Hrishikesh Telang

Work place: Syracuse School of Information Studies/Information Systems, Syracuse, 13244, United States



Research Interests: Data Mining, Image Processing, Natural Language Processing, Computational Learning Theory, Computer systems and computational processes, Data Structures and Algorithms


Mr. Hrishikesh Telang is a student who recently received his B.E. (First Class with Distinction) in Computer Science from Mumbai University (2020). He is highly consistent in his academic track record and is well revered amongst his faculty from the Department of Computer Science. From 2018 to 2019, Hrishikesh was a Research Intern at the R&D cell of his college, pursuing a Water Conservation project from the Internet of Things (IoT). Later, he pursued an internship in Data Science and Business Analytics between August 2020 to September 2020. Since December 2020, Hrishikesh has been working as a Research Assistant under the tutelage of Dr. Kavita Sonawane towards projects pertaining to medical science. His research interests include Image Processing, Machine Learning, Deep Learning, Natural Language Processing, Recommendation Systems, Data Analytics, Data Visualization, Data Mining and Warehousing, and Cloud Data Management and has actively pursued various self-projects to further his knowledge and understand core concepts. Hrishikesh is a prospective M.S. student in Information Management with a Certificate in Advanced Studies (C.A.S) in Data Science at the Syracuse School of Information Studies (2023).

Author Articles
COVID-19 and Malaria Parasite Detection and Classification by Bins Approach with Statistical Moments Using Machine Learning

By Hrishikesh Telang Kavita Sonawane

DOI:, Pub. Date: 8 Jun. 2023

This work introduces the novelty as an application of histogram-based bins approach with statistical moments for detecting and classifying malaria using blood smear images into parasitized and uninfected cell images and the rising disease of COVID-19/Normal lung images. Proposed algorithms greatly vary as compared to the previous work. This work aims to improve accuracy in detection and classification and reduce feature vector dimensionality. It focuses on detailed image contents extracted into 8 bins by considering the significance of the R, G, and B color component relationship in the formation of each pixel. The texture features are represented by the first four moments for each of the three colors separately. This leads to the generation of 12 features vectors, each of size 8 components for each image in the database. Feature dimensionality reduction is achieved by applying different feature selection techniques to obtain desired optimum feature space. The comprehensive feature analysis presented here identifies many useful findings in order to validate the contribution of each image content uniquely in detection and classification. The proposed approach experimented with two image datasets: the malaria dataset obtained from the National Library of Medicine (NLM) and the lung image dataset acquired from the Radiography Database from Kaggle. The performance of work presented here is evaluated and compared with previous work with the same set of parameters, namely precision, recall, F1 score, and the AUC. We have achieved and improved the performances compared to previous work and also achieved better results even for the COVID-19 dataset.

[...] Read more.
Other Articles