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Intelligent Techniques for Predictive Data Analytics
Comprehensive resource covering tools and techniques used for predictive analytics with practical applications across various industries Intelligent Techniques for Predictive Data Analytics provides an in-depth introduction of the tools and techniques used for predictive analytics, covering applications in cyber security, network security, data mining, and machine learning across various industries. Each chapter offers a brief introduction on the subject to make the text accessible regardless of background knowledge. Readers will gain a clear understanding of how to use data processing, classification, and analysis to support strategic decisions, such as optimizing marketing strategies and customer relationship management and recommendation systems, improving general business operations, and predicting occurrence of chronic diseases for better patient management. Traditional data analytics uses dashboards to illustrate trends and outliers, but with large data sets, this process is labor-intensive and time-consuming. This book provides everything readers need to save time by performing deep, efficient analysis without human bias and time constraints. A section on current challenges in the field is also included. Intelligent Techniques for Predictive Data Analytics covers sample topics such as: Models to choose from in predictive modeling, including classification, clustering, forecast, outlier, and time series models Price forecasting, quality optimization, and insect and disease plant and monitoring in agriculture Fraud detection and prevention, credit scoring, financial planning, and customer analytics Big data in smart grids, smart grid analytics, and predictive smart grid quality monitoring, maintenance, and load forecasting Management of uncertainty in predictive data analytics and probable future developments in the field Intelligent Techniques for Predictive Data Analytics is an essential resource on the subject for professionals and researchers working in data science or data management seeking to understand the different models of predictive analytics, along with graduate students studying data science courses and professionals and academics new to the field.
R 4,722.00
Intelligent Techniques for Data Science
This textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions./p> The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for real‐world applications in an intelligent manner. Intelligent Techniques for Data Science also provides real-world cases of extracting value from data in various domains such as retail, health, aviation, telecommunication and tourism.
R 3,114.00
Predictive Analytics
Predictive analytics refers to making predictions about the future based on different parameters which are historical data, machine learning, and artificial intelligence. This book provides the most recent advances in the field along with case studies and real-world examples. It discusses predictive modeling and analytics in reliability engineering and introduces current achievements and applications of artificial intelligence, data mining, and other techniques in supply chain management. It covers applications to reliability engineering practice, presents numerous examples to illustrate the theoretical results, and considers and analyses case studies and real-word examples. The book is written for researchers and practitioners in the field of system reliability, quality, supply chain management, and logistics management. Students taking courses in these areas will also find this book of interest.
R 2,759.71
Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics
The success of any organization is largely dependent on positive feedback and repeat business from patrons. By utilizing acquired marketing data, business professionals can more accurately assess practices, services, and products that their customers find appealing.The Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics features innovative research and implementation practices of analytics in marketing research. Highlighting various techniques in acquiring and deciphering marketing data, this publication is a pivotal reference for professionals, managers, market researchers, and practitioners interested in the observation and utilization of data on marketing trends to promote positive business practices.
R 10,645.92
Intelligent Optimization Techniques for Business Analytics
Today, the convergence of cutting-edge algorithms and actionable insights in business is paramount for success. Scholars and practitioners grapple with the dilemma of optimizing data to drive efficiency, innovation, and competitiveness. The formidable challenge of effectively harnessing the immense power of intelligent optimization techniques and business analytics only increases as the volume of data grows exponentially, and the complexities of navigating the intricate landscape of business analytics becomes more daunting. This pressing issue underscores the critical need for a comprehensive solution, and Intelligent Optimization Techniques for Business Analytics is poised to provide much-needed answers. This groundbreaking book offers an all-encompassing solution to the challenges that academic scholars encounter in the pursuit of mastering the interplay between learning algorithms and intelligent optimization techniques for business analytics. Through a wealth of diverse perspectives and expert case studies, it illuminates the path to effectively implementing these advanced systems in real-world business scenarios. It caters not only to the scholarly community but also to industry professionals and policymakers, equipping them with the necessary tools and insights to excel in the realm of data-driven decision-making. Encompassing a wide range of topics, from foundational principles to practical applications, this book serves as a transformative solution. It is a guide for those seeking to comprehend and harness the potential of machine learning and intelligent optimization in the field of business analytics. It dives into practical challenges, ethical considerations, and showcases the wide-ranging applicability of these techniques across various domains. Intelligent Optimization Techniques for Business Analytics is the solution that empowers scholars to make informed decisions in complex business scenarios, revolutionizing the way data is leveraged and shaping the future of enterprises.
