River Publishers Series in Rapids in Computing and Information Science and Technology – serie

The relentless pace of technological advancement, particularly in telecommunications, is reshaping how we connect and communicate. What once took a decade to standardize now evolves in just a few years, blurring generational divides and delivering seamless transitions to users. Against this backdrop, Considerations on Novel Network Edge Physical Node Architectures explores the complex paradigm of 6G mobile communications—anticipated to emerge around 2030—and casts a longer-term vision toward future networks (FNs).The scientific community has been engaged in 6G research since 2017–2018, defining use cases, key enabling technologies (KETs), and performance indicators (KPIs). Ambitious targets include peak data rates of 1 Tbps, average user rates of 10–100 Gbps, sub-0.1 ms latency, and reliability greater than 10⁻⁸. Application domains span holographic communications, digital twins, multi-sensory extended reality (XR), tactile and haptic interactions, and integrated space–terrestrial networks.While these expectations hold the promise of a more interconnected world, realizing them demands a fundamental shift in approach. The projected density of 10–100 million smart devices per square kilometer in 6G networks cannot be addressed through conventional hardware–software co-design alone. This book proposes new ways of conceiving the physical building blocks of edge intelligence (EI), where intelligence is embedded at the network’s edge.Central to this vision is the use of micro- and nanotechnologies (MEMS/NEMS) to create miniaturized sensors, actuators, and transducers (SATs) that serve as distributed intelligence units. These physical innovations are examined alongside non-physical enablers such as artificial intelligence (AI) and federated learning (FL), illustrating how a holistic integration of technologies can enable practical, scalable applications.By bridging physical and digital innovations, Considerations on Novel Network Edge Physical Node Architectures aims to inject fresh ideas into scientific discourse and provide a foundation for turning the ambitious visions of 6G and future networks into reality. It is an essential resource for researchers, engineers, and industry professionals seeking to shape the next generation of telecommunications infrastructure.

This River Rapid provides a timely analysis of the state of the art and future perspectives of artificial intelligence (AI) in education. Drawing on international expertise and successful case studies, it explores both the opportunities AI creates for teaching and learning and the challenges posed by its uncontrolled use by students and schoolchildren.The book is structured into seven research-analytic chapters authored by leading scholars from Norway, Poland, Ukraine, and the United States. Together, they present a comprehensive picture of AI’s growing role in educational systems worldwide. Topics include:An overview and comparative review of large language models.Analysis of successful AI applications in education based on global best practices.Strategies for bilingual education supported by AI tools.Risks of unsupervised AI use by pupils and students, both in education and on social media.The relationship between machine learning and human learning, with attention to pedagogical advances inspired by AI.Future directions for AI in enhancing teaching and learning processes.The unique dynamics of digital transformation and AI adoption in the Ukrainian education system.By combining international perspectives with focused case studies, AI in Education Systems provides both theoretical insights and practical strategies for educators, policymakers, and researchers. It highlights AI’s potential to significantly improve learning outcomes while addressing ethical concerns, pedagogical challenges, and the importance of guiding its responsible use.

Operations are where strategy becomes reality. For decades, organizations have competed on efficiency, scale, and cost optimization. Today, that foundation is fundamentally reshaped by artificial intelligence (AI). No longer confined to analytics tools or isolated automation systems, AI is now embedded directly into the core of operational decision-making.Artificial Intelligence in Operations: Strategy, Efficiency, and the Future of Execution examines how AI is transforming operational systems across industries and institutions. From supply chains and logistics to healthcare, national security, higher education, criminal justice, diplomacy, and modern warfare, the book reveals how intelligent technologies are redefining how work is planned, executed, monitored, and optimized.Unlike purely technical texts, this book focuses on real-world operational impacts. It explores how AI enables predictive planning, adaptive execution, and autonomous coordination, while also addressing emerging risks related to bias, governance, cybersecurity, workforce displacement, and accountability. Through detailed case studies—ranging from Amazon, Walmart, UPS, Tesla, and IBM to military, diplomatic, and public-sector systems—it demonstrates how AI moves from theory to practice.AI in operations is not about replacing humans. It is about redesigning workflows, redefining roles, and creating effective human–machine collaboration at scale. Organizations that understand this shift will gain resilience, agility, and strategic advantage. Those that do not risk inefficiency, fragility, or long-term irrelevance.Providing a structured and practical framework, this book equips leaders, managers, policymakers, and practitioners to understand how AI reshapes operational systems—and how to adopt it responsibly, strategically, and effectively.

