Surekha Borra – författare

U-Healthcare Monitoring Systems: Volume One: Design and Applications focuses on designing efficient U-healthcare systems which require the integration and development of information technology service/facilities, wireless sensors technology, wireless communication tools, and localization techniques, along with health management monitoring, including increased commercialized service or trial services. These u-healthcare systems allow users to check and remotely manage the health conditions of their parents. Furthermore, context-aware service in u-healthcare systems includes a computer which provides an intelligent service based on the user's different conditions by outlining appropriate information relevant to the user's situation.

This volume will help engineers design sensors, wireless systems and wireless communication embedded systems to provide an integrated u-healthcare monitoring system. This volume provides readers with a solid basis in the design and applications of u-healthcare monitoring systems.

Provides a solid basis in the design and applications of the u-healthcare monitoring systems Illustrates the concept of the u-healthcare monitoring system and its requirements, with a specific focus on the medical sensors and wireless sensors communication Presents a multidisciplinary volume that includes different applications of the monitoring system which highlight the main features for biomedical sensor devices

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented.

The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers.

Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains

The Book presents an overview of newly developed watermarking techniques in various independent and hybrid domains Covers the basics of digital watermarking, its types, domain in which it is implemented and the application of machine learning algorithms onto digital watermarking Reviews hardware implementation of watermarking Discusses optimization problems and solutions in watermarking with a special focus on bio-inspired algorithms Includes a case study along with its MATLAB code and simulation results

Big data and the Internet of Things (IoT) play a vital role in prediction systems used in biological and medical applications, particularly for resolving issues related to disease biology at different scales. Modelling and integrating medical big data with the IoT helps in building effective prediction systems for automatic recommendations of diagnosis and treatment. The ability to mine, process, analyse, characterize, classify and cluster a variety and wide volume of medical data is a challenging task. There is a great demand for the design and development of methods dealing with capturing and automatically analysing medical data from imaging systems and IoT sensors. Addressing analytical and legal issues, and research on integration of big data analytics with respect to clinical practice and clinical utility, architectures and clustering techniques for IoT data processing, effective frameworks for removal of misclassified instances, practicality of big data analytics, methodological and technical issues, potential of Hadoop in managing healthcare data is the need of the hour. This book integrates different aspects used in the field of healthcare such as big data, IoT, soft computing, machine learning, augmented reality, organs on chip, personalized drugs, implantable electronics, integration of bio-interfaces, and wearable sensors, devices, practical body area network (BAN) and architectures of web systems.Key Features: Addresses various applications of Medical Big Data and Internet of Medical Things in real time environment Highlights recent innovations, designs, developments and topics of interest in machine learning techniques for classification of medical data Provides background and solutions to existing challenges in Medical Big Data and Internet of Medical Things Provides optimization techniques and programming models to parallelize the computationally intensive tasks in data mining of medical data Discusses interactions, advantages, limitations, challenges and future perspectives of IoT based remote healthcare monitoring systems. Includes data privacy and security analysis of cryptography methods for the Web of Medical Things (WoMT) Presents case studies on the next generation medical chair, electronic nose and pill cam are also presented.

The Book presents an overview of newly developed watermarking techniques in various independent and hybrid domains Covers the basics of digital watermarking, its types, domain in which it is implemented and the application of machine learning algorithms onto digital watermarking Reviews hardware implementation of watermarking Discusses optimization problems and solutions in watermarking with a special focus on bio-inspired algorithms Includes a case study along with its MATLAB code and simulation results

Big data and the Internet of Things (IoT) play a vital role in prediction systems used in biological and medical applications, particularly for resolving issues related to disease biology at different scales. Modelling and integrating medical big data with the IoT helps in building effective prediction systems for automatic recommendations of diagnosis and treatment. The ability to mine, process, analyse, characterize, classify and cluster a variety and wide volume of medical data is a challenging task. There is a great demand for the design and development of methods dealing with capturing and automatically analysing medical data from imaging systems and IoT sensors. Addressing analytical and legal issues, and research on integration of big data analytics with respect to clinical practice and clinical utility, architectures and clustering techniques for IoT data processing, effective frameworks for removal of misclassified instances, practicality of big data analytics, methodological and technical issues, potential of Hadoop in managing healthcare data is the need of the hour. This book integrates different aspects used in the field of healthcare such as big data, IoT, soft computing, machine learning, augmented reality, organs on chip, personalized drugs, implantable electronics, integration of bio-interfaces, and wearable sensors, devices, practical body area network (BAN) and architectures of web systems.Key Features: Addresses various applications of Medical Big Data and Internet of Medical Things in real time environment Highlights recent innovations, designs, developments and topics of interest in machine learning techniques for classification of medical data Provides background and solutions to existing challenges in Medical Big Data and Internet of Medical Things Provides optimization techniques and programming models to parallelize the computationally intensive tasks in data mining of medical data Discusses interactions, advantages, limitations, challenges and future perspectives of IoT based remote healthcare monitoring systems. Includes data privacy and security analysis of cryptography methods for the Web of Medical Things (WoMT) Presents case studies on the next generation medical chair, electronic nose and pill cam are also presented.

