Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …

Disease prediction from diagnostic reports and pathological images using artificial
intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in …

Data clustering is a complex data mining problem that clusters a massive amount of data
objects into a predefined number of clusters; in other words, it finds symmetric and …

This study aimed to develop a new methodology for evaluating and benchmarking a multi-
agent learning neural network and Bayesian model for real-time skin detectors based on …

Early detection of liver disease is never easy, though it is one of the most important diseases
on earth. This study, thus, attempts to achieve efficient early detection through a Multilayer …

Memory based algorithms, generally referred as similarity based Collaborative Filtering (CF)
algorithm, is one of the most widely accepted approaches to provide service …

Clustering is an unsupervised data analytic technique that can determine the similarity
between data objects and put the similar data objects into one cluster. The similarity among …

In this paper, a new clustering algorithm inspired by magnetic force is proposed. This
algorithm is not sensitive to the initialization problem of cluster centroids. Centroid particles …

Data clustering is an important technique of data mining in which the objective is to partition
N data objects into K clusters that minimize the sum of intra‐cluster distances between each …

Abstract Collaborative filtering based Recommender System is one of the most common
technique used for personalized product ranking. It aids the consumer in decision-making …