Dr. Arfan Ghani currently serves as an Associate Professor in Computer Science and Engineering at the American University of Ras al Khaimah, UAE. He attained academic qualifications and gained valuable experience from UK institutions, including Ulster, Coventry, and Newcastle. Dr Ghani's industrial research and development expertise spans various roles at Intel Research, the University of Cambridge, and Microchip Denmark. With extensive applied research experience, he has made significant contributions to leading journals and conferences and successfully secured substantial collaborative funding from prestigious entities such as EPSRC, EU, Innovate UK, the Royal Academy of Engineering, and the German Aerospace Centre. Dr. Ghani actively engages in scholarly activities, serving as an Associate Editor for Elsevier Neurocomputing, Guest Editor, and Technical Programme Committee member for numerous IEEE/IET conferences. His contributions to the field have been acknowledged with several awards, including the Best Paper award from the European Neural Network Society in 2007. Dr. Ghani specializes in Computer Vision-based healthcare diagnostics, AI chip design, and reconfigurable hardware accelerators for machine learning and deep neural network architectures. His expertise in these areas has led to groundbreaking advancements in applying technology to solve critical healthcare challenges. Dr. Ghani is a distinguished member of the Institution of Engineering and Technology (IET), a Chartered Engineer (CEng), and a Fellow of the Higher Education Academy in the UK.

Introduction.- Accelerating the classification of pandemic data using reconfigurable hardware (FPGA) and machine learning.- Computer vision based automated diagnosis for skin cancer detection.- Design and development of an integrated analytics platform for environmental data classification.- Design and development of multimodal healthcare data sensing and classification using Deep Neural Networks (DNNs).- Low-power analogue design with Spiking Neural Networks (SNN).- Full custom design of a sustainable, low-power environmental monitoring node.- Real-time performance analysis of Maximum-Power-Point Tracking (MPPT) algorithm for energy conversion on hardware platform (FPGA).- Computer-vision based real data generation for object classification.- Conclusion.