Titlebook: Advances in Artificial Intelligence and Machine Learning in Big Data Processing; First International R. Geetha,Nhu-Ngoc Dao,Saeed Khalid C

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Spezielle Arzneiverordnungslehre,ing more common, and in insurance companies, the amount of claim data is rising. Therefore, during the claim re-view procedure, it could be challenging to ascertain the insured claim status. Consequently, the target of the review was to foster an AI model that sorts and conjectures the recurrence of

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https://doi.org/10.1007/978-3-662-55310-7s of users utilizing them on a daily basis. While they have emerged as a means of disseminating information, they have also quickly transformed into a conduit for spreading false information, rumors, unsolicited messages, promotional content, fabricated news, and other undesirable content. Both spam

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https://doi.org/10.1007/978-3-662-55310-7 pigment, are the source of melanoma, a kind of skin cancer. Both the incidence and mortality rates of melanoma have sharply increased in recent years. The ailment that attracted a variety of studies can be treated more effectively by medical professionals with early identification. This article foc

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Grundlagen der anorganischen Chemieger current lines using unexpectedly cutting-edge detection evasion strategies. Options for timely 0-day detection are necessary as standard signature-based approaches become less effective in detecting unknown threats. This study contributes a strategy based on ensemble learning for detecting malic

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Advances in Artificial Intelligence and Machine Learning in Big Data Processing978-3-031-73068-9Series ISSN 1865-0929 Series E-ISSN 1865-0937

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Grundlagen der anorganischen Chemieilarity measures falls into two main areas of machine learning namely supervised learning and Unsupervised Learning. Machine learning algorithms deals with the data and datasets. The main difference between the supervised and unsupervised learning is to predict the labelled and unlabeled data.

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Communications in Computer and Information Sciencehttp://image.papertrans.cn/b/image/167211.jpg