Advances in Data-Driven Computing and Intelligent Systems: Selected Papers from ADCIS 2023, Volume 3 [Paperback]

This book is a collection of best-selected research papers presented at the International Conference on Advances in Data-driven Computing and Intelligent Systems (ADCIS 2023) held at BITS Pilani, K. K. Birla Goa Campus, Goa, India, during September 2123, 2023. It includes state-of-the-art research work in the cutting-edge technologies in the field of data science and intelligent systems. The book presents data-driven computing; it is a new field of computational analysis which uses provided data to directly produce predictive outcomes. The book is useful for academicians, research scholars, and industry persons.A comprehensive review: Sentiment Analysis for Indian local Languages.- Face Recognition-Based Surveillance System.- Comparative Analysis of Malware Classification using Supervised Machine Learning Algorithms.- Noise filtering algorithm based on Machine learning for identification of ground hitting photons in Jaipur city.- Data-driven interior plan generation for residential buildings in Vietnam.- A weekly scheduling of operating theaters using Multi Agent Planner.- Using website content for detecting phishing URLs: A Novel approach.- Attention and Residual-Atrous Convolutional Learning-Based CNN Architecture for Lung Nodule Segmentation and Classification.- Adaptive Segmentation on Extracting Textural and Fractal Patterns for Assessing Mangrove Dynamics using Multi-spectral Data.- Exploring Multivariate Chemometric Tool for Simultaneous Determination of Erectile Dysfunction Drugs in Pharmaceutical Formulation.- Mass transport of combined oscillating electroosmotic and pressure drivenflow through cylindrical nanopore considering ion partitioning effects.- Audio Signal Analysis and Classification of Bluetooth Vulnerabilities using Machine Learning Techniques.- Performance Evaluation of Thresholding-based Segmentation Algorithms for Aerial Imagery.- Estimation of particle Froude Number in Deposited Bed Condition using Hybrid Machine Learning Models.- An Image lóÉ