R 7,941.15
R 782.00
R 410.00
Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing
Today, in this smart era, data analytics and artificial intelligence (AI) play an important role in predictive maintenance (PdM) within the manufacturing industry. This innovative approach aims to optimize maintenance strategies by predicting when equipment or machinery is likely to fail so that maintenance can be performed just in time to prevent costly breakdowns. This book contains up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries.Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing provides an extensive and in-depth exploration of the intersection of data analytics, artificial intelligence, and predictive maintenance in the manufacturing industry and covers fundamental concepts, advanced techniques, case studies, and practical applications. Using a multidisciplinary approach, this book recognizes that predictive maintenance in manufacturing requires collaboration among engineers, data scientists, and business professionals and includes case studies from various manufacturing sectors showcasing successful applications of predictive maintenance. The real-world examples explain the useful benefits and ROI achieved by organizations. The emphasis is on scalability, making it suitable for both small and large manufacturing operations, and readers will learn how to adapt predictive maintenance strategies to different scales and industries. This book presents resources and references to keep readers updated on the latest advancements, tools, and trends, ensuring continuous learning.Serving as a reference guide, this book focuses on the latest advancements, trends, and tools relevant to predictive maintenance and can also serve as an educational resource for students studying manufacturing, data science, or related fields.
R 3,114.00
Intelligent Data Analytics for Solar Energy Prediction and Forecasting
Intelligent Data Analytics for Solar Energy Prediction and Forecasting: Advances in Resource Assessment and PV Systems Optimization explores the utilization of advanced neural networks, machine learning and data analytics techniques for solar radiation prediction, solar energy forecasting, installation and maximum power generation. The book addresses relevant input variable selection, solar resource assessment, tilt angle calculation, and electrical characteristics of PV modules, including detailed methods, coding, modeling and experimental analysis of PV power generation under outdoor conditions. It will be of interest to researchers, scientists and advanced students across solar energy, renewables, electrical engineering, AI, machine learning, computer science, information technology and engineers. In addition, R&D professionals and other industry personnel with an interest in applications of AI, machine learning, and data analytics within solar energy and energy systems will find this book to be a welcomed resource.
R 5,297.00
Computational Business Analytics
Learn How to Properly Use the Latest Analytics Approaches in Your OrganizationComputational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies.The book first covers core descriptive and inferential statistics for analytics. The author then enhances numerical statistical techniques with symbolic artificial intelligence (AI) and machine learning (ML) techniques for richer predictive and prescriptive analytics. With a special emphasis on methods that handle time and textual data, the text: Enriches principal component and factor analyses with subspace methods, such as latent semantic analyses Combines regression analyses with probabilistic graphical modeling, such as Bayesian networks Extends autoregression and survival analysis techniques with the Kalman filter, hidden Markov models, and dynamic Bayesian networks Embeds decision trees within influence diagramsAugments nearest-neighbor and k-means clustering techniques with support vector machines and neural networks These approaches are not replacements of traditional statistics-based analytics; rather, in most cases, a generalized technique can be reduced to the underlying traditional base technique under very restrictive conditions. The book shows how these enriched techniques offer efficient solutions in areas, including customer segmentation, churn prediction, credit risk assessment, fraud detection, and advertising campaigns.
R 9,354.79
Big Data Analytics Methods
Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.
R 2,371.00
Intelligent Business Analytics
This book explores the transformative role of soft computing methods in increasing business analytics, providing a comprehensive look into how these advanced methods can be applied to complex business data for meaningful insights. Through the integration of neural network, fuzzy logic, genetic algorithms, artificial intelligence, machine learning, deep learning, and other innovative approaches, Intelligent Business Analytics: Harnessing the Power of Soft Computing for Data- Driven Insights presents a roadmap for leveraging computational intelligence in diverse areas of business decision- making.Readers will venture from predictive analytics and customer segmentation to real- time decision support systems and many other applications. Soft computing’s flexibility and applicability in the handling of uncertainty, ambiguity, and dynamic data environments shine throughout the book. Each chapter is created to be a base of theory and, at the same time, provide an applied example, so the book is appropriate for students, researchers, and professionals in the field.This book also discusses where the markets are heading and new applications that are in store for intelligent analytics to create a competitive advantage that also supports sustainable growth. At the end, this book is for those who want to learn more about using data-driven approaches and those who are ready to face the changes of the fast-evolving digital world.
