Application Of Soft Computing Machine Learning Deep Learning And Optimizations In Geoengineering And Geoscience

Author : Wengang Zhang,Yanmei Zhang,Xin Gu,Chongzhi Wu,Liang Han
Publisher : Springer Nature
Page : 143 pages
File Size : 44,7 Mb
Release : 2021-10-12
Category : Science
ISBN : 9789811668357

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Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering and Geoscience by Wengang Zhang,Yanmei Zhang,Xin Gu,Chongzhi Wu,Liang Han Pdf

This book summarizes the application of soft computing techniques, machine learning approaches, deep learning algorithms and optimization techniques in geoengineering including tunnelling, excavation, pipelines, etc. and geoscience including the geohazards, rock and soil properties, etc. The book features state-of-the-art studies on use of SC,ML,DL and optimizations in Geoengineering and Geoscience. Considering these points and understanding, this book will be compiled with highly focussed chapters that will discuss the application of SC,ML,DL and optimizations in Geoengineering and Geoscience. Target audience: (1) Students of UG, PG, and Research Scholars: Several applications of SC,ML,DL and optimizations in Geoengineering and Geoscience can help students to enhance their knowledge in this domain. (2) Industry Personnel and Practitioner: Practitioners from different fields can be able to implement standard and advanced SC,ML,DL and optimizations for solving critical problems of civil engineering.

Author : Hoang Nguyen,Xuan Nam Bui,Yosoon Choi,Wengang Zhang,Jian Zhou,Erkan Topal
Publisher : Elsevier
Page : 496 pages
File Size : 50,5 Mb
Release : 2023-11-17
Category : Business & Economics
ISBN : 9780443187643

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Applications of Artificial Intelligence in Mining and Geotechnical Geoengineering by Hoang Nguyen,Xuan Nam Bui,Yosoon Choi,Wengang Zhang,Jian Zhou,Erkan Topal Pdf

Applications of Artificial Intelligence in Mining, Geotechnical and Geoengineering provides recent advances in mining, geotechnical and geoengineering, as well as applications of artificial intelligence in these areas. It serves as the first book on applications of artificial intelligence in mining, geotechnical and geoengineering, providing an opportunity for researchers, scholars, engineers, practitioners and data scientists from all over the world to understand current developments and applications. Topics covered include slopes, open-pit mines, quarries, shafts, tunnels, caverns, underground mines, metro systems, dams and hydro-electric stations, geothermal energy, petroleum engineering, and radioactive waste disposal. In the geotechnical and geoengineering aspects, topics of specific interest include, but are not limited to, foundation, dam, tunneling, geohazard, geoenvironmental and petroleum engineering, rock mechanics, geotechnical engineering, soil mechanics and foundation engineering, civil engineering, hydraulic engineering, petroleum engineering, engineering geology, etc.

Author : Taesam Lee,Vijay P. Singh,Kyung Hwa Cho
Publisher : Springer Nature
Page : 215 pages
File Size : 44,6 Mb
Release : 2021-01-27
Category : Science
ISBN : 9783030647773

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Deep Learning for Hydrometeorology and Environmental Science by Taesam Lee,Vijay P. Singh,Kyung Hwa Cho Pdf

This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.

Author : Prasenjit Chatterjee,Morteza Yazdani,Francisco Fernández-Navarro,Javier Pérez-Rodríguez
Publisher : CRC Press
Page : 339 pages
File Size : 47,8 Mb
Release : 2023-01-09
Category : Computers
ISBN : 9781000642353

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Machine Learning Algorithms and Applications in Engineering by Prasenjit Chatterjee,Morteza Yazdani,Francisco Fernández-Navarro,Javier Pérez-Rodríguez Pdf

Machine Learning (ML) is a sub field of artificial intelligence that uses soft computing and algorithms to enable computers to learn on their own and identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical, transport, supply chain and logistics, to manufacturing and industrial. Through numerous case studies, it will assist researchers and practitioners in selecting the correct options and strategies for managing organizational tasks.

Author : Shuvajit Bhattacharya,Haibin Di
Publisher : Elsevier
Page : 378 pages
File Size : 54,6 Mb
Release : 2022-05-18
Category : Computers
ISBN : 9780128223086

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Advances in Subsurface Data Analytics by Shuvajit Bhattacharya,Haibin Di Pdf

Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume. Covers fundamentals of simple machine learning and deep learning algorithms, and physics-based approaches written by practitioners in academia and industry Presents detailed case studies of individual machine learning algorithms and optimal strategies in subsurface characterization around the world Offers an analysis of future trends in machine learning in geosciences

Author : Mikhail Kanevski,Vadim Timonin,Alexi Pozdnukhov
Publisher : CRC Press
Page : 384 pages
File Size : 50,8 Mb
Release : 2009-06-09
Category : Computers
ISBN : 9780849382376

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Machine Learning for Spatial Environmental Data by Mikhail Kanevski,Vadim Timonin,Alexi Pozdnukhov Pdf

This book discusses machine learning algorithms, such as artificial neural networks of different architectures, statistical learning theory, and Support Vector Machines used for the classification and mapping of spatially distributed data. It presents basic geostatistical algorithms as well. The authors describe new trends in machine learning and their application to spatial data. The text also includes real case studies based on environmental and pollution data. It includes a CD-ROM with software that will allow both students and researchers to put the concepts to practice.

