Wellington Pinheiro Dos Santos – författare

Mining Biomedical Text, Images and Visual Features for Information Retrieval provides broad coverage of the concepts, themes, and instrumentalities of the important, evolving area of biomedical text, images, and visual features towards information retrieval. The book aims to encourage an even wider adoption of IR methods for assisting in problem-solving and to stimulate research that may lead to additional innovations in this area of research. Topics covered include Internet of Things for health informatics; data privacy; smart healthcare; medical image processing; 3D medical images; evolutionary computing; deep learning; medical ontology; linguistic indexing; lexical analysis; and domain specific semantic categories in biomedical applications. This is a valuable resource for researchers and graduate students who are interested to learn more about data mining techniques to improve their research work.Describes many biomedical imaging techniques to detect diseases at the cellular level i.e., image segmentation, classification, or image indexing using a variety of computational intelligence and image processing approachesDiscusses how data mining techniques can be used for noise diminution and filtering MRI, EEG, MEG, fMRI, fNIRS, and PET ImagesPresents text mining techniques used for clinical documents in the areas of medicine and Biomedical NLP Systems

This book presents the fundamentals of swarm intelligence, from classic algorithms to emerging techniques. It presents comprehensive theoretical foundations and examples using the main Computational Intelligence methods in programming languages ​​such as Python, Java and MATLAB®. Real-world applications are also presented in areas as diverse as Medicine, Biology and industrial applications.The book is organized into two parts. The first part provides an introduction to swarming algorithms and hybrid techniques. In the second part, real world applications of swarm intelligence are presented to illustrate how swarm algorithms can be used in applications of optimization and pattern recognition, reviewing the principal methods and methodologies in swarm intelligence.

This book presents the fundamentals of swarm intelligence, from classic algorithms to emerging techniques. It presents comprehensive theoretical foundations and examples using the main Computational Intelligence methods in programming languages ​​such as Python, Java and MATLAB®. Real-world applications are also presented in areas as diverse as Medicine, Biology and industrial applications.The book is organized into two parts. The first part provides an introduction to swarming algorithms and hybrid techniques. In the second part, real world applications of swarm intelligence are presented to illustrate how swarm algorithms can be used in applications of optimization and pattern recognition, reviewing the principal methods and methodologies in swarm intelligence.

This book presents the theoretical basis and applications of biomedical signal analysis and processing. Initially, the nature of the most common biomedical signals, such as electroencephalography, electromyography, electrocardiography and others, is described. The theoretical basis of linear signal processing is summarized, with continuous and discrete representation, linear filters and convolutions, Fourier and Wavelets transforms. Machine learning concepts are also presented, from classic methods to deep neural networks. Finally, several applications in neuroscience are presented and discussed, involving diagnosis and therapy, in addition to other applications. Features: Explains signal processing of neuroscience applications using modern data science techniques. Provides comprehensible review on biomedical signals nature and acquisition aspects. Focusses on selected applications of neurosciences, cardiovascular and muscle-related biomedical areas. Includes computational intelligence, machine learning and biomedical signal processing and analysis. Reviews theoretical basis of deep learning and state-of-the-art biomedical signal processing and analysis. This book is aimed at researchers, graduate students in biomedical signal processing, signal processing, electrical engineering, neuroscience and computer science.

This book presents the theoretical basis and applications of biomedical signal analysis and processing. Initially, the nature of the most common biomedical signals, such as electroencephalography, electromyography, electrocardiography and others, is described. The theoretical basis of linear signal processing is summarized, with continuous and discrete representation, linear filters and convolutions, Fourier and Wavelets transforms. Machine learning concepts are also presented, from classic methods to deep neural networks. Finally, several applications in neuroscience are presented and discussed, involving diagnosis and therapy, in addition to other applications. Features: Explains signal processing of neuroscience applications using modern data science techniques. Provides comprehensible review on biomedical signals nature and acquisition aspects. Focusses on selected applications of neurosciences, cardiovascular and muscle-related biomedical areas. Includes computational intelligence, machine learning and biomedical signal processing and analysis. Reviews theoretical basis of deep learning and state-of-the-art biomedical signal processing and analysis. This book is aimed at researchers, graduate students in biomedical signal processing, signal processing, electrical engineering, neuroscience and computer science.

Advanced Electroencephalography Analytical Methods: Fundamentals, Acquisition, and Applications presents the theoretical basis and applications of electroencephalography (EEG) signals in neuroscience, involving signal analysis, processing, signal acquisition, representation, and applications of EEG signal analysis using non-linear approaches and machine learning. It explains principles of neurophysiology, linear signal processing, computational intelligence, and the nature of signals including machine learning. Applications involve computer-aided diagnosis, brain-computer interfaces, rehabilitation engineering, and applied neuroscience.This book:Includes a comprehensive review on biomedical signals nature and acquisition aspectsFocuses on selected applications of neuroscience/cardiovascular/muscle-related biomedical areasProvides a machine learning update to a classical biomedical signal processing approachExplains deep learning and application to biomedical signal processing and analysisExplores relevant biomedical engineering and neuroscience state-of-the-art applicationsThis book is intended for researchers and graduate students in biomedical signal processing, electrical engineering, neuroscience, and computer science.

"Biomedical Imaging: Principles and Advancements" offers a captivating exploration of the intricate landscapes within the human body, revealing the transformative power of biomedical imaging. Edited by Wellington Pinheiro dos Santos, Juliana Carneiro Gomes, Maíra Araújo de Santana, and Clarisse Lins de Lima, this anthology delves into foundational concepts, from acquisition to ethical considerations, paving the way for in-depth examinations of magnetic resonance imaging, infrared thermography, and electrical impedance tomography. The real-world applications covered in Section II, from Alzheimer's diagnosis to Covid-19 assessment, showcase the diverse impact of these imaging techniques on healthcare. A collective effort, this volume inspires continued exploration in the ever-evolving field of biomedical imaging.

The fourth industrial revolution challenges humanity ethically and morally: mass unemployment, new forms of colonialism, and mass-and-granular surveillance are a few examples of these challenges. Nevertheless, the industrial revolutions have increased human productivity and quality of life. This book aims to review the ethical challenges related to the use of these technologies. It unfolds bioethical perspectives regarding Artificial Intelligence (AI) and its impact on life on earth. It discusses both northern and southern epistemologies of bioethics. Northern bioethics comprises principles of autonomy, beneficence, non-maleficence, and justice. Southern bioethics gives weightage to struggles for human liberation, social justice, and the pluralism of knowledge. The book discusses topics from aging to mass surveillance, to deliver a universal bioethical guideline to a wide range of professions that work with AI and are concerned about its impact on life. This book will not label AI, but broaden the readers' view of an ethical and explainable AI that works for life on earth.

The book explores the prevalence of ASD and the challenges associated with its early detection. Recognizing the limitations of existing diagnostic methods, the volume emphasizes the need for a multidisciplinary approach, utilizing the collective strengths of artificial intelligence (AI), biomedical engineering, and applied neuroscience. This convergence promises not only to enhance diagnostic accuracy but also to streamline the process, facilitating timely interventions for improved treatment. Key Features:Illustrates the latest advancements in AI, biomedical engineering, and applied neuroscience, providing readers with a comprehensive overview of cutting-edge technologies in autism detection.Integrates diverse perspectives from leading experts, merging the fields of AI, biomedical engineering, and neuroscience to present a unified and multidisciplinary approach to autism diagnosis.Demonstrates the practical applications of innovative diagnostic tools, from machine learning algorithms to biomedical devices, offering real-world insights and case studies for effective implementation.Explores the future directions of autism detection, discussing emerging technologies and ethical considerations. Guides readers through a journey of discovery, unraveling the complexities of autism spectrum disorders, and empowering healthcare professionals, researchers, and students with actionable knowledge for enhanced diagnosis and support.