Machine learning

Das Buch gibt eine breite Übersicht über die wichtigsten Optimierungsmethoden, wobei die Verfahren beispielhaft erläutert und mit über 400 Abbildungen illustriert werden. Im ersten Teil des Buches werden verschiedene Klassen von Optimierungsproblemen vorgestellt. Gegenstand des zweiten Teils sind klassische Methoden zur Lösung von Optimierungsproblemen. Im dritten Teil werden naturanaloge Optimierungsmethoden behandelt, darunter physikbasierende Algorithmen, evolutionäre Algorithmen, schwarmintelligente Methoden sowie Optimierungsverfahren, die durch das Immunsystem inspiriert sind. Der letzte Teil des Buches befasst sich mit der Optimierung neuronaler Netze und gibt eine Einführung in ein neues vielversprechendes Forschungsgebiet der KI-basierten, selbstlernenden Optimierungsalgorithmen.

This book reviews recent innovations in the smart agriculture space that use the Internet of Things (IoT) and sensing to deliver Artificial Intelligence (AI) solutionsto agricultural productivity in the agricultural production hubs. In this regard, South and Southeast Asia are one of the major agricultural hubs of the world, facing challenges of climate change and feeding the fast-growing population. To address such challenges, a transboundary approach along with AI and BIG data for bioinformatics are required to increase yield and minimize pre- and post-harvest losses in intangible climates to drive the sustainable development goal (SDG) for feeding a major part of the 9 billion population by 2050 (Society 5.0 SDG 1 & 2). Therefore, this book focuses on the solution through smart IoT and AI-based agriculture including pest infestation and minimizing agricultural inputs for in-house and fields production such as light, water, fertilizer and pesticides to ensure food security aligns with environmental sustainability. It provides a sound understanding for creating new knowledge in line with comprehensive research and education orientation on how the deployment of tiny sensors, AI/Machine Learning (ML), controlled UAVs, and IoT setups for sensing, tracking, collection, processing, and storing information over cloud platforms for nurturing and driving the pace of smart agriculture in this current time. The book will appeal to several audiences and the contents are designed for researchers, graduates, and undergraduate students working in any area of machine learning, deep learning in agricultural engineering, smart agriculture, and environmental science disciplines. Utmost care has been taken to present a varied range of resource areas along with immense insights into the impact and scope of IoT, AI and ML in the growth of intelligent digital farming and smart agriculture which will give comprehensive information to the targeted readers.

Autonomous Mobile Robots: Planning, Navigation, and Simulation presents detailed coverage of the domain of robotics in motion planning and associated topics in navigation. This book covers numerous base planning methods from diverse schools of learning, including deliberative planning methods, reactive planning methods, task planning methods, fusion of different methods, and cognitive architectures. It is a good resource for doing initial project work in robotics, providing an overview, methods and simulation software in one resource. For more advanced readers, it presents a variety of planning algorithms to choose from, presenting the tradeoffs between the algorithms to ascertain a good choice. Finally, the book presents fusion mechanisms to design hybrid algorithms.

This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of  the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers  a comprehensive preamble for further  problem-oriented chapters. The most interesting and open problems of machine learning in the framework of  Deep Learning are discussed in this book and solutions are proposed.  This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks.  This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.

This book constitutes revised selected papers from the refereed proceedings of the 11th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2021, held as a virtual event during December 16-18, 2021.The 13 full papers included in this book were carefully reviewed and selected from 17 submissions. They were organized in topical sections as follows: Computational advances in bio and medical sciences; and computational advances in molecular epidemiology.

Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning. This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a ¿student¿s¿ perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.

This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book,you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will LearnImplement various techniques in time series analysis using Python.Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecastingForecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.

Emerging machine learning techniques bring new opportunities to flexible network control and management. This book focuses on using state-of-the-art machine learning-based approaches to improve the performance of Software-Defined Networking (SDN). It will apply several innovative machine learning methods (e.g., Deep Reinforcement Learning, Multi-Agent Reinforcement Learning, and Graph Neural Network) to traffic engineering and controller load balancing in software-defined wide area networks, as well as flow scheduling, coflow scheduling, and flow migration for network function virtualization in software-defined data center networks. It helps readers reflect on several practical problems of deploying SDN and learn how to solve the problems by taking advantage of existing machine learning techniques. The book elaborates on the formulation of each problem, explains design details for each scheme, and provides solutions by running mathematical optimization processes, conducting simulated experiments, and analyzing the experimental results.

The production control of flexible manufacturing systems is a relevant component that must go along with the requirements of being flexible in terms of new product variants, new machine skills and reaction to unforeseen events during runtime. This work focuses on developing a reactive job-shop scheduling system for flexible and re-configurable manufacturing systems. Reinforcement Learning approaches are therefore investigated for the concept of multiple agents that control products including transportation and resource allocation.