Machine Learning Methods For Engineering Application Development

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Machine Learning Methods for Engineering Application Development

Prasad Lokulwar,Basant Verma,N. Thillaiarasu
Machine Learning Methods for Engineering Application Development Book PDF

Author : Prasad Lokulwar,Basant Verma,N. Thillaiarasu
Publisher : Bentham Science Publishers
Page : 240 pages
File Size : 48,8 Mb
Release : 2022-11-11
Category : Computers
ISBN : 9789815079197

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Machine Learning Methods for Engineering Application Development by Prasad Lokulwar,Basant Verma,N. Thillaiarasu Pdf

This book is a quick review of machine learning methods for engineeringapplications. It provides an introduction to the principles of machine learningand common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field.Next, it offers some guidelines on applying machine learning methods to softwareengineering tasks. Finally, it gives an outlook into some of the futuredevelopments and possibly new research areas of machine learning and artificialintelligence in general.Techniques highlighted in the book include: Bayesian models, supportvector machines, decision tree induction, regression analysis, and recurrent andconvolutional neural network. Finally, it also intends to be a reference book. Key Features:Describes real-world problems that can be solved using machine learningExplains methods for directly applying machine learning techniques to concrete real-world problemsExplains concepts used in Industry 4.0 platforms, including the use and integration of AI, ML, Big Data, NLP, and the Internet of Things (IoT). It does not require prior knowledge of the machine learning This book is meantto be an introduction to artificial intelligence (AI), machine earning, and itsapplications in Industry 4.0. It explains the basic mathematical principlesbut is intended to be understandable for readers who do not have a backgroundin advanced mathematics.

Advances in Machine Learning Applications in Software Engineering

Zhang, Du,Tsai, Jeffery J.P.
Advances in Machine Learning Applications in Software Engineering Book PDF

Author : Zhang, Du,Tsai, Jeffery J.P.
Publisher : IGI Global
Page : 498 pages
File Size : 41,6 Mb
Release : 2006-10-31
Category : Computers
ISBN : 9781591409434

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Advances in Machine Learning Applications in Software Engineering by Zhang, Du,Tsai, Jeffery J.P. Pdf

"This book provides analysis, characterization and refinement of software engineering data in terms of machine learning methods. It depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality while offering readers suggestions by proposing future work in this emerging research field"--Provided by publisher.

Machine Learning Applications in Software Engineering

Du Zhang,Jeffrey J. P. Tsai
Machine Learning Applications in Software Engineering Book PDF

Author : Du Zhang,Jeffrey J. P. Tsai
Publisher : World Scientific
Page : 368 pages
File Size : 54,7 Mb
Release : 2005
Category : Computers
ISBN : 9789812560940

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Machine Learning Applications in Software Engineering by Du Zhang,Jeffrey J. P. Tsai Pdf

A collection of previously published articles from a variety of publications.

Artificial Intelligence Methods For Software Engineering

Meir Kalech,Rui Abreu,Mark Last
Artificial Intelligence Methods For Software Engineering Book PDF

Author : Meir Kalech,Rui Abreu,Mark Last
Publisher : World Scientific
Page : 457 pages
File Size : 40,9 Mb
Release : 2021-06-15
Category : Computers
ISBN : 9789811239939

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Artificial Intelligence Methods For Software Engineering by Meir Kalech,Rui Abreu,Mark Last Pdf

Software is an integral part of our lives today. Modern software systems are highly complex and often pose new challenges in different aspects of Software Engineering (SE).Artificial Intelligence (AI) is a growing field in computer science that has been proven effective in applying and developing AI techniques to address various SE challenges.This unique compendium covers applications of state-of-the-art AI techniques to the key areas of SE (design, development, debugging, testing, etc).All the materials presented are up-to-date. This reference text will benefit researchers, academics, professionals, and postgraduate students in AI, machine learning and software engineering.Related Link(s)

Machine Learning and Optimization for Engineering Design

Apoorva S. Shastri,Kailash Shaw,Mangal Singh
Machine Learning and Optimization for Engineering Design Book PDF

