
Computational Intelligence for Information Retrieval
CRC Press
1st Edition
Published on 15. December 2021
Book
Hardback
278 pages
978-0-367-68080-0 (ISBN)
Description
This book provides a thorough understanding of the integration of computational intelligence with information retrieval including content-based image retrieval using intelligent techniques, hybrid computational intelligence for pattern recognition, intelligent innovative systems, and protecting and analysing big data on cloud platforms. The book aims to investigate how computational intelligence frameworks are going to improve information retrieval systems. The emerging and promising state-of-the-art of human-computer interaction is the motivation behind this book.
The book covers a wide range of topics, starting from the tools and languages of artificial intelligence to its philosophical implications, and thus provides a plethora of theoretical as well as experimental research, along with surveys and impact studies.
Further, the book aims to showcase the basics of information retrieval and computational intelligence for beginners, as well as their integration, and challenge discussions for existing practitioners, including using hybrid application of augmented reality, computational intelligence techniques for recommendation systems in big data, and a fuzzy-based approach for characterization and identification of sentiments.
The book covers a wide range of topics, starting from the tools and languages of artificial intelligence to its philosophical implications, and thus provides a plethora of theoretical as well as experimental research, along with surveys and impact studies.
Further, the book aims to showcase the basics of information retrieval and computational intelligence for beginners, as well as their integration, and challenge discussions for existing practitioners, including using hybrid application of augmented reality, computational intelligence techniques for recommendation systems in big data, and a fuzzy-based approach for characterization and identification of sentiments.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Academic
Illustrations
111 s/w Abbildungen, 41 s/w Photographien bzw. Rasterbilder, 70 s/w Zeichnungen, 31 s/w Tabellen
31 Tables, black and white; 70 Line drawings, black and white; 41 Halftones, black and white; 111 Illustrations, black and white
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 20 mm
Weight
608 gr
ISBN-13
978-0-367-68080-0 (9780367680800)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Dharmender Saini | Gopal Chaudhary | Vedika Gupta
Computational Intelligence for Information Retrieval
Book
10/2024
1st Edition
CRC Press
€71.70
Shipment within 15-20 days

Dharmender Saini | Gopal Chaudhary | Vedika Gupta
Computational Intelligence for Information Retrieval
E-Book
12/2021
1st Edition
CRC Press
€64.49
Available for download

Dharmender Saini | Gopal Chaudhary | Vedika Gupta
Computational Intelligence for Information Retrieval
E-Book
12/2021
1st Edition
CRC Press
€64.49
Available for download
Persons
Gopal Chaudhary is working as an Assistant Professor in Department of Information Technology, at Bharati Vidyapeeth's College of Engineering, New Delhi.
Dharmendra Saini is a Professor in Department of Computer Science & Engineering, at Bharati Vidyapeeth's College of Engineering, New Delhi.
Vedika Gupta is an Assistant Professor at Bharati Vidyapeeth's College of Engineering, New Delhi.
Dharmendra Saini is a Professor in Department of Computer Science & Engineering, at Bharati Vidyapeeth's College of Engineering, New Delhi.
Vedika Gupta is an Assistant Professor at Bharati Vidyapeeth's College of Engineering, New Delhi.
Editor
BVP College of Engineering, India.
BVP College of Engineering, India.
BVP College of Engineering, India.
Content
1. Hybrid Computational Intelligence for Pattern Recognition.
2. Secure Image Transmission Using Nested Images.
3. Accist: Automatic Traffic Accident Detection and Notification with Smartphones.
4. Emotion Prediction through EEG Recordings Using Computational Intelligence.
5. Finger Vein Feature Extraction Using Contrast Enhancement Dynamic Histogram Equalization for Image Enhancement.
6. Song Recommendation Using Computational Techniques Based on Mood Detection.
7. Deep Learning Classification of Retinal Images for the Early Detection of Diabetic Retinopathy Disease.
8. Protecting and Analyzing Big Data on Cloud Platforms.
9. Using Flutter to Develop a Hybrid Application of Augmented Reality.
10. Computational Intelligence Techniques for Recommendation System in Big Data.
11. Predicting Melanoma Tumor Size through Machine Learning Approaches.
12. A Fuzzy-Based Approach for Characterization and Identification of Sentiments.
13. Fingerprint Alterations Type Detection and Gender Recognition Using Convolutional Neural Networks and Transfer Learning.
14. Content-Based Image Retrieval Using Intelligent Techniques.
2. Secure Image Transmission Using Nested Images.
3. Accist: Automatic Traffic Accident Detection and Notification with Smartphones.
4. Emotion Prediction through EEG Recordings Using Computational Intelligence.
5. Finger Vein Feature Extraction Using Contrast Enhancement Dynamic Histogram Equalization for Image Enhancement.
6. Song Recommendation Using Computational Techniques Based on Mood Detection.
7. Deep Learning Classification of Retinal Images for the Early Detection of Diabetic Retinopathy Disease.
8. Protecting and Analyzing Big Data on Cloud Platforms.
9. Using Flutter to Develop a Hybrid Application of Augmented Reality.
10. Computational Intelligence Techniques for Recommendation System in Big Data.
11. Predicting Melanoma Tumor Size through Machine Learning Approaches.
12. A Fuzzy-Based Approach for Characterization and Identification of Sentiments.
13. Fingerprint Alterations Type Detection and Gender Recognition Using Convolutional Neural Networks and Transfer Learning.
14. Content-Based Image Retrieval Using Intelligent Techniques.