Information and Communication Technology for Competitive Strategies (ICTCS 2022): ICT: Applications and Social Interfaces [Paperback]

This book contains best selected research papers presented at ICTCS 2022: Seventh International Conference on Information and Communication Technology for Competitive Strategies. The conference will be held in Chandigarh, India during 9  10 December 2022. The book covers state-of-the-art as well as emerging topics pertaining to ICT and effective strategies for its implementation for engineering and managerial applications. This book contains papers mainly focused on ICT for computation, algorithms and data analytics and IT security. The work is presented in two volumes.

Optimization Accuracy of Tweets for Coronavirus Pandemic using BERT Based CNN Model.- Deep Learning Neural Networks for Forecasting the Abrasive Wear in machining tools for Cryogenic treatment by process parameter optimization.- Real Time Indian Sign Language Detection.- Measuring Impact of Text Features and Image Features on Classifiers performance for Heterogeneous data formats.- Time delay approximation effect on open loop performance of a delayed industrial process model.- Enhancement Of The Effective Bandwidth Available To A Client Host In A Network By Using Multiple Network Devices Concurrently.- Enhanced dynamic source routing based on link capacity and queue optimization in MANET (DSR-LQ).- Statistical Analysis of Feature Selection and Clustering for Building the Optimised Drug Toxicity Prediction Model.- Towards GPU Accelerated Computation for Stock Prediction Based on News Headlines.- A Novel Approach for the Enterprise Hospital Management Application with Full Stack Development.- IoT-Based Platform for Smart Automobile System.- Improving Attitudes of Underrepresented High School Students towards STEM: A Virtual Summer Program.- Diabetic Eye Diseases Detection and Classification Using Deep Learning Techniques - A Survey.- A Review to Analyze Insider Threats Using Machine Learning Techniques.- Machine Learning Model for the Email Spam Detection with Data Mining TechniqueslCñ