Behavior analysis with machine learning using R (Record no. 9310)

MARC details
000 -LEADER
fixed length control field 02500cam a22001938i 4500
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781032067049
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 155.28
Item number CEJ
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Ceja, Enrique Garcia
245 10 - TITLE STATEMENT
Title Behavior analysis with machine learning using R
250 ## - EDITION STATEMENT
Edition statement First edition.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc London
Name of publisher, distributor, etc CRC
Date of publication, distribution, etc 2022
300 ## - PHYSICAL DESCRIPTION
Extent 397.p
520 ## - SUMMARY, ETC.
Summary, etc. "Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data"--
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Behavioral assessment
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Task analysis
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element R (Computer program language)
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Total Renewals Full call number Barcode Checked out Date last seen Date last borrowed Price effective from Koha item type Public note
    Dewey Decimal Classification     Reference BMU Library BMU Library School of Engineering & Technology 23/01/2023 IBD ( Bill No.- 118630, Date- 2/1/22) 5228.30 1 3 155.28 CEJ 14331 01/04/2025 06/02/2023 06/02/2023 23/01/2023 Books School of Engineering & Technology

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