Название: Networks Attack Detection on 5G Networks using Data Mining Techniques
Автор: Sаgаr Dhаnrаj Раndе, Аditуа Кhаmраriа
Издательство: CRC Press
Серия: Wireless Communications and Networking Technologies
Год: 2024
Страниц: 216
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Artificial Intelligence (AI) and its applications have risen to prominence as one of the most active study areas in recent years. In recent years, a rising number of AI applications have been applied in a variety of areas. Agriculture, transportation, medicine, and health are all being transformed by AI technology. The Internet of Things (IoT) market is thriving, having a significant impact on a wide variety of industries and applications, including e-health care, smart cities, smart transportation, and industrial engineering. Recent breakthroughs in artificial intelligence and machine learning techniques have reshaped various aspects of artificial vision, considerably improving the state of the art for artificial vision systems across a broad range of high-level tasks. As a result, several innovations and studies are being conducted to improve the performance and productivity of IoT devices across multiple industries using Machine Learning and Artificial Intelligence. Security is a primary consideration when analyzing the next generation communication network due to the rapid advancement of technology. Additionally, data analytics, deep intelligence, Deep Learning, cloud computing, and intelligent solutions are being employed in medical, agricultural, industrial, and health care systems that are based on the Internet of Things. This book will look at cutting-edge Network Attacks and Security solutions that employ intelligent data processing and Machine Learning (ML) methods.

Название: Data Warehouse and Data Mining: Concepts, techniques and real life applications
Автор: Jugnеsh Кumаr
Издательство:
Год: 2024
Страниц: 214
Язык: английский
Формат: epub (true)
Размер: 10.1 MB

Data warehouse and data mining are essential technologies in the field of data analysis and business intelligence. Data warehouse provides a centralized repository of structured data and facilitates data storage and retrieval. Data mining, on the other hand, utilizes various algorithms and techniques to extract valuable patterns, trends, and insights from large datasets. The book explains the ins and outs of data warehousing by discussing its principles, benefits, and components, differentiating it from traditional databases. The readers will explore warehouse architecture, learn to navigate OLTP and OLAP systems, grasping the crux of the difference between ROLAP and MOLAP. The book is designed to help you discover data mining secrets with techniques like classification and clustering. You will be able to advance your skills by handling multimedia, time series, and text, staying ahead in the evolving data mining landscape. Data Warehousing introduces the foundational concepts behind the creation and management of centralized repositories, offering a blueprint for designing efficient data storage systems.

Название: Machine Learning and Data Mining Annual Volume 2023
Автор: Marco Antonio Aceves-Fernández, Andries Engelbrecht
Издательство: ITexLi
Год: 2023
Страниц: 132
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book offers a multifaceted perspective on Machine Learning and Data Mining. Whether you’re an experienced researcher or a newcomer, this collection is an essential resource for staying at the forefront of these dynamic and influential disciplines. The interest within the academic community regarding AI has experienced exponential growth in recent years. Several key factors have contributed to this surge in interest. Firstly, the rapid advancements in AI technologies have showcased their potential to revolutionize various fields, such as healthcare, finance, and transportation, sparking curiosity and enthusiasm among researchers and scholars. Secondly, the availability of vast amounts of data and computing power has enabled academics to delve deeper into AI research, exploring complex algorithms and models to tackle real-world problems. Additionally, the interdisciplinary nature of AI has encouraged collaboration among experts from diverse fields like Computer Science, neuroscience, psychology, and ethics, fostering a rich exchange of ideas and approaches.

Название: Getting Started with Enterprise Architecture: A Practical and Pragmatic Approach to Learning the Basics of Enterprise Architecture
Автор: Еriс Jаgеr
Издательство: Apress
Год: 2023
Страниц: 280
Язык: английский
Формат: pdf
Размер: 10.2 MB

Implement a basic enterprise architecture from start to finish using a four stage wheel-based approach. Aided by real-world examples, this book shows what elements are needed for the initial implementation of a fundamental enterprise architecture. The book's pragmatic approach keeps existing architecture frameworks and methodologies in mind while providing instructions that are readable and applicable to all. The Enterprise Architecture Implementation Wheel builds on the methodology of existing architecture frameworks and allows you to apply the theory more pragmatically and closer to the reality that an architect encounters in daily practice. While the main focus of the book is the actual steps taken to design an enterprise architecture, other important topics include architecture origin, definition, domains, visualization, and roles. Getting Started with Enterprise Architecture is the ideal handbook for the architect who is asked to implement an Enterprise Architecture in an existing organization. For enterprise architects, project managers and executives.

Название: Big Data Computing: Advances in Technologies, Methodologies, and Applications
Автор: Таnvir Наbib Sаrdаr, Вishwаjееt Кumаr Раndеу
Издательство: CRC Press
Серия: Computational Intelligence Techniques
Год: 2024
Страниц: 397
Язык: английский
Формат: pdf (true)
Размер: 15.0 MB

This book primarily aims to provide an in-depth understanding of recent advances in Big Data computing technologies, methodologies, and applications along with introductory details of Big Data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and Big Data streaming services such as Apache Spark, Kafka, and so forth. It also covers developments in Big Data computing applications such as Machine Learning, Deep Learning, graph processing, and many others. Big Data has become a critical issue for businesses to leverage their data assets to drive business decisions considering the exponential growth of data in today’s age. Machine Learning (ML) and Artificial Intelligence (AI) have emerged as powerful tools to extract insights and value from Big Data. Here we will explore a few of the key applications of ML and AI in Big Data. Predictive analytics is one of the most significant applications of ML in Big Data. It involves using existing data to predict future events or behaviors. Predictive analytics may be used to predict future sales, identify customers at risk of churning, or predict the likelihood of a customer purchasing a specific product . In predictive analytics, algorithms such as decision trees, random forests, and neural networks are commonly used.

