Kotlin programming has been recognized as the main language for deploying Android projects, so this language has tremendous potential. In order to understand the main principles and best applications of Kotlin programming, further research is needed. Principles, Policies, and Applications of Kotlin Programming provides complete knowledge of Kotlin programming. It presents the latest technology of Vert.X, Restful API programming application by Kotlin, as well as authentication techniques for Restful APIs, which are new and important content in backend deployment for software. Covering topics such as array processing, graphic user interface, and string processing, this premier reference source is a valuable resource for software developers, programmers, students and educators of higher education, researchers, and academicians.

The Software Principles of Design for Data Modeling, written by Debabrata Samanta of Rochester Institute of Technology in Kosovo, offers a practical and comprehensive solution to the challenges of designing effective software architecture for data modeling. This book covers key topics such as gathering requirements, modeling requirements with use cases, testing the system, building entity-relationship models, building class models in UML with patterns of data modeling and software quality attributes, and use case modeling. It also includes case studies of relational and object-relational database schema design. The unique approach of this book lies in its unifying method for designing software architecture for data modeling. It addresses specific design issues for various types of software systems, including object-oriented, client/server, service-oriented, component-based, real-time, and software product line architectures. With its practical guidance, standard method for modeling requirements and analysis, and comprehensive coverage of key topics and case studies, this book is a must-read for anyone interested in designing effective software architecture for data modeling, whether you are an academic scholar or a professional in the field.

Machine learning (ML) and the internet of things (IoT) are the top technologies used by businesses to increase efficiency, productivity, and competitiveness in this fast-paced digital era transformation. ML is the key tool for fast processing and decision making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. IoT technology has proven efficient in solving many real-world problems, and ML algorithms combined with IoT means the fusion of product and intelligence to achieve better automation, efficiency, productivity, and connectivity. The Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries highlights the importance of ML for IoT’s success and diverse ML-powered IoT applications. This book addresses the problems and challenges in energy, industry, and healthcare and solutions proposed for ML-enabled IoT and new algorithms in ML. It further addresses their accuracy for existing real-time applications. Covering topics such as agriculture, pattern recognition, and smart applications, this premier reference source is an essential resource for engineers, scientists, educators, students, researchers, and academicians.

Data structures are essential principles applicable to any programming language in computer science. Data structures may be studied more easily with Python than with any other programming language because of their interpretability, interactivity, and object-oriented nature. Computers may store and process data at an extraordinary rate and with outstanding accuracy. Therefore, it is of the utmost importance that the data is efficiently stored and is able to be accessed promptly. In addition, data processing should take as little time as feasible while maintaining the highest possible level of precision. Advanced Applications of Python Data Structures and Algorithms assists in understanding and applying the fundamentals of data structures and their many implementations and discusses the advantages and disadvantages of various data structures. Covering key topics such as Python, linked lists, datatypes, and operators, this reference work is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.

Kotlin is a powerful and pragmatic language, but it's not enough to know about its features. We also need to know when they should be used and in what way. This book is a guide for Kotlin developers on how to become excellent Kotlin developers. It presents and explains in-depth the best practices for Kotlin development. Each item is presented as a clear rule of thumb, supported by detailed explanations and practical examples.

Recent advancements in graph neural networks have expanded their capacities and expressive power. Furthermore, practical applications have begun to emerge in a variety of fields including recommendation systems, fake news detection, traffic prediction, molecular structure in chemistry, antibacterial discovery physics simulations, and more. As a result, a boom of research at the juncture of graph theory and deep learning has revolutionized many areas of research. However, while graph neural networks have drawn a lot of attention, they still face many challenges when it comes to applying them to other domains, from a conceptual understanding of methodologies to scalability and interpretability in a real system. Concepts and Techniques of Graph Neural Networks provides a stepwise discussion, an exhaustive literature review, detailed analysis and discussion, rigorous experimentation results, and application-oriented approaches that are demonstrated with respect to applications of graph neural networks. The book also develops the understanding of concepts and techniques of graph neural networks and establishes the familiarity of different real applications in various domains for graph neural networks. Covering key topics such as graph data, social networks, deep learning, and graph clustering, this premier reference source is ideal for industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.

As artificial intelligence (AI) processing moves from the cloud to the edge of the network, battery-powered and deeply embedded devices are challenged to perform AI functions such as computer vision and voice recognition. Microchip Technology Inc., via its Silicon Storage Technology (SST) subsidiary, is addressing this challenge by significantly reducing power with its analog memory technology, the memBrain Memory Solution. The memBrain solution is being adopted by today’s companies looking to advance machine learning capacities in edge devices. Due to its ability to significantly reduce power, this analog in-memory computer solution is ideal for an AI application. Neuromorphic Computing Systems for Industry 4.0 covers the available literature in the field of neural computing-based microchip technology. It provides further research opportunities in this dynamic field. Covering topics such as emotion recognition, biometric authentication, and neural network protection, this premier reference source is an essential resource for technology developers, computer scientists, engineers, students and educators of higher education, librarians, researchers, and academicians.