Computer vision is an effective solution in a diverse range of real-life applications. With the advent of the machine and deep learning paradigms, this book adopts machine and deep learning algorithms to leverage digital image processing for designing accurate biometrical applications. In this aspect, it presents the advancements made in computer vision to biometric applications design approach using emerging technologies. It discusses the challenges of designing efficient and accurate biometric-based systems, which is a key issue that can be tackled via computer vision-based techniques. Key Features • Discusses real-life applications of emerging techniques in computer vision systems • Offers solutions on real-time computer vision and biometrics applications to cater to the needs of current industry • Presents case studies to offer ideas for developing new biometrics-based products • Offers problem-based solutions in the field computer vision and real-time biometric applications for secured human authentication • Works as a ready resource for professionals and scholars working on emerging topics of computer vision for biometrics. The book is for academic researchers, scholars and students in Computer Science, Information Technology, Electronics and Electrical Engineering, Mechanical Engineering, management, academicians, researchers, scientists and industry people working on computer vision and biometrics applications.
Computer vision is an effective solution in a diverse range of real-life applications. With the advent of the machine and deep learning paradigms, this book adopts machine and deep learning algorithms to leverage digital image processing for designing accurate biometrical applications. In this aspect, it presents the advancements made in the computer vision to biometric applications design approach using emerging technologies. It discusses the challenges of designing efficient and accurate biometric-based systems, which is a key issue that can be tackled via computer vision-based techniques - Discussed real-life applications of emerging techniques in computer vision systems - Offer solutions on real-time computer vision and biometrics applications to cater to current industry problems - Presents case studies to offer ideas for developing new biometrics-based products - Offers problem-based solutions in the field of building construction, electrical, electronic engineering and management. - Works as a ready resource for professionals and scholars working on emerging topics of computer vision for biometrics. The book is for Academic researchers, scholars and students in Computer Science, Information Technology, Electronics and Electrical Engineering, Mechanical Engineering, Management, academicians, researchers, scientists and industry people working on computer vision and biometrics applications.
Exploring Artificial intelligence: A Student’s Handbook is a comprehensive educational guide designed to demystify Al for students, covering foundational theories and practical applications across twenty chapters. It progresses from basic machine learning algorithms to advanced topics, incorporating interactive quizzes, “Did You Know?” facts, and real-world examples to enrich learning. The book emphasizes hands-on interaction with Al through step-by-step activities, aiming to bridge theory and practice. It also addresses the ethical, societal, and futuristic aspects of Al, encouraging readers to consider the broader implications of Al technologies. This handbook serves as a foundational resource for aspiring Al enthusiasts, researchers, and practitioners, fostering a deeper understanding of Al’s impact on the future.
As cybersecurity threats continue to grow in scale and complexity, it is crucial to explore new and innovative solutions to combat them. The application of computer vision (CV) techniques in cybersecurity offers a promising solution to protect sensitive data and systems from malicious attacks. By leveraging CV algorithms, cybersecurity professionals and researchers can design more efficient and effective cybersecurity solutions, making them better equipped to handle the growing number of cyber threats. Global Perspectives on the Applications of Computer Vision in Cybersecurity is a comprehensive guide that offers practical insights into the principles and techniques of computer vision for cybersecurity. The book highlights the real-world applications of CV in various domains, including computer system security, web security, network security, IoT security, and digital forensics. It also emphasizes the importance of responsible CV for cybersecurity, ensuring that CV models adhere to ethical principles and are transparent and interpretable. By reading this book, cybersecurity professionals and researchers can gain a better understanding of how to use CV techniques to design solid cybersecurity solutions and address the challenges involved. With the guidance of the editors, Franklin Tchakounte and Marcellin Atemkeng, who are experts in both cybersecurity and computer vision, readers can leverage the power of CV to secure the future of our digital world. Join the movement today to revolutionize the field of cybersecurity and protect against the growing threat of cyber-attacks.
This book constitutes the refereed post-conference proceedings of the 21st Iberoamerican Congress on Pattern Recognition, CIARP 2016, held in Lima, Peru, in November 2016. The 69 papers presented were carefully reviewed and selected from 131 submissions. The papers feature research results in the areas of pattern recognition, biometrics, image processing, computer vision, speech recognition, and remote sensing. They constitute theoretical as well as applied contributions in many fields related to the main topics of the conference.
This book constitutes thoroughly revised and selected papers from the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017, held in Porto, Portugal, February 27 - March 1, 2017. The 18 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 402 submissions. The papers contribute to the understanding of relevant trends of current research on image and video formation, preprocessing, analysis and understanding; motion, tracking and stereo vision; computer graphics and rendering; data visualization and interactive visual data analysis; agent-based human-robot interactions; and user experience.
Machine Learning Algorithms and Techniques the concepts, popular algorithms, and essential techniques of machine learning. A comprehensive covering supervised, unsupervised, and reinforcement learning methods while exploring key algorithms like decision trees, neural networks, clustering, and more. Practical applications and examples bring each algorithm to life, helping readers understand how these models are used to solve real-world problems. Designed for both beginners and experienced practitioners, this book is an ideal guide for mastering the fundamentals and applications of machine learning.
Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book Second edition of the bestselling book on Machine Learning A practical approach to key frameworks in data science, machine learning, and deep learning Use the most powerful Python libraries to implement machine learning and deep learning Get to know the best practices to improve and optimize your machine learning systems and algorithms Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. What You Will Learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Explore machine learning techniques using challenging real-world data Master deep neural network implementation using the TensorFlow library Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis In Detail Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world. If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn. Style and Approach Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python.
This book is a collection of peer-reviewed best-selected research papers presented at 4th International Conference on Computer Networks and Inventive Communication Technologies (ICCNCT 2021). The book covers new results in theory, methodology, and applications of computer networks and data communications. It includes original papers on computer networks, network protocols and wireless networks, data communication technologies, and network security. The proceedings of this conference are a valuable resource, dealing with both the important core and the specialized issues in the areas of next-generation wireless network design, control, and management, as well as in the areas of protection, assurance, and trust in information security practice. It is a reference for researchers, instructors, students, scientists, engineers, managers, and industry practitioners for advanced work in the area.
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. Audience Researchers 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.
