DEEP LEARNING APPROACHES TO CLOUD SECURITY Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. Deep learning is the fastest growing field in computer science. Deep learning algorithms and techniques are found to be useful in different areas like automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delay in children. However, applying deep learning techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. This book provides state of the art approaches of deep learning in these areas, including areas of detection and prediction, as well as future framework development, building service systems and analytical aspects. In all these topics, deep learning approaches, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. This book is intended for dealing with modeling and performance prediction of the efficient cloud security systems, thereby bringing a newer dimension to this rapidly evolving field. This groundbreaking new volume presents these topics and trends of deep learning, bridging the research gap, and presenting solutions to the challenges facing the engineer or scientist every day in this area. Whether for the veteran engineer or the student, this is a must-have for any library. Deep Learning Approaches to Cloud Security: Is the first volume of its kind to go in-depth on the newest trends and innovations in cloud security through the use of deep learning approaches Covers these important new innovations, such as AI, data mining, and other evolving computing technologies in relation to cloud security Is a useful reference for the veteran computer scientist or engineer working in this area or an engineer new to the area, or a student in this area Discusses not just the practical applications of these technologies, but also the broader concepts and theory behind how these deep learning tools are vital not just to cloud security, but society as a whole Audience: Computer scientists, scientists and engineers working with information technology, design, network security, and manufacturing, researchers in computers, electronics, and electrical and network security, integrated domain, and data analytics, and students in these areas

"This book explores the latest empirical research and best real-world practices for preventing, weathering, and recovering from disasters such as earthquakes or tsunamis to nuclear disasters and cyber terrorism"--Provided by publisher.

In today's global landscape, the need for environmental sustainability is increasing than ever. Organizations find themselves at a crossroads, navigating the intricate web of economic viability, social responsibility, and ecological integrity. The nexus of Human Resource Management (HRM) and Organizational Culture emerges as a critical juncture where transformative change can take root. As businesses grapple with the multifaceted challenges posed by sustainability, a solution-oriented guide is needed to help them reimagine their roles and foster a culture that places environmental well-being at its core. The urgent need for organizations to align HRM practices with environmental sustainability and cultivate a purpose-driven culture is palpable. In this complex terrain, a lack of guidance leaves stakeholders grappling with the intricacies of weaving sustainability into the fabric of their operations. The consequences of overlooking this critical intersection are dire—environmental degradation, disengaged employees, and a missed opportunity for businesses to lead the charge in building a sustainable future. The need for a comprehensive resource that addresses these challenges head-on, providing actionable insights and strategies, is paramount.

Artificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing phenomenon as more realize the plethora of uses and benefits they provide. Due to their complexity, it is vital for researchers to understand ANN capabilities in various fields. The Research Anthology on Artificial Neural Network Applications covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas including medicine, finance, operations research, business, social media, security, and more. Covering everything from the applications and uses of artificial neural networks to deep learning and non-linear problems, this book is ideal for computer scientists, IT specialists, data scientists, technologists, business owners, engineers, government agencies, researchers, academicians, and students, as well as anyone who is interested in learning more about how artificial neural networks can be used across a wide range of fields.

In order to improve competitiveness and performance, corporations must embrace advancements in digitalization. Successful implementation of knowledge management is a huge factor in corporate success. Analyzing the Impacts of Industry 4.0 in Modern Business Environments is a critical scholarly publication that explores digital transformation in business environments and the requirement for not only a substantial management change plan but equally the two essential components of knowledge management: knowledge sharing and knowledge transfer. Featuring a broad range of topics such as strategic planning, knowledge transfer, and cybersecurity risk management, this book is geared toward researchers, academicians, and students seeking current and relevant research on organizational knowledge intensity and monitoring of knowledge management development.

In a world of earthquakes, tsunamis, and terrorist attacks, emergency response plans are crucial to solving problems, overcoming challenges, and restoring and improving communities that have been affected by these catastrophic events. Although the necessity for quick and efficient aid is understood, researchers and professionals continue to strive for the best practices and methodologies to properly handle such significant events. Emergency and Disaster Management: Concepts, Methodologies, Tools, and Applications is an innovative reference source for the latest research on the theoretical and practical components of initiating crisis management and emergency response. Highlighting a range of topics such as preparedness and assessment, aid and relief, and the integration of smart technologies, this multi-volume book is designed for emergency professionals, policy makers, practitioners, academicians, and researchers interested in all aspects of disaster, crisis, and emergency studies.