Titlebook: Intelligent Computing Systems and Applications; Proceedings of the 2 Sivaji Bandyopadhyay,Valentina Emilia Balas,Dalton Conference proceedi

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B. Akoramurthy,B. Surendiranine medizinische Alterssch?tzung angeordnet werden. Diese Dissertation analysiert zum einen, ob die medizinische Alterssch?tzung geeignet ist, eine Minder- oder Vollj?hrigkeit bei Personen nachzuweisen, die ein Alter von 16 oder 17 Jahren angeben, und zum anderen, wie diese Alterssch?tzung medizinet

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Disease Prediction Model Using Deep Transfer Learning,tory tract such as the bronchioles and alveoli. It is typically brought on by inhaled bacteria as well as viruses such as Streptococcus pneumoniae, also known as pneumococcus, which has both of these characteristics. Machine learning technique called transfer learning is, where a model is applied to

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Optimized FOREX Rate Prediction Using Hybrid Machine Learning Algorithm,change (FOREX) data. The statistics used in this investigation include the actual FOREX prices and the high and low values of four different currencies. These statistics begin from January 2012 to November 2022; the data is available on the Reserve Bank of India website. This article investigates th

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,Yarn Quality Assessment and?Monitoring Using ML in?Textile Industry,rded in textile units, but there is no use with this raw data without immediate analysis and necessary actions. Some of the issues in textile automation are lack of automated monitoring systems, poor data analysis after data collection, lack of real-time alerts and inability to integrate with existi

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DEAN: Improving the Efficiency of Disease Network Using Selective Ensemble Network Aggregation Modecations of inter-disease interactions by discovering common gene links. The construction of these networks requires extensive data collection, preprocessing, variance analysis, grouping of features, and connection. Variance analysis and feature clustering needs huge processing time and are responsib

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An Ensemble Technique for Predicting the Human Heart Disease,vention. In this investigation, we explore the potential of a voting classifier algorithm for forecasting heart disease in individuals. The dataset utilized in this research comprises comprehensive clinical and demographic data gathered from medical examinations of patients. A voting classifier harn