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Modern approaches in machine learnin...

Gunjan, Vinit Kumar.

Modern approaches in machine learning and cognitive sciencea walkthrough : latest trends in AI /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modern approaches in machine learning and cognitive scienceedited by Vinit Kumar Gunjan ... [et al.].
Reminder of title:	a walkthrough : latest trends in AI /
other author:	Gunjan, Vinit Kumar.
Published:	Cham :Springer International Publishing :2020.
Description:	viii, 245 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-38445-6
ISBN:	9783030384456$q(electronic bk.)

Modern approaches in machine learning and cognitive sciencea walkthrough : latest trends in AI /
Modern approaches in machine learning and cognitive sciencea walkthrough : latest trends in AI /[electronic resource] :edited by Vinit Kumar Gunjan ... [et al.]. - Cham :Springer International Publishing :2020. - viii, 245 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8851860-949X ;. - Studies in computational intelligence ;v. 216..

Face Recognition using Raspberry PI -- Features Extraction for Network Intrusion Detection using Genetic Algorithm(GA) -- Chemical Sensing through Cogno-Monitoring System for Air Quality Evaluation -- 3 DOF Autonomous Control Analysis of an Quadcopter Using Artificial Neural Network -- Cognitive Demand Forecasting with Novel Features using Word2Vec and Session of the Day -- A Curvelet Transformer Based Computationally Efficient Speech Enhancement for Kalman Filter -- Dexterous Trashbot -- Automated Question Generation and Answer Veriﬁcation using Visual Data -- Comprehensive Survey on Deep Learning Approaches in Predictive Business Process Monitoring -- Machine Learning Based Risk-Adaptive Access Control System to Identify Genuineness of the Requester -- An Approach to End to End Anonymity -- PHT and KELM Based Face Recognition -- Link Failure Detection in MANET: A Survey -- Review of Low Power Techniques for Neural Recording Applications -- Machine Learning Techniques for Thyroid Disease Diagnosis: A Systematic Review -- Heuristic Approach to Evaluate the Performance of Optimization Algorithms in VLSI Floor Planning for ASIC design -- Enhancement in Teaching Quality Methodology by Predicting Attendance using Machine Learning Technique -- Improvement in Extended Object tracking with the Vision-Based Algorithm.

This book discusses various machine learning & cognitive science approaches, presenting high-throughput research by experts in this area. Bringing together machine learning, cognitive science and other aspects of artificial intelligence to help provide a roadmap for future research on intelligent systems, the book is a valuable reference resource for students, researchers and industry practitioners wanting to keep abreast of recent developments in this dynamic, exciting and profitable research field. It is intended for postgraduate students, researchers, scholars and developers who are interested in machine learning and cognitive research, and is also suitable for senior undergraduate courses in related topics. Further, it is useful for practitioners dealing with advanced data processing, applied mathematicians, developers of software for agent-oriented systems and developers of embedded and real-time systems.

ISBN: 9783030384456$q(electronic bk.)

Standard No.: 10.1007/978-3-030-38445-6doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .M634 2020

Dewey Class. No.: 006.31

Modern approaches in machine learning and cognitive sciencea walkthrough : latest trends in AI /
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520 $a This book discusses various machine learning & cognitive science approaches, presenting high-throughput research by experts in this area. Bringing together machine learning, cognitive science and other aspects of artificial intelligence to help provide a roadmap for future research on intelligent systems, the book is a valuable reference resource for students, researchers and industry practitioners wanting to keep abreast of recent developments in this dynamic, exciting and profitable research field. It is intended for postgraduate students, researchers, scholars and developers who are interested in machine learning and cognitive research, and is also suitable for senior undergraduate courses in related topics. Further, it is useful for practitioners dealing with advanced data processing, applied mathematicians, developers of software for agent-oriented systems and developers of embedded and real-time systems.
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950 $a Intelligent Technologies and Robotics (Springer-42732)