
Intelligent Computing
Description
This book presents a selected collection of high-quality papers presented at Computing Conference 2026, held in London, UK, on 9-10 July 2026. The conference witnessed submissions from researchers, academicians, and industry practitioners from worldwide. To ensure the high standards of originality, technical quality, and relevance to the conference themes, all the papers were passed through a double-blind peer-review process.
A wide range of core computer science tracks, including AI and machine learning, data science, computing for society, cybersecurity, privacy and trust, computer vision, technology-enhanced education, future computing architectures, and intelligent systems, encompass the current research and emerging trends. Together, these works highlight innovative methodologies, theoretical advancements, and practical applications that advance the state-of-the-art in computing and digital technologies.
The authors trust that this volume of the proceedings will serve as a valuable reference for researchers and practitioners and contribute to continued exploration, collaboration, and innovation in the rapidly evolving field of computing.
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Content
Optuna-Guided Hyperparameter Tuning for Ensemble-Based Deep Learning Model Selection in Histopathology Image Classification.- Mobile-Friendly Solution for COVID-19 Detection from Computed Tomography Images.- A Machine-Learning Tool-Supported Methodology for Modeling Nonprofit Donor Relations.- Transformer-Based Multi-Scale Fusion of RGB and Point Cloud for Off-Road Drivable Area Detection.- Human Factors and Emotional Responses in FPV Drone Operations: Insights from a Virtual Reality Simulation Framework.- Remote Biometric Advertisements Research in Pandemic Times: Is this a Viable Alternative to Laboratory-Based Studies?.- Exploring Artificial Intelligence Role for Texture Pattern Recognition in Sorting and Recycling of the Circular Economy.- Lag Order Selection for Granger Causality Estimation.- Impact of Linear Dimensionality Reduction on Machine Learning Performance: A Case Study on Fashion-MNIST.- Balanced Sharding for Targeted Machine Unlearning (BSTM).