Kumar Abhishek – författare
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Multi-criteria decision-making (MCDM) has gained vast popularity for its ability to help make decisions in the presence of various similar and conflicting choices.This new volume applies the MCDM theory to solving problems and challenges in manufacturing environments. It discusses using MCDM computational methods to evaluate and select the most optimal solution or method for real-world, real-time manufacutring engineering issues. It details the decision-making process in relation materials selection; identification, assessment, and evaluation of risk; sustainability assessment; selection of green suppliers; and more.
The chapter authors demonstrate the application of myriad MCDM techniques in decision-making, including MADM (multiple attribute decision-making), DEA (data envelopment analysis), fuzzy TOPSIS (technique for order preference by similarities to ideal solution), fuzzy-VIKOR (multicriteria optimization and compromise solution); MOORA (multi-objective optimization on the basis of ratio analysis), EWM (entropy weight method), (AHP) analytic hierarchy process, TODIM (TOmada de Decisao Interativa Multicriterio), and others. The volume illustrates these MCDM models in several industries and industrial processes, including for experimental analysis and optimization of drilling of glass fiber reinforced plastic, in the textile industries, for selection of refrigerants for domestic applications, and others.
2 337 kr
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Multi-criteria decision-making (MCDM) has gained vast popularity for its ability to help make decisions in the presence of various similar and conflicting choices.This new volume applies the MCDM theory to solving problems and challenges in manufacturing environments. It discusses using MCDM computational methods to evaluate and select the most optimal solution or method for real-world, real-time manufacutring engineering issues. It details the decision-making process in relation materials selection; identification, assessment, and evaluation of risk; sustainability assessment; selection of green suppliers; and more.
The chapter authors demonstrate the application of myriad MCDM techniques in decision-making, including MADM (multiple attribute decision-making), DEA (data envelopment analysis), fuzzy TOPSIS (technique for order preference by similarities to ideal solution), fuzzy-VIKOR (multicriteria optimization and compromise solution); MOORA (multi-objective optimization on the basis of ratio analysis), EWM (entropy weight method), (AHP) analytic hierarchy process, TODIM (TOmada de Decisao Interativa Multicriterio), and others. The volume illustrates these MCDM models in several industries and industrial processes, including for experimental analysis and optimization of drilling of glass fiber reinforced plastic, in the textile industries, for selection of refrigerants for domestic applications, and others.
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Take your machine learning expertise to the next level with this essential guide, utilizing libraries like imbalanced-learn, PyTorch, scikit-learn, pandas, and NumPy to maximize model performance and tackle imbalanced data
Key Features
Understand how to use modern machine learning frameworks with detailed explanations, illustrations, and code samplesLearn cutting-edge deep learning techniques to overcome data imbalanceExplore different methods for dealing with skewed data in ML and DL applicationsPurchase of the print or Kindle book includes a free eBook in the PDF formatBook Description
As machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other. Many machine learning algorithms assume an equilibrium between majority and minority classes, leading to suboptimal performance on imbalanced data. This comprehensive guide helps you address this class imbalance to significantly improve model performance.Machine Learning for Imbalanced Data begins by introducing you to the challenges posed by imbalanced datasets and the importance of addressing these issues. It then guides you through techniques that enhance the performance of classical machine learning models when using imbalanced data, including various sampling and cost-sensitive learning methods.As you progress, you’ll delve into similar and more advanced techniques for deep learning models, employing PyTorch as the primary framework. Throughout the book, hands-on examples will provide working and reproducible code that’ll demonstrate the practical implementation of each technique.By the end of this book, you’ll be adept at identifying and addressing class imbalances and confidently applying various techniques, including sampling, cost-sensitive techniques, and threshold adjustment, while using traditional machine learning or deep learning models.What you will learn
Use imbalanced data in your machine learning models effectivelyExplore the metrics used when classes are imbalancedUnderstand how and when to apply various sampling methods such as over-sampling and under-samplingApply data-based, algorithm-based, and hybrid approaches to deal with class imbalanceCombine and choose from various options for data balancing while avoiding common pitfallsUnderstand the concepts of model calibration and threshold adjustment in the context of dealing with imbalanced datasetsWho this book is for
This book is for machine learning practitioners who want to effectively address the challenges of imbalanced datasets in their projects. Data scientists, machine learning engineers/scientists, research scientists/engineers, and data scientists/engineers will find this book helpful. Though complete beginners are welcome to read this book, some familiarity with core machine learning concepts will help readers maximize the benefits and insights gained from this comprehensive resource.
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Skin Image Analysis, and Computer-Aided Pelvic Imaging for Female Health
10th International Workshop, ISIC 2025, and First International Workshop, CAPI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings
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This book contains high-quality papers presented in the conference Recent Advances in Mechanical Infrastructure (ICRAM 2020) held at IITRAM, Ahmedabad, India, from 21-23 August 2020. The topics covered in this book are recent advances in thermal infrastructure, manufacturing infrastructure and infrastructure planning and design.
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