Recommender system with machine learning and artificial intelligence : (Record no. 69294)

000 -LEADER
fixed length control field 05588cam a22005898i 4500
001 - CONTROL NUMBER
control field on1159628189
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220711203607.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200531s2020 nju ob 001 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781119711582
-- (electronic bk. : oBook)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1119711584
-- (electronic bk. : oBook)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781119711605
-- (epub)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1119711606
-- (epub)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781119711599
-- (adobe pdf)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1119711592
-- (adobe pdf)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- (hardback)
082 00 - CLASSIFICATION NUMBER
Call Number 025.04
245 00 - TITLE STATEMENT
Title Recommender system with machine learning and artificial intelligence :
Sub Title practical tools and applications in medical, agricultural and other industries /
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 online resource.
490 0# - SERIES STATEMENT
Series statement Machine learning in biomedical science and healthcare informatics
505 0# - FORMATTED CONTENTS NOTE
Remark 2 An introduction to basic concepts on recommender systems / Pooja Rana, Nishi Jain and Usha Mittal -- A brief model overview of personalized recommendation to citizens in the health-care industry / Subhasish Mohapatra and Kunal Anand -- 2Es of TIS : a review of information exchange and extraction in tourism information systems / Malik M. Saad Missen, Mickaël Coustaty, Hina Asmat, Amnah Firdous, Nadeem Akhtar, Muhammad Akram and V.B. Surya Prasath -- Concepts of recommendation system from the perspective of machine learning / Sumanta Chandra Mishra Sharma, Adway Mitra and Deepayan Chakraborty -- A machine learning approach to recommend suitable crops and fertilizers for agriculture / Govind Kumar Jha, Preetish Ranjan and Manish Gaur -- Accuracy-assured privacy-preserving recommender system using hybrid-based deep learning method / Abhaya Kumar Sahoo and Chittaranjan Pradhan -- Machine learning-based recommender system for breast cancer prognosis / G. Kanimozhi, P. Shanmugavadivu and M. Mary Shanthi Rani -- A recommended system for crop disease detection and yield prediction using machine learning approach / Pooja Akulwar -- Content-based recommender systems / Poonam Bhatia Anand and Rajender Nath -- Content (item)-based recommendation system / R. Balamurali -- Content-based health recommender systems / Soumya Prakash Rana, Maitreyee Dey, Javier Prieto and Sandra Dudley -- Context-based social media recommendation system / R. Sujithra Kanmani and B. Surendiran -- Netflix challenge : improving movie recommendations / Vasu Goel -- Product or item-based recommender system / Jyoti Rani, Usha Mittal and Geetika Gupta -- A trust-based recommender system built on IoT blockchain network with cognitive framework / S. Porkodi and D. Kesavaraja -- Development of a recommender system HealthMudra using blockchain for prevention of diabetes / Rashmi Bhardwaj and Debabrata Datta -- Case study 1 : health care recommender systems / Usha Mittal, Nancy Singla and Geetika Gupta -- Temporal change analysis-based recommender system for Alzheimer Disease classification / S. Naganandhini, P. Shanmugavadivu and M. Mary Shanthi Rani -- Regularization of graphs : sentiment classification / R.S.M. Lakshmi Patibandla -- TSARS : a tree-similarity algorithm-based agricultural recommender system / Madhusree Kuanr, Puspanjali Mohapatra and Sasmita Subhadarsinee Choudhury -- Influenceable targets recommendation analyzing social activities in egocentric online social networks / Soumyadeep Debnath, Dhrubasish Sarkar and Dipankar Das.
520 ## - SUMMARY, ETC.
Summary, etc "The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments"--
590 ## - LOCAL NOTE (RLIN)
Local note John Wiley and Sons
700 1# - AUTHOR 2
Author 2 Mohanty, Sachi Nandan,
700 1# - AUTHOR 2
Author 2 Chatterjee, Jyotir Moy,
700 1# - AUTHOR 2
Author 2 Jain, Sarika,
700 1# - AUTHOR 2
Author 2 Elngar, Ahmed A.,
700 1# - AUTHOR 2
Author 2 Gupta, Priya
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1002/9781119711582
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Hoboken, NJ :
-- Wiley-Scrivener,
-- 2020.
336 ## -
-- text
-- txt
-- rdacontent
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-- computer
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-- rdamedia
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-- online resource
-- nc
-- rdacarrier
520 ## - SUMMARY, ETC.
-- Provided by publisher.
588 ## -
-- Description based on print version record and CIP data provided by publisher; resource not viewed.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Recommender systems (Information filtering)
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machine learning.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
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-- 92
-- DG1

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