Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Terrain classification using machine learning algorithms in a multi-temporal approach A QGIS plug-in implementation

Land cover and land use (LCLU) maps are essential for the successful administration of a nation’s topography, however, conventional on-site data gathering methods are costly and time-consuming. By contrast, remote sensing data can be used to generate up-to-date maps regularly with the help of machine learning algorithms, in turn, allowing for the assessment of a region’s dynamics throughout time. The present dissertation will focus on the implementation of an automated land use and land cover classifier based on remote sensing imagery provided by the mod ern sentinel-2 satellite constellation. The project, with Portugal at its focus, will expand on previous approaches by utilizing temporal data as an input variable in order to harvest the contextual information contained in the vegetation cycles. The pursued solution investigated the implementation of a 9-class classifier plug-in for an industry standard, open-source geographic information system. In the course of the testing procedure, various processing techniques and machine learning algorithms were evaluated in a multi-temporal approach. Resulting in a final overall accuracy of 65,9% across the targeted classes.Mapas de uso e ocupação do solo são cruciais para o entendimento e administração da topografia de uma nação, no entanto, os métodos convencionais de aquisição local de dados são caros e demorados. Contrariamente, dados provenientes de métodos de senso riamento remoto podem ser utilizados para gerar regularmente mapas atualizados com a ajuda de algoritmos de aprendizagem automática. Permitindo, por sua vez, a avaliação da dinâmica de uma região ao longo do tempo. Utilizando como base imagens de sensoriamento remoto fornecidas pela recente cons telação de satélites Sentinel-2, a presente dissertação concentra-se na implementação de um classificador de mapas de uso e ocupação do solo automatizado. O projeto, com foco em Portugal, irá procurar expandir abordagens anteriores através do aproveitamento de informação contextual contida nos ciclos vegetativos pela utilização de dados temporais adicionais. A solução adotada investigou a produção e implementação de um classificador geral de 9 classes num plug-in de um sistema de informação geográfico de código aberto. Durante o processo de teste, diversas técnicas de processamento e múltiplos algoritmos de aprendizagem automática foram avaliados numa abordagem multi-temporal, culminando num resultado final de precisão geral de 65,9% nas classes avaliadas

Deep Learning for Decision Making and Autonomous Complex Systems

Deep learning consists of various machine learning algorithms that aim to learn multiple levels of abstraction from data in a hierarchical manner. It is a tool to construct models using the data that mimics a real world process without an exceedingly tedious modelling of the actual process. We show that deep learning is a viable solution to decision making in mechanical engineering problems and complex physical systems. In this work, we demonstrated the application of this data-driven method in the design of microfluidic devices to serve as a map between the user-defined cross-sectional shape of the flow and the corresponding arrangement of micropillars in the flow channel that contributed to the flow deformation. We also present how deep learning can be used in the early detection of combustion instability for prognostics and health monitoring of a combustion engine, such that appropriate measures can be taken to prevent detrimental effects as a result of unstable combustion. One of the applications in complex systems concerns robotic path planning via the systematic learning of policies and associated rewards. In this context, a deep architecture is implemented to infer the expected value of information gained by performing an action based on the states of the environment. We also applied deep learning-based methods to enhance natural low-light images in the context of a surveillance framework and autonomous robots. Further, we looked at how machine learning methods can be used to perform root-cause analysis in cyber-physical systems subjected to a wide variety of operation anomalies. In all studies, the proposed frameworks have been shown to demonstrate promising feasibility and provided credible results for large-scale implementation in the industry