Ashima Kalra – författare

The rapid convergence of the industrial internet of things (IIoT) and machine learning (ML) is propelling us into a new era known as Industry 5.0. This era is defined by unparalleled levels of automation, customization, and sustainability. Despite these advancements, there is a noticeable absence of comprehensive resources that offer a complete understanding of these transformative technologies and their practical applications across various industrial sectors. Transforing Industries: Capturing the Potential of IIoT and ML in the Era of Industry 5.0 aims to fill the void by providing a timely and relevant guide that explores the theoretical foundations of the technologies while also presenting real-world case studies and best practices.This comprehensive text discusses the profound impact of the IIoT and ML on various sectors, including manufacturing, supply chain management, and energy production. It provides a thorough examination of the opportunities and obstacles associated with these cutting-edge technologies. Through real-world case studies and success stories, readers gain insight into how industry leaders have successfully leveraged IIoT and ML solutions to optimize operational efficiency, foster innovation, and achieve unparalleled excellence. Practical strategies and detailed guidelines are also offered to facilitate the seamless integration of these technologies into existing workflows, empowering businesses to navigate the complexities of digital transformation with confidence. The focus of this publication also extends to future trends and potential disruptors in the era of Industry 5.0, equipping readers with the knowledge needed to anticipate and adapt to emerging challenges. Furthermore, the exploration of how IIoT and ML can streamline resource allocation, minimize waste, and promote sustainable practices underscores the alignment of technological advancements with corporate, environmental, and social responsibilities.By equipping industry leaders, professionals, and entrepreneurs with actionable insights, this book will empower businesses to stay ahead of the curve, foster innovation, optimize resource utilization, and ultimately gain a competitive advantage in the ever-evolving manufacturing landscape.

The rapid convergence of the industrial internet of things (IIoT) and machine learning (ML) is propelling us into a new era known as Industry 5.0. This era is defined by unparalleled levels of automation, customization, and sustainability. Despite these advancements, there is a noticeable absence of comprehensive resources that offer a complete understanding of these transformative technologies and their practical applications across various industrial sectors. Transforing Industries: Capturing the Potential of IIoT and ML in the Era of Industry 5.0 aims to fill the void by providing a timely and relevant guide that explores the theoretical foundations of the technologies while also presenting real-world case studies and best practices.This comprehensive text discusses the profound impact of the IIoT and ML on various sectors, including manufacturing, supply chain management, and energy production. It provides a thorough examination of the opportunities and obstacles associated with these cutting-edge technologies. Through real-world case studies and success stories, readers gain insight into how industry leaders have successfully leveraged IIoT and ML solutions to optimize operational efficiency, foster innovation, and achieve unparalleled excellence. Practical strategies and detailed guidelines are also offered to facilitate the seamless integration of these technologies into existing workflows, empowering businesses to navigate the complexities of digital transformation with confidence. The focus of this publication also extends to future trends and potential disruptors in the era of Industry 5.0, equipping readers with the knowledge needed to anticipate and adapt to emerging challenges. Furthermore, the exploration of how IIoT and ML can streamline resource allocation, minimize waste, and promote sustainable practices underscores the alignment of technological advancements with corporate, environmental, and social responsibilities.By equipping industry leaders, professionals, and entrepreneurs with actionable insights, this book will empower businesses to stay ahead of the curve, foster innovation, optimize resource utilization, and ultimately gain a competitive advantage in the ever-evolving manufacturing landscape.

The rapid convergence of the industrial internet of things (IIoT) and machine learning (ML) is propelling us into a new era known as Industry 5.0. This era is defined by unparalleled levels of automation, customization, and sustainability. Despite these advancements, there is a noticeable absence of comprehensive resources that offer a complete understanding of these transformative technologies and their practical applications across various industrial sectors. Transforing Industries: Capturing the Potential of IIoT and ML in the Era of Industry 5.0 aims to fill the void by providing a timely and relevant guide that explores the theoretical foundations of the technologies while also presenting real-world case studies and best practices.This comprehensive text discusses the profound impact of the IIoT and ML on various sectors, including manufacturing, supply chain management, and energy production. It provides a thorough examination of the opportunities and obstacles associated with these cutting-edge technologies. Through real-world case studies and success stories, readers gain insight into how industry leaders have successfully leveraged IIoT and ML solutions to optimize operational efficiency, foster innovation, and achieve unparalleled excellence. Practical strategies and detailed guidelines are also offered to facilitate the seamless integration of these technologies into existing workflows, empowering businesses to navigate the complexities of digital transformation with confidence. The focus of this publication also extends to future trends and potential disruptors in the era of Industry 5.0, equipping readers with the knowledge needed to anticipate and adapt to emerging challenges. Furthermore, the exploration of how IIoT and ML can streamline resource allocation, minimize waste, and promote sustainable practices underscores the alignment of technological advancements with corporate, environmental, and social responsibilities.By equipping industry leaders, professionals, and entrepreneurs with actionable insights, this book will empower businesses to stay ahead of the curve, foster innovation, optimize resource utilization, and ultimately gain a competitive advantage in the ever-evolving manufacturing landscape.

