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      1. Data och IT
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      Multimodal Biometric and Machine Learning Technologies

      Applications for Computer Vision

      AvSandeep Kumar,Deepika Ghai

      Inbunden, Engelska, 2023

      2 662 kr

      Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

      Beskrivning

      MULTIMODAL BIOMETRIC AND MACHINE LEARNING TECHNOLOGIES With an increasing demand for biometric systems in various industries, this book on multimodal biometric systems, answers the call for increased resources to help researchers, developers, and practitioners.Multimodal biometric and machine learning technologies have revolutionized the field of security and authentication. These technologies utilize multiple sources of information, such as facial recognition, voice recognition, and fingerprint scanning, to verify an individual's identity. The need for enhanced security and authentication has become increasingly important, and with the rise of digital technologies, cyber-attacks and identity theft have increased exponentially. Traditional authentication methods, such as passwords and PINs, have become less secure as hackers devise new ways to bypass them. In this context, multimodal biometric and machine learning technologies offer a more secure and reliable approach to authentication.This book provides relevant information on multimodal biometric and machine learning technologies and focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity. The book provides content on the theory of multimodal biometric design, evaluation, and user diversity, and explains the underlying causes of the social and organizational problems that are typically devoted to descriptions of rehabilitation methods for specific processes. Furthermore, the book describes new algorithms for modeling accessible to scientists of all varieties.AudienceResearchers in computer science and biometrics, developers who are designing and implementing biometric systems, and practitioners who are using biometric systems in their work, such as law enforcement personnel or healthcare professionals.

      Produktinformation

      • Utgivningsdatum:2023-10-27
      • Mått:154 x 230 x 24 mm
      • Vikt:40 g
      • Format:Inbunden
      • Språk:Engelska
      • Antal sidor:336
      • Förlag:John Wiley & Sons Inc
      • ISBN:9781119785408

      Utforska kategorier

      • Artificiell intelligens inom Data och IT

      Mer om författaren

      Sandeep Kumar, PhD, is a professor in Computer Science & Engineering, Koneru Lakshmaiah Educational Foundation, India, He has published more than 150 journal articles and conference papers, 20 patents, and authored 13 books. Deepika Ghai, PhD, is an assistant professor at Lovely Professional University, India. She has published more than 35 research papers in refereed journals and conferences. She received the Dr. C.B. Gupta Award in 2021 at Lovely Professional University. Arpit Jain, PhD, is a professor at the Koneru Lakshmamai University Education Foundation, Vijayawada, A.P., India. He has published more than 40 research papers in international journals, filed 25+ patents as well as authored/edited five books. Suman Lata Tripathi, PhD, is a professor at Lovely Professional University with more than 21 years of experience in academics. She has published more than 105 research papers in refereed journals and conferences. She has published three books and currently has multiple volumes scheduled for publication from Wiley-Scrivener. Shilpa Rani, PhD, is an associate professor at the Neil Gogte Institute of Technology, Hyderabad, India, and specializes in computer science & engineering. She has authored seven books, more than 50 journal articles conference papers, as well as 14 patents.

