Details
Machine Learning for Intelligent Multimedia Analytics
Techniques and ApplicationsStudies in Big Data, Band 82
|
171,19 € |
|
| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 16.01.2021 |
| ISBN/EAN: | 9789811594922 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<p></p><p>This book presents applications of machine learning techniques in processing multimedia large-scale data. Multimedia such as text, image, audio, video, and graphics stands as one of the most demanding and exciting aspects of the information era. The book discusses new challenges faced by researchers in dealing with these large-scale data and also presents innovative solutions to address several potential research problems, e.g., enabling comprehensive visual classification to fill the semantic gap by exploring large-scale data, offering a promising frontier for detailed multimedia understanding, as well as extract patterns and making effective decisions by analyzing the large collection of data.</p><br><p></p>
Chapter 1. Secure Multimodal Access with 2D and 3D Ears.- Chapter 2. Efficient and Low Overhead Detection of Brain Diseases using Deep Learning based Sparse MRI Image Classification.- Chapter 3. Continual Deep Learning Framework for Medical Media Screening and Archival.- Chapter 4. KannadaRes-NeXt: a Deep Residual Network for Kannada Numeral Recognition.- Chapter 5. Secure Image Transmission in Wireless Network using Conventional Neural Network and DOST.- Chapter 6. Robust General Twin Support Vector Machine with Pinball Loss Function.- Chapter 7. Noise Resilient Thresholding based on Fuzzy Logic and Non-linear Filtering.- Chapter 8. Deep Learning Methods for Audio Events Detection.- Chapter 9. A Framework for Multi-lingual Scene Text Detection using K-means++ and Memetic Algorithms.- Chapter 10. Recent Advancements in Medical Imaging: A Machine Learning Approach.- Chapter 11. Solving Image Processing Critical Problems using Machine Learning.- Chapter 12. Spoken Language Identificationof Indian Languages using MFCC Features.- Chapter 13. Performance Evaluation of One-Class Classifiers (OCC) for Damage Detection in Structural Health Monitoring.- Chapter 14. Brain Tumor Classification in MRI Images using Transfer Learning.- Chapter 15. Semantic based Vectorization Technique for Hindi Language.
<p></p><p>Dr. Pardeep Kumar is currently working as an Associate Professor in the Department of Computer Science & Engineering and Information Technology at Jaypee University of Information Technology (JUIT), Wakanaghat, Solan, Himachal Pradesh, India. He has been associated with his current employer since 2008. Prior to joining Jaypee Group, he was associated with Mody University of Technology & Science (Formerly known as Mody Institute of Technology & Science) Laxmangarh, Sikar, Rajasthan. He has completed PhD (Computer Science and Engineering) from Uttarakhand Technical University, Dehradun, India, M.Tech (Computer Science & Engineering) from Guru Jambheshwar University of Science & Technology, Hisar, Haryana, India and B.Tech (Information Technology) from Kurukshetra University, Kurukshetra, Haryana, India. He has served as Executive General Chair of 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), Guest Editor of Special Issue on “Robust andSecure Data Hiding Techniques for Telemedicine Applications”, Multimedia Tools and Applications: An International Journal, Springer (SCI Indexed Journal, IF= 1.346), Lead Guest Editor of Special Issue on “Recent Developments in Parallel, Distributed and Grid Computing for Big Data”, published in the International Journal of Grid and Utility Computing, Inderscience (Scopus Indexed), and Guest Editor of Special Issue on “Advanced Techniques in Multimedia Watermarking”, published in the International Journal of Information and Computer Security, Inderscience (Scopus Indexed). Dr. Kumar has been appointed as an Associate Editor of IEEE Access (SCI Indexed, IF = 3.5) Journal. His area of interests includes machine learning, medical image mining, image processing, health care informatics, etc.</p><p> </p><p><b>Dr. Amit Kumar Singh</b> is currently an Assistant Professor with the Computer Science and Engineering Department, National Institute of Technology Patna, Bihar, India. He received his PhD from National Institute of Technology Kurukshetra, Haryana, India in 2015. He has authored over 100 peer-reviewed journals, conference publications, and book chapters. He has authored three books and edited four books with internationally recognized publishers such Springer and Elsevier. He is the associate editor of IEEE Access (Since 2016), IET Image Processing (Since 2020), and former member of the editorial board of Multimedia Tools and Applications, Springer (2015-2019). He has edited various international journal special issues as a lead guest editor such as such as ACM Transactions on Multimedia Computing, Communications, and Applications, ACM Transactions on Internet Technology, IEEE Consumer Electronics Magazine, IEEE Access, Multimedia Tools and Applications, Springer, International Journal of Information Management, Elsevier, Journal of Ambient Intelligence and Humanized Computing, Springer. He has obtained the memberships from several international academic organizations such as ACM and IEEE. His research interests include multimedia data hiding, image processing, biometrics, & Cryptography.</p><p></p>
<p></p><p>This book presents applications of machine learning techniques in processing multimedia large-scale data. Multimedia such as text, image, audio, video, and graphics stands as one of the most demanding and exciting aspects of the information era. The book discusses new challenges faced by researchers in dealing with these large-scale data and also presents innovative solutions to address several potential research problems, e.g., enabling comprehensive visual classification to fill the semantic gap by exploring large-scale data, offering a promising frontier for detailed multimedia understanding, as well as extract patterns and making effective decisions by analyzing the large collection of data.</p><br><p></p>
Presents applications of machine learning techniques in processing multimedia large-scale data Discusses new challenges faced by researchers in dealing with multimedia data Provides innovative solutions to address several potential research problems
Diese Produkte könnten Sie auch interessieren:
