Handbook Of Research On Deep Learning Based Image Analysis Under Constrained And Unconstrained Environments

Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to provide improved performance in the required task. The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and algorithms for image analysis and addresses its challenges. Highlighting concepts, methods, and tools including convolutional neural networks, edge enhancement, image segmentation, machine learning, and image processing, the book is an essential and comprehensive reference work for engineers, academicians, researchers, and students.

Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data. Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process. Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes.

This book presents the proceedings of the International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2022. The conference provides an interdisciplinary forum for researchers, professional engineers and scientists, educators, and technologists to discuss, debate, and promote research and technology in the upcoming areas of computing, information, communication, and their applications. The book discusses these emerging research areas, providing a valuable resource for researchers and practicing engineers alike.

As the metaverse rapidly evolves, a comprehensive examination of the emerging threats and challenges is imperative. In the groundbreaking exploration within Forecasting Cyber Crimes in the Age of the Metaverse, the intersection of technology, crime, and law enforcement is investigated, and it provides valuable insights into the potential risks and strategies for combating cybercrimes in the metaverse. Drawing upon research and scientific methodologies, this book employs a forward-thinking approach to anticipate the types of crimes that may arise in the metaverse. It addresses various aspects of cybercrime, including crimes against children, financial fraud, ransomware attacks, and attacks on critical infrastructure. The analysis extends to the protection of intellectual property rights and the criminal methods employed against metaverse assets. By forecasting the future of cybercrimes and cyber warfare in the metaverse, this book equips law enforcement agencies, policymakers, and companies with essential knowledge to develop effective strategies and countermeasures. It explores the potential impact of cybercrime on police capabilities and provides valuable insights into the planning and preparedness required to mitigate these threats.

This book covers the latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing, and their applications in real world. The topics covered in machine learning involve feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN), and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modeling from video, 3D object recognition, localization and tracking, medical image analysis, and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multitask, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals, for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG), and electromyogram (EMG).

Even though many data analytics tools have been developed in the past years, their usage in the field of cyber twin warrants new approaches that consider various aspects including unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analysis, sampling, dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection. Further study is required to fully understand the opportunities, benefits, and difficulties of data analytics and the internet of things in today’s modern world. New Approaches to Data Analytics and Internet of Things Through Digital Twin considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. Covering key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

Cancer research is currently a vital field of study as it affects a wide range of the population either directly or indirectly. Breast and cervical cancer are two prevalent types that pose a threat to women’s health and wellness. Due to this, further research on the importance of medical informatics within this field is necessary to ensure patients receive the best possible attention and care. The Research Anthology on Medical Informatics in Breast and Cervical Cancer provides current research and information on how medical informatics are utilized within the field of breast and cervical cancer and considers the best practices and challenges of its implementation. Covering key topics such as women’s health, wellness, oncology, and patient care, this major reference work is ideal for medical professionals, nurses, oncologists, policymakers, researchers, academicians, scholars, practitioners, instructors, and students.

Traditional machining has many limitations in today’s technology-driven world, which has caused industrial professionals to begin implementing various optimization techniques within their machining processes. The application of methods including machine learning and genetic algorithms has recently transformed the manufacturing industry and created countless opportunities in non-traditional machining methods. Significant research in this area, however, is still considerably lacking. Machine Learning Applications in Non-Conventional Machining Processes is a collection of innovative research on the advancement of intelligent technology in industrial environments and its applications within the manufacturing field. While highlighting topics including evolutionary algorithms, micro-machining, and artificial neural networks, this book is ideally designed for researchers, academicians, engineers, managers, developers, practitioners, industrialists, and students seeking current research on intelligence-based machining processes in today’s technology-driven market.