Author : Apoorva S. Shastri,Kailash Shaw,Mangal Singh
Publisher : Springer Nature
Page : 175 pages
File Size : 55,6 Mb
Release : 2024-01-27
Category : Computers
ISBN : 9789819974566

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Machine Learning and Optimization for Engineering Design by Apoorva S. Shastri,Kailash Shaw,Mangal Singh Pdf

This book aims to provide a collection of state-of-the-art scientific and technical research papers related to machine learning-based algorithms in the field of optimization and engineering design. The theoretical and practical development for numerous engineering applications such as smart homes, ICT-based irrigation systems, academic success prediction, future agro-industry for crop production, disease classification in plants, dental problems and solutions, loan eligibility processing, etc., and their implementation with several case studies and literature reviews are included as self-contained chapters. Additionally, the book intends to highlight the importance of study and effectiveness in addressing the time and space complexity of problems and enhancing accuracy, analysis, and validations for different practical applications by acknowledging the state-of-the-art literature survey. The book targets a larger audience by exploring multidisciplinary research directions such as computer vision, machine learning, artificial intelligence, modified/newly developed machine learning algorithms, etc., to enhance engineering design applications for society. State-of-the-art research work with illustrations and exercises along with pseudo-code has been provided here.

Design of Intelligent Applications using Machine Learning and Deep Learning Techniques

Ramchandra Sharad Mangrulkar,Antonis Michalas,Narendra Shekokar,Meera Narvekar,Pallavi Vijay Chavan
Design of Intelligent Applications using Machine Learning and Deep Learning Techniques Book PDF

Author : Ramchandra Sharad Mangrulkar,Antonis Michalas,Narendra Shekokar,Meera Narvekar,Pallavi Vijay Chavan
Publisher : CRC Press
Page : 446 pages
File Size : 42,9 Mb
Release : 2021-08-15
Category : Computers
ISBN : 9781000423839

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Design of Intelligent Applications using Machine Learning and Deep Learning Techniques by Ramchandra Sharad Mangrulkar,Antonis Michalas,Narendra Shekokar,Meera Narvekar,Pallavi Vijay Chavan Pdf

Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent models developed using ML and DL are effectively designed and are fully investigated – bringing in practical applications in many fields such as health care, agriculture and security. These algorithms can only be successfully applied in the context of data computing and analysis. Today, ML and DL have created conditions for potential developments in detection and prediction. Apart from these domains, ML and DL are found useful in analysing the social behaviour of humans. With the advancements in the amount and type of data available for use, it became necessary to build a means to process the data and that is where deep neural networks prove their importance. These networks are capable of handling a large amount of data in such fields as finance and images. This book also exploits key applications in Industry 4.0 including: · Fundamental models, issues and challenges in ML and DL. · Comprehensive analyses and probabilistic approaches for ML and DL. · Various applications in healthcare predictions such as mental health, cancer, thyroid disease, lifestyle disease and cardiac arrhythmia. · Industry 4.0 applications such as facial recognition, feather classification, water stress prediction, deforestation control, tourism and social networking. · Security aspects of Industry 4.0 applications suggest remedial actions against possible attacks and prediction of associated risks. - Information is presented in an accessible way for students, researchers and scientists, business innovators and entrepreneurs, sustainable assessment and management professionals. This book equips readers with a knowledge of data analytics, ML and DL techniques for applications defined under the umbrella of Industry 4.0. This book offers comprehensive coverage, promising ideas and outstanding research contributions, supporting further development of ML and DL approaches by applying intelligence in various applications.

Fundamentals and Methods of Machine and Deep Learning

Pradeep Singh
Fundamentals and Methods of Machine and Deep Learning Book PDF

Author : Pradeep Singh
Publisher : John Wiley & Sons
Page : 484 pages
File Size : 51,5 Mb
Release : 2022-03-02
Category : Computers
ISBN : 9781119821250

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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.

