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Sentiment analysis and deep learningproceedings of ICSADL 2022 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Sentiment analysis and deep learningedited by Subarna Shakya, Ke-Lin Du, Klimis Ntalianis.
其他題名:	proceedings of ICSADL 2022 /
其他題名:	ICSADL 2022
其他作者:	Shakya, Subarna.
團體作者:
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	xviii, 1014 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Deep learning (Machine learning)
電子資源:	https://doi.org/10.1007/978-981-19-5443-6
ISBN:	9789811954436$q(electronic bk.)

Sentiment analysis and deep learningproceedings of ICSADL 2022 /
Sentiment analysis and deep learningproceedings of ICSADL 2022 /[electronic resource] :ICSADL 2022edited by Subarna Shakya, Ke-Lin Du, Klimis Ntalianis. - Singapore :Springer Nature Singapore :2023. - xviii, 1014 p. :ill., digital ;24 cm. - Advances in intelligent systems and computing,v. 14322194-5365 ;. - Advances in intelligent systems and computing ;176..

Ranking roughly tourist destinations using BERT based semantic search -- Discerning the Application of Virtual Laboratory in Curriculum Transaction of Software Engineering Lab Course from the Lens of Critical Pedagogy -- Drought Prediction using Recurrent Neural Networks and Long Short-Term Memory model -- A Deep Learning Framework for Classification of Hyperspectral Images -- Improved Security on Mobile Payments Using IMEI Verification -- Analytics and Data Computing for the Development of the Concept Digitalization in Business and Economic Structures -- Smart Door Locking System using IoT -A Security for Railway Engine Pilots -- Designing and Implementing a Distributed Database for Microservices Cloud-Based Online Travel Portal -- A comparative study of a new customized bert for sentiment analysis -- Twitter Sentiment Analysis Using Naive Bayes Based Machine Learning Technique -- Rainfall Forecasting System Using Machine Learning Technique and IoT Technology for a Localized Region -- Infrastructure as Code (IaC): Insights on Various Platforms -- Breast Cancer Prediction using different Machine Learning Algorithm -- A Proposed System for Understanding the Consumer Opinion of a Product Using Sentiment Analysis -- Comparative study of Machine Learning and Deep learning for Fungi classification -- Personality as a predictor of Computer Science Students' Learning Motivation -- Prediction and analysis of liver disease using extreme learning machine -- Deep-learning based quality assurance of silicon detectors in Compact Muon Solenoid experiment -- An Effectual Analytics and Approach for Avoidance of Malware in Android using Deep Neural Networks -- A One-Stop Service Provider for Farmers Using Machine Learning -- Fuzzy Logic Based Control of SEPIC Converter for Vehicle to Grid Application -- Social media mining to detect mental health disorders using Machine learning -- Using Deep Learning Models for Crop and Weed Classification at Early Stage -- FaceMask detection and social distancing using Machine Learning with Haarcascade algorithm.

This book gathers selected papers presented at International Conference on Sentimental Analysis and Deep Learning (ICSADL 2022), jointly organized by Tribhuvan University, Nepal and Prince of Songkla University, Thailand during 16 - 17 June, 2022. The volume discusses state-of-the-art research works on incorporating artificial intelligence models like deep learning techniques for intelligent sentiment analysis applications. Emotions and sentiments are emerging as the most important human factors to understand the prominent user-generated semantics and perceptions from the humongous volume of user-generated data. In this scenario, sentiment analysis emerges as a significant breakthrough technology, which can automatically analyze the human emotions in the data-driven applications. Sentiment analysis gains the ability to sense the existing voluminous unstructured data and delivers a real-time analysis to efficiently automate the business processes.

ISBN: 9789811954436$q(electronic bk.)

Standard No.: 10.1007/978-981-19-5443-6doiSubjects--Topical Terms:

913129
Deep learning (Machine learning)

LC Class. No.: Q325.73 / .I58 2022

Dewey Class. No.: 006.31

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