This article presents a study detecting Tweets that contain racist text by performing the sentiment analysis of Tweets. The proposed GCR-NN model can detect 97% of the tweets that contain racist comments.

Article is a study proposing an approach for blood cancer disease prediction using the supervised machine learning approach to perform blood cancer prediction with high accuracy using microarray gene data.

Article describes how distributed-denial-of-service (DDoS) attacks can cause a great menace to numerous organizations and their stakeholders. The authors assert that the objective of this research work is to take into account a DDoS afflicted SDN specific dataset and detect the malicious traffic by using various machine learning algorithms namely., K-Nearest Neighbours, Logistic Regression, Multilayer Perceptron, Iterative Dichotomiser 3, and Stochastic Gradient Descent.

Data management plan for the grant, "Collaborative Research: SHF: Small: LEGAS: Learning Evolving Graphs At Scale." This project aims to improve the computation efficiency of graph neural networks (GNNs), which are an emerging class of deep learning models on graphs, with many successful applications, such as, recommendation systems, drug discovery, social network analysis, and code vulnerability detection. This project aims to to design an efficient GNN framework via algorithm and system co-design for both static and dynamic graphs.