Category: Tech & Devices

Artificial Intelligence-Based System for Gaze-Based Communication by B.G.D.A Madhusanka, Sureswaran Ramadass, Premkumar Rajagopal
Requirements: .PDF reader, 10 MB
Overview: This book focuses on the artificial neural network-based system for gaze-based communication. It covers the feasible and practical collaboration of human–computer interaction (HCI) in which a user can intuitively express tasks using gaze-based communication. It will target the vast applications of gaze-based communication using computer vision, image processing, and Artificial Intelligence. Artificial Intelligence-Based System for Gaze-Based Communication introduces a novel method to recognize the implicit intention of users by using nonverbal communication in combination with computer vision technologies. A novel HCI framework is developed to enable implicit and intuitive gaze-based intention communications. This framework allows the users to intuitively express their intention using natural gaze cues. The book also focuses on robot caregiving technology, which can understand the user’s intentions using minimal interactions with the user. The authors examine gaze-based tracking applications for the assisted living of elderly people. The book examines detailed applications of eye-gaze communication for real-life problems. It also examines the advantages that most people can handle gaze-based communications because it requires very little effort, and most of the elderly and impaired can retain visual capability. This book is ideally designed for students, researchers, academicians, and professionals interested in exploring and implementing gaze-based communication strategies and those working in the field of Computer Vision and image processing.
Genre: Non-Fiction > Tech & Devices



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https://katfile.com/ho40truxrlua/Artifi … d.rar.html
https://rapidgator.net/file/43f9fe245dc … d.rar.html.

Linkerd: Up and Running: A Guide to Operationalizing a Kubernetes-native Service Mesh by Jason Morgan, Flynn
Requirements: .ePUB reader, 10 MB
Overview: With the massive adoption of microservices, operators and developers face far more complexity in their applications today. Service meshes can help you manage this problem by providing a unified control plane to secure, manage, and monitor your entire network. This practical guide shows you how the Linkerd service mesh enables cloud-native developers—including platform and site reliability engineers—to solve the thorny issue of running distributed applications in Kubernetes. Jason Morgan and Flynn draw on their years of experience at Buoyant—the creators of Linkerd—to demonstrate how this service mesh can help ensure that your applications are secure, observable, and reliable. You’ll understand why Linkerd, the original service mesh, can still claim the lowest time to value of any mesh option available today. When the Linkerd2 rewrite started, the Rust programming language had been gaining attention for its memory safety, which enables developers to write code that avoids many of the memory management vulnerabilities inherent to C and C++, while still compiling to native code for high performance. This book is meant to help anyone who thinks it’s easier to get things done when creating, running, or debugging microservices applications, and is looking to Linkerd to help with that. You’ll need some basic knowledge of Kubernetes, the overall concept of running things in containers, and the Unix command line to get the most out of this book. Some familiarity with Prometheus, Helm, Jaeger, etc. will also be helpful, but isn’t really critical.
Genre: Non-Fiction > Tech & Devices



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https://ouo.io/z082qW
https://rapidgator.net/file/bd1eba9dbb5 … g.rar.html.

Software Engineering for Data Scientists: From Notebooks to Scalable Systems by Catherine Nelson
Requirements: .ePUB, .PDF reader, 11.2 MB
Overview: Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project’s success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering,and clearly explains how to apply the best practices from software engineering to Data Science. All the code examples in this book are written in Python, and many of the chapters describe Python-specific tools. In recent years, Python has become the most popular programming language for Data Science. Python has an extremely solid set of open source libraries for data science, with good backing and a healthy community of maintainers. Large trend-setting companies have chosen Python for their main ML frameworks, including TensorFlow (Google) and PyTorch (Meta). Because of this, Python appears to be especially popular among data scientists working on production Machine Learning code, where good coding skills are particularly important. In my experience, the Python community has been friendly and welcoming, with many excellent events that have helped me improve my skills. It’s my preferred programming language, so it was an easy choice for this book. This book is aimed at data scientists, but people working in closely related fields such as data analysts, Machine Learning (ML) engineers, and data engineers will also find it useful.
Genre: Non-Fiction > Tech & Devices



