Название: Simulation with Arena, 7th Edition
Автор: W. Dаvid Кеltоn, Nаnсу В. Zuрiсk, Nаthаn J. Ivеу
Издательство: McGraw Hill
Год: 2024
Страниц: 689
Язык: английский
Формат: pdf (true)
Размер: 21.5 MB

Simulation with Arena provides a comprehensive treatment of simulation using industry-standard Arena software. The textbook begins by having the reader develop simple high-level models, and then progresses to advanced modeling and analysis. Statistical design and analysis of simulation experiments is integrated with the modeling chapters, reflecting the importance of mathematical modeling of these activities. An informal, tutorial writing style is used to aid the beginner in fully understanding the ideas and topics presented. The new edition now reflects Arena version 16.2, which contains many new and useful features. Arena simulation software is a tool to remove the guesswork from decision making and improve processes, efficiencies, work teams, and profitability. It uses 2D and 3D modeling and real world data to simulate various process scenarios and optimize system performance. It uses the SIMAN processor and simulation language. We assume neither prior knowledge of simulation nor computer-programming experience. We do assume basic familiarity with computing in general (files, folders, basic editing operations, etc.), but nothing advanced. A fundamental understanding of probability and statistics is needed. We also assume knowledge of basic derivative and integral calculus. Arena combines the ease of use found in high-level simulators with the flexibility of simulation languages and even all the way down to general-purpose procedural languages like the Microsoft Visual Basic programming system or C if you really want. It does this by providing alternative and interchangeable templates of graphical simulation-modeling and analysis modules that you can combine to build a fairly wide variety of simulation models. For specialized needs, like complex decision algorithms or accessing data from an external application, you can write pieces of your model in a procedural language like Visual Basic or C/C++.

Название: Advances in Digital Marketing in the Era of Artificial Intelligence: Case Studies and Data Analysis for Business Problem Solving
Автор: Моеz Ltifi
Издательство: CRC Press
Год: 2025
Страниц: 329
Язык: английский
Формат: pdf (true)
Размер: 14.8 MB

This book highlights the technological advances that are transforming the future of digital marketing and covers important areas of research in this field. The book demonstrates advances in digital marketing as well as tools, techniques, methods and strategies based on Artificial Intelligence (AI). It also identifies gaps in research into effective digital marketing tools, techniques and methods, and it bridges the interaction between digital marketing strategies and organisations’ business plans, on the one hand, and customer relations, on the other, in the age of Artificial Intelligence. This book presents the concepts and applications of digital marketing in the age of Artificial Intelligence to readers in a comprehensive manner. The book highlights the major breakthroughs and technologies in digital marketing for effective interaction, analysis, prediction and design to formulate the best strategy for a business by using Artificial Intelligence solutions. This enables specialists to apply advances in digital marketing in the age of Artificial Intelligence in the right way to serve customers and solve business problems in this competitive age. The advent of ChatGPT, an advanced form of chatbots based on OpenAI’s GPT-3.5 architecture, is distinguished by its ability to understand natural language inputs from users. ChatGPT can assist marketers in creating more human-like interactions with consumers, answering their queries, and offering personalized recommendations.

Название: Introduction to Data Analytics for Accounting, 2nd Edition
Автор: Vеrnоn J. Riсhаrdsоn, Каtiе L. Теrrеll, Rуаn А. Тееtеr
Издательство: McGraw Hill
Год: 2024
Страниц: 689
Язык: английский
Формат: pdf (true)
Размер: 32.4 MB

The combination of computerization and automation of many accounting tasks as well as the explosion of available data is changing the accounting profession. To address this, accountants are increasingly required to have an analytics mindset to perform their jobs. Building upon the fundamentals of accounting learned in prior courses, Introduction to Data Analytics for Accounting explores accounting concepts through the application of data analytics. We recognize students need to not only develop the skills to ask the right questions, but to learn how to use tools they may encounter in the workplace such as Excel, Tableau, and PowerBI to examine and analyze data, and then effectively interpret results to make business decisions. This analytics mindset is crucial early in the study of accounting to meet the demands of today's accounting jobs.

