Procedia Computer Science 

TL;DR: This paper presents k-means clustering algorithm, an unsupervised algorithm used to segment the interest area from the background, and subtractive cluster, a data clustering method, which generates the centroid based on the potential value of the data points.

TL;DR: A network with CNN architecture and data augmentation is developed which can identify the intricate features involved in the classification task such as micro-aneurysms, exudate and haemorrhages on the retina and consequently provide a diagnosis automatically and without user input.

TL;DR: This research proposed a new definition of systems thinking that integrates these components both individually and holistically and was tested for fidelity against a System Test and against three widely accepted system archetypes.

TL;DR: An overview on the data mining techniques that have been used to predict students performance and how the prediction algorithm can be used to identify the most important attributes in a students data is provided.

TL;DR: A performance comparison between different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB) and k Nearest Neighbors (k-NN) on the Wisconsin Breast Cancer datasets is conducted and Experimental results show that SVM gives the highest accuracy with lowest error rate.