Machine Learning for Developers

Rodolfo Bonnin
Machine Learning for Developers Book PDF

Author : Rodolfo Bonnin
Publisher : Packt Publishing Ltd
Page : 264 pages
File Size : 42,9 Mb
Release : 2017-10-26
Category : Computers
ISBN : 9781786466969

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Machine Learning for Developers by Rodolfo Bonnin Pdf

Your one-stop guide to becoming a Machine Learning expert. About This Book Learn to develop efficient and intelligent applications by leveraging the power of Machine Learning A highly practical guide explaining the concepts of problem solving in the easiest possible manner Implement Machine Learning in the most practical way Who This Book Is For This book will appeal to any developer who wants to know what Machine Learning is and is keen to use Machine Learning to make their day-to-day apps fast, high performing, and accurate. Any developer who wants to enter the field of Machine Learning can effectively use this book as an entry point. What You Will Learn Learn the math and mechanics of Machine Learning via a developer-friendly approach Get to grips with widely used Machine Learning algorithms/techniques and how to use them to solve real problems Get a feel for advanced concepts, using popular programming frameworks. Prepare yourself and other developers for working in the new ubiquitous field of Machine Learning Get an overview of the most well known and powerful tools, to solve computing problems using Machine Learning. Get an intuitive and down-to-earth introduction to current Machine Learning areas, and apply these concepts on interesting and cutting-edge problems. In Detail Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This book is a systematic guide teaching you how to implement various Machine Learning techniques and their day-to-day application and development. You will start with the very basics of data and mathematical models in easy-to-follow language that you are familiar with; you will feel at home while implementing the examples. The book will introduce you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement Regression, Clustering, classification, Neural networks, and more with fun examples. As you get to grips with the techniques, you'll learn to implement those concepts to solve real-world scenarios for ML applications such as image analysis, Natural Language processing, and anomaly detections of time series data. By the end of the book, you will have learned various ML techniques to develop more efficient and intelligent applications. Style and approach This book gives you a glimpse of Machine Learning Models and the application of models at scale using clustering, classification, regression and reinforcement learning with fun examples. Hands-on examples will be presented to understand the power of problem solving with Machine Learning and Advanced architectures, software installation, and configuration.

Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications

T. Ananth Kumar,E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Simulation and Analysis of Mathematical Methods in Real Time Engineering Applications Book PDF

Author : T. Ananth Kumar,E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Publisher : John Wiley & Sons
Page : 370 pages
File Size : 52,5 Mb
Release : 2021-09-08
Category : Mathematics
ISBN : 9781119785378

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Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications by T. Ananth Kumar,E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi Pdf

SIMULATIONS AND ANALYSIS of Mathematical Methods Written and edited by a group of international experts in the field, this exciting new volume covers the state of the art of real-time applications of computer science using mathematics. This breakthrough edited volume highlights the security, privacy, artificial intelligence, and practical approaches needed by engineers and scientists in all fields of science and technology. It highlights the current research, which is intended to advance not only mathematics but all areas of science, research, and development, and where these disciplines intersect. As the book is focused on emerging concepts in machine learning and artificial intelligence algorithmic approaches and soft computing techniques, it is an invaluable tool for researchers, academicians, data scientists, and technology developers. The newest and most comprehensive volume in the area of mathematical methods for use in real-time engineering, this groundbreaking new work is a must-have for any engineer or scientist’s library. Also useful as a textbook for the student, it is a valuable contribution to the advancement of the science, both a working handbook for the new hire or student, and a reference for the veteran engineer.

Machine Learning Engineering in Action

Ben Wilson
Machine Learning Engineering in Action Book PDF

Author : Ben Wilson
Publisher : Simon and Schuster
Page : 574 pages
File Size : 50,5 Mb
Release : 2022-04-26
Category : Computers
ISBN : 9781617298714

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Machine Learning Engineering in Action by Ben Wilson Pdf

Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you’ll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You’ll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You’ll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author’s extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists who know machine learning and the basics of object-oriented programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer. Table of Contents PART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING 1 What is a machine learning engineer? 2 Your data science could use some engineering 3 Before you model: Planning and scoping a project 4 Before you model: Communication and logistics of projects 5 Experimentation in action: Planning and researching an ML project 6 Experimentation in action: Testing and evaluating a project 7 Experimentation in action: Moving from prototype to MVP 8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization PART 2 PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML 9 Modularity for ML: Writing testable and legible code 10 Standards of coding and creating maintainable ML code 11 Model measurement and why it’s so important 12 Holding on to your gains by watching for drift 13 ML development hubris PART 3 DEVELOPING PRODUCTION MACHINE LEARNING CODE 14 Writing production code 15 Quality and acceptance testing 16 Production infrastructure

