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Innovative Machine Learning Applications for Cryptography
Data security is paramount in our modern world, and the symbiotic relationship between machine learning and cryptography has recently taken center stage. The vulnerability of traditional cryptosystems to human error and evolving cyber threats is a pressing concern. The stakes are higher than ever, and the need for innovative solutions to safeguard sensitive information is undeniable. Innovative Machine Learning Applications for Cryptography emerges as a steadfast resource in this landscape of uncertainty. Machine learning's prowess in scrutinizing data trends, identifying vulnerabilities, and constructing adaptive analytical models offers a compelling solution. The book explores how machine ...
Cases on Edge Computing and Analytics
Edge computing and analytics are fascinating the whole world of computing. Industry and business are keenly embracing this sound concept to develop customer-centric solutions by enhancing their operations, offerings, and outputs. There is a bevy of advancements in this domain that came with the arrival of IoT devices. The seamless convergence of microservices and serverless computing creates vast opportunities. With the help of IoT devices and these other developments, there has become a deep interest in business automation and additional improvisations in edge computing. With the steady growth of edge devices and applications of IoT fog/edge computing and analytics, there are also distinct ...
Innovations in Modern Cryptography
In today's interconnected digital landscape, cybersecurity threats pose significant challenges to individuals, organizations, and governments worldwide. Cyberattacks, data breaches, and malicious activities continue to escalate in sophistication and frequency, jeopardizing sensitive information, financial assets, and critical infrastructure. Amidst this escalating threat landscape, there's a pressing need for comprehensive solutions to safeguard digital assets and ensure the integrity, confidentiality, and availability of data. Traditional security measures are proving inadequate in the face of evolving cyber threats, necessitating innovative approaches to cybersecurity. Innovations in Moder...
Machine Learning Applications in Non-Conventional Machining Processes
Traditional machining has many limitations in today’s technology-driven world, which has caused industrial professionals to begin implementing various optimization techniques within their machining processes. The application of methods including machine learning and genetic algorithms has recently transformed the manufacturing industry and created countless opportunities in non-traditional machining methods. Significant research in this area, however, is still considerably lacking. Machine Learning Applications in Non-Conventional Machining Processes is a collection of innovative research on the advancement of intelligent technology in industrial environments and its applications within the manufacturing field. While highlighting topics including evolutionary algorithms, micro-machining, and artificial neural networks, this book is ideally designed for researchers, academicians, engineers, managers, developers, practitioners, industrialists, and students seeking current research on intelligence-based machining processes in today’s technology-driven market.
6G Security Education and Multidisciplinary Implementation
The advent of 6G technology introduces significant security challenges that must be addressed to ensure its safe and effective implementation. The increased complexity of 6G infrastructure, encompassing a vast array of devices and networks, expands the potential attack surface, making it more vulnerable to cyber threats. Privacy concerns are heightened with the massive data flow, necessitating stringent protection measures. These rapid developments are outpacing current educational frameworks, highlighting the need for updated programs to equip cybersecurity professionals with the skills to address these challenges. 6G Security Education and Multidisciplinary Implementation explores the critical intersection of technology, security, and education. It provides insights into the implementation of 6G technologies as well as frameworks for security education. Covering topics such as 6G education, learning experience, and privacy concerns, this book is a valuable resource for educators, academicians, scholars, security experts, post-graduate students, pre-service teachers, industry professionals, and researchers.
Navigating the World of Deepfake Technology
The digital age has ushered in an era of incredible innovation, but it's also opened the door to new threats. Deepfakes, hyper-realistic manipulated videos and images, are blurring the lines between truth and fiction. Malicious actors can use deepfakes to create fake news that sways elections, fabricate celebrity scandals to damage reputations, or even launch targeted attacks against businesses. This loss of trust can have a devastating impact on individuals, society as a whole, and especially organizations struggling to maintain brand integrity and secure operations. Navigating the World of Deepfake Technology equips you to understand deepfakes, from their work to the legal issues surrounding them. It also provides practical strategies to identify deepfakes and mitigate their risks, empowering you to protect yourself and your organization. Whether you're a business leader, journalist, or simply someone concerned about the impact of deepfakes, this book is essential reading. It's your key to staying informed and safeguarding yourself in a world increasingly reliant on digital media.
Diagnosis of Neurological Disorders Based on Deep Learning Techniques
This book is based on deep learning approaches used for the diagnosis of neurological disorders, including basics of deep learning algorithms using diagrams, data tables, and practical examples, for diagnosis of neurodegenerative and neurodevelopmental disorders. It includes application of feed-forward neural networks, deep generative models, convolutional neural networks, graph convolutional networks, and recurrent neural networks in the field of diagnosis of neurological disorders. Along with this, data preprocessing including scaling, correction, trimming, and normalization is also included. Offers a detailed description of the deep learning approaches used for the diagnosis of neurological disorders. Demonstrates concepts of deep learning algorithms using diagrams, data tables, and examples for the diagnosis of neurodegenerative, neurodevelopmental, and psychiatric disorders. Helps build, train, and deploy different types of deep architectures for diagnosis. Explores data preprocessing techniques involved in diagnosis. Includes real-time case studies and examples. This book is aimed at graduate students and researchers in biomedical imaging and machine learning.
Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images
Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images comprehensively examines the wide range of AI-based mammogram analysis methods for medical applications. Beginning with an introductory overview of mammogram data analysis, the book covers the current technologies such as ultrasound, molecular breast imaging (MBI), magnetic resonance (MR), and Positron Emission mammography (PEM), as well as the recent advancements in 3D breast tomosynthesis and 4D mammogram. Deep learning models are presented in each chapter to show how they can assist in the efficient processing of breast images. The book also discusses hybrid intelligence approaches for early-stag...
