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"The purpose of this book is to demonstrate the new development and application of Artificial intelligent algorithms for multimedia data processing, to solve the problem from multimedia analysis and multimedia processing to multimedia cybersecurity. This book bridges the gap between AI techniques, Multimedia Signal Processing and cybersecurity. This book connects various interdisciplinary domains related to Multimedia signal processing, cybersecurity for media data particularly the social data and will be highly beneficial for the students, researchers and academicians working in this area as this book will cover state-of-the art technologies around multimedia processing and cybersecurity techniques and their role in Media Data Analysis and performance. Furthermore, this book will be highly beneficial to IT experts working in security management and enhancement from cyber-security point of view as this book will present recent advancements and methods developed and deployed to ensure the high level cyber-security"--
In the age of social media dominance, a staggering amount of textual data floods our online spaces daily. While this wealth of information presents boundless opportunities for research and understanding human behavior, it also poses substantial challenges. The sheer volume of data overwhelms traditional processing methods, and harnessing its potential requires sophisticated tools. Furthermore, the need for ensuring data security and mitigating risks in the digital realm has never been more pressing. Academic scholars, researchers, and professionals grapple with these issues daily, seeking innovative solutions to unlock the true value of multimedia data while safeguarding privacy and integrity. Recent Advancements in Multimedia Data Processing and Security: Issues, Challenges, and Techniques is a groundbreaking book that serves as a beacon of light amidst the sea of data-related challenges. It offers a comprehensive solution by bridging the gap between academic research and practical applications. By delving into topics such as deep learning, emotion recognition, and high-dimensional text clustering, it equips scholars and professionals with the innovative tools and techniques they need to navigate the complex landscape of multimedia data.
This book provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors. The work describes how the history of multimedia data processing can be viewed as a sequence of disruptive innovations. Across the chapters, the discussion covers the practical frameworks, libraries, and open source software that enable the development of ground-breaking research into practical applications. Features: reviews how innovations in mobile, social, cognitive, cloud and organic based computing impacts upon the development of multimedia data mining; provides practical details on implementing the technology for solving real-world problems; includes chapters devoted to privacy issues in multimedia social environments and large-scale biometric data processing; covers content and concept based multimedia search and advanced algorithms for multimedia data representation, processing and visualization.
A timely overview of cutting edge technologies for multimedia retrieval with a special emphasis on scalability The amount of multimedia data available every day is enormous and is growing at an exponential rate, creating a great need for new and more efficient approaches for large scale multimedia search. This book addresses that need, covering the area of multimedia retrieval and placing a special emphasis on scalability. It reports the recent works in large scale multimedia search, including research methods and applications, and is structured so that readers with basic knowledge can grasp the core message while still allowing experts and specialists to drill further down into the analytical sections. Big Data Analytics for Large-Scale Multimedia Search covers: representation learning, concept and event-based video search in large collections; big data multimedia mining, large scale video understanding, big multimedia data fusion, large-scale social multimedia analysis, privacy and audiovisual content, data storage and management for big multimedia, large scale multimedia search, multimedia tagging using deep learning, interactive interfaces for big multimedia and medical decision support applications using large multimodal data. Addresses the area of multimedia retrieval and pays close attention to the issue of scalability Presents problem driven techniques with solutions that are demonstrated through realistic case studies and user scenarios Includes tables, illustrations, and figures Offers a Wiley-hosted BCS that features links to open source algorithms, data sets and tools Big Data Analytics for Large-Scale Multimedia Search is an excellent book for academics, industrial researchers, and developers interested in big multimedia data search retrieval. It will also appeal to consultants in computer science problems and professionals in the multimedia industry.
Recent advances in computing, networking, storage, and information technology have enabled the collection and distribution of vast amounts of multimedia data in a variety of applications such as entertainment, education, environmental protection, e-commerce, public safety, digital government, homeland security, and manufacturing. The proliferation of multimedia data and its rich semantics have created the needs for advanced techniques for in-depth content processing, analysis, indexing, learning, mining, searching, management, and retrieval. The International Journal of Multimedia Data Engineering and Management (IJMDEM) addresses the corresponding issues and challenges and publishes original research on new theories, algorithms, technologies, system design, and implementation in multimedia data engineering and management.
The rapid growth in the demand and consumption of the digital multimedia content in the past decade has led to some valid concerns over issues such as content security, authenticity, and digital rights management. Multimedia data hiding, defined as imperceptible embedding of information into a multimedia host, provides potential solutions, but with many technological challenges. In this thesis, we address several fundamental issues in this field, which provide the framework for the design of practical techniques that can seamlessly be deployed in real-world applications.
