Landslides pose a serious risk to population, property, and environment in mountainous regions and even in flat areas worldwide. Landslides have caused massive casualties and significant losses and damage to property. In recent years, machine learning (ML) techniques, including deep learning methods, have increasingly been used to model complex landslides. Analyses so far have demonstrated promising predictive ability compared to traditional, deterministic solutions, and physical model testing. This Special Issue of Applied Sciences seeks to incorporate the latest developments in machine learning with respect to modeling and prediction of landslide susceptibility, including quantitative and qualitative assessments of the classification, volume (or area) and spatial distribution of landslides, as well as the velocity, intensity, and runout (and consequences) of existing or potential landsliding.

A special issue of Geophysical Prospecting is being planned on machine learning applications in geophysical exploration and monitoring. Artificial intelligence, and in particular its subdomain machine learning, has revolutionized many science and engineering disciplines during the past decade. In many domains such as image recognition, machine translation, and speech analysis, machine learning outperforms conventional techniques and has emerged as the method of choice. It is no surprise that recently geophysicists have also found great value in machine learning to automate workflows, extract valuable information from big data, and create new pathways in solving challenging computational problems. Despite this surge in interest, we are still in the early days of developing machine learning applications for subsurface resource exploration, and the geophysical community at large will benefit from a better understanding of the promise of machine learning in transforming industrial practices.

This trend also brings about a unique opportunity and good assurance for solving different critical problems in medical and healthcare systems as well as engineering applications of Artificial Intelligence (AI) and Operations Research (OR). However, such an assurance strongly depends on the extent to which researchers can discover useful patterns, find informative mechanisms underlying the fragmented and diverse data sets, as well as convert this knowledge into intelligent decisions. AI techniques have been recently studied and applied as promising tools for the development and application of intelligent systems in the healthcare context. AI-based systems can generally learn from data and evolve according to real-time changes and fluctuations by considering the indisputable uncertainty of health data and processes. Many attempts have been made so far that employ different techniques including, inter alia, Machine Learning (ML), neural networks, optimization, computational intelligence and human–machine interface.

Just a few years ago, there were no legions of deep learning scientists developing intelligent products and services at major companies and startups. When the youngest among us (the authors) entered the field, machine learning did not command headlines in daily newspapers. Our parents had no idea what machine learning was, let alone why we might prefer it to a career in medicine or law. Machine learning was a forward-looking academic discipline with a narrow set of real-world applications. And those applications, e.g., speech recognition and computer vision, required so much domain knowledge that they were often regarded as separate areas entirely for which machine learning was one small component. Neural networks then, the antecedents of the deep learning models that we focus on in this book, were regarded as outmoded tools. In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, reinforcement learning, and statistical modeling. With these advances in hand, we can now build cars that drive themselves with more autonomy than ever before (and less autonomy than some companies might have you believe), smart reply systems that automatically draft the most mundane emails, helping people dig out from oppressively large inboxes, and software agents that dominate the worldʼs best humans at board games like Go, a feat once thought to be decades away. Already, these tools exert ever-wider impacts on industry and society, changing the way movies are made, diseases are diagnosed, and playing a growing role in basic sciences--from astrophysics to biology.

This book examines the growing importance of algorithms and automation--including emerging forms of artificial intelligence--in the gathering, composition, and distribution of news. In it the authors connect a long line of research on journalism and computation with scholarly and professional terrain yet to be explored. Taken as a whole, these chapters share some of the noble ambitions of the pioneering publications on'reporting algorithms', such as a desire to see computing help journalists in their watchdog role by holding power to account. However, they also go further, firstly by addressing the fuller range of technologies that computational journalism now consists of: from chatbots and recommender systems to artificial intelligence and atomised journalism. Secondly, they advance the literature by demonstrating the increased variety of uses for these technologies, including engaging underserved audiences, selling subscriptions, and recombining and re-using content. Thirdly, they problematise computational journalism by, for example, pointing out some of the challenges inherent in applying artificial intelligence to investigative journalism and in trying to preserve public service values.

I have read many books during my learning journey but in this article I present a list of the best books that helped me digest important topics. This special book list shaped my understanding of computer science, and kept me going back any time I'm in doubt. This is the book that introduced me to programming design patterns and what an introduction it was! Clear, comprehensive, and with easy to understand and imagine real-world examples that stick to your memory easily. Every time I have a doubt about which design pattern to use, I go back to this book and it solves my problem.

Artificial intelligence (AI) are methods that are applied to transform the way humans will interact with machines and the role that machines will play in all spheres of human life. On one hand, the immense potential of these technologies to enhance and enrich human life has led to a growing exhilaration and excitement on their use, and on the other hand, fear and apprehension of a dystopian future where machines have taken over loom on the horizon. These techniques are considered to be a category in computer science, involved in the research and application of intelligent computers. Traditional methods for modeling and optimizing complex problems require huge amounts of computing resources, and computing-based solutions can often provide valuable alternatives for efficiently solving problems. Due to making nonlinear and complex relationships between dependent and independent variables, these techniques can be performed in the field of bioengineering with a high degree of accuracy.

This book mostly focuses on applying machine learning techniques to solve natural language processing (NLP) problems. All those interested in Natural Language Processing (NLP) with Python should refer to this book. The writing of this book is straightforward and presented in a very tidy fashion. Moreover, the book presents code examples in Python in a precise way. The topics covered in this book are -- extracting features from plain text, analyzing linguistic structure, accessing popular NLP datasets, NLTK, and many more.

Machine learning is a field of computational science which first emerged in the 1950s. However, our ability to effectively harness the power of machine learning techniques was only truly realised in the 1990s. In ecology, the earliest adoption of machine learning came about in the early 2000s, when regression tree algorithms were applied to spatial data to predict species distributions. This was quickly adapted in the field of marine ecology to study the distribution of many pelagic species. Since that time, machine learning algorithms have been adapted and applied in various studies in the marine environment, from population models, image recognition, and experimental studies.

This book introduces probability, statistics and stochastic processes to students. It can be used by both students and practitioners in engineering, various sciences, finance, and other related fields. It provides a clear and intuitive approach to these topics while maintaining mathematical accuracy. You can also find courses and videos online.