Handbook of Decision Support Systems for Neurological Disorders provides readers with complete coverage of advanced computer-aided diagnosis systems for neurological disorders. While computer-aided decision support systems for different medical imaging modalities are available, this is the first book to solely concentrate on decision support systems for neurological disorders. Due to the increase in the prevalence of diseases such as Alzheimer, Parkinson's and Dementia, this book will have significant importance in the medical field. Topics discussed include recent computational approaches, different types of neurological disorders, deep convolution neural networks, generative adversarial networks, auto encoders, recurrent neural networks, and modified/hybrid artificial neural networks. - Includes applications of computer intelligence and decision support systems for the diagnosis and analysis of a variety of neurological disorders - Presents in-depth, technical coverage of computer-aided systems for tumor image classification, Alzheimer's disease detection, dementia detection using deep belief neural networks, and morphological approaches for stroke detection - Covers disease diagnosis for cerebral palsy using auto-encoder approaches, contrast enhancement for performance enhanced diagnosis systems, autism detection using fuzzy logic systems, and autism detection using generative adversarial networks - Written by engineers to help engineers, computer scientists, researchers and clinicians understand the technology and applications of decision support systems for neurological disorders

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies.

The author, a computer science professor diagnosed with terminal cancer, explores his life, the lessons that he has learned, how he has worked to achieve his childhood dreams, and the effect of his diagnosis on him and his family.

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.

"This is a must-have for any researcher in vocational psychology or career counseling, or anyone who wishes to understand the empirical underpinnings of the practice of career counseling." -Mark Pope, EdD College of Education, University of Missouri - St. Louis past president of the American Counseling Association Today's career development professional must choose from a wide array of theories and practices in order to provide services for a diverse range of clients. Career Development and Counseling: Putting Theory and Research to Work focuses on scientifically based career theories and practices, including those derived from research in other disciplines. Driven by the latest empirical and practical evidence, this text offers the most in-depth, far-reaching, and comprehensive career development and counseling resource available. Career Development and Counseling includes coverage of: Major theories of career development, choice, and adjustment Informative research on occupational aspirations, job search success, job satisfaction, work performance, career development with people of color, and women's career development Assessment of interests, needs and values, ability, and other important constructs Occupational classification and sources of occupational information Counseling for school-aged youth, diverse populations, choice-making, choice implementation, work adjustment, and retirement Special needs and applications including those for at-risk, intellectually talented, and work-bound youth; people with disabilities; and individuals dealing with job loss, reentry, and career transitions Edited by two of the leading figures in career development, and featuring contributions by many of the most well-regarded specialists in the field, Career Development and Counseling: Putting Theory and Research to Work is the one book that every career counselor, vocational psychologist, and serious student of career development must have.

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.