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Author: Archana Patel
Publisher: John Wiley & Sons
ISBN: 111986531X
Category : Computers
Languages : en
Pages : 469
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Book Description
DATA SCIENCE WITH SEMANTIC TECHNOLOGIES This book will serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for scholars, researchers, professionals, and practitioners in this field. To create intelligence in data science, it becomes necessary to utilize semantic technologies which allow machine-readable representation of data. This intelligence uniquely identifies and connects data with common business terms, and it also enables users to communicate with data. Instead of structuring the data, semantic technologies help users to understand the meaning of the data by using the concepts of semantics, ontology, OWL, linked data, and knowledge-graphs. These technologies help organizations to understand all the stored data, adding the value in it, and enabling insights that were not available before. As data is the most important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. Data Science with Semantic Technologies provides a roadmap for the deployment of semantic technologies in the field of data science. Moreover, it highlights how data science enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book provides answers to various questions like: Can semantic technologies be able to facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is knowledge data science? How does knowledge data science relate to other domains? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of researchers? Audience Researchers in the fields of data science, semantic technologies, artificial intelligence, big data, and other related domains, as well as industry professionals, software engineers/scientists, and project managers who are developing the software for data science. Students across the globe will get the basic and advanced knowledge on the current state and potential future of data science.
Author: Archana Patel
Publisher: John Wiley & Sons
ISBN: 111986531X
Category : Computers
Languages : en
Pages : 469
Get Book Here
Book Description
DATA SCIENCE WITH SEMANTIC TECHNOLOGIES This book will serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for scholars, researchers, professionals, and practitioners in this field. To create intelligence in data science, it becomes necessary to utilize semantic technologies which allow machine-readable representation of data. This intelligence uniquely identifies and connects data with common business terms, and it also enables users to communicate with data. Instead of structuring the data, semantic technologies help users to understand the meaning of the data by using the concepts of semantics, ontology, OWL, linked data, and knowledge-graphs. These technologies help organizations to understand all the stored data, adding the value in it, and enabling insights that were not available before. As data is the most important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. Data Science with Semantic Technologies provides a roadmap for the deployment of semantic technologies in the field of data science. Moreover, it highlights how data science enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book provides answers to various questions like: Can semantic technologies be able to facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is knowledge data science? How does knowledge data science relate to other domains? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of researchers? Audience Researchers in the fields of data science, semantic technologies, artificial intelligence, big data, and other related domains, as well as industry professionals, software engineers/scientists, and project managers who are developing the software for data science. Students across the globe will get the basic and advanced knowledge on the current state and potential future of data science.
Author: Pete Warden
Publisher: O'Reilly Media
ISBN: 1492052019
Category : Computers
Languages : en
Pages : 504
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Book Description
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size
Author: K. Mohaideen Abdul Kadhar
Publisher: Apress
ISBN: 9781484268247
Category : Computers
Languages : en
Pages : 239
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Book Description
Implement real-time data processing applications on the Raspberry Pi. This book uniquely helps you work with data science concepts as part of real-time applications using the Raspberry Pi as a localized cloud. You’ll start with a brief introduction to data science followed by a dedicated look at the fundamental concepts of Python programming. Here you’ll install the software needed for Python programming on the Pi, and then review the various data types and modules available. The next steps are to set up your Pis for gathering real-time data and incorporate the basic operations of data science related to real-time applications. You’ll then combine all these new skills to work with machine learning concepts that will enable your Raspberry Pi to learn from the data it gathers. Case studies round out the book to give you an idea of the range of domains where these concepts can be applied. By the end of Data Science with the Raspberry Pi, you’ll understand that many applications are now dependent upon cloud computing. As Raspberry Pis are cheap, it is easy to use a number of them closer to the sensors gathering the data and restrict the analytics closer to the edge. You’ll find that not only is the Pi an easy entry point to data science, it also provides an elegant solution to cloud computing limitations through localized deployment. What You Will Learn Interface the Raspberry Pi with sensors Set up the Raspberry Pi as a localized cloud Tackle data science concepts with Python on the Pi Who This Book Is For Data scientists who are looking to implement real-time applications using the Raspberry Pi as an edge device and localized cloud. Readers should have a basic knowledge in mathematics, computers, and statistics. A working knowledge of Python and the Raspberry Pi is an added advantage.