R 10,900.33
Big Data Analytics and Intelligent Techniques for Smart Cities
Big Data Analytics and Intelligent Techniques for Smart Cities covers fundamentals, advanced concepts, and applications of big data analytics for smart cities in a single volume. This comprehensive reference text discusses big data theory modeling and simulation for smart cities and examines case studies in a single volume. The text discusses how to develop a smart city and state-of-the-art system design, system verification, real-time control and adaptation, Internet of Things, and testbeds. It covers applications of smart cities as they relate to smart transportation/connected vehicle (CV) and intelligent transportation systems (ITS) for improved mobility, safety, and environmental protection. It will be useful as a reference text for graduate students in different areas including electrical engineering, computer science engineering, civil engineering, and electronics and communications engineering.Features: Technologies and algorithms associated with the application of big data for smart cities Discussions on big data theory modeling and simulation for smart cities Applications of smart cities as they relate to smart transportation and intelligent transportation systems (ITS) Discussions on concepts including smart education, smart culture, and smart transformation management for social and societal changes
R 7,580.72
R 489.00
Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing
In this smart era, Data Analytics and Artificial Intelligence (AI) play an important role in Predictive Maintenance (PdM) within the manufacturing industry. This innovative approach aims to optimize maintenance strategies by predicting when equipment or machinery is likely to fail so that maintenance can be performed in time to prevent costly breakdowns. This book will contain up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries.Data Analytics and Artificial Intelligence for Predictive Maintenance in Manufacturing provides an extensive and in-depth exploration of the intersection of data analytics, artificial intelligence, and predictive maintenance in the manufacturing industry, and covers fundamental concepts, advanced techniques, case studies, and practical applications. Using a multi-disciplinary approach, this book recognizes that predictive maintenance in manufacturing requires collaboration among engineers, data scientists, and business professionals and includes case studies from various manufacturing sectors showcasing successful applications of predictive maintenance. The real-world examples explain the useful benefits and ROI achieved by organizations. Emphasizes is on scalability, making it suitable for both small and large manufacturing operations. Readers will learn how to adapt predictive maintenance strategies to different scales and industries.The book presents resources and references to keep readers updated on the latest advancements, tools, and trends, ensuring continuous learning. Serving as a reference guide this book focuses on the latest advancements, trends, and tools relevant to predictive maintenance and can also serve as an educational resource for students studying manufacturing, data science, or related fields.
R 9,338.77
Predictive Analytics for Marketers
Predictive analytics has revolutionized marketing practice. It involves using many techniques from data mining, statistics, modelling, machine learning and artificial intelligence, to analyse current data and make predictions about unknown future events. In business terms, this enables companies to forecast consumer behaviour and much more. Predictive Analytics for Marketers will guide marketing professionals on how to apply predictive analytical tools to streamline business practices. Including comprehensive coverage of an array of predictive analytic tools and techniques, this book enables readers to harness patterns from past data, to make accurate and useful predictions that can be converted to business success. Truly global in its approach, the insights these techniques offer can be used to manage resources more effectively across all industries and sectors. Written in clear, non-technical language, Predictive Analytics for Marketers contains case studies from the author's more than 25 years of experience and articles from guest contributors, demonstrating how predictive analytics can be used to successfully achieve a range of business purposes.