Название: Cybersecurity and Artificial Intelligence: Transformational Strategies and Disruptive Innovation
Автор: Наmid Jаhаnkhаni, Gоrdоn Воwеn, Мhd Sаееd Shаrif, Оsаmа Нussiеn
Издательство: Springer
Год: 2024
Страниц: 329
Язык: английский
Формат: pdf (true), epub
Размер: 40.3 MB

This book discusses a range of topics that are essential to understanding cyber security, including legal implications and technical aspects, cyber detection, and minimising the threats so that governments and organisations can function without noticeable degradation of service. Unlike other technological threats, cyber security threats have the potential to destroy governments and undermine democratic processes – which makes an overarching cyber security strategy essential for all functioning governments. Thus, the book serves as a guide for developing strategies and ideas in the field and as a motivator for other governments and interested parties to develop and implement effective strategies. The development of cyber security strategies calls for new skills at the technical and user levels alike. However, IT skills are sometimes in short supply, and without a government policy on cyber security training, the lack of these skills could hamper the full potential of cyber security. The book explores various aspects and challenges of cyber security strategy and highlights the benefits and drawbacks, offering in-depth insights into the field. Artificial Intelligence (AI), Machine Learning, and Deep Learning are powerful and intelligent technologies that have prevalent applications in the finance domain. These technologies enable financial institutions to develop advanced systems such as fraud detection, portfolio management, market segmentation, stock price prediction, and security anomaly detection. Recent decades have shown a great deal of research applications of AI in various areas of finance.

Название: Statistical Thinking: Analyzing Data in an Uncertain World
Автор: Russеll А. Роldrасk
Издательство: Princeton University Press
Год: 2023
Страниц: 281
Язык: английский
Формат: pdf (true)
Размер: 50.5 MB

An essential introduction to statistics for students of psychology and the social sciences. Statistical thinking is increasingly essential to understanding our complex world and making informed decisions based on uncertain data. This incisive undergraduate textbook introduces students to the main ideas of statistics in a way that focuses on deep comprehension rather than rote application or mathematical immersion. The presentation of statistical concepts is thoroughly modern, sharing cutting-edge ideas from the fields of machine learning and data science that help students effectively use statistical methods to ask questions about data. Statistical Thinking provides the tools to describe complex patterns that emerge from data and to make accurate predictions and decisions based on data. The only way to really learn statistics is to do statistics. While many statistics courses historically have been taught using point-and-click statistical software, it is increasingly common for statistical education to use open source languages in which students can code their own analyses. I think that being able to code one’s analyses is essential in order to gain a deep appreciation for statistical analysis, which is why the students in my course at Stanford are expected to learn to use the R statistical programming language to analyze data, alongside the theoretical knowledge that they learn from this book. There are two openly available companions to this textbook that can help the reader get started learning to program; one focuses on the R programming language, and another focuses on the Python language.

Название: Management Science using Excel: Harnessing Excel's advanced features for business optimization
Автор: Isааc Gоttliеb
Издательство: BPB Publications
Год: 2024
Страниц: 606
Язык: английский
Формат: pdf, epub
Размер: 23.9 MB

A practical guide to using Excel for decision making, forecasting, optimization, and more. With this book, you will acquire a strong understanding of Management Science in the decision-making field while mastering the solutions with Microsoft Excel. This book alleviates complicated mathematical models with easy Microsoft Excel tools. Management science is a practice of problem-solving and decision-making in organizations, business, and other fields. It uses various analytical methods, including mathematical modeling and statistics, to improve the ability of better decision’s making. The techniques in this book range from maximization of profit, performance, and Return on Investment (ROI) to minimization (cost, time, risk) through Monte Carlos simulations, sensitivity analysis, and other tools. The readers will find countless real-life examples and case studies from banking, finance, transportation, manufacturing, manpower assignment, scheduling, and inventory management through food and product mix. This book's linear and nonlinear techniques utilize the Excel Solver for the solutions. Once you get the hang of using the Solver – you can apply the tools learned to solve problems of your background, experience, and choice. This book takes a hands-on approach using Excel to solve these problems. Instead of using complex mathematical formulations and algorithms – you will learn to set up and solve these problems in an easy-to-follow manner in Excel.

