Women In Computational Intelligence

This book provides a breadth of innovative and impactful research in the field computational intelligence led by women investigators. Topics include intelligent data analytics, optimization of complex systems, approximation of human reasoning, robotic path planning, and intelligent control systems. These topics touch on many of the technological challenges facing the world today and these solutions by women researcher teams are valuable for their excellence and their non-traditional perspective. As an important part of the Women in Science and Engineering book series, the work highlights the contribution of women leaders in computational intelligence, inspiring women and men, girls, and boys to enter and apply themselves to this exciting multi-disciplinary field.

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI.

Artificial Intelligence and Machine Learning for Women’s Health Issues: Challenges, Impact, and Solutions discusses the applications, challenges, and solutions that machine learning can bring to women’s health challenges. This book illustrates advanced, innovative techniques, frameworks, concepts, and methodologies of machine learning, which enhance the future healthcare system. This book's primary focus is on women’s health issues and machine learning's role in providing solutions to these challenges, providing novel ideas for feasible implementation. It also provides an early-stage analysis for early diagnosis of women’s health issues. Provides fundamental concepts and analysis of machine learning algorithms used to aid in the diagnosis of women’s health issues Guides researchers to specific ideas, tools, and practices most applicable to product/service development, innovation problems, and opportunities Provides hands-on chapters that describe frameworks, applications, best practices, and case studies of future directions of applied machine learning in women’s healthcare

'Football looked at in a very different way' Pat Nevin, former Chelsea and Everton star and football media analyst Football – the most mathematical of sports. From shot statistics and league tables to the geometry of passing and managerial strategy, the modern game is filled with numbers, patterns and shapes. How do we make sense of them? The answer lies in the mathematical models applied in biology, physics and economics. Soccermatics brings football and mathematics together in a mind-bending synthesis, using numbers to help reveal the inner workings of the beautiful game. This new and expanded edition analyses the current big-name players and teams using mathematics, and meets the professionals working inside football who use numbers and statistics to boost performance. Welcome to the world of mathematical modelling, expressed brilliantly by David Sumpter through the prism of football. No matter who you follow – from your local non-league side to the big boys of the Premiership, La Liga, the Bundesliga, Serie A or the MLS – you'll be amazed at what mathematics has to teach us about the world's favourite sport.

This book is divided in the three main areas where Professor Antonina Starita was most active in the last period of her research activity: clustering and learning applications, biomedical applications, and motor control and evaluation. The part on clustering and learning applications opens with a contribution concerning the clustering of short-text corpora by Particle Swarm Optimization (PSO). The second contribution in this part investigates the use of Neural Networks (NN), and specifically of Recurrent NN, to interpret brain images obtained by functional Magnetic Resonance Imaging (fMRI). The first part of the book is closed by a contribution on the System for Paleographic Inspections (SPI) software suite.

"This book deals with the computational intelligence field, particularly business applications adopting computational intelligence techniques"--Provided by publisher.

As new technological challenges are perpetually arising, Artificial Intelligence research interests are focusing on the incorporation of improvement abilities into machines in an effort to make them more efficient and more useful. Recent reports indicate that the demand for scientists with Artificial Intelligence skills significantly exceeds the market availability and that this shortage will intensify further in the years to come. A potential solution includes attracting more women into the field, as women currently make up only 26 percent of Artificial Intelligence positions in the workforce. The present book serves a dual purpose: On one hand, it sheds light on the very significant research led by women in areas of Artificial Intelligence, in hopes of inspiring other women to follow studies in the area and get involved in related research. On the other hand, it highlights the state-of-the-art and current research in selected Artificial Intelligence areas and applications. The book consists of an editorial note and an additional thirteen (13) chapters, all authored by invited women-researchers who work on various Artificial Intelligence areas and stand out for their significant research contributions. In more detail, the chapters in the book are organized into three parts, namely (i) Advances in Artificial Intelligence Paradigms, (ii) Advances in Artificial Intelligence Applications, and (iii) Recent Trends in Artificial Intelligence Areas and Applications. This research book is directed towards professors, researchers, scientists, engineers and students in Artificial Intelligence-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Artificial Intelligence-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the Artificial Intelligence areas of interest to them.

The main focus of this book is the examination of women’s health issues and the role machine learning can play as a solution to these challenges. This book will illustrate advanced, innovative techniques/frameworks/concepts/machine learning methodologies, enhancing the future healthcare system. Combating Women’s Health Issues with Machine Learning: Challenges and Solutions examines the fundamental concepts and analysis of machine learning algorithms. The editors and authors of this book examine new approaches for different age-related medical issues that women face. Topics range from diagnosing diseases such as breast and ovarian cancer to using deep learning in prenatal ultrasound diagnosis. The authors also examine the best machine learning classifier for constructing the most accurate predictive model for women’s infertility risk. Among the topics discussed are gender differences in type 2 diabetes care and its management as it relates to gender using artificial intelligence. The book also discusses advanced techniques for evaluating and managing cardiovascular disease symptoms, which are more common in women but often overlooked or misdiagnosed by many healthcare providers. The book concludes by presenting future considerations and challenges in the field of women’s health using artificial intelligence. This book is intended for medical researchers, healthcare technicians, scientists, programmers and graduate-level students looking to understand better and develop applications of machine learning/deep learning in healthcare scenarios, especially concerning women’s health conditions.