Manju Kaushik – författare

Software Engineering Approaches to Enable Digital Transformation Technologies features contributions reflecting ideas and research in enabling digital transformation technologies through software engineering. To date, multiple, different approaches have been adopted to develop software solutions for a variety of different problems. Of all the available approaches, the main approaches are level-oriented, data flow-oriented, data structure-oriented, and object-oriented design approaches. The other focus of the book is digital transformation, which can be defined as the adoption of digital technology to improve efficiency, value, and innovation Digitalization is more than just putting additional technological systems and services in place. Rather than improving conventional methods, a true digital transformation initiative includes radically rethinking company structures and procedures. There are four types of digital transformation: business process, business model, domain, and cultural and organizational. Companies are being challenged to develop new business models that consider and harness digitalization. From the standpoint of software engineering, digital transformation alters how software is built. Current trends include the development of mobile applications, cloud applications, and Internet of Things (IoT) applications. Emerging trends are the development of digital twins, robotics, artificial intelligence, machine learning, augmented reality, and additive manufacturing. This book examines the challenges that arise due to digitization in society and presents plausible solutions that could be applied to counter these challenges and convert them into opportunities. These solutions may further be improvised and worked out for the software companies from the technological perspective, organizational perspective, and management perspective.

Software Engineering Approaches to Enable Digital Transformation Technologies features contributions reflecting ideas and research in enabling digital transformation technologies through software engineering. To date, multiple, different approaches have been adopted to develop software solutions for a variety of different problems. Of all the available approaches, the main approaches are level-oriented, data flow-oriented, data structure-oriented, and object-oriented design approaches. The other focus of the book is digital transformation, which can be defined as the adoption of digital technology to improve efficiency, value, and innovation Digitalization is more than just putting additional technological systems and services in place. Rather than improving conventional methods, a true digital transformation initiative includes radically rethinking company structures and procedures. There are four types of digital transformation: business process, business model, domain, and cultural and organizational. Companies are being challenged to develop new business models that consider and harness digitalization. From the standpoint of software engineering, digital transformation alters how software is built. Current trends include the development of mobile applications, cloud applications, and Internet of Things (IoT) applications. Emerging trends are the development of digital twins, robotics, artificial intelligence, machine learning, augmented reality, and additive manufacturing. This book examines the challenges that arise due to digitization in society and presents plausible solutions that could be applied to counter these challenges and convert them into opportunities. These solutions may further be improvised and worked out for the software companies from the technological perspective, organizational perspective, and management perspective.

Deep Learning Applications in Operations Research explores cutting-edge applications of deep learning and optimization techniques across various domains. By exploring innovative approaches and emerging trends in advanced intelligent applications, the book examines how innovation and emerging technologies can be leveraged to drive intelligent solutions across diverse domains. It covers such key areas as follows:A comparative study of deep learning algorithms and genetic algorithms as stochastic optimizers, analyzing their effectiveness in operations research applicationsAn updated approach to the critical path method (CPM) that combines traditional scheduling with modern computational methods for dynamic project environmentsA bibliometric analysis of smart warehousing trends in logistics operations management using R, providing data- driven insights into industry developmentsAn examination of edge computing optimization for real-time decision-making in operations research, focusing on latency reduction and computational efficiencyDevelopment of a hybrid intrusion detection system for IoT networks, combining machine learning with anomaly and signature-based detection approachesIntroduction of SAI-GAN, a novel approach for masked face reconstruction, paired with a DCNN-ELM classifier for enhanced biometric authenticationAnalysis of deep learning-driven mHealth applications in India’s healthcare system, demonstrating how predictive analytics and real-time monitoring can improve healthcare accessibilityExploration of machine learning-driven ontology evolution in multi-tenant cloud architectures, advancing automated knowledge engineering through deep learning modelsProviding a wide-ranging overview of the field, the book helps researchers navigate the rapidly evolving landscape of advanced intelligent applications. It demonstrates the transformative impact of deep learning on operations research by offering practical insights and establishing a foundation for future innovations.

Global and Local Trends in Media Innovation and Entrepreneurship explores the cutting-edge technologies and entrepreneurial shifts transforming how media is created, distributed, and consumed worldwide. The book offers readers a clear understanding of how emerging technologies—such as VR, AR, AI, the metaverse, and OTT platforms—are disrupting traditional media practices. Through real-world examples, case studies, and expert analysis, it bridges theory and practice to showcase how media innovation is driving new business models, reshaping audience engagement, and redefining global-local media dynamics.A vital resource for media professionals, entrepreneurs, academics, and students, this book is designed for those seeking to navigate and leverage the evolving media landscape, whether for strategic insight, research, or innovation-driven practice.