This is an essential resource for beginners and experienced practitioners in machine learning. This comprehensive guide covers a broad spectrum of machine learning topics, starting with an in-depth exploration of popular machine learning libraries. Readers will gain a thorough understanding of Scikit-learn, TensorFlow, PyTorch, Keras, and other pivotal libraries like XGBoost, LightGBM, and CatBoost, which are integral for efficient model development and deployment.The book delves into various neural network architectures, providing readers with a solid foundation in understanding and applying these models. Beginning with the basics of the Perceptron and its application in digit classification, it progresses to more complex structures such as multilayer perceptrons for financial forecasting, radial basis function networks for air quality prediction, and convolutional neural networks (CNNs) for image classification. Additionally, the book covers recurrent neural networks (RNNs) and their variants like long short-term memory (LSTM) and gated recurrent units (GRUs), which are crucial for time-series analysis and sequential data applications.Supervised machine learning algorithms are meticulously explained, with practical examples to illustrate their application. The book covers logistic regression and its use in predicting sports outcomes, decision trees for plant classification, random forests for traffic prediction, and support vector machines for house price prediction. Gradient boosting machines and their applications in genomics, AdaBoost for bioinformatics data classification, and extreme gradient boosting (XGBoost) for churn prediction are also discussed, providing readers with a robust toolkit for various predictive tasks.Unsupervised learning algorithms are another significant focus of the book, introducing readers to techniques for uncovering hidden patterns in data. Hierarchical clustering for gene expression data analysis, principal component analysis (PCA) for climate predictions, and singular value decomposition (SVD) for signal denoising are thoroughly explained. The book also explores applications like robot navigation and network security, demonstrating the versatility of these techniques.Natural language processing (NLP) is comprehensively covered, highlighting its fundamental concepts and various applications. The book discusses the overview of NLP, its fundamental concepts, and its diverse applications such as chatbots, virtual assistants, clinical NLP applications, and social media analytics. Detailed sections on text pre-processing, syntactic analysis, machine translation, text classification, named entity recognition, and sentiment analysis equip readers with the knowledge to build sophisticated NLP models.The final chapters of the book explore generative AI, including generative adversarial networks (GANs) for image generation, variational autoencoders for vibrational encoder training, and autoregressive models for time series forecasting. It also delves into Markov chain models for text generation, Boltzmann machines for pattern recognition, and deep belief networks for financial forecasting. Special attention is given to the application of recurrent neural networks (RNNs) for generation tasks, such as wind power plant predictions and battery range prediction, showcasing the practical implementations of generative AI in various fields.

This book provides a thorough and comprehensive introduction to natural language processing (NLP), a critical field at the intersection of artificial intelligence and computational linguistics. It explores key techniques such as sentiment analysis, which enables the detection of emotional tone in text, machine translation, facilitating the conversion of text between languages, and named entity recognition (NER), which identifies and classifies entities like names, dates, and locations within text data.The book delves into deep learning advancements, particularly the use of neural networks such as transformers and recurrent models, which have revolutionized NLP applications. Readers will gain insights into how these models drive innovations in areas such as text classification, language generation, and speech recognition.In addition to technical concepts, the book also addresses the ethical considerations surrounding NLP, emphasizing the responsible use of AI technologies to mitigate issues like bias, misinformation, and privacy concerns. Practical case studies and real-world examples are included to illustrate how NLP is applied in various sectors, including healthcare, finance, and customer service.This book is an invaluable resource for students, researchers, and industry professionals seeking to understand the foundational concepts, cutting-edge advancements, and broader implications of NLP, equipping them with the knowledge to innovate and apply these technologies effectively in their respective fields.

This book explores the fundamentals and applications of Blockchain technology. Readers will learn about the decentralized peer-to-peer network, distributed ledger, and the trust model that defines Blockchain technology. They will also be introduced to the basic components of Blockchain (transaction, block, block header, and the chain), its operations (hashing, verification, validation, and consensus model), underlying algorithms, and essentials of trust (hard fork and soft fork). Private and public Blockchain networks similar to Bitcoin and Ethereum will be introduced, as will concepts of Smart Contracts, Proof of Work and Proof of Stack.Blockchain is an emerging technology that can radically improve transaction security at banking, supply chain, and other transaction networks. It’s estimated that Blockchain will generate $3.1 trillion in new business value by 2030. Essentially, it provides the basis for a dynamic distributed ledger that can be applied to save time when recording transactions between parties, remove costs associated with intermediaries, and reduce risks of fraud and tampering.

This book introduces the Internet of Things (IoT), which is the convergence of connecting people, things, data and processes; it is transforming our life, business and everything in between. By 2025, experts forecast that up to 75 billion devices will be connected to the Internet with only one third of them being computers, smartphones, smartwatches, and tablets. The remaining two thirds will be other "devices" – sensors, terminals, household appliances, thermostats, televisions, automobiles, production machinery, urban infrastructure and many other "things", which traditionally have not been Internet enabled. This "Internet of Things" (IoT) represents a remarkable transformation of the way in which our world will soon interact. Much like the World Wide Web connected computers to networks, and the next evolution mobile devices connected people to the Internet and other people, IoT looks poised to interconnect devices, people, environments, virtual objects and machines in ways that only science fiction writers could have imagined.