Tradition K-means clustering algorithm is easy to fall into local optimum, poor clustering
effect on large capacity data and uneven distribution of clustering centroids. To solve these …

The improvement of enterprise competitiveness depends on the ability to match segmented
customers in a competitive market. In this study, we propose a customer segmentation …

In this research, we propose two variants of the Firefly Algorithm (FA), namely inward
intensified exploration FA (IIEFA) and compound intensified exploration FA (CIEFA), for …

Clustering is a popular data analysis and data mining problem. Symmetry can be
considered as a pre-attentive feature, which can improve shapes and objects, as well as …

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm
intended to improve the optimization capabilities of the conventional grey wolf optimizer in …

Android-based mobile devices have attracted a large number of users because they are
easy to use and possess a wide range of capabilities. Because of its popularity, Android has …

The application of metaheuristic algorithms is one of the most promising approaches for
solving real-world problems. The marine predators algorithm (MPA) is a recently proposed …

Applications of the Internet of Things (IoT) are used in several areas to create a smart
environment such as healthcare, smart agriculture, smart cities, transportation, and water …

In this research, we propose a variant of the Particle Swarm Optimization (PSO) algorithm,
namely hybrid learning PSO (HLPSO), for skin lesion segmentation and classification …

The processes of retrieving useful information from a dataset are an important data mining
technique that is commonly applied, known as Data Clustering. Recently, nature-inspired …