Full Description
This book compiles a curated selection of insightful, rigorously researched, and state-of-the-art papers presented at the Computing Conference 2025, hosted in London, UK, on June 19-20, 2025. Drawing submissions from across the globe, the conference received 473 papers, each subjected to a stringent double-blind peer-review process. Of these, 169 papers were accepted for inclusion, reflecting exceptional scholarship and innovation across disciplines such as IoT, artificial intelligence, computing, data science, networking, data security, and privacy.
Researchers, academics, and industry leaders converged to share pioneering ideas, transformative methodologies, and practical solutions to real-world challenges. By bridging academic theory and industrial application, the conference catalyzed opportunities for knowledge synthesis and interdisciplinary progress.
The diverse contributions within this proceedings not only address contemporary technological issues but also anticipate future trends, offering frameworks for continued exploration. We trust this collection will serve as an indispensable reference for researchers, practitioners, and policymakers navigating the evolving landscapes of computing and digital innovation.
As we reflect on the conference's outcomes, we are confident that the insights and collaborations forged here will inspire sustained advancements in these critical fields. May the ideas within these pages spark further inquiry, drive technological evolution, and contribute meaningfully to solving the challenges of our interconnected world.
Contents
Towards AI Consciousness: A Phased Approach to Functional Organization, Embodiment, and Continuous Learning.- Metrological Validation of Medical AI Algorithms in International Data Spaces.- Enhanced Liver and Tumor Segmentation in Multi-Modal Medical Images Using Deep Learning Techniques.- Advanced AI and Temporal Analysis for Accurate Cricket Shot Classification.- Prompt-Driven Approach for Cattle Image Segmentation Based on SAM.- Vision Transformer Based Photometric Stellar Classification.