Author: Vikram Bali
Publisher: CRC Press
ISBN: 1000413454
Category : Business & Economics
Languages : en
Pages : 283
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Book Description
The aim of this book is to provide insight into Data Science and Artificial Learning Techniques based on Industry 4.0, conveys how Machine Learning & Data Science are becoming an essential part of industrial and academic research. Varying from healthcare to social networking and everywhere hybrid models for Data Science, Al, and Machine Learning are being used. The book describes different theoretical and practical aspects and highlights how new systems are being developed. Along with focusing on the research trends, challenges and future of AI in Data Science, the book explores the potential for integration of advanced AI algorithms, addresses the challenges of Data Science for Industry 4.0, covers different security issues, includes qualitative and quantitative research, and offers case studies with working models. This book also provides an overview of AI and Data Science algorithms for readers who do not have a strong mathematical background. Undergraduates, postgraduates, academicians, researchers, and industry professionals will benefit from this book and use it as a guide.
Author: Philip Meitiner
Publisher: Apress
ISBN: 9781484257654
Category : Computers
Languages : en
Pages : 0
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Book Description
Learn to use technology to undertake data science and to leverage the Internet of Things (IoT) in your experimentation. Designed to take you on a fascinating journey, this book introduces the core concepts of modern data science. You'll start with simple applications that you can undertake on a BBC micro:bit and move to more complex experiments with additional hardware. The skills and narrative are as generic as possible and can be implemented with a range of hardware options. One of the most exciting and fastest growing topics in education is data science. Understanding how data works, and how to work with data, is a key life skill in the 21st century. In a world driven by information it is essential that students are equipped with the tools they need to make sense of it all. For instance, consider how data science was the key factor that identified the dangers of climate change -- and continues to help us identify and react to the threats it presents. This book explores the power of data and how you can apply it using hardware you have at hand. You'll learn the core concepts of data science, how to apply them in the real world and how to utilize the vast potential of IoT. By the end, you'll be able to execute sophisticated and meaningful data science experiments - why not become a citizen scientist and make a real contribution to the fight against climate change. There is something of a digital revolution going these days, especially in the classroom. With increasing access to microprocessors, classrooms are are incorporating them more and more into lessons. Close to 5 million BBC micro:bits will be in the hands of young learners by the end of the year and millions of other devices are also being used by educators to teach a range of topics and subjects. This presents an opportunity: microprocessors such as micro:bit provide the perfect tool to use to build 21st century data science skills. Beginning Data Science and IoT on the BBC micro:bit provides you with a solid foundation in applied data science. What You'll Learn · Use sensors with a microprocessor to gather or "create" data · Extract, tabulate, and utilize data it from the microprocessor · Connect a microprocessor to an IoT platform to share and then use the data we collect · Analyze and convert data into information Who This Book Is For Educators, citizen scientists, and tinkerers interested in an introduction to the concepts of IoT and data on a broad scale.
Author: Biswajeet Pradhan
Publisher: Springer Nature
ISBN: 3031686020
Category :
Languages : en
Pages : 479
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Book Description
Author: Perry Xiao
Publisher: John Wiley & Sons
ISBN: 1119560012
Category : Computers
Languages : en
Pages : 480
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Book Description
Learn practical uses for some of the hottest tech applications trending among technology professionals We are living in an era of digital revolution. On the horizon, many emerging digital technologies are being developed at a breathtaking speed. Whether we like it or not, whether we are ready or not, digital technologies are going to penetrate more and more, deeper and deeper, into every aspect of our lives. This is going to fundamentally change how we live, how we work, and how we socialize. Java, as a modern high-level programming language, is an excellent tool for helping us to learn these digital technologies, as well as to develop digital applications, such as IoT, AI, Cybersecurity, Blockchain and more. Practical Java Programming uses Java as a tool to help you learn these new digital technologies and to be better prepared for the future changes. Gives you a brief overview for getting started with Java Programming Dives into how you can apply your new knowledge to some of the biggest trending applications today Helps you understand how to program Java to interact with operating systems, networking, and mobile applications Shows you how Java can be used in trending tech applications such as IoT (Internet of Things), AI (Artificial Intelligence), Cybersecurity, and Blockchain Get ready to find out firsthand how Java can be used for connected home devices, healthcare, the cloud, and all the hottest tech applications.
