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Название: Construct, Merge, Solve & Adapt: A Hybrid Metaheuristic for Combinatorial Optimization
Автор: Christian Blum
Издательство: Springer
Серия: Computational Intelligence Methods and Applications
Год: 2024
Страниц: 202
Язык: английский
Формат: pdf (true), epub
Размер: 30.7 MB

This book describes a general hybrid metaheuristic for combinatorial optimization labeled Construct, Merge, Solve & Adapt (CMSA). The general idea of standard CMSA is the following one. At each iteration, a number of valid solutions to the tackled problem instance are generated in a probabilistic way. Hereby, each of these solutions is composed of a set of solution components. The components found in the generated solutions are then added to an initially empty sub-instance. Next, an exact solver is applied in order to compute the best solution of the sub-instance, which is then used to update the sub-instance provided as input for the next iteration. In this way, the power of exact solvers can be exploited for solving problem instances much too large for a standalone application of the solver. Important research lines on CMSA from recent years are covered in this book. After an introductory chapter about standard CMSA, subsequent chapters cover a self-adaptive CMSA variant as well as a variant equipped with a learning component for improving the quality of the generated solutions over time. Furthermore, on outlining the advantages of using set-covering-based integer linear programming models for sub-instance solving, the author shows how to apply CMSA to problems naturally modelled by non-binary integer linear programming models. First of all, note that all approaches described in this book were implemented in C++ and compiled with one of the latest GNU compilers. Moreover, as ILP solver we used CPLEX version 22.1, if not otherwise stated. The book will be valuable and is intended for researchers, professionals and graduate students working in a wide range of fields, such as combinatorial optimization, algorithmics, metaheuristics, mathematical modeling, evolutionary computing, operations research, Artificial Intelligence, or statistics.

Название: Predictive Data Modelling for Biomedical Data and Imaging
Автор: Poonam Tanwar, Tapas Kumar, K. Kalaiselvi, Haider Raza
Издательство: River Publishers
Год: 2024
Страниц: 392
Язык: английский
Формат: pdf (true)
Размер: 19.4 MB

In this book, we embark on a journey into the realm of predictive data modeling for biomedical data and imaging in healthcare. It explores the potential of predictive analytics in the field of medical science through utilizing various tools and techniques to unravel insights and enhance patient care. This volume creates a medium for an interchange of knowledge from expertise and concerns in the field of predictive data modeling. In detail, the research work on this will include the effective use of predictive data modeling algorithms to run image analysis tasks for understanding. Predictive Data Modelling for Biomedical Data and Imaging is divided into three sections, namely Section I – Beginning of Predictive Data Modeling for Biomedical Data and Imaging/Healthcare, Section II – Data Design and Analysis for Biomedical Data and Imaging/Healthcare, and Section III – Case Studies of Predictive Analytics for Biomedical Data and Imaging/Healthcare. We hope this book will inspire further research and innovation in the field of predictive data modeling for biomedical data and imaging in healthcare. Better healthcare and precise medicine may be developed using AI and multipurpose ML systems. These platforms may be used to find the best routes to customize and affordable therapies. Deep Learning algorithms focus on inducement of alike conclusions as human beings ensure by incessantly examining data conferring to a specified rational structure. TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. TensorFlow is designed to be a flexible, scalable, and efficient platform for building and training Machine Learning models. Keras is an open-source neural network library written in Python.

Название: Artificial Intelligent Algorithms for Image Dehazing and Non-Uniform Illumination Enhancement
Автор: Teena Sharma, Nishchal K. Verma
Издательство: Springer
Серия: Algorithms for Intelligent Systems
Год: 2024
Страниц: 158
Язык: английский
Формат: pdf (true)
Размер: 10.5 MB

Artificial Intelligence (AI) has introduced a range of effective image processing tools that play a pivotal role in fortifying vision-based applications. The success of these applications hinges on the quality of input images, particularly those containing intricate textural details. The merit of an image is typically gauged through visible aspects such as illumination, color, and shapes, all of which can be adversely impacted by atmospheric factors like haze and uneven illumination. When haze particles are present, they impede the transmission of reflected light rays to the observer, resulting in an elevation of airlight and causing the image to exhibit a hazy appearance. Conversely, non-uniform illumination conditions contribute to a darkened depiction of the captured scene, leading to the obscuring of visible features. Consequently, image enhancement emerges as a crucial preliminary step, aiming to revive textural intricacies and refine image details. This enhancement process proves indispensable in bolstering the resilience of vision-based applications, ensuring their optimal performance even in challenging environmental circumstances. This book provides a detailed explanation of AI algorithms for image dehazing and non-uniform illumination enhancement. It outlines unsupervised and supervised methods with their advantages and limitations. The structure of this book will be helpful for a reader first to understand the unsupervised methods considering the scenario when it is impractical to have a large set of datasets for training an AI algorithm for image enhancement. Since there have been extensive advancements in Deep Learning methods and the availability of resources, this book provides a detailed insight into the run-time and accuracy adjustments of deep learning-based AI algorithms to learn the pattern better while performing enhancement.

Название: Сад. Город. Насіння
Номер: № 7(8) липень, 2017
Изд-во: ФОП О. В. Самуляк
К-во стр: 50
Формат: pdf
Размер: 33,58 Мб
Язык: Українська

В новому номері журнала "Сад. Город. Насіння" читайте:
- "САД": садові турботи - чому не плодоносять вишня і слива?; Дерева у другий період вегетації; Клумба: догляд улітку; Подбайте про яблуню та грушу; Боротьба з плодовою гниллю та інш.*
- "ГОРОД": визначаємо дозу добрива; підв’язуємо помідори; чому гірчать огірки? тощо*
- "НАСІННЯ": помідори: збір і зберігання насіння; Пора викопувати цибулини;

Название: Сборник газеты "Бабушкины пироги"
Издатель: ЧП Тригорло С.М.
Год/ месяц/ номер: 2005 / май / 05
Страниц: 64
Формат: pdf
Размер: 22.42 MB

Тематический кулинарный журнал, в котором много рецептов на все случаи жизни.
Тема выпуска: "Консервация".

Название: Другие Звёзды
Автор: Сергеев Артем
Издательство: СИ
Год: 2024
Формат: fb2, rtf
Размер: 10 Мб
Качество: Хорошее
Язык: Русский

Не вернулся с боевого задания – для лётчика-штурмовика эти слова не всегда означают гибель в бою. Ещё можно выжить, ещё можно вернуться, ещё можно выйти к своим, да всё можно, если тебе отчаянно повезёт, если звёзды окажутся на твоей стороне, но что делать, если эти звёзды будут совсем другими?

Название: Краткий справочник по открытым горным работам
Автор: Мельников Н.В.
Издательство: Недра
Год издания: 1982
Количество страниц: 418
Язык: русский
Формат: DJVU
Размер: 12,25 Мб

Приведены сведения, необходимые при проектировании открытой разработки месторождений, даны схемы вскрытия и системы разработки, способы проведения траншей, определены условия устойчивости откосов уступов и бортов карьеров, рассмотрены буровзрывные, экскаваторные и скреперные работы, гидромеханизация, схемы транспортирования, устройство отвалов и рекультивация.
Четвертое издание (3-е изд. - 1974 г.) дополнено новыми материалами по результатам научных исследований в области открытых разработок, приведены данные по усреднению качества полезных ископаемых, включена уточненная методика расчета технико-экономических показателей при проектировании карьеров.
Для широкого круга инженерно-технических работников горнодобывающих предприятий, проектных и научно-исследовательских институтов.