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This book provides an overview about the open challenges in software verification. Software verification is a branch of software engineering aiming at guaranteeing that software applications satisfy some requirements of interest. Over the years, the software verification community has proposed and considered several techniques: abstract interpretation, data-flow analysis, type systems, model checking are just a few examples. The theoretical advances have been always motivated by practical challenges that have led to an equal evolution of both these sides of software verification. Indeed, several verification tools have been proposed by the research community and any software application, in order to guarantee that certain software requirements are met, needs to integrate a verification phase in its life cycle, independently of the context of application or software size. This book is aimed at collecting contributions discussing recent advances in facing open challenges in software verification, relying on a broad spectrum of verification techniques. This book collects contributions ranging from theoretical to practical arguments, and it is aimed at both researchers in software verification and their practitioners.

This book focuses on research aspects of ensemble approaches of machine learning techniques that can be applied to address the big data problems.In this book, various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance are presented in several chapters. Each of them has separate functionalities, which can be leveraged to solve a specific set of big data applications. This book is a potential resource for various advances in the field of machine learning and data science to solve big data problems with many objectives. It has been observed from the literature that several works have been focused on the advancement of machine learning in various fields like biomedical, stock prediction, sentiment analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems.

The book discusses Explainable (XAI) and Responsive Artificial Intelligence (RAI) for biomedical and healthcare applications. It will discuss the advantages in dealing with big and complex data by using explainable AI concepts in the field of biomedical sciences. The book explains both positive as well as negative findings obtained by explainable AI techniques. It features real time experiences by physicians and medical staff for applied deep learning based solutions. The book will be extremely useful for researchers and practitioners in advancing their studies.

This book presents thoughts and pathways to build revolutionary machine learning models with the new paradigm of machine learning to adapt behaviorism. It focuses on two aspects - one focuses on architecting a choice process to lead users on the certain choice path while the second focuses on developing machine learning models based on choice paradigm. This book is divided in three parts where part one deals with human choice and choice architecting models with stories of choice architects. Second part closely studies human choosing models and deliberates on developing machine learning models based on the human choice paradigm. Third part takes you further to look at machine learning based choice architecture. The proposed pioneering choice-based paradigm for machine learning presented in the book will help readers to develop products - help readers to solve problems in a more humanish way and to negotiate with uncertainty in a more graceful but in an objective way. It will help to create unprecedented value for business and society. Further, it will unveil a new paradigm for modern intelligent businesses to embark on the new journey; the journey of transition from shackled feature rich and choice poor systems to feature flexible and choice rich natural behaviors.