Dr. D. Krishna Wins Best Paper Award at ICIICS-2026

Anurag University is proud to congratulate Dr. D. Krishna, Associate Professor in the Department of Electrical and Electronics Engineering, on earning the Best Paper Award at the IEEE International Conference on Integrated Intelligence and Communication Systems (ICIICS-2026).
His winning paper, 'Hybrid LSTM and SVM Framework for Uncertainty Reduction in Renewable Energy Forecasting,' addresses a key challenge in the clean energy transition: the inherent variability and unpredictability of renewable sources such as solar and wind. Accurate forecasting is essential for integrating renewables reliably into power grids, and Dr. Krishna's hybrid approach, combining Long Short-Term Memory (LSTM) networks with Support Vector Machines (SVM), offers a promising method to reduce uncertainty and improve prediction accuracy. By uniting two powerful machine learning techniques, the work contributes meaningfully to smarter, more dependable renewable energy systems.
Winning the Best Paper Award at an IEEE conference is a distinguished honour, reflecting the technical strength and relevance of the research amid competitive peer evaluation. This achievement underscores Anurag University's commitment to research in sustainable energy and intelligent systems. The university congratulates Dr. Krishna on this excellent recognition and celebrates his contribution to advancing reliable renewable energy forecasting.
Written By
By AU Media Team
More Stories You Might Like

Advancing Hexaferrite–Spinel Nanocomposite Research
Dr. M. Srinivasa Reddy, Professor & HoD, Department of Physics, has published a research paper in Next Materials on the comparative synthesis of hexaferrite–spinel nanocomposites, featured in a Q1 journal indexed in WoS, SCIE and Scopus.

Advancing Image Understanding Through Deep Learning
Ms. Palla Chamundeswari, Assistant Professor, Department of AIML, has published the book “Deep Learning Pathways for Image Understanding and Computer Vision Analytics”, contributing to knowledge in Deep Learning and Computer Vision.


