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Welcome to IgMin Research – an Open Access journal uniting Biology, Medicine, and Engineering. We’re dedicated to advancing global knowledge and fostering collaboration across scientific fields.
Welcome to IgMin, a leading platform dedicated to enhancing knowledge dissemination and professional growth across multiple fields of science, technology, and the humanities. We believe in the power of open access, collaboration, and innovation. Our goal is to provide individuals and organizations with the tools they need to succeed in the global knowledge economy.
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We believe in bridging the gaps between fields to strengthen interdisciplinary understanding.
Biography
Dr. Kandati Dasaradharami Reddy is an accomplished Associate Professor in the Department of Computer Science and Engineering at NBKR Institute of Science and Technology (Autonomous), located in Vidyanagar, Andhra Pradesh, India, an institution founded in 1979 and spread across a 184‑acre campus. A holder of a Ph.D., he brings a rich academic and research background with a particular emphasis on machine learning, deep learning, and federated learning.
Dr. Reddy has authored over 49 scholarly publications, with significant contributions in privacy‑preserving AI systems for healthcare applications. Most notably, he published “A Comprehensive Review of Federated Learning in Cancer Diagnosis and Prognosis Prediction” in April 2025, exploring secure, decentralized model training frameworks for medical data across hospitals. Other notable works include studies on federated learning for industrial applications, digital twins, and early COVID‑19 detection.
His contributions extend to editorial and professional service roles: he is a member of the editorial board at IgMin Research, where he reviews and curates content relating to AI and engineering. He also serves as an IEEE member and fellow of professional organizations such as LMISTE and LMIAENG
Beyond research, Dr. Reddy is a skilled software developer proficient in Python, C++, Java, and related technologies. He recently spearheaded the inauguration of the AI & ML Club at NBKRIST on 6 March 2025, fostering innovation and collaboration within the academic community
Through his dedication to research, teaching, and community-building, Dr. K. Dasaradharami Reddy exemplifies academic excellence and leadership in artificial intelligence and computer science.
Research Interest
Dr. K. Dasaradharami Reddy's research interests lie at the intersection of artificial intelligence, machine learning, and data privacy, with a strong focus on federated learning. His work is particularly centered on designing decentralized and privacy-preserving models for sensitive domains such as healthcare and industrial applications. He is deeply invested in exploring how AI can be made more ethical and secure, especially in contexts where data sharing is restricted due to confidentiality concerns. Dr. Reddy’s research also extends to deep learning, digital twin systems, IoT-based data modeling, and early disease detection using AI frameworks. He aims to bridge the gap between theoretical advancements and practical applications by integrating domain-specific constraints into scalable machine learning models. Through his contributions, Dr. Reddy seeks to enhance the accessibility and trustworthiness of AI technologies across sectors. His commitment to innovation in secure, distributed AI systems positions him as a key contributor in the evolving landscape of ethical machine learning.
Open Access Policy refers to a set of principles and guidelines aimed at providing unrestricted access to scholarly research and literature. It promotes the free availability and unrestricted use of research outputs, enabling researchers, students, and the general public to access, read, download, and distribute scholarly articles without financial or legal barriers. In this response, I will provide you with an overview of the history and latest resolutions related to Open Access Policy.
Federated learning (FL) has emerged as a promising approach for collaborative model training across multiple institutions without sharing sensitive patient data. In the context of cancer diagnosis and prognosis prediction, FL offers a potential solution to the challenges associated with data privacy and security. This paper reviews the application of FL in cancer diagnosis and prognosis prediction, highlighting its key benefits, limitations, and future research directions. We discuss the potential of FL to improve the accuracy and generalizabil...ity of predictive models by leveraging diverse and distributed datasets while preserving data privacy. Furthermore, we examine the technical and regulatory considerations associated with implementing FL in the healthcare domain. Finally, we identify opportunities for future research and development in FL for cancer diagnosis and prognosis prediction.
