Beschreibung:
This book is a collection of selected papers presented at the Third Congress on Intelligent Systems (CIS 2022), organized by CHRIST (Deemed to be University), Bangalore, India, under the technical sponsorship of the Soft Computing Research Society, India, during September 5-6, 2022. It includes novel and innovative work from experts, practitioners, scientists, and decision-makers from academia and industry. It covers topics such as the Internet of Things, information security, embedded systems, real-time systems, cloud computing, big data analysis, quantum computing, automation systems, bio-inspired intelligence, cognitive systems, cyber-physical systems, data analytics, data/web mining, data science, intelligence for security, intelligent decision-making systems, intelligent information processing, intelligent transportation, artificial intelligence for machine vision, imaging sensors technology, image segmentation, convolutional neural network, image/video classification, soft computing for machine vision, pattern recognition, human-computer interaction, robotic devices and systems, autonomous vehicles, intelligent control systems, human motor control, game playing, evolutionary algorithms, swarm optimization, neural network, deep learning, supervised learning, unsupervised learning, fuzzy logic, rough sets, computational optimization, and neuro-fuzzy systems.
This book is a collection of selected papers presented at the Third Congress on Intelligent Systems (CIS 2022), organized by CHRIST (Deemed to be University), Bangalore, India, under the technical sponsorship of the Soft Computing Research Society, India, during September 5–6, 2022. It includes novel and innovative work from experts, practitioners, scientists, and decision-makers from academia and industry. It covers topics such as the Internet of Things, information security, embedded systems, real-time systems, cloud computing, big data analysis, quantum computing, automation systems, bio-inspired intelligence, cognitive systems, cyber-physical systems, data analytics, data/web mining, data science, intelligence for security, intelligent decision-making systems, intelligent information processing, intelligent transportation, artificial intelligence for machine vision, imaging sensors technology, image segmentation, convolutional neural network, image/video classification, soft computing for machine vision, pattern recognition, human-computer interaction, robotic devices and systems, autonomous vehicles, intelligent control systems, human motor control, game playing, evolutionary algorithms, swarm optimization, neural network, deep learning, supervised learning, unsupervised learning, fuzzy logic, rough sets, computational optimization, and neuro-fuzzy systems.
Chapter 1. Design and Analysis of Genetic Algorithm Optimization based ANFIS Controller for Interleaved DC-DC Converter fed PEMFC System.- Chapter 2. A Digital Transformation Model for Intelligent Organizational Systems: Key Constructs for successful DT.- Chapter 3. Smart Sewage Monitoring Systems.- Chapter 4. Explainable Stacking Machine Learning Ensemble for Predicting Airline Customer Satisfaction.- Chapter 5. A Water Cycle Algorithm for Optimal Design of IIR Filters.- Chapter 6. Comparative Evaluation of Machine Learning Algorithms for Credit Card Fraud Detection.- Chapter 7. Future Commercial Prospects of Unmanned Aerial Vehicles (UAVs).- Chapter 8. Experimental Analysis of Deep Learning algorithms used in Brain Tumor Classification.- Chapter 9. Optimized Grabcut Algorithm in Medical Image Analyses.- Chapter 10. Improvement of Speech Emotion Recognition by Deep Convolutional Neural Network and Speech Features.- Chapter 11. GABC Genetic Artificial Bee Colony for Mapping Onto Network on Chip.- Chapter 12. A Study of Machine Translation Models for Kannada - Tulu.- Chapter 13. Multi-Technology Gateway Management for IoT: Review, Analysis and Open Issues.- Chapter 14. BGR Images based Human Fall detection using resnet-50 and lstm.- Chapter 15. Presaging Cancer Stage Classification by extracting influential features from Breast/Lung/Prostate cancer clinical datasets based on TNM model.