R 1,470.69
Intelligent Optimization Techniques for Business Analytics
Today, the convergence of cutting-edge algorithms and actionable insights in business is paramount for success. Scholars and practitioners grapple with the dilemma of optimizing data to drive efficiency, innovation, and competitiveness. The formidable challenge of effectively harnessing the immense power of intelligent optimization techniques and business analytics only increases as the volume of data grows exponentially, and the complexities of navigating the intricate landscape of business analytics becomes more daunting. This pressing issue underscores the critical need for a comprehensive solution, and Intelligent Optimization Techniques for Business Analytics is poised to provide much-needed answers. This groundbreaking book offers an all-encompassing solution to the challenges that academic scholars encounter in the pursuit of mastering the interplay between learning algorithms and intelligent optimization techniques for business analytics. Through a wealth of diverse perspectives and expert case studies, it illuminates the path to effectively implementing these advanced systems in real-world business scenarios. It caters not only to the scholarly community but also to industry professionals and policymakers, equipping them with the necessary tools and insights to excel in the realm of data-driven decision-making. Encompassing a wide range of topics, from foundational principles to practical applications, this book serves as a transformative solution. It is a guide for those seeking to comprehend and harness the potential of machine learning and intelligent optimization in the field of business analytics. It dives into practical challenges, ethical considerations, and showcases the wide-ranging applicability of these techniques across various domains. Intelligent Optimization Techniques for Business Analytics is the solution that empowers scholars to make informed decisions in complex business scenarios, revolutionizing the way data is leveraged and shaping the future of enterprises.
R 10,436.08
Intelligent Analytics for Industry 4.0 Applications
The advancements in intelligent decision-making techniques have elevated the efficiency of manufacturing industries and led to the start of the Industry 4.0 era. Industry 4.0 is revolutionizing the way companies manufacture, improve, and distribute their products. Manufacturers are integrating new technologies, including the Internet of Things (IoT), cloud computing and analytics, and artificial intelligence and machine learning, into their production facilities throughout their operations. In the past few years, intelligent analytics has emerged as a solution that examines both historical and real-time data to uncover performance insights. Because the amount of data that needs analysis is growing daily, advanced technologies are necessary to collect, arrange, and analyze incoming data. This approach enables businesses to detect valuable connections and trends and make decisions that boost overall performance. In Industry 4.0, intelligent analytics has a broader scope in terms of descriptive, predictive, and prescriptive subdomains. To this end, the book will aim to review and highlight the challenges faced by intelligent analytics in Industry 4.0 and present the recent developments done to address those challenges.
R 6,674.44
Intelligent Techniques for Security of Medical Data
The rapid digital transformation of healthcare has created unprecedented opportunities for improving patient outcomes, operational efficiency, and accessibility of medical services. At the same time, the increasing reliance on connected devices, cloud infrastructures, and data-driven decision-making has introduced significant challenges related to security, privacy, trust, and regulatory compliance. Emerging technologies such as artificial intelligence, machine learning, blockchain, federated learning, post-quantum cryptography, and neuromorphic computing are playing a pivotal role in addressing these challenges while enabling the next generation of intelligent healthcare systems. Their potential extends from secure data sharing and privacy-preserving analytics to predictive diagnostics, personalized medicine, remote patient monitoring, and autonomous healthcare applications.The book is intended for undergraduate and postgraduate students, researchers, academicians, healthcare professionals, cybersecurity practitioners, AI engineers, data scientists, and industry experts working in the fields of artificial intelligence, cybersecurity, healthcare informatics, medical data analytics, blockchain technologies, Internet of Things (IoT), and intelligent healthcare systems. It also serves as a valuable reference for professionals seeking to understand the practical implementation of advanced computational and security technologies in modern healthcare environments.Key Features: Explores novel cutting-edge techniques for the protection of health data.Provides a better understanding and analysis of the telemedicine applications with these techniques.Discusses the integration of intelligent systems with cutting edge techniques for e- health care management.Reviews the development of the different state of the art techniques for social good.Examines how the developed techniques will result in better remote health care services with improved and efficient diagnosis.
