disha M. Prajapati, Rashmin B. Upadhyay, Keyur N. Valani, Nisha S. Soni, Neha R. Patel, Shrina Sardar Vallabhbhai Patel Institute of Technology Dept. of Computer Engineering Gujarat Vasad India

ISBN: (纸本)9798350300093

the identification of enzyme proteins is crucial for understanding the underlying mechanisms of various biological processes. In recent years, the development of machine learning techniques has made it possible to automate this task with high accuracy. In this study, we propose a method that uses 3-gram features and machine learning models for recognizing human enzyme protein sequences. First, the 3-gram features are extracted from the amino acid sequences of enzymes and use these features as input to several machine learning models, including naive bayes, decision tree, random forest, and Extra tree (ET). We compare the performance of these models using metrics such as accuracy, precision, recall, and F1 score. the results obtained show that the ET model performs the best, achieving an accuracy of 96%, precision of 93%, recall of 93%, and F1 score of 93%. Overall, the proposed approach demonstrates the potential of using machine learning models and 3-gram features for accurate identification of human enzyme protein sequences. © 2023 IEEE.

关键词： Decision trees

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Unraveling Pulmonary Fibrosis Deep learning Transfer learning for Diagnosis 4

Unraveling Pulmonary Fibrosis Deep Learning Transfer Learnin...

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

作者： Rekha, G. Pongiannan, R.K. Pachipulusu, Venkata Sreeram Mogili, Sai Balaji Brindha, R. Maddileti, Telugu Srm Institute of Science and Technology Kattankulathur Dept. Computing Technologies Chennai603 203 India Srm Institute of Science and Technology Kattankulathur Computing Technologies Chennai603 203 India Guru Nanak Institutions Technical Campus Department of Electronics & Commn Engg Telangana Ibrahimpatnam501506 India

ISBN: (纸本)9798331523923

Pulmonary Fibrosis is a chronic lung disease demands prompt diagnosis for effective management. the proposed model develops an automated system using deep learning for feature extraction and machine learning for classification, reducing reliance on manual assessments. In the context, we use InceptionV3, VGG16, and MobileNetV2 for feature extraction as they are constantly successful and able to detect fine-grained patterns from medical images. the extracted features are further analyzed with popular machine learning techniques including but not limited to Random Forest and SVM. the proposed new hybrid model, leads to a faster diagnosis even if it is comparatively more accurate, than the previously used models. the system performance are evaluated using the accuracy, precision, recall, and F1-score. these are integrated into the Flask-based web application and it enables quick and accurate Pulmonary Fibrosis diagnosis, aiding timely treatment and better patient outcomes. © 2025 IEEE.

关键词： Lung cancer

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Regression and machine learning Methods for Predicting Human Movements Based on Skeletal Data 4

Regression and Machine Learning Methods for Predicting Human...

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4th IEEE international conference on Smart Information Systems and Technologies, SIST 2024

作者： Safibullayevna, Beknazarova Saida Khanatovna, Kozhamzharova Dinara Karamatdinkizi, Jaumitbaeva Mexriban Faxriddinovich, Saidov Farrux Khairullayevna, Shaikhanova Aigul Department Television and media technologies Tashkent University of Information Technologies named after Muhammad Al- Khwarizmi Tashkent Uzbekistan Department International Information Technology University Almaty Kazakhstan The Department of Information Security L.N. Gumilyov Eurasian National Univeristy Astana Kazakhstan

