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Titlebook: Artificial Intelligence and Knowledge Processing; Third International Hemachandran K,Raul Villamarin Rodriguez,Kok-Leong Conference procee

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發(fā)表于 2025-3-21 17:39:53 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Artificial Intelligence and Knowledge Processing
期刊簡稱Third International
影響因子2023Hemachandran K,Raul Villamarin Rodriguez,Kok-Leong
視頻videohttp://file.papertrans.cn/168/167576/167576.mp4
學(xué)科分類Communications in Computer and Information Science
圖書封面Titlebook: Artificial Intelligence and Knowledge Processing; Third International  Hemachandran K,Raul Villamarin Rodriguez,Kok-Leong Conference procee
影響因子.This book constitutes the Revised Selected Papers of the Third International Conference on Artificial Intelligence and Knowledge Processing, AIKP 2023, held in Hyderabad, India, during October 6–8, 2023...The 20 full papers and 8 short papers were carefully selected from 118 submissions. The research areas include: Artificial Intelligence and Machine Learning; Deep Learning and Computer Vision; Natural Language Processing; Intelligent Control..
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https://doi.org/10.1007/978-3-662-64378-5ffering from high blood pressure which is the major causes of death. Patients are in the resting stage and asymptotic until fatal event occurs. Selecting important features from the available data by applying the bayes technique for the COPD patients is an important classification technique that can
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Functional Design of Workplaces, the field of education, the researcher examines the awareness and knowledge of Artificial Intelligence among school students and their computer science mentors. Researchers assessed the conceptions of students about features of AI, tools of AI and its use for learning purposes as well as demerits o
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Handbook Factory Planning and Designample, predictive maintenance solutions utilize raw analog signals to identify trends and patterns. In a few scenarios, an engineer working in a factory setting can tell when a machine is behaving abnormally just by hearing unexpected sounds (e.g., the loudness of sound) that are well within the hum
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Data Integration in Health Carement outcomes and patient survival rates. In our study, we propose a novel approach to address this challenge by utilizing granular computing, a method that allows for the efficient analysis of complex data by dividing it into smaller, more manageable subsets. Our proposed model harnesses the capabi
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