R 6,118.00
Big Data Analytics Techniques for Market Intelligence
The ever-expanding realm of Big Data presents a formidable challenge for academic scholars and professionals. The sheer magnitude and diversity of data types, coupled with the continuous influx of information from various sources, make it exceedingly difficult to extract valuable insights. The need to effectively manipulate and analyze this vast amount of data is crucial for companies and organizations to uncover new market intelligence and make informed decisions. Without the proper guidance and understanding of the techniques and methodologies in Big Data analytics, scholars may struggle to navigate this complex landscape and maximize the potential benefits of their research. Big Data Analytics Techniques for Market Intelligence is a meticulously curated collection of original research which addresses the specific challenges faced by scholars and professionals in the field of Big Data analytics. By offering a comprehensive exploration of different techniques and methodologies, this book provides a solution to the hurdles encountered in extracting meaningful information from Big Data. It covers the entire lifecycle of Big Data analytics and also showcases the application of machine learning and deep learning techniques, compares various analysis methods, and presents real-world case studies, enabling readers to develop a holistic understanding of this complex domain. Big Data Analytics Techniques for Market Intelligence empowers academic scholars and professionals to overcome the obstacles inherent in working with Big Data. By gaining insights into the latest advancements in preprocessing, analysis, visualization, and utilization of analysis results, readers can enhance their research capabilities and contribute to their respective fields. Armed with the knowledge and tools provided in this book, scholars can unlock the immense potential of Big Data, generate valuable market intelligence, and drive strategic decision-making. The practical guidance offered in this book ensures that scholars and professionals can effectively harness the power of Big Data analytics and stay at the forefront of this rapidly evolving field.
R 13,721.01
Mastering Predictive Analytics with scikit-learn and TensorFlow
Learn advanced techniques to improve the performance and quality of your predictive modelsKey FeaturesUse ensemble methods to improve the performance of predictive analytics modelsImplement feature selection, dimensionality reduction, and cross-validation techniquesDevelop neural network models and master the basics of deep learningBook DescriptionPython is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics.By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis.What you will learnUse ensemble algorithms to obtain accurate predictionsApply dimensionality reduction techniques to combine features and build better modelsChoose the optimal hyperparameters using cross-validationImplement different techniques to solve current challenges in the predictive analytics domainUnderstand various elements of deep neural network (DNN) modelsImplement neural networks to solve both classification and regression problemsWho this book is forMastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.
R 1,140.00
Predictive Analytics in System Reliability
This book provides engineers and researchers knowledge to help them in system reliability analysis using machine learning, artificial intelligence, big data, genetic algorithm, information theory, multi-criteria decision making, and other techniques. It will also be useful to students learning reliability engineering.The book brings readers up to date with how system reliability relates to the latest techniques of AI, big data, genetic algorithm, information theory, and multi-criteria decision making and points toward future developments in the subject.
R 3,037.18
Computational Intelligence and Predictive Analysis for Medical Science
This book uncovers stakes and possibilities offered by Computational Intelligence and Predictive Analytics to Medical Science. The main focus is on data technologies,classification, analysis and mining, information retrieval, and in the algorithms needed to elaborate the informations. A section with use cases and applications follows the two main parts of the book, respectively dedicated to the foundations and techniques of the discipline.
R 7,242.29
AI-Powered Business Analytics
This book offers an integrated framework that actively leverages AI-driven analytics, empowering businesses to transform raw data into strategic insights, actionable intelligence, and informed decision-making. It expertly covers core AI techniques and presents real-world case studies from diverse industries. Furthermore, the book provides step-by-step methodologies for constructing effective AI analytics solutions, guiding readers through the practical application of these powerful tools.The author empowers the reader to adopt strategies that actively increase predictive accuracy, automate complex analyses, and improve business processes, ultimately leading to better outcomes. The clear and structured chapters facilitate understanding, starting with the basics and progressing to advanced machine learning algorithms, equipping readers with hands-on skills for future practical applications. Key topics covered include neural networks, deep learning fundamentals, and convolutional and recurrent networks. The book illustrates this essential knowledge with real-world applications for solving complex business problems. Furthermore, it delves into Natural Language Processing (NLP) methods, sentiment analysis, text classification, and document analytics within the context of business cases like customer feedback analysis and market intelligence. By actively engaging with the material, the reader will learn and understand the tools for both predictive and prescriptive analytics, gaining valuable competitive insights and enhanced strategic decision-making capabilities. This comprehensive approach ensures readers can confidently apply AI to drive tangible business improvements.This book is targeted at upper-level undergraduates and graduates in Business Analytics, Data Science, AI in Business, MBA in Analytics, and Digital Transformation. Industry professionals like business analysts, data analysts, data scientists, AI specialists, and decision-makers seeking strategic AI analytics implementation will also benefit.
