Third Congress on Intelligent Systems : proceedings of CIS 2022. Volume 2 / Sandeep Kumar, Harish Sharma, K. Balachandran, Joong Hoon Kim, Jagdish Chand Bansal, editors.
Congress on Intelligent Systems (3rd : 2022 : Bangalore, India); Kumar, Sandeep (Professor of computer science and engineering), editor.; Sharma, Harish, editor.; Balachandran, K. editor.; Kim, Joong Hoon, editor.; Bansal, Jagdish Chand, editor.
2023
Q334
Available Online

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Title
Third Congress on Intelligent Systems : proceedings of CIS 2022. Volume 2 / Sandeep Kumar, Harish Sharma, K. Balachandran, Joong Hoon Kim, Jagdish Chand Bansal, editors.
Meeting Name
Congress on Intelligent Systems (3rd : 2022 : Bangalore, India)
ISBN
9789811993794 (electronic bk.)
9811993793 (electronic bk.)
9811993785
9789811993787
DOI
https://doi.org/10.1007/978-981-19-9379-4
Published
Singapore : Springer, 2023.
Language
English
Description
1 online resource (816 pages) : illustrations (black and white, and color).
Item Number
10.1007/978-981-19-9379-4 doi
Call Number
Q334
Dewey Decimal Classification
006.3
Summary
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 56, 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.
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Access limited to authorized users.
Source of Description
Description based on print version record.
Added Author
Kumar, Sandeep (Professor of computer science and engineering), editor.
Sharma, Harish, editor.
Balachandran, K. editor.
Kim, Joong Hoon, editor.
Bansal, Jagdish Chand, editor.
Series
Lecture notes in networks and systems ; v. 613.
Available in Other Form
THIRD CONGRESS ON INTELLIGENT SYSTEMS.
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Patch Extraction and Classifier for Abnormality Classification in Mammography Imaging
Improving the Performance of Fuzzy Rule Based Classification Systems using Particle Swarm Optimization
Tuning Extreme Learning Machine by Hybrid Planet Optimization Algorithm: An Application for Diabetes Classification
Towards Computation Offloading Approaches in IoT-Fog-Cloud Environment: Survey on Concepts, Architectures, Tools and Methodologies
Prediction of COVID-19 Pandemic spread using Graph Neural Networks
Event Based Time-To-Contact Estimation with Depth Image Fusion
mCD and Clipped RBM based DBN for Optimal Classification of Breast Cancer
Digital Disruption in Major Ports with Special Reference to Chennai Port, Kamarajar Port and Tuticorin Port
SmartTour: A Blockchain-based Smart Tourism Platform using Improvised SHA
Detection of Starch in Turmeric using Machine Learning Methods
A study of Crypto-Ransomware using Detection Techniques for Defence Research
Internet of Things (IOT) Based Smart Agriculture System Implementation and Current Challenges
Physical Unclonable Function and Smart Contract based Authentication Protocol for Medical Sensor Network
Developing Prediction Model for Hospital Appointment No-Shows using Logistic Regression.

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