Author: Ambika Nagaraj
Publisher: Bentham Science Publishers
ISBN: 9815165712
Category : Computers
Languages : en
Pages : 205
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Book Description
This book explores the dynamic intersection of three cutting-edge technologies—Artificial Intelligence (AI), Internet of Things (IoT), and Cloud Computing—and their profound impact on diverse domains. Beginning with an introduction to AI and its challenges, it delves into IoT applications in fields like transportation, industry 4.0, healthcare, and agriculture. The subsequent chapter explores AI in the cloud, covering areas such as banking, e-commerce, smart cities, healthcare, and robotics. Another section investigates the integration of AI and IoT-Cloud, discussing applications like smart meters, smart cities, smart agriculture, smart healthcare, and smart industry. Challenges like data privacy and security are examined, and the future direction of these technologies, including fog computing and quantum computing, is explored. The book concludes with use cases that highlight the real-world applications of these transformative technologies across various sectors. Each chapter is also supplemented with a list of scholarly references for advanced readers. The book is intended primarily as a resource for students in information technology and technology courses. And as a secondary resource for industry professionals who want to learn about these technologies in the context of digital transform
Author: Kai Hwang
Publisher: MIT Press
ISBN: 026203641X
Category : Computers
Languages : en
Pages : 626
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Book Description
The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies. This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage, transmission and mashup, and AI techniques to analyze this data. This textbook is designed to train college students to master modern cloud computing systems in operating principles, architecture design, machine learning algorithms, programming models and software tools for big data mining, analytics, and cognitive applications. The book will be suitable for use in one-semester computer science or electrical engineering courses on cloud computing, machine learning, cloud programming, cognitive computing, or big data science. The book will also be very useful as a reference for professionals who want to work in cloud computing and data science. Cloud and Cognitive Computing begins with two introductory chapters on fundamentals of cloud computing, data science, and adaptive computing that lay the foundation for the rest of the book. Subsequent chapters cover topics including cloud architecture, mashup services, virtual machines, Docker containers, mobile clouds, IoT and AI, inter-cloud mashups, and cloud performance and benchmarks, with a focus on Google's Brain Project, DeepMind, and X-Lab programs, IBKai HwangM SyNapse, Bluemix programs, cognitive initiatives, and neurocomputers. The book then covers machine learning algorithms and cloud programming software tools and application development, applying the tools in machine learning, social media, deep learning, and cognitive applications. All cloud systems are illustrated with big data and cognitive application examples.
Author: Sujata Dash
Publisher: Springer Nature
ISBN: 3030756572
Category : Technology & Engineering
Languages : en
Pages : 443
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Book Description
This book plays a significant role in improvising human life to a great extent. The new applications of soft computing can be regarded as an emerging field in computer science, automatic control engineering, medicine, biology application, natural environmental engineering, and pattern recognition. Now, the exemplar model for soft computing is human brain. The use of various techniques of soft computing is nowadays successfully implemented in many domestic, commercial, and industrial applications due to the low-cost and very high-performance digital processors and also the decline price of the memory chips. This is the main reason behind the wider expansion of soft computing techniques and its application areas. These computing methods also play a significant role in the design and optimization in diverse engineering disciplines. With the influence and the development of the Internet of things (IoT) concept, the need for using soft computing techniques has become more significant than ever. In general, soft computing methods are closely similar to biological processes than traditional techniques, which are mostly based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis. Soft computing techniques are anticipated to complement each other. The aim of these techniques is to accept imprecision, uncertainties, and approximations to get a rapid solution. However, recent advancements in representation soft computing algorithms (fuzzy logic,evolutionary computation, machine learning, and probabilistic reasoning) generate a more intelligent and robust system providing a human interpretable, low-cost, approximate solution. Soft computing-based algorithms have demonstrated great performance to a variety of areas including multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, biomedical and health informatics, etc. Soft computing approaches such as genetic programming (GP), support vector machine–firefly algorithm (SVM-FFA), artificial neural network (ANN), and support vector machine–wavelet (SVM–Wavelet) have emerged as powerful computational models. These have also shown significant success in dealing with massive data analysis for large number of applications. All the researchers and practitioners will be highly benefited those who are working in field of computer engineering, medicine, biology application, signal processing, and mechanical engineering. This book is a good collection of state-of-the-art approaches for soft computing-based applications to various engineering fields. It is very beneficial for the new researchers and practitioners working in the field to quickly know the best performing methods. They would be able to compare different approaches and can carry forward their research in the most important area of research which has direct impact on betterment of the human life and health. This book is very useful because there is no book in the market which provides a good collection of state-of-the-art methods of soft computing-based models for multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, and biomedical and health informatics.