R 8,572.00
Predictive Analysis on Large Data for Actionable Knowledge
Data analysis forms the basis of many modes of research ranging from scientific discoveries to governmental findings. With the advent of machine intelligence and neural networks, extracting and modeling, approaching data has been unimpeachably altered. These changes, seemingly small, affect the way societies organize themselves, deliver services, or interact with each other. Predictive Analysis on Large Data for Actionable Knowledge: Emerging Research and Opportunities provides emerging information on extraction and prediction patterns in data mining along with knowledge discovery. While highlighting the current issues in data extraction, readers will learn new methodologies comprising of different algorithms that automate the multidimensional schema that remove the manual processes. This book is a vital resource for researchers, academics, and those seeking new information on data mining techniques and trends.
R 5,234.42
Predictive Analytics
Predictive analytics refers to making predictions about the future based on different parameters which are historical data, machine learning, and artificial intelligence. This book provides the most recent advances in the field along with case studies and real-world examples. It discusses predictive modeling and analytics in reliability engineering and introduces current achievements and applications of artificial intelligence, data mining, and other techniques in supply chain management. It covers applications to reliability engineering practice, presents numerous examples to illustrate the theoretical results, and considers and analyses case studies and real-word examples. The book is written for researchers and practitioners in the field of system reliability, quality, supply chain management, and logistics management. Students taking courses in these areas will also find this book of interest.
R 9,317.75
Data Analytics and Business Intelligence
Business Analytics (BA) is an evolving phenomenon that showcases the increasing importance of using huge volumes of data to generate value for businesses. Advances in BA have offered great opportunities for organisations to improve, innovate, and develop existing or new processes, products, and services. BA is the process of transforming data into actionable insight by using statistical and mathematical analysis, descriptive, prescriptive, and predictive models, machine learning, information systems and network science methods, among others, along with a variety of data, expert knowledge, and fact-based management to support better and faster decision-making.BA and Business Intelligence (BI) generate capabilities for companies to compete in the market effectively and has become one of the main functional areas in most companies. BA tools are used in diverse ways, for example, to identify consumer behaviour patterns and market trends, to derive valuable insights on the performance of stocks, to find information on the attrition rate of employees, to analyse and solve healthcare problems, to offer insight into inventory management and supply chain management, to analyse data from social networks, and to infer traffic behaviour and develop traffic management policy, among others.BA and BI have become one of the most popular research areas in academic circles, as well as in the industry, driven by the increasing demand in the business world. This book aims to become a stimulus for innovative business solutions covering a wide range of aspects of business analytics, such as management science, information technology, descriptive, prescriptive, and predictive models, machine learning, network science, mathematical and statistical techniques. The book will encompass a valuable collection of chapters exploring and discussing computational frameworks, practices, and applications of BA that can assist industries and relevant stakeholders in decision-making and problem-solving exercises, with a view to driving competitive advantage.
R 9,315.40
Evolving Predictive Analytics in Healthcare
A major use of practical predictive analytics in medicine has been in the diagnosis of current diseases, particularly through medical imaging. Now there is sufficient improvement in AI, IoT and data analytics to deal with real time problems with an increased focus on early prediction using machine learning and deep learning algorithms. With the power of artificial intelligence alongside the internet of 'medical' things, these algorithms can input the characteristics/data of their patients and get predictions of future diagnoses, classifications, treatment and costs. Evolving Predictive Analytics in Healthcare: New AI techniques for real-time interventions discusses deep learning algorithms in medical diagnosis, including applications such as Covid-19 detection, dementia detection, and predicting chemotherapy outcomes on breast cancer tumours. Smart healthcare monitoring frameworks using IoT with big data analytics are explored and the latest trends in predictive technology for solving real-time health care problems are examined. By using real-time data inputs to build predictive models, this new technology can literally 'see' your future health and allow clinicians to intervene as needed. This book is suitable reading for researchers interested in healthcare technology, big data analytics, and artificial intelligence.
