Kumar Abhishek – författare

his book covers the history, biology, pathogenesis, clinical manifestations, and the latest advancements in diagnosis, treatment, and control strategies against leishmaniasis. Chapter 1 presents the life cycle of Leishmania parasites, focusing on vector biology and transmission dynamics, while elucidating the mechanisms of infection and disease progression. The clinical manifestations of cutaneous, visceral, and mucocutaneous leishmaniasis are examined in detail, alongside the immune response of the host and the evasion strategies by the parasite. It also investigates the genetic and molecular diversity of Leishmania, highlighting their virulence mechanisms, and reviews diagnostic methods from traditional techniques to advanced molecular diagnostics. The chapter also covers the current therapeutic approaches, including potential drugs and combination therapies along with integrated vector management strategies for effective disease control. The book explores preventive measures, community interventions, and public health strategies, addressing challenges in leishmaniasis research and identifying knowledge gaps. It presents real-world applications and outcomes through case studies and clinical trials. This book is useful for researchers and students of infectious diseases, tropical medicine, and parasitology.Key Features:Explores the pathogenesis and epidemiology of leishmaniasis, providing a global perspective on its public health impactDetails the life cycle of Leishmania parasites, focusing on vector biology, transmission dynamics, and mechanisms of infectionExamines the clinical manifestations of cutaneous, visceral, and mucocutaneous leishmaniasis, including the host immune response and parasite evasion strategiesReviews current therapeutic approaches, including potential drugs, combination therapies, and integrated vector management strategies for effective controlPresents preventive measures, community interventions, public health strategies, and the challenges in leishmaniasis research

The text provides a comprehensive and forward-looking exploration of the integration between low-power CMOS VLSI design and cutting-edge technologies such as artificial intelligence and machine learning.Explores how artificial intelligence techniques are revolutionizing VLSI design, focusing on optimizing low-power circuits and systems.Presents the latest advancements and research in both artificial intelligence and VLSI design, such as FinFET and nanosheet-based logic, analog integrated circuits design, and optimization with artificial intelligence.Offers perspectives on future trends and potential developments in the integration of artificial intelligence with VLSI design to reduce the complexity and time-consuming process of semiconductor IC design.Addresses techniques for achieving energy efficiency in both digital and analog components and presents future trends in low-power VLSI design.Discusses topics such as artificial intelligence-driven adaptive power management in VLSI design and neural networks in low-power VLSI architectures.The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, microelectronics, VLSI design, artificial intelligence, computer science and engineering.

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.

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 dataKey FeaturesUnderstand 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 DescriptionAs 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 learnUse 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 forThis 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.

This book constitutes the refereed joint proceedings of the First International Workshop on Computer-Aided Pelvic Imaging for Female Health, CAPI 2025, and the 10th International Workshop on Skin Image Analysis, ISIC 2025, held in conjunction with the 28th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2025, in , Daejeon, South Korea, in September 2025.The 7 full papers and 1 short paper presented at CAPI 2025, and the 6 full papers presented at ISIC 2018 were carefully reviewed and selected.CAPI 2025 focuses on women’s reproductive health by linking the growing community with researchers working in Computer Assisted Intervention.ISIC 2025 focuses on advancements and knowledge dissemination in the field of skin image analysis, raising awareness and interest for these socially valuable tasks.

The book presents latest research-based innovations in the field of mechanical infrastructure presented in the International Conference on Recent Advances in Mechanical Infrastructure (ICRAM 2021).

The book presents latest research-based innovations in the field of mechanical infrastructure presented in the International Conference on Recent Advances in Mechanical Infrastructure (ICRAM 2021).

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.

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 broad research topics included in this volume are material characterization, polymer composites and smart materials, manufacturing techniques, green manufacturing, sustainable manufacturing, instrumentation and control.