Описание: Chapter 1. Introducing Data Representation FeaturesSet the context for the reader with important data representation features, present the need for adaptive algorithms to compute them and demonstrate how these algorithms are important in multiple disciplines. Additionally, discuss a common methodology adopted to derive all our algorithms.Sub-topics: 1. Data representation features2. Computational models for time-varying multi-dimensional data3. Multi-disciplinary origin of adaptive algorithms4. Common Methodology for Derivations of Algorithms5. Outline of The Book Chapter 2. General Theories and NotationsIntroduce the reader to types of data in real-world streaming applications, discuss practical use cases and derive adaptive algorithms for mean, normalized mean, median, and covariances. Support the results with experiments on real data.Sub-topics: 1. Introduction2. Stationary and Non-Stationary Sequences3. Use Cases for Algorithms Covered in this Chapter 4. Adaptive Mean and Covariance of Nonstationary Sequences5. Adaptive Covariance and Inverses6. Adaptive Normalized Mean Algorithm7. Adaptive Median Algorithm8. Experimental Results Chapter 3. Square Root and Inverse Square RootIntroduce readers to practical applications of square roots and inverse square roots of streaming data matrices, then present algorithms to compute them. Support the algorithms with real data.Sub-topics: 1. Introduction and Use Cases2. Adaptive Square Root Algorithms3. Adaptive Inverse Square Root Algorithms4. Experimental Results Chapter 4. First Principal EigenvectorIntroduce the reader to adaptive computation of first principal component of streaming data, discuss the use cases with examples, derive ten algorithms with the common methodology adopted here. Demonstrate the algorithms with real-world non-stationary streaming data examples.Sub-topics: 1. Introduction and Use Cases2. Algorithms and Objective Functions3. OJA Algorithm4. RQ, OJAN, and LUO Algorithms5. IT and XU Algorithms6. Penalty Function Algorithm 7. Augmented Lagrangian Algorithms8. Summary of Algorithms9. Experimental Results Chapter 5. Principal and Minor EigenvectorsIntroduce the reader to adaptive computation of all principal components, discuss powerful use cases with examples, derive 21 adaptive algorithms and demonstrate the algorithms on real-world time-varying data.Sub-topics: 1. Introduction and Use Cases2. Algorithms and Objective Functions3. OJA Algorithms4. XU Algorithms5. PF Algorithms6. AL1 Algorithms7. AL2 Algorithms8. IT Algorithms9. RQ Algorithms10. Summary of Adaptive Eigenvector Algorithms11. Experimental Results Chapter 6. Accelerated Computation eigenvectorsIntroduce the reader to methods to speed up the adaptive algorithms presented in this book. Help the reader speed up a few algorithms and demonstrate their usefulness and acceleration on real-world stationery and non-stationary data.Sub-topics: 1. Introduction2. Gradient Descent Algorithm3. Steepest Descent Algorithm4. Conjugate Direction Algorithm5. Newton-Raphson Algorithm6. Experimental Results Chapter 7. Generalized EigenvectorsIntroduce the reader to the adaptive computation of generalized eigenvectors of streaming data matrices in real-time applications. Dis
Автор: Williams, Steve Название: Business Intelligence Strategy and Big Data Analytics ISBN: 0128091983 ISBN-13(EAN): 9780128091982 Издательство: Elsevier Science Рейтинг: Цена: 5388.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Business Intelligence Strategy and Big Data Analytics is written for business leaders, managers, and analysts - people who are involved with advancing the use of BI at their companies or who need to better understand what BI is and how it can be used to improve profitability. It is written from a general management perspective, and it draws on observations at 12 companies whose annual revenues range between $500 million and $20 billion. Over the past 15 years, my company has formulated vendor-neutral business-focused BI strategies and program execution plans in collaboration with manufacturers, distributors, retailers, logistics companies, insurers, investment companies, credit unions, and utilities, among others. It is through these experiences that we have validated business-driven BI strategy formulation methods and identified common enterprise BI program execution challenges.
In recent years, terms like "big data" and "big data analytics" have been introduced into the business and technical lexicon. Upon close examination, the newer terminology is about the same thing that BI has always been about: analyzing the vast amounts of data that companies generate and/or purchase in the course of business as a means of improving profitability and competitiveness. Accordingly, we will use the terms BI and business intelligence throughout the book, and we will discuss the newer concepts like big data as appropriate. More broadly, the goal of this book is to share methods and observations that will help companies achieve BI success and thereby increase revenues, reduce costs, or both.
