Outlier reduction is crucial in computer science for improving data quality, analysis accuracy, and modeling robustness. Selection and modification of DBSCAN parameters are essential for optimal clustering accuracy and outlier detection. We developed an ...

Evaluation of recommendation systems continues evolving, especially in recent years. There have been several attempts to standardize the assessment processes and propose replacement metrics better oriented toward measuring effective personalization. ...

This study examines the integration of multimedia technology with English language instruction in higher education, focusing on its potential to enhance personalised and independent learning. Anchored in constructivist learning theory, the research ...

Clustering arranges the data objects into clusters and similar data objects put into same cluster. There is not a single algorithm that can work effectively with all types of clustering problems. Other side, quality of clusters is also an important issue ...

Cloud computing is the most popular way of delivering on-demand computational resources. Recently, the research in this area has started to focus on carbon-aware clouds. Here, the most challenging aspects are related to defining strategies for ...

Personalization services offered by Point-of-Interest (POI) recommender systems are becoming increasingly popular, especially in the context of mobile devices. However, data privacy regulations and user concerns regarding privacy often prevent the ...

Immersive reality technologies, such as Virtual and Augmented Reality, have ushered a new era of user-centric systems, in which every aspect of the coding-delivery-rendering chain is tailored to the interaction of the users. Understanding the actual ...

In this paper, we examine the possibility of employing the idea of progressive-inductive (π) aggregation in the k-means algorithm. We base our work on the interactive visualization framework called Skydive which is a tightly coupled system that ...

The data mining accuracy of e-commerce users' consumption behaviour is low and the data clustering effect is poor, so a deep mining method of e-commerce users' consumption behaviour data based on clustering and deep learning is proposed. The consumption ...

Existing low-rank representation-based methods adopt a two-step framework, which must employ an extra clustering method to gain labels after representation learning. In this paper, a novel one-step representation-based method, i.e., One-step Low-Rank ...

In this paper, an Improved version of the Multi-Trial Vector-based Differential Evolution (IMTDE) algorithm is proposed and adapted for clustering data. The purpose here is to enhance the balance between the exploration and exploitation mechanisms in ...

Multi-center clustering algorithms have attracted the attention of researchers because they can deal with complex data sets more effectively. However, the reasonable determination of cluster centers and their number as well as the final clusters ...

Data clustering is widely applied in many real world domain including marketing, anthropology, medical science, engineering, economics, and others. It concerns with the partition of unlabelled dataset objects into clusters (groups) based on a similarity ...

Data clustering aims to partition a dataset into k subsets according to a prespecified similarity measure. It is NP-hard, and has lots of real applications. This paper presents a ranking paired based artificial bee colony algorithm (RPABC) to solve data ...

The disease diagnosis in the medical field enhances better medical service to patients and also leads to a decrease in their mortality rate. The prediction of the survival rate of the patients purely depends on the accurate diagnosis of the diseases, but ...

In real‐world applications, large amounts of data from multiple sources come in the form of streams. This makes multi‐view feature learning cost much time when new instances rise incrementally. Dealing with these growing multi‐view data becomes a ...

Addressing information overload in current e-participation platforms, we present a lightweight web application consisting of a simple HTML-based data panel that, through the use of date, location and category based filters, and several interactive ...

Proper data items distribution may seriously improve the performance of data processing in distributed environment. However, typical datastorage systems as well as distributed computational frameworks do not pay special attention to that aspect. ...

Krill herd algorithm (KHA) is an emerging nature-inspired approach that has been successfully applied to optimization. However, KHA may get stuck into local optima owing to its poor exploitation. In this paper, the orthogonal learning (OL) ...

We investigate the terminal location method in 5G‐Low Earth Orbit (5G‐LEO) satellite communication systems. To overcome the dependence on the external Global Navigation Satellite System (GNSS), we propose to use a single LEO satellite in 5G‐LEO ...

The terminal location method with NLOS exclusion based on unsupervised learning in 5G‐LEO satellite communication systems is proposed. We use a single LEO satellite for terminal location and utilize the downlink synchronization detection for pseudorange ...