About
9
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Introduction
My research centers on machine learning algorithms with a focus on clustering methods, multi-view learning, and Edge AI. These areas are explored through a rigorous theoretical framework and extensive simulation-based studies, ensuring the solutions are well-founded and generalizable. The work emphasizes developing efficient and robust algorithms that can operate under real-world constraints, aligning closely with the needs of cyber-physical systems and modern edge computing environments.
Education
September 2016 - June 2020
Publications
Publications (9)
The k-means clustering algorithm is the oldest and most known method in cluster analysis. It has been widely studied with various extensions and applied in a variety of substantive areas. Since internet, social network, and big data grow rapidly, multi-view data become more important. For analyzing multi-view data, various multi-view k-means cluste...
The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the k-means algorithm and its extensions are always influenced by initializations with a necessary...
Fuzzy c-means (FCM) clustering had been extended for handling multi-view data with collaborative idea. However, these collaborative multi-view FCM treats multi-view data under equal importance of feature components. In general, different features should take different weights for clustering real multi-view data. In this paper, we propose a novel mu...
The increasing effect of Internet of Things (IoT) unlocks the massive volume of the availability of Big Data in many fields. Generally, these Big Data may be in a non-independently and identically distributed fashion (non-IID). In this paper, we have contributions in such a way enable multi-view k-means (MVKM) clustering to maintain the privacy of...
In this study, we propose extension of fuzzy c-means (FCM) clustering in multi-view environments. First, we introduce an exponential multi-view FCM (E-MVFCM). E-MVFCM is a centralized MVC with consideration to heat-kernel coefficients (H-KC) and weight factors. Secondly, we propose an exponential bi-level multi-view fuzzy c-means clustering (EB-MVF...
The rapid development in information technology makes it easier to collect vast numbers of data through the cloud, internet and other sources of information. Multiview clustering is a significant way for clustering multiview data that may come from multiple ways. The fuzzy c-means (FCM) algorithm for clustering (single-view) datasets was extended t...
The k-means algorithm with its extensions is the most used clustering method in the literature. But, the k-means and its various extensions are generally affected by initializations with a given number of clusters. On the other hand, most of k-means always treat data points with equal importance for feature components. There are several feature-wei...
Questions
Questions (2)
Dear ResearchGate community,
I am looking for someone with endorsement rights on arXiv in the fields of computer science -computer vision and pattern recognition (cs.CV). I would like to submit a preprint paper for visibility and need an endorsement.
If you're able to endorse and willing to help, please visit the following URL:
or visit the link and enter the code:
Endorsement Code: 88LWI9
I have a few peer-reviewed publications in the area of endorsement which can be checked from my profile.
Thank you for your consideration.
Dear ResearchGate community,
I am looking for someone with endorsement rights on arXiv in the fields of computer science - computer vision and pattern recognition (cs.CV). I would like to submit a preprint paper for visibility and need an endorsement.
If you're able to endorse and willing to help, please visit the following URL:
or visit the link and enter the code:
Endorsement Code: 88LWI9
I have a few peer-reviewed publications in the area of endorsement which can be checked from my profile.
Thank you for your consideration.
