Novel and Efficient Hybrid Model for Classification of Heart Disease

Propose an effective cardiac disease categorization method that can predict disease early on and cut death rates. The study used a hybrid intelligence model of Genetic Algorithm (GA) and Support Vector Machine (SVM) for prediction, and the Cleveland dataset from

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Anomaly Detection of Outlier Features from Spatio-temporal Databases of Landsat-8 Sensor, using Cloud Computing Platform

There has recently been a surge in the number of research articles published in peer-reviewed journals about machine learning and specialized algorithms for feature identification, feature selection, and feature extraction studies. This article distinguishes proof-of-concept applications from domains such as

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