Muhammad Shamsul Alam

University of Bisha, University of Technology Malaysia

Papers

Total Citations

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About

Muhammad Shamsul Alam is a researcher at the forefront of computer vision and autonomous robotics, with a specialized focus on indoor camera localization and navigation systems. His work primarily explores the application of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to solve the challenging problem of estimating camera pose from single images or video sequences in indoor environments. Alam’s most significant contribution is his comprehensive review of RNN-based camera localization techniques (2023, 42 citations), which has become a key reference for researchers in robotics and computer vision. His experimental work evaluating RNN performance for indoor localization (2022, 8 citations) provides critical insights into the practical challenges of implementing these models with large image datasets. Notably, Alam has extended his research to socially impactful applications, developing self-localization methods for guide robots designed to assist visually impaired individuals (2024, 4 citations). His work bridges the gap between theoretical deep learning architectures and real-world robotic navigation, addressing fundamental challenges in autonomous systems. With a growing citation footprint and a clear trajectory toward applied robotics for accessibility, Alam is establishing himself as an emerging voice in intelligent navigation systems.