Computational Intelligence for Information Retrieval

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.

About the author

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.

Summary

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.

Product details

• Assisted by: Gopal Chaudhary (Editor), Dharmender Saini (Editor), Vedika Gupta (Editor)
• Authors: Dharmender (Bvp College of Engineering Saini
• Publisher: Taylor & Francis Ltd.
• Content: Book
• Product form: Paperback / Softback
• Publication date: 07.10.2024
• Subject: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communication
• EAN: 9780367680831
• ISBN: 978-0-367-68083-1
• Pages: 278
• Series: Sensors Communication for Urban Intelligence
• Subjects: machine learning, Artificial Intelligence, Deep Learning, AI, AR, Smartphones, pattern recognition, Random Forest, Artificial Intelligence (AI), Computational Intelligence, COMPUTERS / Artificial Intelligence / General, QR Code, CF, Convolutional Neural Networks, medical image analysis, recommendation system, Deep learning applications, Hybrid Computational Intelligence, image enhancement, Big Data Security, Emotion Prediction, Biometric Authentication, gender recognition, Membership Function, cloud data protection, pattern recognition methods, CBIR, sentiment analysis techniques, advanced neural network models for retrieval, POS Tag, SVM Classifier, CNN Model, Convolutional Layer, Ml Model, computational intelligence methods, CNN Architecture, Emotiv EPOC, Max Pooling Layers, song recommendation, Construct Feature Vector, Vein Image, Finger Vein, fuzzy-based approach, CBIR Technique, SVM Classification Model, melanoma tumor, mood detection, finger vein feature extraction, diabetic retinopathy disease, dynamic histogram equalization