Provides ideas for improving the business performance of one's company or business functions
Emphasizes proven, practical, step-by-step methods that readers can readily apply in their companies
Includes exercises and case studies with road-tested advice about formulating BI strategies and program plans
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 179981193X ISBN-13(EAN): 9781799811930 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 27027.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 1799811921 ISBN-13(EAN): 9781799811923 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 35897.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Название: Application of big data and business analytics ISBN: 1800438850 ISBN-13(EAN): 9781800438859 Издательство: Emerald Рейтинг: Цена: 16870.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Application of Big Data and Business Analytics uses advanced analytic tools to explore the solutions to problems in society, environment and industry. The chapters within bring together researchers, engineers and practitioners, encompassing a wide and diverse set of topics in almost every field.
With the increase in the availability of data, analytics has now become a major element in both the top line and the bottom line of any organization. With this in mind, Application of Big Data and Business Analytics brings together researchers, engineers and practitioners, encompassing a wide and diverse set of topics in almost every field.
The primary target audience of this book includes researchers, academicians and data scientists from a variety of disciplines interested in analyzing and application of big data analytics. However, this work will also be of general interest to postgraduates and undergraduates pursuing advanced study in big data.
Session 5: Data-driven co-design of communication, computing and control for IoT security
Design of a Force Balance Geophone Utilizing Bandwidth Extension and Data Acquisition Interface
Application of 3ds Max Technology in Archaeology
The Application of Virtual Reality Technology in ESP Teaching
Application of Simulation Method Based on Computer Bionic Design
The Implementation and Application of Computer Simulation Technology in PE Teaching
Construction of College Communities in the New Media Based on Network Environment
Political and Ideological Personnel Management Mode Based on Computer Network
Analysis of Mapping Knowledge Domain on Health and Wellness Tourism in the Perspective of Cite Space
Application of Smart Retail Mode in Suning.Com
Construction and Development of High-tech Smart City
Design and Implementation of Self-Service Tourism Management Information System Based on B/S Architecture
Chinese Culture Penetration in Teaching Chinese as a Foreign Language in the Era of Mobile Internet
Application and Outlook of Digital Media Technology in Smart Tourism
Accounting Informationization in Computer Network Environment
Mobile Phone GPS and Sensor Technology in College Students' Extracurricular Exercises
Design of Networking Network Model Based on Network Function Virtualization Technology
Intelligent Media Technology Empowered Brand Communication of Chinese Intangible Cultural Heritage
Construction Strategy of Smart English Teaching Platform from the Perspective of "Internet + Education"
Online Writing Effectiveness under the Blended Teaching Mode of Moscotech APP
A Narrative Environment Model for the Sustainability of Intangible Cultural Heritage under the 5G Era
Application Study of VPN on the Network of Hydropower Plant
Prediction of Technology Trend of Educational Robot Industry Based On Patent Map Analysis
Coal Handling System of Power Plant Based On PLC
Discussion on the Construction of Wireless Campus Network Based On SDN Architecture
Applicational Status Analysis of Artificial Intelligence Technology in Middle School Education and Teaching
Virtual Enterprise Partner Selection by Improved Analytic Hierarchy Process with Entropy Weight and Range Method
Research and implementation of Intelligent Tourism Guide System Based on cloud computing platform
Analysis of financial needs of new agricultural operators based on K-means clustering algorithm
Research on the application of virtual network technology in computer network security
Application of Bionics in Underwater Acoustic Covert Communication
Energy-saving and efficient underwater wireless sensor network security data aggregation model
False Data Filtering in Underwater Wireless Sensor Networks
Research on Underwater Bionic Covert Communication
Session 6: Authentication and access control for data usage in IoT
The Application of Virtual Reality Technology in Architectural Design
Computer-assisted Teaching and Cultivate Students' Innovative Thinking Ability
The Reform Progress and Practical Difficulties of State-owned Hospitals under Information Age―Case Analysis Based on the Reform in a Medical Institution of A Group in China
Financing Efficiency of SMEs in New Third Board Market in the Information Times
Application of Virtual Instrument Technology in Electronic Course Teaching
A Solution for Internet of Things based on Blockchain and Edge Computer
Discovery and Advice of Free Charging of Electronic Devices
Design and Implementation of Tourism Information Management System Based on .NET
A Computer Model for Decision of Equipment Maintenance Spare Parts Reserve
Описание: The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data.
