Artificial intelligence models are being used to make labor and delivery safer for mothers and newborns. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. This is a critical area of study as maternal and infant health are indispensable for a healthy society. Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning considers the recent advances, challenges, and best practices of artificial intelligence and machine learning in relation to pregnancy complications. Covering key topics such as pregnancy complications, wearable sensors, and healthcare technologies, this premier reference source is ideal for nurses, doctors, computer scientists, medical professionals, industry professionals, researchers, academicians, scholars, instructors, and students.

This volume contains the proceedings of the twenty-second International Conference on Medical Informatics Europe MIE 2009, that was held in Sarajevo, Bosnia and Herzegovina, from 30 August to 2 September 2009. The scientific topics present in this proceedings range from national and trans-national eHealth roadmaps, health information and electronic health record systems, systems interoperability and communication standards, medical terminology and ontology approaches, and social networks to Web, Web 2.0, nd Semantic Web solutions for patients, health personnel, and researchers. Furthermore, they include quality assurance and usability of medical informatics systems, specific disease management and telemedicine systems, including a section on devices and snsors, drug safety, clinical decision support and medical expert systems, clinical practice guidelines and protocols, as well as issues on privacy and security. Moreover, bioinformatics, biomedical modeling and simulation, medical imaging and visualizatio and, last but not least, learning and education through medical informatics systems are parts of the included topics.

The lack of comprehensive, innovative insights into the intricate world of pregnancy complication prediction is a pressing concern, as these complications can severely impact the health and wellbeing of pregnant patients. As the complexities of maternal healthcare continue to evolve, scholars grapple with the challenge of staying at the forefront of research and innovation in this critical field. The unpredictability of pregnancy complications poses significant risks to positive patient outcomes, demanding novel approaches to diagnosis and prevention. The academic community seeks a solution that can bridge the gap between traditional research and the transformative potential of technological advancements in healthcare. Technological Tools for Predicting Pregnancy Complications not only identify the problem but offer an authoritative solution. It serves as a beacon of knowledge for academic scholars, providing a holistic exploration of how Artificial Intelligence (AI) and Machine Learning (ML) technologies can revolutionize maternal healthcare. With a laser focus on predictive models, comprehensive health data analysis, and innovative algorithmic approaches, this book equips scholars with the tools they need to navigate the ever-evolving landscape of pregnancy complications. Academic scholars will find a treasure trove of insights, spanning from the fundamentals of AI and ML in healthcare to the application of IoT devices and wearable sensors for expectant mothers.

In the world of academia, scholars and researchers are confronted with a rapidly expanding knowledge base in Artificial Intelligence (AI) and nanotechnology. The integration of these two groundbreaking fields presents an intricate web of concepts, innovations, and interdisciplinary applications that can overwhelm even the most astute academic minds. Staying up to date with the latest developments and effectively navigating this complex terrain has become a pressing challenge for those striving to contribute meaningfully to these fields. Artificial Intelligence in the Age of Nanotechnology is a transformative solution meticulously crafted to address the academic community's knowledge gaps and challenges. This comprehensive book serves as the guiding light for scholars, researchers, and students grappling with the dynamic synergy between AI and Nanotechnology. It offers a structured and authoritative exploration of the core principles and transformative applications of these domains across diverse fields. By providing clarity and depth, it empowers academics to stay at the forefront of innovation and make informed contributions.

In the dynamic world of manufacturing, the industry has grappled with ongoing issues such as expensive machine maintenance, operational inefficiencies, and the production of defective products. The need for informed decision-making to maintain quality, meet deadlines, and prevent disruptions is more crucial than ever. Enter Using Real-Time Data and AI for Thrust Manufacturing, a groundbreaking book that addresses these challenges head-on. As Industry 4.0 transforms the manufacturing sector through the integration of the Internet of Things (IoT) and artificial intelligence (AI), this book serves as a beacon for academic scholars and industry professionals alike, offering profound insights into the world of AI-driven industry solutions. The objective of this book is clear—to revolutionize the manufacturing sector by leveraging human expertise and AI-driven data technologies. By delving into the realms of Industry 4.0, IoT, and AI, the book systematically tackles issues such as costly downtime, inefficient processes, and the production of substandard products. With a focus on turning raw data into meaningful insights, the book explores AI applications like machine learning and deep learning, natural language processing, and machine vision. From predictive maintenance to improved demand forecasting, quality assurance, inspection, and warehouse automation, the book positions AI as the linchpin of "Industry 4.0," ensuring not only cost savings but also safety improvements and supply-chain efficiencies.

The Handbook of Research on AI and ML for Intelligent Machines and Systems offers a comprehensive exploration of the pivotal role played by artificial intelligence (AI) and machine learning (ML) technologies in the development of intelligent machines. As the demand for intelligent machines continues to rise across various sectors, understanding the integration of these advanced technologies becomes paramount. While AI and ML have individually showcased their capabilities in developing robust intelligent machine systems and services, their fusion holds the key to propelling intelligent machines to a new realm of transformation. By compiling recent advancements in intelligent machines that rely on machine learning and deep learning technologies, this book serves as a vital resource for researchers, graduate students, PhD scholars, faculty members, scientists, and software developers. It offers valuable insights into the key concepts of AI and ML, covering essential security aspects, current trends, and often overlooked perspectives that are crucial for achieving comprehensive understanding. It not only explores the theoretical foundations of AI and ML but also provides guidance on applying these techniques to solve real-world problems. Unlike traditional texts, it offers flexibility through its distinctive module-based structure, allowing readers to follow their own learning paths.