Название: Computational Intelligence and Blockchain in Biomedical and Health Informatics
Автор: Раnkаj Вhаmbri, Sitа Rаni, Мuhаmmаd Fаhim
Издательство: CRC Press
Год: 2024
Страниц: 361
Язык: английский
Формат: pdf (true)
Размер: 30.6 MB

Advancements in computational intelligence, which encompasses Artificial Intelligence, Machine Learning, and data analytics, have revolutionized the way we process and analyze biomedical and health data. These techniques offer novel approaches to understanding complex biological systems, improving disease diagnosis, optimizing treatment plans, and enhancing patient outcomes. Computational Intelligence and Blockchain in Biomedical and Health Informatics introduces the role of computational intelligence and blockchain in the biomedical and health informatics fields and provides a framework and summary of the various methods. The book emphasizes the role of advanced computational techniques and offers demonstrative examples throughout. Techniques to analyze the impacts on the biomedical and health Informatics domains are discussed along with major challenges in deployment. Rounding out the book are highlights of the transformative potential of computational intelligence and blockchain in addressing critical issues in healthcare from disease diagnosis and personalized medicine to health data management and interoperability along with two case studies. This book is highly beneficial to educators, researchers, and anyone involved with health data. Machine Learning, on the other hand, takes advantage of methods that allow computers to discover patterns and make predictions without being explicitly programmed to do so. Models are trained using labelled data in supervised learning, while unlabelled data is used in unsupervised learning. Decision trees, support vector machines, and neural networks are all examples of common Machine Learning methods.

Название: Ransomware and Data Extortion: The Shifting Threat Landscape
Автор: Rуаn Gоldеn, Аnthоnу М. Frееd
Издательство: O’Reilly Media, Inc.
Год: 2024-04-19
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Ransomware poses an existential threat to organizations of all sizes in any vertical. These attacks are extremely lucrative, with ransom demands and recovery costs bleeding victim organizations out of millions of dollars. This book provides an overview of the ransomware economy, including the threat that this complex attack ecosystem poses to organizations, and reveals how you can best prepare to be resilient in the face of this threat and remain operational. Ransomware is a major threat to businesses and organizations of all kinds. Ransomware and data extortion attacks are not only disruptive to business operations but also costly to resolve. They spur regulatory actions and civil litigation, including class action lawsuits against victim organizations, and more recently have even resulted in criminal charges being lodged against company officers. A dedicated anti-ransomware solution utilizes Artificial Intelligence (AI) and Machine Learning (ML) with behavioral analytics to identify and stop polymorphic and repacked variants of ransomware that EPP, EDR, and XDR continue to miss. This is because AI/ML endpoint protection models were trained on characteristics that all malware share, including a subset of ransomware.

Название: Spatiotemporal Data Analytics and Modeling: Techniques and Applications
Автор: Jоhn А, Sаthееsh Аbimаnnаn, Еl-Sауеd М. Еl-Аlfу
Издательство: Springer
Серия: Big Data Management
Год: 2024
Страниц: 253
Язык: английский
Формат: pdf (true), epub
Размер: 18.9 MB

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.

Название: Deep Learning in Internet of Things for Next Generation Healthcare
Автор: Lаvаnуа Shаrmа, Рrаdеер Кumаr Gаrg
Издательство: CRC Press
Год: 2024
Страниц: 311
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

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.