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The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out.

Technology: general issues --- History of engineering & technology --- unmanned aerial vehicle --- UAV positioning --- machine learning --- wireless communications --- drones --- network --- DTN --- mobility schedule --- routing algorithms --- data delivery --- Internet of drones --- communication --- security --- privacy --- UAV base station --- MIMO --- millimeter-wave band --- blind beamforming --- signal recovery --- UAV relay networks --- resource management --- transmit time allocation --- unmanned aerial vehicles --- dynamic spectrum access --- quality of service --- reinforcement learning --- multi-armed bandit --- aerial communication --- FANET --- not-spots --- stratospheric communication platform --- UAV --- UAV-assisted network --- 5G --- global positioning system --- GPS spoofing attacks --- detection techniques --- dynamic selection --- hyperparameter tuning --- IoT --- RF radio communication --- Wi-Fi direct --- D2D --- drone-based mobile secure zone --- friendly jamming --- mobility --- internet of things --- non-orthogonal multiple access --- resource allocation --- ultra reliable low latency communication --- uplink transmission --- Deep Q-learning (DQL) --- Double Deep Q-learning (DDQL) --- dynamic spectrum sharing --- High Altitude Platform Station (HAPS) --- cellular communications --- power control --- interference management --- cognitive UAV networks --- clustered two-stage-fusion cooperative spectrum sensing --- continuous hidden Markov model --- SNR estimation --- n/a

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The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out.

Technology: general issues --- History of engineering & technology --- unmanned aerial vehicle --- UAV positioning --- machine learning --- wireless communications --- drones --- network --- DTN --- mobility schedule --- routing algorithms --- data delivery --- Internet of drones --- communication --- security --- privacy --- UAV base station --- MIMO --- millimeter-wave band --- blind beamforming --- signal recovery --- UAV relay networks --- resource management --- transmit time allocation --- unmanned aerial vehicles --- dynamic spectrum access --- quality of service --- reinforcement learning --- multi-armed bandit --- aerial communication --- FANET --- not-spots --- stratospheric communication platform --- UAV --- UAV-assisted network --- 5G --- global positioning system --- GPS spoofing attacks --- detection techniques --- dynamic selection --- hyperparameter tuning --- IoT --- RF radio communication --- Wi-Fi direct --- D2D --- drone-based mobile secure zone --- friendly jamming --- mobility --- internet of things --- non-orthogonal multiple access --- resource allocation --- ultra reliable low latency communication --- uplink transmission --- Deep Q-learning (DQL) --- Double Deep Q-learning (DDQL) --- dynamic spectrum sharing --- High Altitude Platform Station (HAPS) --- cellular communications --- power control --- interference management --- cognitive UAV networks --- clustered two-stage-fusion cooperative spectrum sensing --- continuous hidden Markov model --- SNR estimation

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Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such example. Due to the rapid development of computer technology, AI has been actively explored for a variety of academic and practical purposes in the context of financial markets. This book focuses on the broad topic of “AI and Financial Markets”, and includes novel research associated with this topic. The book includes contributions on the application of machine learning, agent-based artificial market simulation, and other related skills to the analysis of various aspects of financial markets.

Economics, finance, business & management --- algorithmic trading --- Stop Loss --- Turtle --- ATR --- community finances --- fiscal flexibility --- individualized financial arrangements --- sustainable financial services --- price momentum --- hidden markov model --- asset allocation --- blockchain --- BlockCloud --- Artificial Intelligence --- consensus algorithms --- exchange rates --- fundamentals --- prediction --- random forest --- support vector machine --- neural network --- deep reinforcement learning --- financial market simulation --- agent based simulation --- artificial market --- simulation --- CAR regulation --- portfolio --- contract for difference --- CfD --- reinforcement learning --- RL --- neural networks --- long short-term memory --- LSTM --- Q-learning --- deep learning --- uncertainty --- economic policy --- text mining --- topic model --- yield curve --- term structure of interest rates --- machine learning --- autoencoder --- interpretability

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Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.

star image --- image denoising --- reinforcement learning --- maximum likelihood estimation --- mixed Poisson–Gaussian likelihood --- machine learning-based classification --- non-uniform foundation --- stochastic analysis --- vehicle–pavement–foundation interaction --- forest growing stem volume --- coniferous plantations --- variable selection --- texture feature --- random forest --- red-edge band --- on-shelf availability --- semi-supervised learning --- deep learning --- image classification --- machine learning --- explainable artificial intelligence --- wildfire --- risk assessment --- Naïve bayes --- transmission-line corridors --- image encryption --- compressive sensing --- plaintext related --- chaotic system --- convolutional neural network --- color prior model --- object detection --- piston error detection --- segmented telescope --- BP artificial neural network --- modulation transfer function --- computer vision --- intelligent vehicles --- extrinsic camera calibration --- structure from motion --- convex optimization --- temperature estimation --- BLDC --- electric machine protection --- touchscreen --- capacitive --- display --- SNR --- stylus --- laser cutting --- quality monitoring --- artificial neural network --- burr formation --- cut interruption --- fiber laser --- semi-supervised --- fuzzy --- noisy --- real-world --- plankton --- marine --- activity recognition --- wearable sensors --- imbalanced activities --- sampling methods --- path planning --- Q-learning --- neural network --- YOLO algorithm --- robot arm --- target reaching --- obstacle avoidance

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Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms.

Technology: general issues --- global optimization --- cuckoo search algorithm --- Q-learning --- mutation --- self-adaptive step size --- evolutionary computation --- playtesting --- game feature --- game simulation --- game trees --- playtesting metric --- validation --- Pareto optimality --- h-index --- ranking --- dominance --- Pareto-front --- multi-indicators --- multi-metric --- multi-resources --- citation --- universities ranking --- swarm intelligence --- simulated annealing --- krill herd --- particle swarm optimization --- quantum --- elephant herding optimization --- engineering optimization --- metaheuristic --- constrained optimization --- multi-objective optimization --- single objective optimization --- differential evolution --- success-history --- premature convergence --- turning-based mutation --- opposition-based learning --- ant colony optimization --- opposite path --- traveling salesman problems --- whale optimization algorithm --- WOA --- binary whale optimization algorithm --- bWOA-S --- bWOA-V --- feature selection --- classification --- dimensionality reduction --- menu planning problem --- evolutionary algorithm --- decomposition-based multi-objective optimisation --- memetic algorithm --- iterated local search --- diversity preservation --- single-objective optimization --- knapsack problem --- travelling salesman problem --- seed schedule --- many-objective optimization --- fuzzing --- bug detection --- path discovery --- evolutionary algorithms (EAs) --- coevolution --- dynamic learning --- performance indicators --- magnetotelluric --- one-dimensional inversions --- geoelectric model --- optimization problem --- multi-task optimization --- multi-task evolutionary computation --- knowledge transfer --- assortative mating --- unified search space --- quantum computing --- grey wolf optimizer --- 0-1 knapsack problem --- green shop scheduling --- fuzzy hybrid flow shop scheduling --- discrete artificial bee colony algorithm --- minimize makespan --- minimize total energy consumption