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https://katfile.com/dwbrrnwmf38y/Softwa … s.rar.html
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Spatiotemporal Data Analytics and Modeling: Techniques and Applications by John A, Satheesh Abimannan, El-Sayed M. El-Alfy, Yue-Shan Chang
Requirements: .ePUB, .PDF reader, 18.9 MB
Overview: With the growing advances in technology and transformation to digital services, the world is becoming more connected and more complex. Huge heterogeneous data are generated at rapid speed from various types of sensors. Augmented with Artificial Intelligence and Machine Learning and Internet of Things, latent relations, and new insights can be captured helping in optimizing plans and resource utilization, improving infrastructure, and enhancing quality of services. A “spatial data management system” is a way to take care of data that has something to do with space. This could include data such as maps, satellite images, and GPS data. A temporal data management system is a system designed to manage data that has a temporal component. This could include data such as weather data, financial data, and social media data. Some advanced techniques used in spatial and temporal data management systems include geospatial indexing for efficient querying and retrieval of location-based data, time-series analysis for understanding and predicting temporal patterns in datasets like weather or financial trends, machine learning algorithms for uncovering hidden patterns and correlations in large and complex datasets, and integration with Internet of Things (IoT) technologies for real-time data collection and analysis. These techniques, augmented with Artificial Intelligence, enable the extraction of latent relations and insights, thereby optimizing plans, improving infrastructure, and enhancing the quality of services. This book provides essential technical knowledge, best practices, and case studies on the state-of-the-art techniques of Artificial Intelligence and Machine Learning for spatiotemporal data analysis and modeling. It can be used as academic reference at graduate level or by professionals in science and engineering related fields such as Data Science and engineering, Big Data analytics and mining, Artificial Intelligence, Machine Learning and Deep Learning, cloud computing, and Internet of Things.
Genre: Non-Fiction > Tech & Devices



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Deep Learning in Internet of Things for Next Generation Healthcare by Lavanya Sharma, Pradeep Kumar Garg
Requirements: .PDF reader, 10.2 MB
Overview: This book presents the latest developments in Deep Learning-enabled healthcare tools and technologies and offers practical ideas for using the IoT with Deep Learning (motion-based object data) to deal with human dynamics and challenges including critical application domains, technologies, medical imaging, drug discovery, insurance fraud detection and solutions to handle relevant challenges. This book covers real-time healthcare applications, novel solutions, current open challenges, and the future of Deep Learning for next-generation healthcare. It includes detailed analysis of the utilization of the IoT with Deep Learning and its underlying technologies in critical application areas of emergency departments such as drug discovery, medical imaging, fraud detection, and genomes. Convolutional neural networks (CNNs), in particular, are excellent at extracting hierarchical characteristics from images using Deep Learning models. These models learn to recognize edges, textures, forms, and even intricate patterns inside images in the context of human vision. CNNs may automatically identify pertinent features for IoT applications from unprocessed visual data without the need for explicit feature engineering. The classifcation and recognition of objects is one of the most important uses of Deep Learning and human vision in IoT. Deep Learning models can be taught to identify and categorize objects in frames of pictures or videos. Security (intruder detection), retail (product identifcation), healthcare (medical image analysis), and other felds all make use of these capabilities. Postgraduate students and researchers in the departments of Computer Science, working in the areas of the IoT, Deep Learning, Machine Learning, image processing, Big Data, cloud computing, and remote sensing will find this book useful.
Genre: Non-Fiction > Tech & Devices



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https://ouo.io/7wfCtR
https://katfile.com/p1xbp6v99exb/Deep_L … e.pdf.html
https://rapidgator.net/file/8ceb52837ae … e.pdf.html.