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Titlebook: Machine Learning Paradigms; Applications of Lear George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain Book 2019 Springer Nature Switzerland AG

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樓主: 母牛膽小鬼
41#
發(fā)表于 2025-3-28 15:17:16 | 只看該作者
Machine Learning Approaches for Pap-Smear Diagnosis: An Overviewl data analysis problems, such as optimizing the Pap-Smear or Pap-Test diagnosis. Pap-Smear or Pap-Test is a method for diagnosing Cervical Cancer (4th leading cause of female cancer and 2nd common female cancer in the women aged 14–44?years old), invented by Dr. George Papanicolaou in 1928 (Bruni e
42#
發(fā)表于 2025-3-28 19:00:08 | 只看該作者
Multi-kernel Analysis Paradigm Implementing the Learning from Loads Approach for Smart Power Systemsa new machine learning paradigm is presented focusing on the analysis of recorded electricity load data. The presented paradigm utilizes a set of multiple kernel functions to analyze a load signal into a set of components. Each component models a set of different data properties, while the coefficie
43#
發(fā)表于 2025-3-28 23:48:43 | 只看該作者
Conceptualizing and Measuring Energy Security: Geopolitical Dimensions, Data Availability, Quantitat to energy security research that aims to estimate a quantitative energy security index with a geopolitical focus, by providing an in-depth dynamic geopolitical look into the history, evolution, dimensions, data, estimation, taxonomy, and forecasts of energy security. Discussion is complemented with
44#
發(fā)表于 2025-3-29 06:19:03 | 只看該作者
Automated Stock Price Motion Prediction Using Technical Analysis Datasets and Machine Learning in which the process of forecasting is based on Machine Learning. The system has been developed following the cloud computing paradigm consisting of a backend application in Google’s API Hosting Cloud and using an Android front end using inspiring, contemporary styles of tools and libraries and har
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發(fā)表于 2025-3-29 09:41:43 | 只看該作者
46#
發(fā)表于 2025-3-29 12:12:05 | 只看該作者
47#
發(fā)表于 2025-3-29 16:38:39 | 只看該作者
48#
發(fā)表于 2025-3-29 21:16:11 | 只看該作者
Analytics and Evolving Landscape of Machine Learning for Emergency Responseision makers. This has resulted in new challenges related to the effective management of large volumes of data. In this regard, the role of machine learning in mass emergency and humanitarian crises is constantly evolving and gaining traction. As a branch of artificial intelligence, machine learning
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