Intelligent Computing for Sustainable Development First International Conference, ICICSD 2023, Hyderabad, India, August 25–26, 2023, Revised Selected Papers, Part II

The two-volume proceedings set CCIS 2121 and 2122 constitutes the refereed proceedings of the First International Conference on Intelligent Computing for Sustainable Development, ICICSD 2023, which took place in Hyderabad, India, during August 25–26, 2023.

The 46 papers included in these proceedings were carefully reviewed and selected from 138 submissions. They focus on digital healthcare, renewable energy, smart cities, digital farming, and autonomous systems.

1 A Cognitive Architecture based Conversation Agent Technology for Secure Communication.- 2 Darwinian Lion Swarm Optimization-based Extreme Learning Machine with Adaptive Weighted Smote for Heart Disease Prediction.- 3 Alzheimer’s Disease Detection Using Convolution Neural Networks.- 4 A Comparison Study of Cyberbullying Detection Using Various Machine Learning Algorithms.- 5 State of the Art Analysis of Word Sense Disambiguation.- 6 Oral Cancer Classification using GLRLM Combined with Fuzzy Cognitive Map and Support Vector Machines from Dental Radiograph.- 7 Machine Learning Based Delta Sigma Modulator Using Memristor for Neuromorphic Computing.- 8 An Effective Framework for the Background Removal of Tomato Leaf Disease using Residual Transformer Network.- 9 An Intelligent Ensemble Architecture to Accurately Predict Housing Price for Smart Cities.- 10 Detection of Leaf Blight Disease in Sorghum using Convolutional Neural Network.- 11 Data Security for Internet of Things (IoT) using Lightweight Cryptography (LWC) Method.- 12 A Hybrid Optimization Driven Deep Residual Network for Sybil Attack Detection and Avoidance in Wireless Sensor Networks.- 13 Feature Engineering Techniques for Stegware Analysis: An Extensive Survey.- 14 Text Summarization using Deep Learning: An Empirical Analysis of Various Algorithms.- 15 An Improved Detection of Fetal Heart Disease using Multilayer Perceptron.- 16 Application of Deep Learning Techniques for Coronary Artery Disease Detection and Prediction: A Systematic Review.