Sagar Pande

TL;DR: In this article, different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset, which consists of 14 main attributes used for performing the analysis.

TL;DR: In this article, a novel framework which integrates Spark with a deep learning approach is proposed in order to detect the fraud transactions in the Mastercard data set, which achieves 96% accuracy for both training and testing datasets.

TL;DR: In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds.

TL;DR: This study aimed to present a case study of the recent research related to the coronav virus and proposed technology related to coronavirus and what control measure should be taken to stop the virus from further spreading.

TL;DR: DDoS attack was performed using ping of death technique and detected using machine learning technique by using WEKA tool and 99.76% of the samples were correctly classified.