Spatially Explicit Hyperparameter Optimization For Neural Networks
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Spatially Explicit Hyperparameter Optimization For Neural Networks
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Author : Minrui Zheng
language : en
Publisher: Springer Nature
Release Date : 2021-10-18
Spatially Explicit Hyperparameter Optimization For Neural Networks written by Minrui Zheng and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-18 with Computers categories.
Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks in the GIScience field. Also, the approach improves the computing performance at both model and computing levels. This book is written for researchers of the GIScience field as well as social science subjects.
Landslide Susceptibility Risk Assessment And Sustainability
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Author : Gopal Krishna Panda
language : en
Publisher: Springer Nature
Release Date : 2024-05-14
Landslide Susceptibility Risk Assessment And Sustainability written by Gopal Krishna Panda and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-14 with Nature categories.
The book illustrates a geospatial and geostatistical approach to data analysis, modeling, risk assessment, and visualization, as well as landslide hazard management in the hilly region. This book investigates cutting-edge methodologies based on open source software and R statistical programming and modeling in current decision-making procedures, with a particular emphasis on recent advances in data mining techniques and robust modeling in torrential rainfall and earthquake induced landslide hazard.
Ai 2023 Advances In Artificial Intelligence
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Author : Tongliang Liu
language : en
Publisher: Springer Nature
Release Date : 2023-11-26
Ai 2023 Advances In Artificial Intelligence written by Tongliang Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-26 with Computers categories.
This two-volume set LNAI 14471-14472 constitutes the refereed proceedings of the 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, held in Brisbane, QLD, Australia during November 28 – December 1, 2023. The 23 full papers presented together with 59 short papers were carefully reviewed and selected from 213 submissions. They are organized in the following topics: computer vision; deep learning; machine learning and data mining; optimization; medical AI; knowledge representation and NLP; explainable AI; reinforcement learning; and genetic algorithm.
Ecai 2020
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Author : G. De Giacomo
language : en
Publisher: IOS Press
Release Date : 2020-09-11
Ecai 2020 written by G. De Giacomo and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-11 with Computers categories.
This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.
Intelligent Systems And Applications
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Author : Kohei Arai
language : en
Publisher: Springer Nature
Release Date : 2024-01-09
Intelligent Systems And Applications written by Kohei Arai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-09 with Computers categories.
The book is a unique collection of studies involving intelligent systems and applications of artificial intelligence in the real world to provide solutions to most vexing problems. IntelliSys received an overwhelming 605 papers which were put under strict double-blind peer-review for their novelty, originality and exhaustive research. Finally, 227 papers were sieved and chosen to be published in the proceedings. This book is a valuable collection of all the latest research in the field of artificial intelligence and smart systems. It provides a ready-made resource to all the readers keen on gaining information regarding the latest trends in intelligent systems. It also renders a sneak peek into the future world governed by artificial intelligence.
Machine Learning For Advanced Functional Materials
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Author : Nirav Joshi
language : en
Publisher: Springer Nature
Release Date : 2023-05-22
Machine Learning For Advanced Functional Materials written by Nirav Joshi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-22 with Science categories.
This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material’s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.
Ai Systems
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Author : Anand V Vemula
language : en
Publisher: Anand Vemula
Release Date :
Ai Systems written by Anand V Vemula and has been published by Anand Vemula this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
This book provides a comprehensive exploration of Artificial Intelligence systems, spanning foundational concepts, technical underpinnings, design principles, human interaction, ethics, applications, and future directions. It begins by establishing core definitions, historical context, and the various types of AI, from narrow task-specific models to the visionary goal of artificial general intelligence (AGI). The technical foundations delve into key algorithms, machine learning models, deep learning architectures, natural language processing, computer vision, reinforcement learning, and knowledge representation techniques that empower AI capabilities. Moving into design and architecture, the book examines data acquisition, model training, validation, deployment, and the challenges of scalability and optimization critical to building robust AI systems. The section on human-AI interaction addresses user interfaces, explainability, collaboration, and trust—highlighting the importance of transparency and interpretability for real-world adoption. Ethical considerations form a substantial focus, investigating issues of bias, fairness, privacy, safety, and governance frameworks necessary to ensure responsible AI development. The applications section showcases AI’s transformative impact across healthcare, finance, robotics, communication, and creative arts, illustrating both current achievements and future potential. Finally, the book surveys emerging technologies, explores the frontier of general AI, and reflects on societal impacts, including opportunities and risks. Overall, this work serves as a foundational guide for understanding the multidisciplinary landscape of AI systems, blending theory and practice while emphasizing the technical, ethical, and societal dimensions shaping the future of artificial intelligence.
Cross Modal Learning Adaptivity Prediction And Interaction
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Author : Jianwei Zhang
language : en
Publisher: Frontiers Media SA
Release Date : 2023-02-02
Cross Modal Learning Adaptivity Prediction And Interaction written by Jianwei Zhang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-02 with Science categories.
The purpose of this Research Topic is to reflect and discuss links between neuroscience, psychology, computer science and robotics with regards to the topic of cross-modal learning which has, in recent years, emerged as a new area of interdisciplinary research. The term cross-modal learning refers to the synergistic synthesis of information from multiple sensory modalities such that the learning that occurs within any individual sensory modality can be enhanced with information from one or more other modalities. Cross-modal learning is a crucial component of adaptive behavior in a continuously changing world, and examples are ubiquitous, such as: learning to grasp and manipulate objects; learning to walk; learning to read and write; learning to understand language and its referents; etc. In all these examples, visual, auditory, somatosensory or other modalities have to be integrated, and learning must be cross-modal. In fact, the broad range of acquired human skills are cross-modal, and many of the most advanced human capabilities, such as those involved in social cognition, require learning from the richest combinations of cross-modal information. In contrast, even the very best systems in Artificial Intelligence (AI) and robotics have taken only tiny steps in this direction. Building a system that composes a global perspective from multiple distinct sources, types of data, and sensory modalities is a grand challenge of AI, yet it is specific enough that it can be studied quite rigorously and in such detail that the prospect for deep insights into these mechanisms is quite plausible in the near term. Cross-modal learning is a broad, interdisciplinary topic that has not yet coalesced into a single, unified field. Instead, there are many separate fields, each tackling the concerns of cross-modal learning from its own perspective, with currently little overlap. We anticipate an accelerating trend towards integration of these areas and we intend to contribute to that integration. By focusing on cross-modal learning, the proposed Research Topic can bring together recent progress in artificial intelligence, robotics, psychology and neuroscience.
Ai 2018 Advances In Artificial Intelligence
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Author : Tanja Mitrovic
language : en
Publisher: Springer
Release Date : 2018-12-03
Ai 2018 Advances In Artificial Intelligence written by Tanja Mitrovic and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-03 with Computers categories.
This book constitutes the proceedings of the 31st Australasian Joint Conference on Artificial Intelligence, AI 2018, held in Wellington, New Zealand, in December 2018. The 50 full and 26 short papers presented in this volume were carefully reviewed and selected from 125 submissions. The paper were organized in topical sections named: agents, games and robotics; AI applications and innovations; computer vision; constraints and search; evolutionary computation; knowledge representation and reasoning; machine learning and data mining; planning and scheduling; and text mining and NLP.
Automated Machine Learning
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Author : Frank Hutter
language : en
Publisher: Springer
Release Date : 2019-05-17
Automated Machine Learning written by Frank Hutter and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-17 with Computers categories.
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.