The integration of artificial intelligence (AI) with Internet of Things (IoT), known as AIoT. It is recognized for its transformative potential across various sectors by enhancing automation, efficiency, and decision-making capabilities. The book delves into the technological, human, and organizational dimensions of deploying AIoT systems, highlighting the importance of sociotechnical readiness and human-centric approaches to ensure sustainable and ethical business transformation. The book discusses key insights emphasizing the need for integrated sociotechnical thinking and human-centric strategies in AIoT deployment. It calls on business leaders, technologists, and policymakers to collaborate in shaping an AIoT-powered future that is sustainable, inclusive, and ethically grounded.

Intelligent prediction and decision support systems　 are based on signal processing, computer vision (CV), machine learning (ML), software engineering (SE), knowledge based systems (KBS), data mining, artificial intelligence (AI) and 　include several systems developed from the study of expert systems (ES), genetic algorithms (GA), artificial neural networks (ANN) and fuzzy-logic systems The use of automatic decision support systems in design and manufacturing industry, healthcare and commercial software development systems has the following benifits: Cost savings in companies, due to employment of expert system technology. Fast decision making, completion of projects in time and development of new products. Improvement in decision making capability and quality. Usage of Knowledge database and Preservation of expertise of individuals Eases complex decision problems. Ex: Diagnosis in Healthcare To address the issues and challenges related to development, implementation and application of automatic and intelligent prediction and decision support systems in domains such as manufacturing, healthcare and software product design, development and optimization, this book aims to collect and publish wide ranges of quality articles such as original research contributions, methodological reviews, survey papers, case studies and/or reports covering intelligent systems, expert prediction systems, evaluation models, decision support systems and Computer Aided Diagnosis (CAD).

This book on classification in biomedical image applications presents original and valuable research work on advances in this field, which covers the taxonomy of both supervised and unsupervised models, standards, algorithms, applications and challenges.Further, the book highlights recent scientific research on artificial neural networks in biomedical applications, addressing the fundamentals of artificial neural networks, support vector machines and other advanced classifiers, as well as their design and optimization.In addition to exploring recent endeavours in the multidisciplinary domain of sensors, the book introduces readers to basic definitions and features, signal filters and processing, biomedical sensors and automation of biomeasurement systems. The target audience includes researchers and students at engineering and medical schools, researchers and engineers in the biomedical industry, medical doctors and healthcare professionals.

The performance of the proposed method is evaluated using various transform bases like Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and Fast Discrete Curvelet Transform (FDCuT).

This book on classification in biomedical image applications presents original and valuable research work on advances in this field, which covers the taxonomy of both supervised and unsupervised models, standards, algorithms, applications and challenges.Further, the book highlights recent scientific research on artificial neural networks in biomedical applications, addressing the fundamentals of artificial neural networks, support vector machines and other advanced classifiers, as well as their design and optimization.In addition to exploring recent endeavours in the multidisciplinary domain of sensors, the book introduces readers to basic definitions and features, signal filters and processing, biomedical sensors and automation of biomeasurement systems. The target audience includes researchers and students at engineering and medical schools, researchers and engineers in the biomedical industry, medical doctors and healthcare professionals.

This book introduces medical imaging, its security requirements, and various security mechanisms using data hiding approaches. The book in particular provides medical data hiding techniques using various advanced image transforms and encryption methods. The book focuses on two types of data hiding techniques: steganography and watermarking for medical images. The authors show how these techniques are used for security and integrity verification of medical images and designed for various types of medical images such as grayscale image and color image. The implementation of techniques are done using discrete cosine transform (DCT), discrete wavelet transform (DWT), singular value decomposition (SVD), redundant DWT (RDWT), fast discrete curvelet transform (FDCuT), finite ridgelet transform (FRT) and non-subsampled contourlet transform (NSCT). The results of these techniques are also demonstrated after description of each technique. Finally, some future research directions are provided forsecurity of medical images in telemedicine application.