      Innehållsförteckning

      • Preface xiii1 Multimodal Biometric in Computer Vision 1Sunayana Kundan Shivthare, Yogesh Kumar Sharma and Ranjit D. Patil1.1 Introduction 21.2 Importance of Artificial Intelligence, Machine Learning and Deep Learning in Biometric System 21.3 Machine Learning 41.4 Deep Learning 61.5.1 Discussions 111.6 Biometric System 111.7 Need for Multimodal Biometric 151.8 Databases Used by Biometric System 171.9 Impact of DL in the Current Scenario 191.10 Conclusion 242 A Vaccine Slot Tracker Model Using Fuzzy Logic for Providing Quality of Service 31Mohammad Faiz, Nausheen Fatima and Ramandeep Sandhu2.1 Introduction 322.2 Related Research 332.3 Novelty of the Proposed Work 372.4 Proposed Model 382.5 Proposed Fuzzy-Based Vaccine Slot Tracker Model 422.6 Simulation 442.7 Conclusion 472.8 Future Work 503 Enhanced Text Mining Approach for Better Ranking System of Customer Reviews 53Ramandeep Sandhu, Amritpal Singh, Mohammad Faiz, Harpreet Kaur and Sunny Thukral3.1 Introduction 533.2 Techniques of Text Mining 553.3 Related Research 583.4 Research Methodology 633.5 Conclusion 674 Spatial Analysis of Carbon Sequestration Mapping Using Remote Sensing and Satellite Image Processing 71Prashantkumar B. Sathvara, J. Anuradha, R. Sanjeevi, Sandeep Tripathi and Ankitkumar B. Rathod4.1 Introduction 724.2 Materials and Methods 754.3 Results 774.4 Conclusion 795 Applications of Multimodal Biometric Technology 85Shivalika Goyal and Amit Laddi5.1 Introduction 855.2 Components of MBS 875.3 Biometrics Modalities 895.4 Applications of Multimodal Biometric Systems 895.5 Conclusion 976 A Study of Multimodal Colearning, Application in Biometrics and Authentication 103Sandhya Avasthi, Tanushree Sanwal, Ayushi Prakash and Suman Lata Tripathi6.1 Introduction 1046.2 Multimodal Deep Learning Methods and Applications 1086.3 MMDL Application in Biometric Monitoring 1136.4 Fusion Levels in Multimodal Biometrics 1166.5 Authentication in Mobile Devices Using Multimodal Biometrics 1196.6 Challenges and Open Research Problems 1226.7 Conclusion 1237 A Structured Review on Virtual Reality Technology Application in the Field of Sports 129Harmanpreet Kaur, Arpit Kulshreshtha and Deepika Ghai7.1 Introduction 1307.2 Related Work 1327.3 Conclusion 1428 A Systematic and Structured Review of Fuzzy Logic-Based Evaluation in Sports 145Harmanpreet Kaur, Sourabh Chhatiye and Jimmy Singla8.1 Introduction 1468.2 Related Works 1488.3 Conclusion 1599 Machine Learning and Deep Learning for Multimodal Biometrics 163Danvir Mandal and Shyam Sundar Pattnaik9.1 Introduction 1639.2 Machine Learning Using Multimodal Biometrics 1659.3 Deep Learning Using Multimodal Biometrics 1679.4 Conclusion 16910 Machine Learning and Deep Learning: Classification and Regression Problems, Recurrent Neural Networks, Convolutional Neural Networks 173R. K. Jeyachitra and Manochandar, S.10.1 Introduction 17410.2 Classification of Machine Learning 17410.3 Supervised Learning 17510.4 Unsupervised Learning 20110.5 Reinforcement Learning 20310.6 Hybrid Approach 20410.7 Other Common Approaches 20510.8 DL Techniques 21010.9 Conclusion 21911 Handwriting and Speech-Based Secured Multimodal Biometrics Identification Technique 227Swathi Gowroju, V. Swathi and Ankita Tiwari11.1 Introduction 22811.2 Literature Survey 23011.3 Proposed Method 23111.4 Results and Discussion 23711.5 Conclusion 24812 Convolutional Neural Network Approach for Multimodal Biometric Recognition System for Banking Sector on Fusion of Face and Finger 251Sandeep Kumar, Shilpa Choudhary, Swathi Gowroju and Abhishek Bhola12.1 Introduction 25212.2 Literature Work 25312.3 Proposed Work 25612.4 Results and Discussion 26012.5 Conclusion 26513 Secured Automated Certificate Creation Based on Multimodal Biometric Verification 269Shilpa Choudhary, Sandeep Kumar, Monali Gulhane and Munish Kumar13.1 Introduction 27013.2 Literature Work 27413.3 Proposed Work 27613.4 Experiment Result 27813.5 Conclusion and Future Scope 27914 Face and Iris-Based Secured Authorization Model Using CNN 283Munish Kumar, Abhishek Bhola, Ankita Tiwari and Monali Gulhane14.1 Introduction 28414.2 Related Work 28514.3 Proposed Methodology 28714.4 Results and Discussion 29114.5 Conclusion and Future Scope 296References 297Index 301
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