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The results of computational model simulations allow researchers and clinicians to make predictions about what will happen in the biological systems that are being studied in response to changing conditions for a disease or disorder. With a well-developed computational model, researchers and clinicians can better understand the cause of a disease or a disorder and predict treatment results. Computational Models for Biomedical Reasoning and Problem Solving is a critical scholarly publication that provides insightful strategies to developing computational models that allow for the better understanding and treatment of various diseases and disorders. Featuring topics such as biomedicine, neuroscience, and artificial intelligence, this book is ideal for practitioners, clinicians, researchers, psychologists, and engineers.
The many drawbacks of conventional dosage forms and delivery systems are overcome by designing and developing controlled release drug delivery systems, and pharmaceutical and other scientists have carried out extensive and intensive investigations in the field to explore their applications. A controlled-release drug formulation can improve product efficacy and extend patent protection. As controlled drug delivery systems continue to play a vital role in delivering various types of therapeutic agents in a controlled manner, researchers are only just scratching the surface of their full potential. Advancements in Controlled Drug Delivery Systems supplies information on translating the physicochemical properties of drugs into drug delivery systems, explores how drugs are administered via various routes, and discusses recent advancements in the fabrication and development of controlled drug delivery systems. It also underlines the methodology of controlled drug delivery system preparation and the significance, disadvantages, detailed classifications, and relevant examples. Covering topics such as machine learning and oral-controlled drug delivery, this book is ideal for pharmacists, healthcare professionals, researchers, academicians, research centers, health units, students, and pharmaceutical and scientific laboratories.
Low temperature plasma in medicine is a new field that rose from the research in the application of cold plasmas in bioengineering. Plasma medicine is an innovative and promising multidisciplinary novel field of research covering plasma physics, life sciences, and clinical medicine to apply physical plasma for therapeutic applications. Emerging Developments and Applications of Low Temperature Plasma explores all areas of experimental, computational, and theoretical study of low temperature and atmospheric plasmas and provides a collection of exciting new research on the fundamental aspects of low temperature and pressure plasmas and their applications. Covering topics such as carbon nanotubes, foodborne pathogens, and plasma formation, this book is an essential resource for research groups, plasma-based industries, plasma aerodynamics industries, metal and cutlery industries, medical institutions, researchers, and academicians.
Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.
This book features selected papers presented at the 4th International Conference on Recent Innovations in Computing (ICRIC 2021), held on May 8–9, 2021, at the Central University of Jammu, India, and organized by the university’s Department of Computer Science and Information Technology. The book is divided into two volumes, and it includes the latest research in the areas of software engineering, cloud computing, computer networks and Internet technologies, artificial intelligence, information security, database and distributed computing, and digital India.
In studying biology, one of the more difficult factors to predict is how parents’ attributes will affect their children and how those children will affect their own children. Organizing and calculating those vast statistics can become extremely tedious without the proper mathematical and reproductive knowledge. Attractors and Higher Dimensions in Population and Molecular Biology: Emerging Research and Opportunities is a collection of innovative research on the methods and applications of population logistics. While highlighting topics including gene analysis, crossbreeding, and reproduction, this book is ideally designed for academics, researchers, biologists, and mathematicians seeking current research on modeling the reproduction process of a biological population.
Machine learning continues to have myriad applications across industries and fields. To ensure this technology is utilized appropriately and to its full potential, organizations must better understand exactly how and where it can be adapted. Further study on the applications of machine learning is required to discover its best practices, challenges, and strategies. The Research Anthology on Machine Learning Techniques, Methods, and Applications provides a thorough consideration of the innovative and emerging research within the area of machine learning. The book discusses how the technology has been used in the past as well as potential ways it can be used in the future to ensure industries continue to develop and grow. Covering a range of topics such as artificial intelligence, deep learning, cybersecurity, and robotics, this major reference work is ideal for computer scientists, managers, researchers, scholars, practitioners, academicians, instructors, and students.
Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.
Neural Engineering for Autism Spectrum Disorder, Volume One: Imaging and Signal Analysis Techniques presents the latest advances in neural engineering and biomedical engineering as applied to the clinical diagnosis and treatment of Autism Spectrum Disorder (ASD). Advances in the role of neuroimaging, infrared spectroscopy, sMRI, fMRI, DTI, social behaviors and suitable data analytics useful for clinical diagnosis and research applications for Autism Spectrum Disorder are covered, including relevant case studies. The application of brain signal evaluation, EEG analytics, feature selection, and analysis of blood oxygen level-dependent (BOLD) signals are presented for detection and estimation of the degree of ASD. - Presents applications of Neural Engineering and other Machine Learning techniques for the diagnosis of Autism Spectrum Disorder (ASD) - Includes in-depth technical coverage of imaging and signal analysis techniques, including coverage of functional MRI, neuroimaging, infrared spectroscopy, sMRI, fMRI, DTI, and neuroanatomy of autism - Covers Signal Analysis for the detection and estimation of Autism Spectrum Disorder (ASD), including brain signal analysis, EEG analytics, feature selection, and analysis of blood oxygen level-dependent (BOLD) signals for ASD - Written to help engineers, computer scientists, researchers and clinicians understand the technology and applications of Neural Engineering for the detection and diagnosis of Autism Spectrum Disorder (ASD)