In today's sensor network research, numerous technologies are used for the enhancement
of earlier studies that focused on cost-effectiveness in addition to time-saving and novel …

Partitional data clustering with K-means algorithm is the dividing of objects into smaller and
disjoint groups that has the most similarity with objects in a group and most dissimilarity from …

Abstract The Quadratic Assignment Problem (QAP) has attracted considerable research
efforts due to its importance for a number of real life problems, in addition to its …

The practical driving cycle is of great significance in studying the control strategy of vehicles,
and effective clustering of micro-trips is the key to obtaining the typical driving cycle. A novel …

In the current paper, we have developed two bio-inspired fuzzy clustering algorithms by
incorporating the optimization techniques, namely differential evolution and particle swarm …

In this paper, we treat a multi-robot formation problem. In this problem, multiple robots move
from their respective initial positions, and they achieve to make a given target formation by …

With the rapid development of the Internet, malware traffic is seriously endangering the
security of cyberspace. Convolutional neural networks (CNNs)-based malware traffic …

Recommendation Systems (RS) save the time of users in their hectic life schedules for
purchasing their interested products. RS faces challenges of data sparsity, cold start …

Among the data clustering algorithms, k-means (KM) algorithm is one of the most popular
clustering techniques due to its simplicity and efficiency. However, k-means is sensitive to …

Among the data clustering algorithms, the k-means (KM) algorithm is one of the most
popular clustering techniques because of its simplicity and efficiency. However, KM is …