The digital transformation of healthcare delivery is in full swing. Health monitoring is increasingly becoming more effective, efficient, and timely through mobile devices that are now widely available. This, as well as wireless technology, is essential to assessing, diagnosing, and treating medical ailments. However, systems and applications that boost wellness must be properly designed and regulated in order to protect the patient and provide the best care. Optimizing Health Monitoring Systems With Wireless Technology is an essential publication that focuses on critical issues related to the design, development, and deployment of wireless technology solutions for healthcare and wellness. Highlighting a broad range of topics including solution evaluation, privacy and security, and policy and regulation, this book is ideally designed for clinicians, hospital directors, hospital managers, consultants, health IT developers, healthcare providers, engineers, software developers, policymakers, researchers, academicians, and students.

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

Demographics reveal that the proportion of elderly individuals in the population is growing at a significant rate. Advances in medicine have allowed populations to live longer than ever; however, ensuring that these individuals have the tools necessary to sustain a productive and happy lifestyle as they age remains a concern. Optimizing Assistive Technologies for Aging Populations focuses on the development and improvement of devices intended to assist elderly individuals in coping with various physical limitations and disabilities. Highlighting the available tools and technologies for supporting the mobility, agility, and self-sufficiency of the aging population as well as the challenges associated with the integration of these technologies into the everyday lives of elderly individuals, this publication is ideally designed for reference use by healthcare workers, medical students, gerontologists, and IT developers in the field of medicine.

Multidrug-resistant bacteria play a significant role in public health by destroying the potency of existing antibiotics. Meanwhile, cancer remains one of the most common health problems that impact society, resulting in many deaths worldwide. Novel strategies are required to combat antimicrobial resistance and create efficient anticancer drugs that could revolutionize treatment. Nanomedicine is one such innovation that plays a significant role in developing alternative and more effective treatment strategies for antimicrobial resistance and cancer theranostics. The Handbook of Research on Nano-Strategies for Combatting Antimicrobial Resistance and Cancer is an essential scholarly resource that examines (1) how to overcome the existing, traditional approaches to combat antimicrobial resistance and cancer; (2) how to apply multiple mechanisms to target the cancer cells and microbes; and (3) how the nanomaterials can be used as carriers. Featuring a range of topics such as bacteriophage, nanomedicine, and oncology, this book is ideal for molecular biologists, microbiologists, nanotechnologists, academicians, chemists, pharmacists, oncologists, researchers, healthcare professionals, and students.

Cancer continues to be a growing problem as it is the foremost cause of death worldwide, killing millions of people each year. The number of people battling cancer continues to increase, owing to different reasons, such as lifestyle choices. Clinically, determining the cause of cancer is very challenging and often inaccurate. Incorporating efficient and accurate algorithms to detect cancer cases is becoming increasingly beneficial for scientists in computer science and healthcare, as well as a long-term benefit for doctors, patients, clinic practitioners, and more. Specifically, an automation of computation in machine learning could be a solution in the next generation of big data science technology. Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis presents algorithms that have been developed to evaluate big data approaches and cancer research. The chapters include artificial intelligence and machine learning approaches, as well as case studies to solve the predictive issues in colon cancer research. This book includes concepts and techniques used to run tasks in an automated manner with the intent to improve better accuracy in comparison with previous studies and methods. This book also covers the processes of research design, development, and outcome analytics in this field. Doctors, IT consultants, IT specialists, medical software professionals, data scientists, researchers, computer scientists, healthcare practitioners, academicians, and students can benefit from this critical resource.

SMART AND SUSTAINABLE APPROACHES FOR OPTIMIZING PERFORMANCE OF WIRELESS NETWORK Explores the intersection of sustainable growth, green computing and automation, and performance optimization of 5G wireless networks Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks explores how wireless sensing applications, green computing, and Big Data analytics can increase the energy efficiency and environmental sustainability of real-time applications across areas such as healthcare, agriculture, construction, and manufacturing. Bringing together an international team of expert contributors, this authoritative volume highlights the limitations of conventional technologies and provides methodologies and approaches for addressing Quality of Service (QOS) issues and optimizing network performance. In-depth chapters cover topics including blockchain-assisted secure data sharing, smart 5G Internet of Things (IoT) scenarios, intelligent management of ad hoc networks, and the use of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) techniques in smart healthcare, smart manufacturing, and smart agriculture. Covers design, implementation, optimization, and sustainability of wireless and sensor-based networks Discusses concepts of sustainability and green computing as well as their relevance to society and the environment Addresses green automation applications in various disciplines such as computer science, nanoscience, information technology (IT), and biochemistry Explores various smart and sustainable approaches for current wireless and sensor-based networks Includes detailed case studies of current methodologies, applications, and implementations Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks: Real-time Applications is an essential resource for academic researchers and industry professionals working to integrate sustainable development and Information and Communications Technology (ICT).

Recent advancements in the technology of medical imaging, such as CT and MRI scanners, are making it possible to create more detailed 3D and 4D images. These powerful images require vast amounts of digital data to help with the diagnosis of the patient. Artificial intelligence (AI) must play a vital role in supporting with the analysis of this medical imaging data, but it will only be viable as long as healthcare professionals and AI interact to embrace deep thinking platforms such as automation in the identification of diseases in patients. AI Innovation in Medical Imaging Diagnostics is an essential reference source that examines AI applications in medical imaging that can transform hospitals to become more efficient in the management of patient treatment plans through the production of faster imaging and the reduction of radiation dosages through the PET and SPECT imaging modalities. The book also explores how data clusters from these images can be translated into small data packages that can be accessed by healthcare departments to give a real-time insight into patient care and required interventions. Featuring research on topics such as assistive healthcare, cancer detection, and machine learning, this book is ideally designed for healthcare administrators, radiologists, data analysts, computer science professionals, medical imaging specialists, diagnosticians, medical professionals, researchers, and students.