The International Conference on Intelligent Technologies (CONIT)

Bengaluru, India — The IEEE 5th International Conference on Intelligent Technologies (CONIT 2025) concluded its prestigious annual gathering this June in Bengaluru, honoring four research teams with the Best Paper Award for their outstanding contributions to the field of intelligent systems. Selected from a pool of rigorously peer-reviewed submissions, the winning papers represent some of the most innovative and impactful research in Quantum-AI, Edge Computing, Artificial Intelligence, Federated Learning, Robotics and Smart Infrastructure.

Organized under the IEEE banner and adhering to a double-blind peer review process, CONIT 2025 attracted global participation from academia, industry, and research institutions. The conference provided a forum for presenting novel research on data-driven intelligent systems, with accepted papers slated for publication in the IEEE Xplore Digital Library and indexing in Scopus and Web of Science.

2025 Best Paper Award Recipients:

Paper 1: Quantum-AI Hybrid Algorithms for Solving Large-Scale Engineering Problems Using LLM-Driven Optimizations

Track :Computational Intelligence

Authors : Anil Kumar Jonnalagadda, Sudeep Acharya, Venkat Reddy Pasam, RohanShahane

The paper titled “Quantum-AI Hybrid Algorithms for Solving Large-Scale Engineering Problems Using LLM-Driven Optimizations” is authored by four accomplished professionals and researchers whose combined expertise spans data engineering, cloud computing, AI, and quantum-driven optimizations. This paper introduces a Quantum-AI Hybrid Framework that merges quantum computing and Large Language Models (LLMs) to solve complex engineering problems. This approach leverages quantum parallelism and LLM intelligence for improved convergence, accuracy, and scalability in fields like structural design, power systems, and fluid dynamics

Anil Kumar Jonnalagadda is the primary author, a Senior Data Engineer and Cloud Architect with over 17 years of experience delivering large-scale data-driven solutions. His expertise includes cloud-native architectures, big data engineering, quantum computing, and AI-powered automation across GCP, AWS, and Azure. Anil is a passionate researcher, with multiple contributions in AI, data privacy, and scalable systems. His commitment to bridging research with real-world solutions led to this paper being recognized with the Best Paper Award in the Computational Intelligence track at CONIT 2025.

Sudeep Acharya is a Manager in Application Development and Data Architecture based in the USA. He brings deep expertise in Azure, Databricks, and AI-powered data platforms, focusing on solving complex business problems with scalable analytics, automation, and modern data architectures.

Venkata Reddy Pasam is a seasoned Data Engineer and Cloud Architect with 14 years of experience. His contributions span AI, machine learning, cloud security, and DevSecOps. Venkata is recognized for driving high-impact cloud migrations, AI automation, and database modernization. He is also a hackathon judge, community mentor, and a thought leader in enterprise AI solutions.

Rohan is a technology-driven professional with 25 years of IT experience, including 8 years focused on Data Architecture and Engineering. He specializes in transforming complex data landscapes, ensuring data quality, legacy system modernization, and applying advanced analytics to drive enterprise success.

Together, the authors bring a unique blend of research, innovation, and industry leadership that reflects in their award-winning contribution to advancing computational intelligence.

 

Paper 2 : Swarm-Based Federated Learning for Scalable Optimization of Edge and Cloud Resource Allocation
Track : Data Science & Engineering

Authors : Sooraj George Thomas, Satya Prakash, Direesh Reddy Aunugu

This paper introduces a novel Quantum-AI Hybrid Framework for solving complex engineering problems. It combines quantum computing’s probabilistic sampling with LLMs’ reasoning and code generation capabilities. This hybrid approach, tested on structural design, power systems, and fluid dynamics, shows significant improvements in convergence speed and solution accuracy over traditional methods. It highlights the potential of integrating LLM intelligence into quantum-AI for future computational advancements.

Paper 3: Edge AI and Federated LLMOps for Latency-Critical IoT Systems in Smart Infrastructure.
Track : Computing Technologies

Authors: Praveen Kumar Myakala, Prudhvi Naayini ,  Srikanth Kamatala
This paper introduces an integrated Edge-Federated LLMOps framework designed to bring real-time intelligence to smart infrastructure using distributed large language models. The approach demonstrated a 47% improvement in model convergence, 60% reduction in communication overhead, and significant gains in energy efficiency across practical IoT applications such as traffic systems and surveillance.

Paper 4 : Federated AI for Surgical Robotics: Enhancing Precision, Privacy, and Real-Time Decision-Making in Smart Healthcare

Track : Robotics & Healthcare

Authors: Gokul Narain Natarajan,  Satya Manesh Veerapaneni, Vijayalaxmi Methuku, Vivek Venkatesan, Rajesh kumar kanji.
This paper proposes a Quantum-AI Hybrid Framework combining quantum computing and LLMs to solve complex engineering problems. It achieves faster convergence and improved accuracy in structural design, power systems, and fluid dynamics by leveraging quantum parallelism and LLM intelligence. The research demonstrates the significant potential of this integrated approach for future computational advancements.