ISBN: (纸本)9798350374865

In recent years, the advancement of computer vision technologies has facilitated the extraction of skeletal data from human movements captured in video sequences. Leveraging this wealth of information, this article explores the application of regression and machine learning methods to predict human movements based on skeletal data. the objective is to develop models that can accurately forecast and understand the dynamics of various human motions. the methodology involves the use of computer vision techniques for skeleton detection and tracking, followed by careful preprocessing of the skeletal data to extract relevant features. these features, representing key aspects of the skeletal structure and motion, serve as inputs to regression and machine learning models. Various algorithms, including linear regression, support vector machines, random forests, and deep neural networks, are employed to learn the intricate relationships between skeletal features and corresponding movements. A crucial aspect of the study is the creation of a comprehensive training dataset that captures diverse human movements. Data augmentation techniques are applied to enhance the variability and robustness of the training set. the trained models are then rigorously evaluated using metrics such as mean squared error to quantify their predictive performance. Results indicate that the proposed regression and machine learning approaches exhibit promising capabilities in predicting human movements. the article discusses the strengths and limitations of different algorithms, highlighting their applicability in various contexts, including virtual reality, augmented reality, security systems, and robotics. Furthermore, the study emphasizes the importance of continuous refinement and adaptation of models to accommodate the complexity and variability inherent in human motion. this research contributes to the growing body of knowledge at the intersection of computer vision and human movement prediction

关键词： computer vision human movement prediction machine learning motion forecasting predictive modeling regression methods skeletal data analysis virtual reality

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Soft kind of Computing Based Least Resource Data finding which is Used for Analysis purpose 4

Soft kind of Computing Based Least Resource Data finding whi...

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4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Rani, L. Pattathu Surendran, S. Monica, D. Kumar, V. Senthil Rajaram, Gnanajeyaraman Dwibedi, Rajat Kumar Simats University Saveetha School of Engineering Chennai India Tagore Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering India Aarupadai Veedu Institute of Technology Department of Ece Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

Deep learning (DL) approaches are utilized across three distinct contexts to showcase their applicability in commercial environments with restricted data for training. this study showcases the application of unsupervised DL with autoencoders that vary & illustrates how federation learning serves as a successful collaboration approach for machine learning (ML) that safeguards confidentiality of data. © 2024 IEEE.

关键词： Federated learning

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An AI-powered smart health companion that detects stress to aid medical support using artificial intelligence/machine learning 4

An AI-powered smart health companion that detects stress to ...

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Gunasekaran, S. Kathirvel, S. Kavin, A. Logeshwaran, K. Patmesh, S. V.S.B. Engineering College Department of Computer Science and Engineering Karur India

ISBN: (纸本)9798350364729

the name of the project is "An AI-powered smart health companion that detects stress to aid medical support using artificial intelligence/machine learning."It uses advanced technologies such as AI and ML, which has created a revolution in healthcare and changed people's approach to medical care. this initiative ensures personalized care and support by implementing innovative methods in medical aid coupled with stress detection through what can only be described as an elaborate blend of different technologies. Among the components used are React, ***, Python-based machine learning algorithms, MongoDB, and Firebase;they work together harmoniously to produce features like NLP chat application that allows users to easily engage in discussions about their health issues while receiving customized medical advice and suggestions. © 2024 IEEE.

关键词： Electronic health record

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Psychological Restrain for Fault Detection in Reinforcement learning 4

Psychological Restrain for Fault Detection in Reinforcement ...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Phour, Himanshi Gautam, Neha Gaurav, Abhilash Chandigarh University Department of Computer Science and Engineering Punjab Gharuan India

ISBN: (纸本)9798350306118

Reinforcement learning is a sub part of machine learning that aims for adapting its agent or agents to the unfeasible environment with a solo purpose of achieving the maximum rewards. In common RL agents are trained to find the optimum policy for the varied actions and to achieve the maximum rewards possible. Markov Decision processes that call a rule of penalty if and only if it got the penalty or transit to achieve the penalty. But however, getting all the data set values that shows all those transit states or chances where it couldn't be able to get the rewards. In this paper, we put forward the Reinforcement learning psychological restrain for fault detection that will visualize the restrains that block the agent to optimize the rewards. these bugs can be in the logical part of the code or there might be a misplaced value of rewards or penalty that got missed by the programmer. Its effectiveness can be shown via snake game. © 2023 IEEE.

关键词： Fault detection

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