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Full Description
This book includes high-quality research papers presented at the Ninth International Conference on Innovative Computing and Communication (ICICC 2026), which is held at the Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India, on 6-7 February 2026. Introducing the innovative works of scientists, professors, research scholars, students, and industrial experts in the field of computing and communication, the book promotes the transformation of fundamental research into institutional and industrialized research and the conversion of applied exploration into real-time applications.
Contents
Comparative Study of Deep Neuro-Fuzzy Systems with Artificial Neural Networks for Diabetes Prediction in Siddha Healthcare.- Adaptive Artificial Hummingbird Algorithm-Based Single Diode Photovoltaic Model Parameter Estimation.- Enhancing the Flood Risk Management By using Machine Learning Algorithms.- ZeroDCE-CBAM: Lightweight Spatial-Channel Aware Illumination Enhancement for Images.- Generating Coherent Children Stories in English Using Language Models: A Comparative Study.- Machine Learning-Based Clustering of Global Labor Markets Using Unemployment Profiles.- A Comprehensive Research and Analysis Study on AyurMate: The Healthcare Platform.- Evaluation of Accuracy and Efficiency of Machine Learning Models for Environmental Monitoring with COTS Edge-IoT Architectures using Virtualization in Development.- AI That Fixes Its Own Mistakes: A Self-Healing Agentic Model without Retraining.- Online Monitoring of Battery Parameters and SoH Prediction.- Building Future- Proof IoT Networks with Quantum Cryptography and Quantum Machine Learning.- Hybrid Stacking Ensemble Model for Cardiovascular Disease Risk Prediction.- A Machine Learning-Driven Framework for Early Heart Disease Detection Using Multivariate Clinical Indicators.- Hybrid Religion Algorithm and Improved Conjugate Gradient Method for Solving Non Linear optimization problems.- A Novel Gamma-Log Transform based image enhancement approach for low contrast plant disease detection.