This book provides a comprehensive examination of artificial intelligence techniques that integrate heterogeneous healthcare data to support intelligent, secure, and sustainable healthcare systems. It presents both theoretical foundations and practical methodologies for multimodal data fusion, representation learning, and decision-making using medical images, bio signals, electronic health records, wearable sensors, text, and multimedia data.Features:Illustrates analyzing and integrating multiple modalities of healthcare data, such as medical images, electronic health records, bio signals, and genomic data.Emphasizes the role of multimedia intelligence in healthcare by leveraging advanced artificial intelligence techniques to analyze diverse data formats, including text, images, audio, and video.Presents practical applications that ensure secure healthcare data management and sustainable solutions, focusing on improving patient care and enhancing diagnostics.Covers the different security mechanisms applied in multimodal and multimedia-based healthcare applications.Showcases the role of advanced artificial intelligence and machine learning methods, including privacy-preserving technologies, blockchain, and explainable artificial intelligence tailored for secure healthcare.The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and biomedical engineering.

The book highlights emerging developments in intelligent computing, communication technologies, robotics, embedded systems, and data-driven innovations that are shaping the future of smart and autonomous systems.This volume brings together multidisciplinary research focusing on artificial intelligence, computational intelligence, smart communication systems, embedded and VLSI technologies, robotics, automation, cybersecurity, cloud computing, data analytics, IoT-enabled platforms, and intelligent decision-making systems. The book explores innovative methodologies, mathematical models, and practical applications that support next-generation technological ecosystems. Contributions include theoretical advancements, simulation-based studies, experimental implementations, and industrial applications in smart environments and AI-integrated systems. The book also discusses recent trends in machine learning, predictive analytics, human-robot interaction, autonomous systems, and sustainable computing architectures, offering valuable insights into the rapidly evolving landscape of communication and computational sciences.The book is intended for researchers, academicians, scientists, industry professionals, postgraduate students, and practitioners working in the domains of artificial intelligence, communication engineering, computer science, robotics, embedded systems, electronics, and data analytics. It serves as a valuable reference for both academic research and industrial innovation.

The book highlights emerging developments in intelligent computing, communication technologies, robotics, embedded systems, and data-driven innovations that are shaping the future of smart and autonomous systems.This volume brings together multidisciplinary research focusing on artificial intelligence, computational intelligence, smart communication systems, embedded and VLSI technologies, robotics, automation, cybersecurity, cloud computing, data analytics, IoT-enabled platforms, and intelligent decision-making systems. The book explores innovative methodologies, mathematical models, and practical applications that support next-generation technological ecosystems. Contributions include theoretical advancements, simulation-based studies, experimental implementations, and industrial applications in smart environments and AI-integrated systems. The book also discusses recent trends in machine learning, predictive analytics, human-robot interaction, autonomous systems, and sustainable computing architectures, offering valuable insights into the rapidly evolving landscape of communication and computational sciences.The book is intended for researchers, academicians, scientists, industry professionals, postgraduate students, and practitioners working in the domains of artificial intelligence, communication engineering, computer science, robotics, embedded systems, electronics, and data analytics. It serves as a valuable reference for both academic research and industrial innovation.

Master's Thesis from the year 2008 in the subject Computer Science - Miscellaneous, , course: Implementation And Performance Study of Edge Detection Of Images, language: English, abstract: Edge detection is a research field within Image processing and Computer vision, in particular within the area of feature extraction. It is extensively used in image segmentation when we want to divide the image into areas corresponding to different objects. Representing an image by its edges has the further advantage that the amount of data is reduced significantly while retaining most of the image information. Since edges consist of mainly high frequencies, we can, in theory, detect edges by applying a high pass frequency filter in the Fourier domain or by convolving the image with an appropriate kernel in the spatial domain. In practice, edge detection is performed in the spatial domain, because it is computationally less expensive and often yields better results. Since edges correspond to strong illumination gradients, we can highlight them by calculating the derivatives of the image. The present Thesis aims at extracting a good & accurate edge detected image from the application of various masks or edge detection operators on the image.Convolution is the mathematical tool, that is used to implement the various masks operators to get an edge detected image from the original image. Our thesis provides the implementation of the following edge detection techniques to get a better edge detected image: a) 1 Dimensional operators : Kirch ,Prewitt, Sobel and Quick Masking; b) 2 Dimensional operators: LOG( Laplacian of Guassian) and DOG( difference of Guassian).