Sandeep Kumar Mathivanan – författare

Biomedical engineering is a rapidly growing interdisciplinary area that is providing solutions to biological and medical problems and improving the healthcare system. It is connected to various applications like protein structure prediction, computer-aided drug design, and computerized medical diagnosis based on image and signal data, which accomplish low-cost, accurate, and reliable solutions for improving healthcare services. With the recent advancements, machine learning (ML) and deep learning (DL) techniques are widely used in biomedical engineering to develop intelligent decision-making healthcare systems in real-time. However, accuracy and reliability in model performance can be a concern in tackling data generated from medical images and signals, making it challenging for researchers and practitioners. Therefore, optimized models can produce quality healthcare services to handle the complexities involved in biomedical research.Various optimization techniques have been employed to optimize parameters, hyper-parameters, and architectural information of ML/DL models explicitly applied to biological, medical, and signal data. The swarm intelligence approach has the potential to solve complex non-linear optimization problems. It mimics the collective behavior of social swarms such as ant colonies, honey bees, and bird flocks. The cooperative nature of swarms can search global settings of ML/DL models, which efficiently provide the solution to biomedical engineering applications. Finally, the book aims to provide the utility of swarm optimization and similar optimization techniques to design ML/DL models to improve the solutions related to biomedical engineering.

Biomedical engineering is a rapidly growing interdisciplinary area that is providing solutions to biological and medical problems and improving the healthcare system. It is connected to various applications like protein structure prediction, computer-aided drug design, and computerized medical diagnosis based on image and signal data, which accomplish low-cost, accurate, and reliable solutions for improving healthcare services. With the recent advancements, machine learning (ML) and deep learning (DL) techniques are widely used in biomedical engineering to develop intelligent decision-making healthcare systems in real-time. However, accuracy and reliability in model performance can be a concern in tackling data generated from medical images and signals, making it challenging for researchers and practitioners. Therefore, optimized models can produce quality healthcare services to handle the complexities involved in biomedical research.Various optimization techniques have been employed to optimize parameters, hyper-parameters, and architectural information of ML/DL models explicitly applied to biological, medical, and signal data. The swarm intelligence approach has the potential to solve complex non-linear optimization problems. It mimics the collective behavior of social swarms such as ant colonies, honey bees, and bird flocks. The cooperative nature of swarms can search global settings of ML/DL models, which efficiently provide the solution to biomedical engineering applications. Finally, the book aims to provide the utility of swarm optimization and similar optimization techniques to design ML/DL models to improve the solutions related to biomedical engineering.

The volume explores a wide-range of applications of computer vision in the field of healthcare. Computer vision, an interdisciplinary field that combines image processing, pattern recognition, and artificial intelligence, has the potential to revolutionize healthcare by enabling automated analysis and interpretation of medical images and videos. This chapter will provide an overview of the current advancements and potential future directions of computer vision applications in healthcare. Specifically, the book examines the application of computer vision techniques in various healthcare domains, including diagnostic imaging, surgical interventions, clinical decision support, and healthcare management. It reviews the challenges and limitations in implementing computer vision systems in real-world healthcare settings and discusses potential solutions.Overall, this book aims to shed light on the current and potential applications of computer vision in healthcare. It will summarize the state-of-the-art techniques, their benefits, challenges, and implications, while also discussing potential ethical considerations. The objective is to provide healthcare professionals, researchers, and practitioners with an understanding of the transformative impact of computer vision in the healthcare sector and inspire further research and development in this exciting field.

The integration ML with robotics and weaponry is revolutionizing mechanical engineering by enabling intelligent systems that can adapt, learn, and operate autonomously. In robotics, ML allows systems to process vast amounts of data from sensors to make real-time decisions. Robots, whether in industrial settings or autonomous vehicles, can navigate environments, recognize objects, and optimize tasks through reinforcement learning algorithms. In military applications, robotics combined with ML enhances autonomous weapon systems. Unmanned aerial vehicles (UAVs) and autonomous ground systems are increasingly utilized for surveillance, targeting, and even combat roles. These systems employ ML to improve target recognition, threat analysis, and adaptive decision-making in dynamic battle environments . This reduces human risk in conflict zones and can lead to more precise operational outcomes. Mechanical engineering plays a critical role in designing the physical systems that enable robotic mobility, structure, and function. Advanced mechanical systems integrate machine learning for predictive maintenance, fault diagnosis, and condition monitoring in weaponry and industrial robotics.Mechanical engineers design robots with complex actuators, sensors, and control mechanisms that respond to real-time data processed by machine learning algorithms. The combination of robotics, ML, and mechanical engineering is driving the development of next-generation intelligent systems. These innovations not only improve automation but are also crucial for defence systems, manufacturing, and autonomous vehicle technologies. This synergy promises greater efficiency, adaptability, and autonomy in a range of applications.Key Features:Highlights Real-World ApplicationsExplores Advanced AI TechniquesAddresses Ethical and Security ConcernsEquips Readers with Hands-On KnowledgeForecasts Future Technological Trends

Reviews the convergence AI and medicine. As healthcare systems face rising demands, clinician shortages, and the complexity of patient data; machine learning and deep learning are stepping in as transformative forces - to improve diagnosis, personalize treatments, and enhance patient outcomes. This book is a comprehensive guide to technologies, applications, and implications of AI in healthcare. It navigates through the foundational principles of machine learning, dives into real-world implementations such as radiology diagnostics, robotic surgery, and predictive analytics, and addresses key topics like natural language processing of EHRs and AI assisted drug discovery.

Advancements in sensor technology have enabled autonomous systems to operate efficiently and safely in the Internet of Vehicles environment. Multisensor image fusion is a crucial component in enhancing the capabilities of these autonomous systems by combining information from multiple sensors such as cameras, LiDAR, radar, and ultrasonic sensors. This book delves into the role of multisensor image fusion in the Internet of Vehicles for autonomous systems. It will cover the fundamental concepts of multisensor image fusion, different fusion methods, and their applications in autonomous systems for the IoV. It will also address the challenges associated with multisensor fusion, such as sensor calibration, synchronization, and noise reduction and discuss the benefits of multisensor fusion in improving object detection, tracking, and decision-making processes in autonomous vehicles operating in the IoV. This book is a comprehensive overview of multisensor image fusion in the context of IoV for autonomous systems, highlighting its importance in achieving reliable and robust autonomous navigation in dynamic and complex environments.

This book explores the applications of machine learning techniques in the healthcare industry to optimize decision-making processes. The book delves into the ways in which machine learning can be used to analyze large and complex healthcare data sets, such as electronic health records, medical imaging, and wearable device data, to extract valuable insights and improve patient care.