Tabla de Contenidos: “…6.4.2.2 Gradient Tree Boosting -- 6.4.2.3 XGBoost -- 6.4.3 Stacking -- 6.5 Results -- 6.5.1 Bagging Meta Estimator -- 6.5.2 Random Forest -- 6.5.3 AdaBoost -- 6.5.4 Gradient Tree Boosting -- 6.5.5 XGBoost -- 6.5.6 Stacking -- 6.5.7 Comparison with Single Classifiers -- 6.6 Conclusion -- Acknowledgement -- References -- Chapter 7 Feature Engineering and Selection Approach Over Malicious Image -- 7.1 Introduction -- 7.2 Feature Engineering Techniques -- 7.2.1 Methodologies in Feature Engineering -- 7.2.2 Strides in Feature Engineering -- 7.2.3 Feature Extraction -- 7.2.4 Feature Selection -- 7.2.5 Feature Engineering in Image Processing -- 7.2.6 Importance of Feature Engineering in Image Processing -- 7.3 Malicious Feature Engineering -- 7.4 Image Processing Technique -- 7.4.1 Steps Involved in Image Processing Technique -- 7.4.2 Image Processing Task -- 7.4.2.1 Image Enhancement -- 7.4.2.2 Image Restoration -- 7.4.2.3 Coloring Image Processing -- 7.4.2.4 Wavelets Processing and Multiple Solutions -- 7.4.2.5 Image Compression -- 7.4.2.6 Character Recognition -- 7.4.2.7 Characteristics of Image Processing -- 7.5 Image Processing Techniques for Analysis on Malicious Images -- 7.6 Conclusion -- References -- Blog -- Chapter 8 Cubic-Regression and Likelihood Based Boosting GAM to Model Drug Sensitivity for Glioblastoma -- 8.1 Introduction -- 8.1.1 Glioblastoma -- 8.2 Literature Survey -- 8.3 Materials and Methods -- 8.3.1 Methodology -- 8.3.1.1 Generalized Additive Models (GAMs) -- 8.3.1.2 Model-Based Boosting - Boosted GAM -- 8.3.2 Datasets Description -- 8.4 Evaluations, Results and Discussions -- 8.4.1 Akaike Information Criterion (AIC) -- 8.4.2 Adjusted R-Squared -- 8.4.3 Discussion -- Conclusion -- References -- Chapter 9 Unobtrusive Engagement Detection through Semantic Pose Estimation and Lightweight ResNet for an Online Class Environment…”

“…A promising decentralized hybrid PV-SOFC system is investigated for providing useful energy supply to commercial buildings, capable of power and heat generation at a lower cost. A hybrid solar-combined cycle power plant integrated with a packed-bed thermal energy storage system with a novel recycling configuration enables robust control of collector temperature and net power during times of high solar activity. …”

Tabla de Contenidos: “…3.8 Tensor metadata: Size, offset, and stride -- 3.8.1 Views of another tensor's storage -- 3.8.2 Transposing without copying -- 3.8.3 Transposing in higher dimensions -- 3.8.4 Contiguous tensors -- 3.9 Moving tensors to the GPU -- 3.9.1 Managing a tensor's device attribute -- 3.10 NumPy interoperability -- 3.11 Generalized tensors are tensors, too -- 3.12 Serializing tensors -- 3.12.1 Serializing to HDF5 with h5py -- 3.13 Conclusion -- 3.14 Exercises -- 3.15 Summary -- 4 Real-world data representation using tensors -- 4.1 Working with images -- 4.1.1 Adding color channels -- 4.1.2 Loading an image file -- 4.1.3 Changing the layout -- 4.1.4 Normalizing the data -- 4.2 3D images: Volumetric data -- 4.2.1 Loading a specialized format -- 4.3 Representing tabular data -- 4.3.1 Using a real-world dataset -- 4.3.2 Loading a wine data tensor -- 4.3.3 Representing scores -- 4.3.4 One-hot encoding -- 4.3.5 When to categorize -- 4.3.6 Finding thresholds -- 4.4 Working with time series -- 4.4.1 Adding a time dimension -- 4.4.2 Shaping the data by time period -- 4.4.3 Ready for training -- 4.5 Representing text -- 4.5.1 Converting text to numbers -- 4.5.2 One-hot-encoding characters -- 4.5.3 One-hot encoding whole words -- 4.5.4 Text embeddings -- 4.5.5 Text embeddings as a blueprint -- 4.6 Conclusion -- 4.7 Exercises -- 4.8 Summary -- 5 The mechanics of learning -- 5.1 A timeless lesson in modeling -- 5.2 Learning is just parameter estimation -- 5.2.1 A hot problem -- 5.2.2 Gathering some data -- 5.2.3 Visualizing the data -- 5.2.4 Choosing a linear model as a first try -- 5.3 Less loss is what we want -- 5.3.1 From problem back to PyTorch -- 5.4 Down along the gradient -- 5.4.1 Decreasing loss -- 5.4.2 Getting analytical -- 5.4.3 Iterating to fit the model -- 5.4.4 Normalizing inputs -- 5.4.5 Visualizing (again)…”

