Advancing Artificial Intelligence through Biological Process Applications


Book Description

As science continues to advance, researchers are continually gaining new insights into the way living beings behave and function, and into the composition of the smallest molecules. Most of these biological processes have been imitated by many scientific disciplines with the purpose of trying to solve different problems, one of which is artificial intelligence. Advancing Artificial Intelligence through Biological Process Applications presents recent advances in the study of certain biological processes related to information processing that are applied to artificial intelligence. Describing the benefits of recently discovered and existing techniques to adaptive artificial intelligence and biology, this book will be a highly valued addition to libraries in the neuroscience, molecular biology, and behavioral science spheres.




Artificial Intelligence in Healthcare


Book Description

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data




Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications


Book Description

"This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro-psychological models and computational models"--




Artificial Intelligence in Drug Discovery


Book Description

Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.




Advanced AI Techniques and Applications in Bioinformatics


Book Description

The advanced AI techniques are essential for resolving various problematic aspects emerging in the field of bioinformatics. This book covers the recent approaches in artificial intelligence and machine learning methods and their applications in Genome and Gene editing, cancer drug discovery classification, and the protein folding algorithms among others. Deep learning, which is widely used in image processing, is also applicable in bioinformatics as one of the most popular artificial intelligence approaches. The wide range of applications discussed in this book are an indispensable resource for computer scientists, engineers, biologists, mathematicians, physicians, and medical informaticists. Features: Focusses on the cross-disciplinary relation between computer science and biology and the role of machine learning methods in resolving complex problems in bioinformatics Provides a comprehensive and balanced blend of topics and applications using various advanced algorithms Presents cutting-edge research methodologies in the area of AI methods when applied to bioinformatics and innovative solutions Discusses the AI/ML techniques, their use, and their potential for use in common and future bioinformatics applications Includes recent achievements in AI and bioinformatics contributed by a global team of researchers




Advancement of Data Processing Methods for Artificial and Computing Intelligence


Book Description

This book emphasizes the applications of advances in data processing methods for Artificial Intelligence in today's fast-changing world, as well as to serve society through research, innovation, and development in this field. This book is applicable to a wide range of data that contribute to data science concerns and can be used to promote research in this high-potential new field. People's perceptions of the world and how they conduct their lives have changed dramatically as a result of technological advancements. The world has been gripped by technology, and the advances that are being made every day are undeniably transforming the planet. In the domains of Big Data, engineering, and data science, this cutting-edge technology is ready to support us. Artificial intelligence (AI) is a current research topic because it can be applied to a wide range of applications and disciplines to solve complicated problems and find optimal solutions. In research, medicine, technology, and the social sciences, the benefits of AI have already been proven. Data science, also known as pattern analytics and mining, is a technique for extracting useful and relevant information from databases, enabling better decision-making and strategy formulation in a range of fields. As a result of the exponential growth of data in recent years, the combined notions of big data and AI have given rise to many study areas, such as scale-up behaviour from classical algorithms. Furthermore, combining numerous AI technologies from other areas (such as vision, security, control, and biology) in order to build efficient and durable systems that interact in the real world is a new problem. Despite recent improvements in fundamental AI technologies, the integration of these skills into larger, trustworthy, transparent, and maintainable systems is still in its development. Both conceptually and practically, there are a number of unanswered issues.







Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence


Book Description

The International Conference on Intelligent Computing (ICIC) was formed to p- vide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring together researchers and practitioners from both academia and ind- try to share ideas, problems and solutions related to the multifaceted aspects of intelligent computing. ICIC 2008, held in Shanghai, China, September 15–18, 2008, constituted the 4th International Conference on Intelligent Computing. It built upon the success of ICIC 2007, ICIC 2006 and ICIC 2005 held in Qingdao, Kunming and Hefei, China, 2007, 2006 and 2005, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Emerging Intelligent Computing Technology and Applications”. Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology.




Advanced Photonics Methods for Biomedical Applications


Book Description

Advanced photonics methods for biomedical applications give researchers in universities and industries, and clinicians an overview of the novel tools for cancer diagnostics and treatment. This book provides researchers and professionals in the area of biomedical photonics with a toolbox of novel methodologies for biomedical applications, including health diagnostics, cancer detection, and treatment. It covers the theory, modeling, and design of each method, alongside their applications, fabrication, characterization, and measurements in clinical practice. A wide scope of concepts concerning innovative science and technologies of medicine will be covered, providing the readers with the latest research, developments, and technologies. It will also be a valuable resource for students and early-career researchers, alongside those involved in the design of the novel photonics-based techniques for health diagnostics and cancer detection and treatment. Key features • Discusses novel methods of cancer diagnostics and cancer treatment. • Details non and minimally invasive photonics techniques. • Explores the applications of machine learning and artificial intelligence to these novel techniques.




Advanced Mathematical Applications in Data Science


Book Description

Advanced Mathematical Applications in Data Science comprehensively explores the crucial role mathematics plays in the field of data science. Each chapter is contributed by scientists, researchers, and academicians. The 13 chapters cover a range of mathematical concepts utilized in data science, enabling readers to understand the intricate connection between mathematics and data analysis. The book covers diverse topics, including, machine learning models, the Kalman filter, data modeling, artificial neural networks, clustering techniques, and more, showcasing the application of advanced mathematical tools for effective data processing and analysis. With a strong emphasis on real-world applications, the book offers a deeper understanding of the foundational principles behind data analysis and its numerous interdisciplinary applications. This reference is an invaluable resource for graduate students, researchers, academicians, and learners pursuing a research career in mathematical computing or completing advanced data science courses. Key Features: Comprehensive coverage of advanced mathematical concepts and techniques in data science Contributions from established scientists, researchers, and academicians Real-world case studies and practical applications of mathematical methods Focus on diverse areas, such as image classification, carbon emission assessment, customer churn prediction, and healthcare data analysis In-depth exploration of data science's connection with mathematics, computer science, and artificial intelligence Scholarly references for each chapter Suitable for readers with high school-level mathematical knowledge, making it accessible to a broad audience in academia and industry.