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About

Sahil Verma is a researcher specializing in computational intelligence, path planning, and wireless sensor networks. His work focuses on optimizing complex navigation and communication systems using swarm intelligence and hybrid metaheuristic algorithms. Verma’s most significant contribution is in optimal path planning, where he developed novel hybrid techniques—such as combining Bat Algorithm and Cuckoo Search—to determine collision-free, shortest paths for applications in robotics, simulation, virtual reality, and bioinformatics. His 2019 paper on “Optimal Path Planning Using Swarm Intelligence Based Hybrid Techniques” has garnered 29 citations, reflecting its impact on the field. Verma has also advanced wireless sensor network efficiency with his “SC-MCHMP” protocol, a score-based cluster-level hybrid multi-channel MAC approach designed to reduce collisions and energy consumption. More recently, he has explored deep learning applications, including handwritten digit recognition using convolutional neural networks. With a portfolio spanning path planning, network protocols, and machine learning, Verma demonstrates a versatile approach to solving real-world computational challenges, making his work valuable for researchers in robotics, AI, and IoT systems.