R 4,559.00
Burnout Intervention Mechanisms for Online Learning Processes Enabled by Predictive Learning Analytics
This book aims to fully demonstrate the burnout of learners in online learning processes. The authors propose a series of feasible and reliable solutions to sufficiently obtain and analyze massive instances of online learning behavior.In order to flexibly perceive and intervene in the "burnout state" and improve online learning processes and learning effectiveness, the authors design and construct various novel data analysis models and decision prediction methods using technological means and data-driven learning strategies. Their innovative methods, techniques, and decisions would benefit autonomous learning behavior tracking and stimulate the learning interest of online learning processes enabled by predictive learning analytics. By employing behavioral science research strategies, they build adaptive prediction and optimization measures for positive online learning patterns, improve learning behaviors, optimize learning states, and establish dynamic and sustainable knowledge tracing paths and behavior scheduling methods, enabling users to achieve self-organization and self-mobilization in their overall learning processes.The book will appeal to scholars and learners in Europe, North America, and Asia, especially those majoring in educational statistics and measurement, educational big data, learning analytics, educational psychology, artificial intelligence in education, computer science, and online collaborative learning.
R 9,306.30
Intelligent Techniques for Data Analysis in Diverse Settings
Data analysis forms the basis of many forms of research ranging from the scientific to the governmental. With the advent of machine intelligence and neural networks, extracting, modeling, and approaching data has been unimpeachably altered. These changes, seemingly small, affect the way societies organize themselves, deliver services, or interact with each other.Intelligent Techniques for Data Analysis in Diverse Settings addresses the specialized requirements of data analysis in a comprehensive way. This title contains a comprehensive overview of the most innovative recent approaches borne from intelligent techniques such as neural networks, rough sets, fuzzy sets, and metaheuristics. Combining new data analysis technologies, applications, emerging trends, and case studies, this publication reviews the intelligent, technological, and organizational aspects of the field. This book is ideally designed for IT professionals and students, data analysis specialists, healthcare providers, and policy makers.
R 7,571.49
Applied Modeling Techniques and Data Analysis 2
BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 2 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.
R 5,795.00
Machine Learning Techniques for Improved Business Analytics
Analytical tools and algorithms are essential in business data and information systems. Efficient economic and financial forecasting in machine learning techniques increases gains while reducing risks. Providing research on predictive models with high accuracy, stability, and ease of interpretation is important in improving data preparation, analysis, and implementation processes in business organizations.Machine Learning Techniques for Improved Business Analytics is a collection of innovative research on the methods and applications of artificial intelligence in strategic business decisions and management. Featuring coverage on a broad range of topics such as data mining, portfolio optimization, and social network analysis, this book is ideally designed for business managers and practitioners, upper-level business students, and researchers seeking current research on large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques.
R 5,821.38
Foundations of Predictive Analytics
Drawing on the authors’ two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It also discusses a variety of practical topics that are frequently missing from similar texts. The book begins with the statistical and linear algebra/matrix foundation of modeling methods, from distributions to cumulant and copula functions to Cornish–Fisher expansion and other useful but hard-to-find statistical techniques. It then describes common and unusual linear methods as well as popular nonlinear modeling approaches, including additive models, trees, support vector machine, fuzzy systems, clustering, naïve Bayes, and neural nets. The authors go on to cover methodologies used in time series and forecasting, such as ARIMA, GARCH, and survival analysis. They also present a range of optimization techniques and explore several special topics, such as Dempster–Shafer theory.An in-depth collection of the most important fundamental material on predictive analytics, this self-contained book provides the necessary information for understanding various techniques for exploratory data analysis and modeling. It explains the algorithmic details behind each technique (including underlying assumptions and mathematical formulations) and shows how to prepare and encode data, select variables, use model goodness measures, normalize odds, and perform reject inference.Web ResourceThe book’s website at www.DataMinerXL.com offers the DataMinerXL software for building predictive models. The site also includes more examples and information on modeling.
R 6,678.43
Foundations of Predictive Analytics
Drawing on the authors’ two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It also discusses a variety of practical topics that are frequently missing from similar texts. The book begins with the statistical and linear algebra/matrix foundation of modeling methods, from distributions to cumulant and copula functions to Cornish–Fisher expansion and other useful but hard-to-find statistical techniques. It then describes common and unusual linear methods as well as popular nonlinear modeling approaches, including additive models, trees, support vector machine, fuzzy systems, clustering, naïve Bayes, and neural nets. The authors go on to cover methodologies used in time series and forecasting, such as ARIMA, GARCH, and survival analysis. They also present a range of optimization techniques and explore several special topics, such as Dempster–Shafer theory.An in-depth collection of the most important fundamental material on predictive analytics, this self-contained book provides the necessary information for understanding various techniques for exploratory data analysis and modeling. It explains the algorithmic details behind each technique (including underlying assumptions and mathematical formulations) and shows how to prepare and encode data, select variables, use model goodness measures, normalize odds, and perform reject inference.Web ResourceThe book’s website at www.DataMinerXL.com offers the DataMinerXL software for building predictive models. The site also includes more examples and information on modeling.
