Computer Vision and Machine Intelligence Paradigms for SDGs [electronic resource] : Select Proceedings of ICRTAC-CVMIP 2021 /

PTZ-camera-based facial expression analysis using faster R-CNN for student engagement recognition -- Convergence Perceptual Model for Computing Time-Series-Data on Fog-Environment -- Localized Super Resolution for Foreground Images using U-Net and MR-CNN -- SMS Spam Classification Using PSO-C4.5 -- Automated Sorting, Grading of Fruits Based on Internal and External Quality Assessment Using HSI, Deep CNN -- Pest Detection using Improvised YOLO Architecture -- Classification of Fungi Effected Psidium Guajava Leaves using ML and DL Techniques -- Deep Learning Based Recognition of Plant Diseases -- Artificial Cognition of Temporal Events using Recurrent Point Process Networks -- On the Performance of Energy Efficient Video Transmission over LEACH based protocol in WSN -- Hybridization of Texture Features for Identification of Bi-lingual Scripts from Camera Images at Wordlevel -- Advanced Algorithmic Techniques for Topic Prediction and Recommendation - An Analysis -- Implementation of an automatic EEG feature extraction with Gated Recurrent Neural Network for Emotion Recognition.

This book constitutes refereed proceedings of the 4th International Conference on Recent Trends in Advanced Computing - Computer Vision and Machine Intelligence Paradigms for Sustainable Development Goals. This book covers novel and state-of-the-art methods in computer vision coupled with intelligent techniques including machine learning, deep learning, and soft computing techniques. The contents of this book will be useful to researchers from industry and academia. This book includes contemporary innovations, trends, and concerns in computer vision with recommended solutions to real-world problems adhering to sustainable development from researchers across industry and academia. This book serves as a valuable reference resource for academics and researchers across the globe.