' profile based on the Multiple Intelligences theory and clustering algorithms. The findings revealed correlations, which may …

problems. The learning algorithms are widely used for classification purposes. But choosing the learners that perform best for … assessment of various state-of-the-art learning algorithms over multi-domain data sets to search the effective classifier(s) for …

are a number of clustering algorithms like k-means, k-medoids, normalized k-means, etc. So, the focus remains on … efficiency and accuracy of algorithms. The focus is also on the time it takes for clustering and reducing overlapping between … clusters. K-means is one of the simplest unsupervised learning algorithms that solves the well-known clustering problem. The k …

application in practical problems in multiple areas make it one of the most studied problems in the literature. Algorithms …

these samples. To enhance the segmentation process, this article proposes to use genetic algorithms to evolve the initial … further classification of a multispectral satellite image. Results are obtained by injecting genetic algorithms in sampling …

Among some of the competent optimization algorithms, nature inspired algorithms are quite popular due to their …

, algorithms and techniques for protection of the key in cloud computing are proposed. The algorithms to select the number of …