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    1. Data och IT
    2. Systemvetenskap och AI

    Computation of Artificial Intelligence and Machine Learning

    First International Conference, ICCAIML 2024, Jaipur, India, January 18–19, 2024, Proceedings, Part II

    AvAmit Kumar Bairwa,Varun Tiwari

    Häftad, Engelska, 2024

    Del 2185 i serien Communications in Computer and Information Science

    839 kr

    Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

    Beskrivning

    The two-volume set, CCIS 2184-2185, constitutes the refereed proceedings of the First International Conference on Computation of Artificial Intelligence and Machine Learning, ICCAIML 2024, held in Jaipur, India, in January 18–19, 2024. The 60 papers included in these volumes were carefully reviewed and selected from 645 submissions. These papers focus on various subject areas within the field of Artificial Intelligence and Machine Learning, such as Neural Networks and Deep Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, Data Mining and Big Data Analytics, AI in Healthcare and Biomedical Applications, Autonomous Systems and Robotics, AI Ethics and Fairness, AI in Finance and Eco-nomics.

    Produktinformation

    • Utgivningsdatum:2024-10-23
    • Mått:155 x 235 x 22 mm
    • Vikt:593 g
    • Format:Häftad
    • Språk:Engelska
    • Serie:Communications in Computer and Information Science
    • Antal sidor:367
    • Förlag:Springer International Publishing AG
    • ISBN:9783031714832

    Utforska kategorier

    • Systemvetenskap och AI inom Data och IT

    Innehållsförteckning

    • .- A Machine Learning Approach for Driver Drowsiness Detection in Automotive Safety Systems..- Dynamic COVID-19 endurance indicator system for scientific decisions using ensemble learning approach with rapid data processing..- Facial Recognition Advancements with Siamese Networks: A Comprehensive Survey..- Improving PCOS Diagnosis Accuracy with CNN-based Image Analysis..- Adoption of Quantum NLP for Improved Healthcare System..- Algorithm Visualization Using Deep Learning..- An Efficient Ontology Based Chronic Disease Diagnosis Model..- Deep Learning Models for Building and Shadow Detection in Aerial Imagery: A Comparative Study..- A Comparative study and analysis of Transfer Learning Models for Early Detection of Parkinson's Disease..- Development of Rule-based Chunker for Sindhi..- Smart Meter Security and Solutions with IoT in India..- Optimization of Positioning of Recloser in the High Voltage Category A Distribution Network of Alibori (Benin)..- An efficient machine learning based framework for analysis and prediction of link in social media platform..- Deep learning segmentation method for unveiling lower limb deformative on the knee..- Development of HMM based Part of Speech tagger for Hadoti..- Sentiment Analysis of Tokopedia Product Reviews Using Naïve Bayes Algorithm..- Combating Toxicity on the Internet: An Efficient HateSwarm Algorithm for Classifying Hate Speech..- Automated Categorization of Stack Overflow Queries: Solution to Highlight the Similar Kind of Queries..- A Modified U-Net for Semantic Segmentation of Liver and Liver Tumors From CT Scans..- Enhancing Histopathological Image Analysis: A Study on Effect of Color Normalization and Activation Functions..- Smart cities in India: A bibliometric analysis..- Generative Adversarial Networks for Generation of synthetic images: A comprehensive review..- Abstractive Text Summarization with domain knowledge for biomedical data..- Enhanced Residual U-Net with Attention for Optic Disc and Cup Segmentation in Fundus Images..- A Comprehensive Survey of Evaluation Techniques for Recommendation Systems..- eAuthenticate: Enhancing Security with Machine Learning based Real-Time Open-World Anti-Spoofing Method..- Utilization of computer vision techniques of ai for the attendance management system in online sessions..- Machine Learning and the Internet of Things for Industrial Process Improvement..- A Novel Method to Detect the Faults in an Underground Cable Using Robot..- Enhancing Time Series Forecasting with Machine Learning and Deep Learning Models.