Author : Neeraj Kumar,S. Balamurugan,Karthikeyan N,Sivakumar M,Dinesh Goyal
Publisher : Wiley-Scrivener
Page : 400 pages
File Size : 52,8 Mb
Release : 2021-05-18
Category : Electronic
ISBN : 1119710162

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Deep Learning Application in Image Processing by Neeraj Kumar,S. Balamurugan,Karthikeyan N,Sivakumar M,Dinesh Goyal Pdf

Author : Pradeep Singh
Publisher : John Wiley & Sons
Page : 480 pages
File Size : 44,9 Mb
Release : 2022-02-01
Category : Computers
ISBN : 9781119821885

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Fundamentals and Methods of Machine and Deep Learning by Pradeep Singh Pdf

FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.

Author : Paresh Chandra Deka
Publisher : CRC Press
Page : 201 pages
File Size : 51,7 Mb
Release : 2019-10-28
Category : Computers
ISBN : 9780429836657

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A Primer on Machine Learning Applications in Civil Engineering by Paresh Chandra Deka Pdf

Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included. Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil engineering disciplines Provides ideas on how and where to apply machine learning techniques for problem solving Covers water resources and hydrological modeling, geotechnical engineering, construction engineering and management, coastal and marine engineering, and geographical information systems Includes MATLAB® exercises

Author : Hitoshi Iba,Nasimul Noman
Publisher : Springer Nature
Page : 437 pages
File Size : 51,7 Mb
Release : 2020-05-20
Category : Computers
ISBN : 9789811536854

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Deep Neural Evolution by Hitoshi Iba,Nasimul Noman Pdf

This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and automatic game playing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data. Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL. EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes. Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research —from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN). This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice.

Author : Uday Shankar Shanthamallu,Andreas Spanias
Publisher : Morgan & Claypool Publishers
Page : 123 pages
File Size : 49,6 Mb
Release : 2021-12-22
Category : Technology & Engineering
ISBN : 9781636392660

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Machine and Deep Learning Algorithms and Applications by Uday Shankar Shanthamallu,Andreas Spanias Pdf

This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning to address a variety of needs and applications. The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new software platforms. They have strong capabilities in terms of learning complex mapping functions for different types of data. We organize the book as follows. The book starts by introducing concepts in supervised, unsupervised, and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. We then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, we cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts.

Author : Daniel Asante Otchere
Publisher : CRC Press
Page : 368 pages
File Size : 41,9 Mb
Release : 2024-02-06
Category : Science
ISBN : 9781003860228

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Data Science and Machine Learning Applications in Subsurface Engineering by Daniel Asante Otchere Pdf

This book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments. This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.

Author : J. Joshua Thomas
Publisher : Unknown
Page : 128 pages
File Size : 41,6 Mb
Release : 2019-11
Category : Big data
ISBN : 179981193X

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Deep Learning Techniques and Optimization Strategies in Big Data Analytics by J. Joshua Thomas Pdf

"This book examines the application of artificial intelligence in machine learning, data mining in unstructured data sets or databases, web mining, and information retrieval"--

Author : Punit Gupta,Mayank Kumar Goyal,Sudeshna Chakraborty,Ahmed A Elngar
Publisher : CRC Press
Page : 232 pages
File Size : 48,6 Mb
Release : 2022-02-17
Category : Computers
ISBN : 9781000542257

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Machine Learning and Optimization Models for Optimization in Cloud by Punit Gupta,Mayank Kumar Goyal,Sudeshna Chakraborty,Ahmed A Elngar Pdf

Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition. This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud. Key Features · Comprehensive introduction to cloud architecture and its service models. · Vulnerability and issues in cloud SAAS, PAAS and IAAS · Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models · Detailed study of optimization techniques, and fault management techniques in multi layered cloud. · Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network. · Advanced study of algorithms using artificial intelligence for optimization in cloud · Method for power efficient virtual machine placement using neural network in cloud · Method for task scheduling using metaheuristic algorithms. · A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment. This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.

Author : Kundan Meshram
Publisher : Elsevier
Page : 220 pages
File Size : 55,5 Mb
Release : 2023-10-01
Category : Technology & Engineering
ISBN : 9780443153631

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Machine Learning Applications in Civil Engineering by Kundan Meshram Pdf

Machine Learning Applications in Civil Engineering discusses machine learning and deep learning models for different civil engineering applications. These models work for stochastic methods wherein internal processing is done using randomized prototypes. The book explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency. It introduces Machine Learning and its applications to different Civil Engineering tasks, including Basic Machine Learning Models for data pre-processing, models for data representation, classification models for Civil Engineering Applications, Bioinspired Computing models for Civil Engineering, and their case studies. Using this book, civil engineering students and researchers can deep dive into Machine Learning, and identify various solutions to practical Civil Engineering tasks. Introduces various ML models for Civil Engineering Applications that will assist readers in their analysis of design and development interfaces for building these applications Reviews different lacunas and challenges in current models used for Civil Engineering scenarios Explores designs for customized components for optimum system deployment Explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency