This book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as pneumonia detection in chest X-ray images, object detection and classification, RGB and depth image fusion, NLP tasks, dimensionality estimation, time series forecasting, building electric power grid for controllable energy resources, guiding charities in maximizing donations, and robotic control in industrial environments. Novel ways of using convolutional neural networks, recurrent neural network, autoencoder, deep evidential active learning, deep rapid class augmentation techniques, BERT models, multi-task learning networks, model compression and acceleration techniques, and conditional Feature Augmented and Transformed GAN (cFAT-GAN) for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and algorithms in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

This book focuses on cases and studies of interest to mechanical engineers and industrial technicians. The considered applications in this volume are widely used in several industrial fields particularly in the automotive and aviation industries. Readers will understand the theory and techniques which are used in each application covered in each chapter. Volume 3 includes the following topics: Numerical simulations of three-dimensional laminar mixed convection heat transfer of water-based-Al2O3 nanofluid in an open cubic cavity with a heated block. Nonlinear formulations of Element-Free Galerkin Method (EFGM) for large deformation analysis of Ogden’s hyperelastic materials, emphasizing incompressibility and mesh distortion avoidance. Development of a 3D numerical model with LS-DYNA using a coupled SPH-FEM method to simulate hydraulic behavior of a Ski-Jump Spillway with dentates, showcasing precision through validation. Exploration of enhancing the inlet system of an LPG-H2 fueled engine using a static inclined blade turbine, analyzed through Computational Fluid Dynamics (CFD) simulations. Effective utilization of Artificial Neural Networks (ANN) in heat transfer applications, addressing issues like fouling in heat exchangers, showcasing their accuracy compared to experimental data. Investigation of the impact of nitrogen concentration on the structure and properties of ZrN coatings deposited by magnetron sputtering, evaluating variations in structural and mechanical properties. Forced convection in a horizontal cylindrical pipe with pseudoplastic fluid, considering uniform constant heat flux and uniform temperature as boundary conditions. Modeling and experimental study of a water solar collector coupled to an optimized solar still, aiming to enhance freshwater production in a solar distillation system under specific climatic conditions. Exploration of the effect of film thickness on the structure and properties of Ti-N films deposited by magnetron sputtering, utilizing theoretical and experimental analysis to confirm the rock salt TiN structure. The presented case studies and development approaches aim to provide readers with basic and applied information broadly related to mechanical engineering and technology. Readership Graduate students, PhD candidates and professionals seeking basic and applied information related to mechanical engineering and technology.

This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Deep learning is a branch of machine learning similar to artificial intelligence. The applications of deep learning vary from medical imaging to industrial quality checking, sports, and precision agriculture. This book is divided into two sections. The first section covers deep learning architectures and the second section describes the state of the art of applications based on deep learning.

This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

This book presents a compilation of extended versions of selected papers from 20th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2021). It focuses on deep learning networks and their applications in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers. The book is fourth in the series published since 2017.

The Building Cognitive Applications with IBM Watson Services series is a seven-volume collection that introduces IBM® Watson cognitive computing services. The series includes an overview of specific Watson services with their associated architectures and simple code examples. Each volume describes how you can use and implement these services in your applications through practical use cases. The series includes the following volumes: Volume 1 Getting Started, SG24-8387 Volume 2 Conversation, SG24-8394 Volume 3 Visual Recognition, SG24-8393 Volume 4 Natural Language Classifier, SG24-8391 Volume 5 Language Translator, SG24-8392 Volume 6 Speech to Text and Text to Speech, SG24-8388 Volume 7 Natural Language Understanding, SG24-8398 Whether you are a beginner or an experienced developer, this collection provides the information you need to start your research on Watson services. If your goal is to become more familiar with Watson in relation to your current environment, or if you are evaluating cognitive computing, this collection can serve as a powerful learning tool. This IBM Redbooks® publication, Volume 3, introduces the IBM Watson® Visual Recognition service. The Watson Visual Recognition service uses deep learning algorithms to analyze images for scenes, objects, faces, and other content. This book introduces concepts that you need to understand in order to use this Watson service and provides simple code examples to illustrate the use of the APIs. This book includes examples of applications that demonstrate how to use the Watson Visual Recognition service in practical use cases. You can develop and deploy the sample applications by following along in a step-by-step approach and using provided code snippets. Alternatively, you can download an existing Git project to more quickly deploy the application.