Titlebook: Computation of Artificial Intelligence and Machine Learning; First International Amit Kumar Bairwa,Varun Tiwari,Thittaporn Ganokrat Confer

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https://doi.org/10.1007/978-3-531-92733-6ad of such toxicity can cause severe damage to people’s mental health. Therefore, the need for an intelligent system capable of detecting and classifying hate speech in real time has become more pressing than ever. This paper aims to devise an effective feature selection algorithm for detecting and

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1865-0929 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 Le

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A Machine Learning Approach for Driver Drowsiness Detection in Automotive Safety Systems,hine learning techniques have been used. Detecting Drowsiness can be done with the help of cameras by detecting the driver’s facial expressions and extracting the driver’s eye image. This paper gives you a real-world idea of how the detection system using machine learning could be reasonable and effective.

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Conference proceedings 2025ch 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..

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Conference proceedings 2025nce 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, su

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Validierung analytischer Verfahren, a resource for aspiring researchers who wish to explore this area of Hadoti Language Processing. The system was evaluated for accuracy and produced 99.87% accurate results on seen data and 98.78% on unseen data. The system was able to produce an accuracy of 99.33% on the entire test corpus.

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Development of HMM Based Parts of Speech Tagger for Hadoti, a resource for aspiring researchers who wish to explore this area of Hadoti Language Processing. The system was evaluated for accuracy and produced 99.87% accurate results on seen data and 98.78% on unseen data. The system was able to produce an accuracy of 99.33% on the entire test corpus.