Intelligent multimedia surveillance concerns the analysis of multiple sensing inputs including video and audio streams, radio-frequency identification (RFID), and depth data. These data are processed for the automated detection and tracking of people, vehicles, and other objects. The goal is to locate moving targets, to understand their behavior, and to detect suspicious or abnormal activities for crime prevention. Despite its benefits, there is societal apprehension regarding the use of such technology, so an important challenge in this research area is to balance public safety and privacy. This edited book presents recent findings in the field of intelligent multimedia surveillance emerging from disciplines such as multimedia computing, computer vision, and artificial intelligence. It consists of nine chapters addressing intelligent video surveillance, video analysis of crowds, privacy issues in intelligent multimedia surveillance, RFID technology for localization of objects, object tracking using visual saliency information, estimating multiresolution depth using active stereo vision, and performance evaluation for video surveillance systems. The book will be of value to researchers and practitioners working on related problems in security, multimedia, and artificial intelligence.
The interaction of various service models, including edge computing and cloud computing, are quickly changing to better support microservices. This intricate weave of technology and information sharing is necessary to build systems that run faster and more efficiently. The interplay between these computing methods and microservices is emerging as the field of Osmotic Computing. Experts can now embark on an intellectual journey into data-driven exploration and ingenuity with the guidance of the book, Advanced Applications in Osmotic Computing. As ethical considerations become rising concerns, the potential biases, privacy encumbrances, and equitable conundrums of osmotic computing are investigated. This book offers judicious strategies to navigate these quandaries conscientiously, adding a layer of responsibility to the discourse. Within these pages, the very fabric of understanding in IoT, Cloud, Edge, Fog, and Machine Learning is redefined, marking a pivotal shift in the paradigm of technological comprehension. This book is an epicenter for the latest evolutions in osmotic computing, unfurling unconventional methodologies that shape the trajectory of data-driven decision-making. Readers will plunge into the theoretical bedrock, simultaneously witnessing pragmatic applications that adeptly bridge the schism between the theoretical constructs and pragmatic realization. The intended audience is multifaceted, encompassing data scientists, machine learning engineers, researchers, academics, educators, students, industry practitioners, interdisciplinary experts, and technology and business leaders.
The need for tailored data for machine learning models is often unsatisfied, as it is considered too much of a risk in the real-world context. Synthetic data, an algorithmically birthed counterpart to operational data, is the linchpin for overcoming constraints associated with sensitive or regulated information. In high-dimensional data, where the dimensions of features and variables often surpass the number of available observations, the emergence of synthetic data heralds a transformation. Applications of Synthetic High Dimensional Data delves into the algorithms and applications underpinning the creation of synthetic data, which surpass the capabilities of authentic datasets in many cases. Beyond mere mimicry, synthetic data takes center stage in prioritizing the mathematical domain, becoming the crucible for training robust machine learning models. It serves not only as a simulation but also as a theoretical entity, permitting the consideration of unforeseen variables and facilitating fundamental problem-solving. This book navigates the multifaceted advantages of synthetic data, illuminating its role in protecting the privacy and confidentiality of authentic data. It also underscores the controlled generation of synthetic data as a mechanism to safeguard private information while maintaining a controlled resemblance to real-world datasets. This controlled generation ensures the preservation of privacy and facilitates learning across datasets, which is crucial when dealing with incomplete, scarce, or biased data. Ideal for researchers, professors, practitioners, faculty members, students, and online readers, this book transcends theoretical discourse.
With the immense amount of data that is now available online, security concerns have been an issue from the start, and have grown as new technologies are increasingly integrated in data collection, storage, and transmission. Online cyber threats, cyber terrorism, hacking, and other cybercrimes have begun to take advantage of this information that can be easily accessed if not properly handled. New privacy and security measures have been developed to address this cause for concern and have become an essential area of research within the past few years and into the foreseeable future. The ways in which data is secured and privatized should be discussed in terms of the technologies being used, the methods and models for security that have been developed, and the ways in which risks can be detected, analyzed, and mitigated. The Research Anthology on Privatizing and Securing Data reveals the latest tools and technologies for privatizing and securing data across different technologies and industries. It takes a deeper dive into both risk detection and mitigation, including an analysis of cybercrimes and cyber threats, along with a sharper focus on the technologies and methods being actively implemented and utilized to secure data online. Highlighted topics include information governance and privacy, cybersecurity, data protection, challenges in big data, security threats, and more. This book is essential for data analysts, cybersecurity professionals, data scientists, security analysts, IT specialists, practitioners, researchers, academicians, and students interested in the latest trends and technologies for privatizing and securing data.