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Data, engineering and applicationsselect proceedings of IDEA 2021 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data, engineering and applicationsedited by Sanjeev Sharma ... [et al.].
Reminder of title:	select proceedings of IDEA 2021 /
remainder title:	IDEA 2021
other author:	Sharma, Sanjeev.
corporate name:
Published:	Singapore :Springer Nature Singapore :2022.
Description:	ix, 705 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Big dataCongresses.
Online resource:	https://doi.org/10.1007/978-981-19-4687-5
ISBN:	9789811946875$q(electronic bk.)

Data, engineering and applicationsselect proceedings of IDEA 2021 /
Data, engineering and applicationsselect proceedings of IDEA 2021 /[electronic resource] :IDEA 2021edited by Sanjeev Sharma ... [et al.]. - Singapore :Springer Nature Singapore :2022. - ix, 705 p. :ill., digital ;24 cm. - Lecture notes in electrical engineering,v. 9071876-1119 ;. - Lecture notes in electrical engineering ;v.132..

1. Medical Assistance Chatbot using Deep Learning -- 2. Distortion Controlled Secure Reversible Data Hiding in H.264 videos -- 3. A Method for improving Efficiency and Security of FANET using Chaotic Black Hole Optimization based Routing (BHOR) Technique -- 4. Machine Learning Techniques for Intrusion Detection System: A Survey -- 5. Software Fault Detection by using Rider Optimization Algorithm (ROA) based Deep Neural Network (DNN) -- 6. An Approach for Predicting Admissions in Post Graduate Program by using Machine Learning -- 7. A Survey on Various Representation Learning of Hypergraph for Unsupervised Feature Selection -- 8. A brief study of time series forecasting technique using linear regression, SVM, LSTM, ARIMA and SARIMA -- 9. Adoption of Blockchain Technology for Storage & Verification of Educational Documents -- 10. Obstacle Collision Prediction model for Path Planning Using Obstacle Trajectory Clustering.

The book contains select proceedings of the 3rd International Conference on Data, Engineering, and Applications (IDEA 2021) It includes papers from experts in industry and academia that address state-of-the-art research in the areas of big data, data mining, machine learning, data science, and their associated learning systems and applications. This book will be a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of big data applications.

ISBN: 9789811946875$q(electronic bk.)

Standard No.: 10.1007/978-981-19-4687-5doiSubjects--Topical Terms:

592065
Big data
--Congresses.

LC Class. No.: QA76.9.B45 / I57 2021

Dewey Class. No.: 005.7

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