1) Dr. Vilas Ramakant Wani, Head, Assistant Professor, Computer Science

Published research article titled "A Study on Blockchain, Applications, Challenges, and Its Impact on Business" in JETIR Volume 11, Issue 9, on September 24, 2024.

The article examines the transformative potential of blockchain technology across various sectors, including business, healthcare, taxation, and more. It explores blockchain’s applications, challenges, and future implications, with a focus on enhancing transparency, security, and efficiency. The study also addresses scalability issues, smart contracts, and regulatory considerations related to blockchain. The full article can be accessed at http://doi.one/10.1729/Journal.41624

Published research article titled "A Study: 5G Next Generation Wireless Technology Test Issues, Implications, and Research Challenges" in JETIR Volume 11, Issue 9, on September 24, 2024.

The article explores the challenges, testing methodologies, and implications of 5G wireless technology. It addresses the research challenges associated with 5G deployment, its potential impact on various sectors, and the innovations required to ensure its effective implementation. The full article is available at http://doi.one/10.1729/Journal.41623

2) Ulhas Baban Langote, Assistant Professor, Mathematics

Published a research article titled "A Study of Algorithm for PTH Root of Matrix" in the IJISRT (International Journal of Innovative Science and Research Technology), Volume 0, Issue 9, in September 2024.

The article explores algorithms for calculating the PTH root of matrices and its applications in various mathematical and computational fields.

3) Pranita D. Sherkhane, Assistant Professor, Computer Science

Published research paper titled "Deep Learning Techniques Used in Agriculture: A Review" in the International Journal for Research in Applied Science & Engineering Technology (IJRASET) in March 2024.

This paper explores the application of deep learning algorithms, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in agriculture. It reviews various case studies related to crop yield estimation, disease detection, weed identification, and irrigation management, emphasizing the significant potential of these algorithms in revolutionizing agricultural practices. The paper also discusses the challenges and future directions in the field.