MUMP
发表于 2025-3-25 04:41:59
https://doi.org/10.1007/978-981-99-7820-5Models and Algorithms; Data Science Applications; Data Science Challenges; machine learning; Data Mining
Nmda-Receptor
发表于 2025-3-25 09:35:30
978-981-99-7819-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
泥土谦卑
发表于 2025-3-25 13:03:24
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真实的人
发表于 2025-3-25 19:00:32
Ulrich Spandau,Gabor B. Schariothegative impacts on society. Many businesses have started automating their operations through technologies such as Artificial intelligence, Machine Learning, Blockchain, and Cyber Security, improving efficiency and security. However, these technologies have also been misused, causing data breaches an
crucial
发表于 2025-3-25 20:02:48
Ulrich Spandau,Gabor B. Scharioth voice, and other nonverbal cues. It is an important research topic in computer vision and has a wide range of applications. Emotion recognition from facial expressions is a challenging task in computer vision, and deep learning has shown remarkable performance in this area. In this study, we presen
menopause
发表于 2025-3-26 03:11:23
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吗啡
发表于 2025-3-26 04:37:08
Compilation of All Pits and Pearlsroposed. Thus, there is a need to keep up with the said advancements’ framework. This paper reviews the progress of a covert information exchange technique, Steganography, over the recent few years by sifting through some cornerstone techniques and papers. We have listed and reviewed the various typ
征税
发表于 2025-3-26 10:28:20
https://doi.org/10.1007/978-3-642-54449-1f the company based on the percentage of common shares held by that shareholder. A capitalization table, often referred to as a cap table, is a record stored in formats like spreadsheets or database tables. It provides comprehensive information about ownership distribution within a company. This doc
变白
发表于 2025-3-26 15:26:39
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评论性
发表于 2025-3-26 20:17:28
https://doi.org/10.1007/978-3-642-54449-1e less severe and this may decrease the death rates. Distinguishing risk factors utilizing deep learning models is a hopeful methodology. Our research is based on the “Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques”