Bentham Science Publishers

Intelligent Computational Systems presents current and future developments in intelligent computational systems in a multi-disciplinary context. Readers will learn about the pervasive and ubiquitous roles of artificial intelligence (AI) and gain a perspective about the need for intelligent systems to behave rationally when interacting with humans in complex and realistic domains.

This reference covers widespread applications of AI discussed in 11 chapters which cover topics such as AI and behavioral simulations, AI schools, automated negotiation, language analysis and learning, financial prediction, sensor management, Multi-agent systems, and much more.

This reference work is will assist researchers, advanced-level students and practitioners in information technology and computer science fields interested in the broad applications of AI.

Intelligent Systems for IoE Based Smart Cities provides simplified information about complexities of cyber physical systems, the Internet of Everything (IoE) and smart city infrastructure. It presents 11 edited chapters that reveal how intelligent systems and IoE are driving the evolution of smart cities, making them more efficient, interconnected, and responsive to the needs of citizens.

The book content represents comprehensive exploration of the transformative potential and challenges of IoE-based smart cities, fueled by Artificial Intelligence (AI) and Machine Learning (ML) innovations.

Key Topics:

Physical layer design considerations that underpin smart city infrastructure

Enabling technologies for intelligent systems within the context of smart computing environments

Smart sensors and actuators, their applications, challenges, and future trends in IoE-based smart cities

Applications, enabling technologies, challenges, and future trends of IoE for smart cities.

The integration of Artificial Intelligence, Natural Language Processing, and smart cities for enhanced urban experiences

machine learning-based intrusion detection techniques for countering attacks on the Internet of Vehicles

Smartphone-based indoor positioning applications using trilateration and the role of sensors in IoT ecosystems

IoT, blockchain, and cloud-based technology for secure frameworks and data analytics

Blockchain and smart contracts in shaping the future of smart cities.

This is a timely reference for researchers, professionals, and students interested in the convergence of IoT, intelligent systems and urban studies into smart city planning and design.

This volume explores diverse applications for automated machine learning and predictive analytics. The content provides use cases for machine learning in different industries such as healthcare, agriculture, cybersecurity, computing and transportation.

Key highlights of this volume include topics on engineering for underwater navigation, and computer vision for healthcare and biometric applications.

Chapters 1-4 delve into innovative signal detection, biometric authentication, underwater AUV localization, and COVID-19 face mask detection. Chapters 5-9 focus on wireless pH sensing, differential pattern identification, economic considerations in off-grid hybrid power, high optimization of image transmission, and ANN-based IoT-bot traffic detection. Chapters 10-12 cover mixed-signal VLSI design, pre-placement 3D floor planning, and bio-mimic robotic fish. Finally, Chapters 13 and 14 explore underwater robotic fish and IoT-based automatic irrigation systems, providing a comprehensive overview of cutting-edge technological advancements.

The book is a resource for academics, researchers, educators and professionals in the technology sector who want to learn about current trends in intelligent technologies.

Interest Rate Modeling for Risk Management introduces a theoretical framework - the ‘real-world’ model - that allows us to estimate the market price of interest rate risk based on practical and real life situations. The model can be briefly summarized as a process of estimating the market prices of risk through discretization of forward rates with a ‘space-state setup’ whilst considering historical data trends. The book starts with a brief explanation of interest rate stochastic analysis fundamentals before delving into standard models such as Heath-Jarrow-Morton, Hull-White and LIBOR models. The real-world model is then explained in subsequent chapters while applying different frameworks. Additionally, the book also explains some properties of the real-world model, along with the negative price tendency of the market price for risk and a positive market price for risk (with an example of this actually occurring). Readers will also find a handy appendix with proofs to complement the numerical methods explained in the book.

This book is intended as a primer for practitioners in financial institutions involved in interest rate risk management. It also presents a new perspective for researchers and graduates in econometrics and finance on the study of interest rate models.

International Journal of Sensors, Wireless Communications and Control publishes timely research articles, full-length/ mini reviews and communications on these three strongly related areas, with emphasis on networked control systems whose sensors are interconnected via wireless communication networks.
The emergence of high speed wireless network technologies allows a cluster of devices to be linked together economically to form a distributed system. Wireless communication is playing an increasingly important role in such distributed systems. Transmitting sensor measurements and control commands over wireless links allows rapid deployment, flexible installation, fully mobile operation and prevents the cable wear and tear problem in industrial automation, healthcare and environmental assessment. Wireless networked systems has raised and continues to raise fundamental challenges in the fields of science, engineering and industrial applications, hence, more new modelling techniques, problem formulations and solutions are required.

With the explosion of video and image data available on the Internet, desktops and mobile devices, multimedia search has gained immense importance. Moreover, mining semantics and other useful information from large-scale multimedia data to facilitate online and local multimedia content analysis, search, and other related applications has also gained an increasing attention from the academia and industry. The rapid increase of multimedia data has brought new challenges to multimedia content analysis and multimedia retrieval, especially in terms of scalability. While on the other hand, large-scale multimedia data has also provided new opportunities to address these challenges and other conventional problems in multimedia analysis. The massive associated metadata, context and social information available on the Internet, desktops and mobile devices, and the large number of grassroots users, are a valuable resource that could be leveraged to solve the these difficulties. This is the first reference book on the subject of internet multimedia search and mining and it will be extremely useful for graduates, researchers and working professionals in the field of information technology and multimedia content analysis.

Introduction to Advanced Soft Robotics is an introductory textbook on soft body robotics. The content is designed to enable readers to better understand soft body robotics. Starting with an introduction to the subject, contents explain fundamental concepts such as perception and sensing, fabrication techniques and material design. These introductory chapters demonstrate the design concept and related design structures of soft robots from multiple perspectives, which can provide considerable design references for robotics learners and enthusiasts. Next, the book explains modeling and control for soft robotics and the applications. Key features of this book include easy-to-understand language and format, simple illustrations and a balanced overview of the subject (including a section on challenges and future prospects for soft robotics), and scientific references.

Introduction to Computer Data Representation introduces readers to the representation of data within computers. Starting from basic principles of number representation in computers, the book covers the representation of both integer and floating point numbers, and characters or text. It comprehensively explains the main techniques of computer arithmetic and logical manipulation. The book also features chapters covering the less usual topics of basic checksums and ‘universal’ or variable length representations for integers, with additional coverage of Gray Codes, BCD codes and logarithmic representations. The description of character coding includes information on both MIME and Unicode formats. Introduction to Computer Data Representation also includes historical aspects of data representation, explaining some of the steps that developers took (and the mistakes they made) that led to the present, well-defined and accepted standards of data representation techniques. The book serves as a primer for advanced computer science graduates and a handy reference for anyone wanting to learn about numbers and data representation in computers.

Machine learning is a subfield of artificial intelligence, broadly defined as a machine's capability to imitate intelligent human behavior. Like humans, machines become capable of making intelligent decisions by learning from their past experiences. Machine learning is being employed in many applications, including fraud detection and prevention, self-driving cars, recommendation systems, facial recognition technology, and intelligent computing. This book helps beginners learn the art and science of machine learning. It presens real-world examples that leverage the popular Python machine learning ecosystem,

The topics covered in this book include machine learning basics: supervised and unsupervised learning, linear regression and logistic regression, Support Vector Machines (SVMs). It also delves into special topics such as neural networks, theory of generalisation, and bias and fairness in machine learning. After reading this book, computer science and engineering students - at college and university levels - will receive a complete understanding of machine learning fundamentals and will be able to implement neural network solutions in information systems, and also extend them to their advantage.

The definition of occupational health hazards is constantly changing over time as technology advances and as the awareness of work place safety issues continues to grow. Introduction to Occupational Health Hazards focuses on work-related hazards which have evolved with the advent of more recent professions. The book introduces readers to the basic concept of occupational hazards. From this starting point, readers are introduced to the types of medical disorders that can occur as a result of occupational hazards, such as cancer and neurological disorders. The next section explains health hazards to medical and laboratory staff. The book concludes with a chapter that explains mental health issues (stress and psychosocial factors) that are related to occupational health – which is a recent addition to the spectrum of health risks in the working environment. As a handbook, the book provides information about occupational health risks for science and medical students along with professionals working in the health care and laboratory industries, respectively.