Автор: Abu-Salih Bilal, Wongthongtham Pornpit, Zhu Dengya Название: Social Big Data Analytics: Practices, Techniques, and Applications ISBN: 9813366516 ISBN-13(EAN): 9789813366510 Издательство: Springer Цена: 17074.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Chapter 1: Big data technologies
Big data is no more "all just hype" but widely applied in nearly all aspects of our business, governments, and organizations with the technology stack of AI. Its influences are far beyond a simple technique innovation but involves all rears in the world. This chapter will first have historical review of big data; followed by discussion of characteristics of big data, i.e. the 3V's to up 10V's of big data. The chapter then introduces technology stacks for an organization to build a big data application, from infrastructure/platform/ecosystem to constructional units/components; following by several successful examples. Finally, we provide some big data online resources for reference.
Chapter 2: Credibility and influence in social big data
Online Social Networks (OSNs) are a fertile medium through which users can express their sentiments and share their opinions, experiences and knowledge of several topics. There is a deficiency of assessment mechanisms that incorporate domain-based trustworthiness. In OSNs, determining users' influence in a particular domain has been driven by its significance in a broad range of applications such as personalized recommendation systems, opinion analysis, expertise retrieval, to name a few. This chapter presents a comprehensive framework that aims to infer value from BSD by measuring the domain-based trustworthiness of OSN users, addressing the main features of big data, and incorporating semantic analysis and the temporal factor.
Chapter 3: Semantic data discovery from social big data
The challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academia and industry. Social big data is an important big data island; thus, social data analytics are intended to make sense of data and to obtain value from data. Social big data provides a wealth of information that businesses, political governments, organisations, etc. can mine and analyse to exploit value in a variety of areas. This chapter discusses the development of an approach that aims to semantically analyse social content, thus enriching social data with semantic conceptual representation for domain-based discovery.
Chapter 4: Predictive analytics using social big data and machine learning
Previous works in the area of topic distillation and discovery lack an appropriate and applicable technical solution that can handle the complex task of obtaining an accurate interpretation of the contextual social content. This is evident through the inadequacy of these endeavours in addressing the topics of microblogging short messages like tweets, and their inability to classify and predict the messages' actual and precise domains of interest at the user level. Hence, this chapter intends to address this problem by presenting solutions to domain-based classification and prediction of social big data at the user and tweet levels incorporating comprehensive knowledge discovery tools and well-known machine learning algorithms.
Chapter 5: Affective design in the era of big social data
In today's competitive market, product designers not only need to optimize functional qualities when developing a new product, but also they need to optimize the affective qualities of the product. The reason is that products with high affective qualities is more likely to attract more potential consumers to buy. In the past, affective design is generally conducted based on the limited amount of customer survey data which is collected from marketing questionnaires and consumer interviews. Since the data amount is limited, the affective design cannot fully reflect the current or even the recent situation of the marketplaces. Thanks to the advanced computing and web technologies, big data from social media or product reviews in w
Session 1: Novel machine learning methods for IoT security
The Innovation of UI Design Courses in Higher Vocational Colleges Based on the Internet Perspective
Predicting the Getting-on and Getting-off Points Based on the Trafic Big Data
The Prediction analysis of Passengers Boarding and Alighting Points Based on the Big Data of Urban Traffic
Verifiable Random Number Based on B-Spline Curve
An Improved Particle Swarm Optimization Algorithm Based on DFC&HRS
Application and Practice of ID3 Algorithms in College Students' Education
Construction of Human Knee Joint Mechanics Model and Study on Mechanical Characteristics of Flexion movement based on neural network algorithm
Application of Artificial Intelligence Technology in Physical Fitness Test of College Students
Theoretical Research on College Students' Professional Literacy Design Based on Deep Learning
Artificial Intelligence is the Technical Guarantee of Network Security
Intelligent Question Answering System of Medical Knowledge Map Based on Deep Learning
Construction and Practice of Red Teaching Resources Based on Machine Learning
Reasonable Approach and Development Trend of Artificial Intelligence Sports Development
Returnee Migrant Workers' Entrepreneurship Based on Artificial Intelligence
Improvement of College Teachers' Teaching Ability under the Background of the Development of Artificial Intelligence Platform
Influence Factors of Using Modern Teaching Technology in the Classroom of Junior Middle School Teachers under the Background of Artificial Intelligence--Analysis Based on HLM
Analysis and Design of Personalized Learning System Based on Decision Tree Technology
Deep Learning Classification and Recognition Model Construction of Face Living Image Based on Multi-Feature Fusion
A Prediction Method of Blood Glucose Concentration Based On Nonlinear Auto-Regressive Model
Signal Processing Based On Machine Learning Optical Communication
Refined Management of Installation Engineering Cost based on Artificial Intelligence Technology
Application of Artificial Intelligence Technology in International Trade Finance
An Improved Genetic Algorithm for Vehicle Routing Problem
A Machine-Learning Based Store Layout Strategy in Shopping Mall
Risk Analysis in Online Peer-to-Peer Loaning Based on Machine Learning: A Decision Tree Implementation on PPDai.com
The Application of BP Neural Network in the Opioid Crisis
Design of Personalized Intelligent Learning Assistant System under Artificial Intelligence Background
Application of Artificial Intelligence in Intelligent Decision-making of Human Resource Allocation
Research on Early Warning of Security Risk of Hazardous Chemicals Storage Based on BP-PSO
The Application of Artificial Intelligence and Machine Learning in Financial Stability
Application of alternative routing configuration mechanism based on Genetic Algorithm in power communication network
Safety Situation Assessment of Underwater Nodes based on BP Neural Network
Session 2: Big data analytics for IoT security
The Innovation of College Counsellor's Work Based On Big Data Analysis
Analysis of India's Big Data Industry
The Application of Computer Virtual Technology in Modern Sports Training
The Research on the Development and Utilization of Hospital Archive Information in the Big Data Era
Integration and Optimization of College English Teaching Information Resources in the Context of Big Data
Discussion on the Training of Cross-border E-commerce Application Talents Based on the Internet Era
Level of Technology Innovation Development and Promotion Strategies of High Technology Industry in Hubei Province Based on Smart City
Описание: This book presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis.
Описание: This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets—i.e., big data—to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions.
Автор: Choo Kim-Kwang Raymond, Dehghantanha Ali Название: Handbook of Big Data Analytics and Forensics ISBN: 3030747522 ISBN-13(EAN): 9783030747527 Издательство: Springer Рейтинг: Цена: 21953.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT security, privacy, and forensics literature, focusing on IoT and unmanned aerial vehicles (UAVs). The authors propose a deep learning-based approach to process cloud’s log data and mitigate enumeration attacks in the third chapter. The fourth chapter proposes a robust fuzzy learning model to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. Advanced and fair clustering approach for industrial data, which is capable of training with huge volume of data in a close to linear time is introduced in the fifth chapter, as well as offering an adaptive deep learning model to detect cyberattacks targeting cyber physical systems (CPS) covered in the sixth chapter. The authors evaluate the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS) in chapter 7, and the next chapter presents a robust fuzzy Bayesian approach for ICS’s cyber threat hunting. This handbook also evaluates the performance of supervised machine learning methods in identifying cyberattacks against CPS. The performance of a scalable clustering algorithm for CPS’s cyber threat hunting and the usefulness of machine learning algorithms for MacOS malware detection are respectively evaluated. This handbook continues with evaluating the performance of various machine learning techniques to detect the Internet of Things malware. The authors demonstrate how MacOSX cyberattacks can be detected using state-of-the-art machine learning models. In order to identify credit card frauds, the fifteenth chapter introduces a hybrid model. In the sixteenth chapter, the editors propose a model that leverages natural language processing techniques for generating a mapping between APT-related reports and cyber kill chain. A deep learning-based approach to detect ransomware is introduced, as well as a proposed clustering approach to detect IoT malware in the last two chapters. This handbook primarily targets professionals and scientists working in Big Data, Digital Forensics, Machine Learning, Cyber Security Cyber Threat Analytics and Cyber Threat Hunting as a reference book. Advanced level-students and researchers studying and working in Computer systems, Computer networks and Artificial intelligence will also find this reference useful.
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