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Congress on Intelligent Systems = proceedings of CIS 2021.. Volume 1 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Congress on Intelligent Systems/ edited by Mukesh Saraswat ... [et al.].
其他題名:	proceedings of CIS 2021.
其他題名:	CIS 2021
其他作者:	Saraswat, Mukesh.
團體作者:	Congress on Intelligent Systems
出版者:	Singapore :Springer Nature Singapore : : 2022.,
面頁冊數:	xv, 951 p. :ill., digital ;24 cm.
內容註:	The Extraction of Automated Vehicles Traffic Accident Factors and Scenarios using Real-World data -- Leaf Disease Identification in Rice Plants Using CNN Model -- Twitter Sentiment Analysis Based on Neural Network Techniques -- Enhanced Stock Market Prediction using Hybrid LSTM Ensemble -- Classifying Microarray Gene Expression Cancer Data using Statistical Feature Selection and Machine Learning Methods -- Pythagorean Fuzzy Information Measure with Application to Multicriteria Decision Making -- Developing an Improved Software Architecture Framework for Smart Manufacturing -- Intelligent Water Drops Algorithm Implementation using Mathematical Function -- A Study of Decision Tree Classifier to Predict Learner's Progression -- An Overview of Blockchain and IoT in e-Healthcare System.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence - Congresses. -
電子資源:	https://doi.org/10.1007/978-981-16-9416-5
ISBN:	9789811694165

Congress on Intelligent Systems = proceedings of CIS 2021.. Volume 1 /
Congress on Intelligent Systemsproceedings of CIS 2021.Volume 1 /[electronic resource] :CIS 2021edited by Mukesh Saraswat ... [et al.]. - Singapore :Springer Nature Singapore :2022. - xv, 951 p. :ill., digital ;24 cm. - Lecture notes on data engineering and communications technologies,v. 1142367-4520 ;. - Lecture notes on data engineering and communications technologies ;v. 114..

The Extraction of Automated Vehicles Traffic Accident Factors and Scenarios using Real-World data -- Leaf Disease Identification in Rice Plants Using CNN Model -- Twitter Sentiment Analysis Based on Neural Network Techniques -- Enhanced Stock Market Prediction using Hybrid LSTM Ensemble -- Classifying Microarray Gene Expression Cancer Data using Statistical Feature Selection and Machine Learning Methods -- Pythagorean Fuzzy Information Measure with Application to Multicriteria Decision Making -- Developing an Improved Software Architecture Framework for Smart Manufacturing -- Intelligent Water Drops Algorithm Implementation using Mathematical Function -- A Study of Decision Tree Classifier to Predict Learner's Progression -- An Overview of Blockchain and IoT in e-Healthcare System.

This book is a collection of selected papers presented at the Second Congress on Intelligent Systems (CIS 2021), organized by Soft Computing Research Society and CHRIST (Deemed to be University), Bengaluru, India, during September 4 - 5, 2021. It includes novel and innovative work from experts, practitioners, scientists, and decision-makers from academia and industry. It covers topics such as 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.

ISBN: 9789811694165

Standard No.: 10.1007/978-981-16-9416-5doiSubjects--Topical Terms:

606815
Artificial intelligence
--Congresses.

LC Class. No.: Q334 / .C65 2021

Dewey Class. No.: 006.3

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