Boris Galitsky - Böcker

Artificial Intelligence for Healthcare Applications and Management introduces application domains of various AI algorithms across healthcare management. Instead of discussing AI first and then exploring its applications in healthcare afterward, the authors attack the problems in context directly, in order to accelerate the path of an interested reader toward building industrial-strength healthcare applications. Readers will be introduced to a wide spectrum of AI applications supporting all stages of patient flow in a healthcare facility. The authors explain how AI supports patients throughout a healthcare facility, including diagnosis and treatment recommendations needed to get patients from the point of admission to the point of discharge while maintaining quality, patient safety, and patient/provider satisfaction.

AI methods are expected to decrease the burden on physicians, improve the quality of patient care, and decrease overall treatment costs. Current conditions affected by COVID-19 pose new challenges for healthcare management and learning how to apply AI will be important for a broad spectrum of students and mature professionals working in medical informatics. This book focuses on predictive analytics, health text processing, data aggregation, management of patients, and other fields which have all turned out to be bottlenecks for the efficient management of coronavirus patients.

Presents an in-depth exploration of how AI algorithms embedded in scheduling, prediction, automated support, personalization, and diagnostics can improve the efficiency of patient treatment Investigates explainable AI, including explainable decision support and machine learning, from limited data to back-up clinical decisions, and data analysis Offers hands-on skills to computer science and medical informatics students to aid them in designing intelligent systems for healthcare Informs a broad, multidisciplinary audience about a multitude of applications of machine learning and linguistics across various healthcare fields Introduces medical discourse analysis for a high-level representation of health texts

Healthcare Applications of Neuro-Symbolic Artificial Intelligence provides a comprehensive introduction to the field of neuro-symbolic (NS) artificial intelligence (AI), presenting the most recent advances in deep learning and integration of NS systems and large language models (LLMs). This book evaluates traditional approaches, current approaches, as well as the author’s own approach to NS, to create hybrid architectures and reasoning techniques to overcome the limitations of most existing AI systems such as deep learning, neural networks, and symbolic AI.This book will be a welcome resource for researchers and graduate students in AI, natural language processing, and biomedical informatics, as well as professionals in software development looking to redesign current systems to leverage LLMs through the health application of NS architecture.Presents a comprehensive introduction to the field of neuro-symbolic AI in health, explaining hybrid architectures and reasoning techniquesProvides hands-on training in navigating deep learning resources and APIs, including HuggingFace and GitHub, as well as reasoning systems like PrologDescribes buildable, scalable neuro-symbolic systems that can perform search, recommendation, content writing, diagnosis, treatment planning, and educational support

A chatbot is expected to be capable of supporting a cohesive and coherent conversation and be knowledgeable, which makes it one of the most complex intelligent systems being designed nowadays. Designers have to learn to combine intuitive, explainable language understanding and reasoning approaches with high-performance statistical and deep learning technologies.  Today, there are two popular paradigms for chatbot construction:1.     Build a bot platform with universal NLP and ML capabilities so that a bot developer  for a particular enterprise, not being an expert, can populate it with training data;2.    Accumulate a huge set of training dialogue data, feed it to a deep learning network and expect the trained chatbot to automatically learn “how to chat”. Although these two approaches are reported to imitate some intelligent dialogues, both of them are unsuitable for enterprise chatbots, being unreliable and too brittle.The latter approach is based on a belief that some learning miracle will happen and a chatbot will start functioning without a thorough feature and domain engineering by an expert and interpretable dialogue management algorithms.Enterprise high-performance chatbots with extensive domain knowledge require a mix of statistical, inductive, deep machine learning and learning from the web, syntactic, semantic and discourse NLP, ontology-based reasoning and a state machine to control a dialogue. This book will provide a comprehensive source of algorithms and architectures for building chatbots for various domains based on the recent trends in computational linguistics and machine learning. The foci of this book are applications of discourse analysis in text relevant assessment, dialogue management and content generation, which help to overcome the limitations of platform-based and data driven-based approaches.Supplementary material and code is available at https://github.com/bgalitsky/relevance-based-on-parse-trees

This research monograph brings AI to the field of Customer Relationship Management (CRM) to make a customer experience with a product or service smart and enjoyable.

This research monograph brings AI to the field of Customer Relationship Management (CRM) to make a customer experience with a product or service smart and enjoyable.

The second volume of this research monograph describes a number of applications of Artificial Intelligence in the field of Customer Relationship Management with the focus of solving customer problems.

The second volume of this research monograph describes a number of applications of Artificial Intelligence in the field of Customer Relationship Management with the focus of solving customer problems.

This book offers a bold vision for the future of artificial intelligence, showing how the next wave of innovation will blend the power of large language models with the rigor of logical reasoning and human-like understanding. Instead of settling for systems that simply generate text, it explores how AI can be designed to think, engage, and interact in ways that feel meaningful, personal, and trustworthy.Readers will discover how AI can move beyond common pitfalls like hallucinations through smarter frameworks that combine reinforcement learning and symbolic logic. They’ll see how new approaches make AI more empathetic, more explainable, and better at understanding human intent. From copilots that supercharge social media promotion to next-generation recommendation engines for Web 3.0, the book highlights real-world applications that bring these ideas to life. It also dives into AI’s role in creativity and industry, showing how it can craft compelling ad campaigns, spark invention dialogues, and even support specific fields like chemical engineering. Throughout, the focus is on building AI that doesn’t just serve as a tool, but as a partner—enhancing decision-making, supporting innovation, and opening new opportunities for human–machine collaboration.The book is a must-read for anyone curious about where AI is heading next.

This book explores and evaluates accounts and models of autistic reasoning and cognition from a computational standpoint. The author investigates the limitations and peculiarities of autistic reasoning and sets out a remediation strategy to be used by a wide range of psychologists and rehabilitation personnel and will also be appreciated by computer scientists who are interested in the practical implementation of reasoning.The author subjects the Theory of Mind (ToM) model to a formal analysis to investigate the limitations of autistic reasoning and proposes a formal model regarding mental attitudes and proposes a method to help those with autism navigate everyday living. Based on the concept of playing with computer based mental simulators, the NL_MAMS, is examined to see whether it is capable of modeling mental and emotional states of the real world to aid the emotional development of autistic children. Multiple autistic theories and strategies are also examined for possible computational cross-overs, providing researchers with a wide range of examples, tools and detailed case studies to work from. Computational Autism will be an essential read to behavioral specialists, researcher’s, developers and designers who are interested in understanding and tackling the increasing prevalence of autism within modern society today.