Tabla de Contenidos: “…Chapter 10 Cloud Computing Security, Risk, and Challenges: A Detailed Analysis of Preventive Measures and Applications -- 10.1 Introduction -- 10.2 Background -- 10.2.1 History of Cloud Computing -- 10.2.1.1 Software-as-a-Service Model -- 10.2.1.2 Infrastructure-as-a-Service Model -- 10.2.1.3 Platform-as-a-Service Model -- 10.2.2 Types of Cloud Computing -- 10.2.3 Cloud Service Model -- 10.2.4 Characteristics of Cloud Computing -- 10.2.5 Advantages of Cloud Computing -- 10.2.6 Challenges in Cloud Computing -- 10.2.7 Cloud Security -- 10.2.7.1 Foundation Security -- 10.2.7.2 SaaS and PaaS Host Security -- 10.2.7.3 Virtual Server Security -- 10.2.7.4 Foundation Security: The Application Level -- 10.2.7.5 Supplier Data and Its Security -- 10.2.7.6 Need of Security in Cloud -- 10.2.8 Cloud Computing Applications -- 10.3 Literature Review -- 10.4 Cloud Computing Challenges and Its Solution -- 10.4.1 Solution and Practices for Cloud Challenges -- 10.5 Cloud Computing Security Issues and Its Preventive Measures -- 10.5.1 General Security Threats in Cloud -- 10.5.2 Preventive Measures -- 10.6 Cloud Data Protection and Security Using Steganography -- 10.6.1 Types of Steganography -- 10.6.2 Data Steganography in Cloud Environment -- 10.6.3 Pixel Value Differencing Method -- 10.7 Related Study -- 10.8 Conclusion -- References -- Chapter 11 Internet of Drone Things: A New Age Invention -- 11.1 Introduction -- 11.2 Unmanned Aerial Vehicles -- 11.2.1 UAV Features and Working -- 11.2.2 IoDT Architecture -- 11.3 Application Areas -- 11.3.1 Other Application Areas -- 11.4 IoDT Attacks -- 11.4.1 Counter Measures -- 11.5 Fusion of IoDT With Other Technologies -- 11.6 Recent Advancements in IoDT -- 11.7 Conclusion -- References -- Chapter 12 Computer Vision-Oriented Gesture Recognition System for Real-Time ISL Prediction -- 12.1 Introduction -- 12.2 Literature Review…”

Tabla de Contenidos: “…L'entreprise publique au Cameroun post-colonial -- II. L'État et la question énergétique -- A. …”