Automated Software Engineering: A Deep Learning-Based Approach

Suresh Chandra Satapathy,Ajay Kumar Jena,Jagannath Singh,Saurabh Bilgaiyan
Automated Software Engineering A Deep Learning Based Approach Book PDF

Author : Suresh Chandra Satapathy,Ajay Kumar Jena,Jagannath Singh,Saurabh Bilgaiyan
Publisher : Springer Nature
Page : 118 pages
File Size : 45,8 Mb
Release : 2020-01-07
Category : Technology & Engineering
ISBN : 9783030380069

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Automated Software Engineering: A Deep Learning-Based Approach by Suresh Chandra Satapathy,Ajay Kumar Jena,Jagannath Singh,Saurabh Bilgaiyan Pdf

This book discusses various open issues in software engineering, such as the efficiency of automated testing techniques, predictions for cost estimation, data processing, and automatic code generation. Many traditional techniques are available for addressing these problems. But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software’s complexity. Hence, many previously used methods are proving insufficient to solve the problems now arising in software development. The book highlights a number of unique problems and effective solutions that reflect the state-of-the-art in software engineering. Deep learning is the latest computing technique, and is now gaining popularity in various fields of software engineering. This book explores new trends and experiments that have yielded promising solutions to current challenges in software engineering. As such, it offers a valuable reference guide for a broad audience including systems analysts, software engineers, researchers, graduate students and professors engaged in teaching software engineering.

Data Mining and Machine Learning Applications

Rohit Raja,Kapil Kumar Nagwanshi,Sandeep Kumar,K. Ramya Laxmi
Data Mining and Machine Learning Applications Book PDF

Author : Rohit Raja,Kapil Kumar Nagwanshi,Sandeep Kumar,K. Ramya Laxmi
Publisher : John Wiley & Sons
Page : 500 pages
File Size : 54,6 Mb
Release : 2022-01-26
Category : Computers
ISBN : 9781119792505

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Data Mining and Machine Learning Applications by Rohit Raja,Kapil Kumar Nagwanshi,Sandeep Kumar,K. Ramya Laxmi Pdf

DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

Handbook of Deep Learning in Biomedical Engineering

Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
Handbook of Deep Learning in Biomedical Engineering Book PDF

Author : Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
Publisher : Academic Press
Page : 320 pages
File Size : 46,8 Mb
Release : 2020-11-12
Category : Science
ISBN : 9780128230473

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Handbook of Deep Learning in Biomedical Engineering by Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar Pdf

Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of Deep Learning applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer’s, ADHD, and ASD, tumor prediction, as well as translational multimodal imaging analysis. Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer’s, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography

Applications of Machine Learning

Prashant Johri,Jitendra Kumar Verma,Sudip Paul
Applications of Machine Learning Book PDF

Author : Prashant Johri,Jitendra Kumar Verma,Sudip Paul
Publisher : Springer Nature
Page : 404 pages
File Size : 55,8 Mb
Release : 2020-05-04
Category : Technology & Engineering
ISBN : 9789811533570

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Applications of Machine Learning by Prashant Johri,Jitendra Kumar Verma,Sudip Paul Pdf

This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics.

Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering

Gebrail Bekdas,Sinan Melih Nigdeli,Melda Yucel
Artificial Intelligence and Machine Learning Applications in Civil Mechanical and Industrial Engineering Book PDF

Author : Gebrail Bekdas,Sinan Melih Nigdeli,Melda Yucel
Publisher : Engineering Science Reference
Page : 312 pages
File Size : 42,7 Mb
Release : 2019
Category : Artificial intelligence
ISBN : 1799803023

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Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering by Gebrail Bekdas,Sinan Melih Nigdeli,Melda Yucel Pdf

"This book examines the application of artificial intelligence and machine learning civil, mechanical, and industrial engineering"--