Название: Learn Data Mining Through Excel: A Step-by-Step Approach for Understanding Machine Learning Methods, 2nd Edition
Автор: Hong Zhou
Издательство: Apress
Год: 2023
Страниц: 289
Язык: английский
Формат: pdf (true), epub (true)
Размер: 31.9 MB

Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Most software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help, and this book will show you exactly how. This updated edition demonstrates how to work with data in a transparent manner using Excel. When you open an Excel file, data is visible immediately and you can work with it directly. You’ll see how to examine intermediate results even as you are still conducting your mining task, offering a deeper understanding of how data is manipulated, and results are obtained. These are critical aspects of the model construction process that are often hidden in software tools and programming language packages. Anyone who is interested in learning data mining or Machine Learning, especially data science visual learners and people skilled in Excel who would like to explore Data Science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended.

Название: Embedding Knowledge Graphs with RDF2vec
Автор: Heiko Paulheim, Petar Ristoski, Jan Portisch
Издательство: Springer
Год: 2023
Страниц: 165
Язык: английский
Формат: pdf (true), epub
Размер: 19.6 MB

This book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reusing pre-trained knowledge graph embeddings to training tailored vectors for a knowledge graph at hand. They also demonstrate different extensions of RDF2vec and how they affect not only the downstream performance, but also the expressivity of the resulting vector representation, and analyze the resulting vector spaces and the semantic properties they encode. Knowledge graph embedding is a technique which projects entities and relations in a knowledge graph into a continuous vector space. Many other components of AI systems, especially machine learning components, can work with those continuous representations better than operating on the graph itself, and often yield superior result quality compared to those trying to extract non-continuous features from a graph.

Название: Data Fabric Architectures: Web-Driven Applications
Автор: Vandana Sharma, Balamurugan Balusamy, J. Joshua Thomas
Издательство: De Gruyter
Год: 2023
Страниц: 228
Язык: английский
Формат: pdf (true), epub
Размер: 58.2 MB

The immense increase on the size and type of real time data generated across various edge computing platform results in unstructured databases and data silos. This edited book gathers together an international set of researchers to investigate the possibilities offered by data-fabric solutions; the volume focuses in particular on data architectures and on semantic changes in future data landscapes. Big Data platform components like Hadoop, data lakes, and NoSQL have made Big Data architectures more logical, enabling businesses to pursue insight-driven competitive advantage. Moving corporate data to these platforms, especially when dealing with distributed data across data centers, is hampered by security issues, complicated data structures, issues with moving historical data, big volumes, latency issues, and variable speed of ingestion.

Название: Practical Data Mining Techniques and Applications
Автор: Ketan Shah, Neepa Shah, Vinaya Sawant
Издательство: CRC Press
Год: 2023
Страниц: 215
Язык: английский
Формат: pdf (true)
Размер: 12.5 MB

Data mining techniques and algorithms are extensively used to build real-world applications. A practical approach can be applied to data mining techniques to build applications. Once deployed, an application enables the developers to work on the users’ goals and mold the algorithms with respect to users’ perspectives. Practical Data Mining Techniques and Applications focuses on various concepts related to data mining and how these techniques can be used to develop and deploy applications. The book provides a systematic composition of fundamental concepts of data mining blended with practical applications. The aim of this book is to provide access to practical data mining applications and techniques to help readers gain an understanding of data mining in practice. Readers also learn how relevant techniques and algorithms are applied to solve problems and to provide solutions to real-world applications in different domains. This book can help academicians to extend their knowledge of the field as well as their understanding of applications based on different techniques to gain greater insight. It can also help researchers with real-world applications by diving deeper into the domain. Computing science students, application developers, and business professionals may also benefit from this examination of applied Data Science techniques.

Название: Exploring Data Science with R and the Tidyverse, A Concise Introduction
Автор: Jerry Bonnell, Mitsunori Ogihara
Издательство: CRC Press
Год: 2024
Страниц: 492
Язык: английский
Формат: pdf (true)
Размер: 20.8 MB

This book introduces the reader to data science using R and the tidyverse. No prerequisite knowledge is needed in college-level programming or mathematics (e.g., calculus or statistics). The book is self-contained so readers can immediately begin building data science workflows without needing to reference extensive amounts of external resources for onboarding. The contents are targeted for undergraduate students but are equally applicable to students at the graduate level and beyond. The book develops concepts using many real-world examples to motivate the reader. An accompanying R package "edsdata" contains synthetic and real datasets used by the textbook and is meant to be used for further practice. An exercise set is made available and designed for compatibility with automated grading tools for instructor use.