Tabla de Contenidos: “…Chapter 4 Analysis of Smart Technologies in Healthcare -- 4.1 Introduction -- 4.2 Emerging Technologies in Healthcare -- 4.2.1 Internet of Things -- 4.2.2 Blockchain -- 4.2.3 Machine Learning -- 4.2.4 Deep Learning -- 4.2.5 Federated Learning -- 4.3 Literature Review -- 4.4 Risks and Challenges -- 4.5 Conclusion -- References -- Chapter 5 Enhanced Neural Network Ensemble Classification for the Diagnosis of Lung Cancer Disease -- 5.1 Introduction -- 5.2 Algorithm for Classification of Proposed Weight-Optimized Neural Network Ensembles -- 5.2.1 Enhanced Raphson's Most Likelihood and Minimum Redundancy Preprocessing -- 5.2.2 Maximum Likelihood Boosting in a Weighted Optimized Neural Network -- 5.3 Experimental Work and Results -- 5.4 Conclusion -- References -- Chapter 6 Feature Selection for Breast Cancer Detection -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Design and Implementation -- 6.3.1 Feature Selection -- 6.4 Conclusion -- References -- Chapter 7 An Optimized Feature-Based Prediction Model for Grouping the Liver Patients -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Proposed Methodology -- 7.4 Results and Discussions -- 7.5 Conclusion -- References -- Chapter 8 A Robust Machine Learning Model for Breast Cancer Prediction -- 8.1 Introduction -- 8.2 Literature Review -- 8.2.1 Comparative Analysis -- 8.3 Proposed Mythology -- 8.4 Result and Discussion -- 8.4.1 Accuracy -- 8.4.2 Error -- 8.4.3 TP Rate -- 8.4.4 FP Rate -- 8.4.5 F-Measure -- 8.5 Concluding Remarks and Future Scope -- References -- Chapter 9 Revolutionizing Pneumonia Diagnosis and Prediction Through Deep Neural Networks -- 9.1 Introduction -- 9.2 Literature Work -- 9.3 Proposed Section -- 9.3.1 Input Image -- 9.3.2 Pre-Processing -- 9.3.3 Identification and Classification Using ResNet50 -- 9.4 Result Analysis -- 9.5 Conclusion and Future Scope -- References…”

Tabla de Contenidos: “…Borrás Jarque, Eufemiano Olaria, Samuel Garrido y José Auserec. Año 1942. Fondo -- Modelo de instancia que los maestros, para ser depurados, debían presentar en estos términos como so -- Aval de Falange Española Tradicionalista y de las jons. …”
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“…Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. …”

“…Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. …”

“…What you will learn Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application’s performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Who this book is for If you’r..…”

“…Beginning with the fundamentals of digital sculpting as well as a thorough introduction to the user interface, Getting Started in ZBrush will have you creating a variety of professional-level 3D models in no-time. More than just another button-pushing manual, this comprehensive guide is packed with start-to-finish projects that ease you into the workflow of the program, while at the same time providing tips and tricks that will allow you to achieve certain tasks much more quickly. …”

“…Streamline deployment with Kickstart Find, install, update, remove, and verify software Detect, analyze, and manage hardware Manage storage with LVM, RAID, ACLs, and quotas Use Red Hat Enterprise Linux 5 on 64-bit and multi-core systems Administer users and groups more efficiently and securely Ensure trustworthy backup and rapid recovery Script and schedule tasks to run automatically Provide unified identity management services Configure Apache, BIND, Samba, and Sendmail Monitor and tune the kernel and individual applications Protect against intruders with SELinux and ExecShield Set up firewalls with iptables Enable the Linux Auditing System Use virtualization to run multiple operating systems concurrently Part I Installation and Configuration Chapter 1 Installing Red Hat Enterprise Linux Chapter 2 Post-Installation Configuration Chapter 3 Operating System Updates Part II Operating System Core Concepts Chapter 4 Understanding Linux Concepts Chapter 5 Working with RPM Software Chapter 6 Analyzing Hardware Chapter 7 Managing Storage Chapter 8 64-Bit, Multi-Core, and Hyper-Threading Technology Processors Part III System Administration Chapter 9 Managing Users and Groups Chapter 10 Techniques for Backup and Recovery Chapter 11 Automating Tasks with Scripts Part IV Network Services Chapter 12 Identity Management Chapter 13 Network File Sharing Chapter 14 Granting Network Connectivity with DHCP Chapter 15 Creating a Web Server with the Apache HTTP Server Chapter 16 Hostname Res..…”

“…Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learnImplement text and music generating models using PyTorchBuild a deep Q-network (DQN) model in PyTorchExport universal PyTorch models using Open Neural Network Exchange (ONNX)Become well-versed with rapid prototyping using PyTorch with fast.aiPerform neural architecture search effectively using AutoMLEasily interpret machine learning (ML) models written in PyTorch using CaptumDesign ResNets, LSTMs, Transformers, and more using PyTorchFind out how to use PyTorch for distributed training using the torch.distributed APIWho this book is forThis book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. …”

“…PyTorch is a Python framework developed by Facebook to develop and deploy deep learning models. It is one of the most popular deep-learning frameworks nowadays. You will begin with learning the deep learning concept. …”

“…Muchas empresas reconocen la necesidad de estas prácticas pero no las llevan a cabo por el desconocimiento, el coste o el poco interés. Este trabajo pretende estudiar y dar conocer estas medidas de modo que su consulta sea de especial interés para los agentes del sector. …”
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