For Admissions Enquire: 8712225044 | admissions@aceec.ac.in
Associate Professor & HOD of AI & DS
Qualification:
B.Tech(CSE),. M.Tech(CSE), Ph.D(CSE)
Professional Exp.:
17 Years
Registration Number:
0862-150505-162448
Dr. Ralla Suresh is an accomplished academician and researcher, currently serving as Associate Professor and Head of the Department of Artificial Intelligence and Data Science (AI & DS) at ACE Engineering College, Medchal Malkajgiri, Telangana. With more than 16 years of teaching and research experience, he has significantly contributed to curriculum design, academic leadership, and research advancement in the fields of Artificial Intelligence, Machine Learning, and Deep Learning.
He obtained his Ph.D. and M.Tech in Computer Science from Osmania University, Hyderabad, and his B.Tech in Computer Science from Kakatiya University, Khammam. His doctoral research focused on heart disease prediction using machine learning models for early detection and risk stratification.
Dr. Suresh has authored over 26 research publications, including Scopus-indexed journals, Springer book chapters, and conference proceedings. His publications focus on intelligent algorithms, predictive analytics, and feature selection techniques for healthcare applications. His work is indexed on Google Scholar.
He has also authored a textbook on Artificial Intelligence (ISBN: 978-81-966517-3-2) and holds two patents, one on Flood Monitoring Devices with Alert Mechanism and another on Ad Hoc Model Building and Machine Learning Services for Radiology Quality Dashboards.
Heart disease prediction using machine learning is to develop accurate and reliable models that can assist healthcare professionals in early detection, risk stratification, and personalized treatment planning for patients at risk of heart disease
The flood monitoring device with alert mechanism is a sophisticated tool designed to detect and mitigate the risks associated with flooding. At its core, this device utilizes a network of sensors strategically placed in flood-prone areas, such as riverbanks, coastal regions, or urban areas susceptible to flash floods. These sensors can detect changes in water levels, pressure, and flow rates, providing real-time data on impending flood conditions.
The present invention discloses a system for AD HOC model building and machine learning services for radiology quality dashboard and method thereof. The system includes, but not limited to, a medical report database including one or more diagnostic multidimensional image data and non-image patient metadata; a processing unit configured to communicate with the medical report database to retrieve a patient-specific input dataset; a machine learning interface to parse the patient-specific input dataset using learned models to determine a clinical domain and relevant image annotations and populate an annotation data using the relevant image annotations; a user interface to apply one or more domain-specific scriptable rules to populate a report template based on the annotation data; and a computation server configured to identify one or more clinically relevant findings based on the populated report template and sending to the user interface through Ad-hoc data modeling.