R 3,716.06
Data Analytics and Business Intelligence
Business Analytics (BA) is an evolving phenomenon that showcases the increasing importance of using huge volumes of data to generate value for businesses. Advances in BA have offered great opportunities for organisations to improve, innovate, and develop existing or new processes, products, and services. BA is the process of transforming data into actionable insight by using statistical and mathematical analysis, descriptive, prescriptive, and predictive models, machine learning, information systems and network science methods, among others, along with a variety of data, expert knowledge, and fact-based management to support better and faster decision-making.BA and Business Intelligence (BI) generate capabilities for companies to compete in the market effectively and has become one of the main functional areas in most companies. BA tools are used in diverse ways, for example, to identify consumer behaviour patterns and market trends, to derive valuable insights on the performance of stocks, to find information on the attrition rate of employees, to analyse and solve healthcare problems, to offer insight into inventory management and supply chain management, to analyse data from social networks, and to infer traffic behaviour and develop traffic management policy, among others.BA and BI have become one of the most popular research areas in academic circles, as well as in the industry, driven by the increasing demand in the business world. This book aims to become a stimulus for innovative business solutions covering a wide range of aspects of business analytics, such as management science, information technology, descriptive, prescriptive, and predictive models, machine learning, network science, mathematical and statistical techniques. The book will encompass a valuable collection of chapters exploring and discussing computational frameworks, practices, and applications of BA that can assist industries and relevant stakeholders in decision-making and problem-solving exercises, with a view to driving competitive advantage.
R 3,152.47
R 671.00
Predictive Analytics For Dummies
Use Big Data and technology to uncover real-world insights You don't need a time machine to predict the future. All it takes is a little knowledge and know-how, and Predictive Analytics For Dummies gets you there fast. With the help of this friendly guide, you'll discover the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. In no time, you'll learn how to incorporate algorithms through data models, identify similarities and relationships in your data, and predict the future through data classification. Along the way, you'll develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get you stakeholder buy-in. Big Data has taken the marketplace by storm, and companies are seeking qualified talent to quickly fill positions to analyze the massive amount of data that are being collected each day. If you want to get in on the action and either learn or deepen your understanding of how to use predictive analytics to find real relationships between what you know and what you want to know, everything you need is a page away! Offers common use cases to help you get startedCovers details on modeling, k-means clustering, and moreIncludes information on structuring your dataProvides tips on outlining business goals and approaches The future starts today with the help of Predictive Analytics For Dummies.
R 684.00
Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry
This book covers aspects of data science and predictive analytics used in oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital well, value chain integration, crude basket forecasting and so forth. It further discusses theoretical, methodological, well-established, and validated empirical work dealing with various related topics. Special focus has been given to experimental topics with various case studies.Features:Provides understanding of basics of IT technologies applied in Oil and Gas sectorIncludes deep comparison between different artificial intelligence techniquesAnalyses different simulators in Oil and Gas sector as well as discussion of AI applicationsFocuses on in-depth experimental and applied topicsDetails different case studies for upstream and downstreamThis book is aimed at professionals and graduate students in petroleum engineering, upstream industry, data analytics and digital transformation process in oil and gas.
R 6,654.18
AI-Powered Business Analytics
This book offers an integrated framework that actively leverages AI-driven analytics, empowering businesses to transform raw data into strategic insights, actionable intelligence, and informed decision-making. It expertly covers core AI techniques and presents real-world case studies from diverse industries. Furthermore, the book provides step-by-step methodologies for constructing effective AI analytics solutions, guiding readers through the practical application of these powerful tools.The author empowers the reader to adopt strategies that actively increase predictive accuracy, automate complex analyses, and improve business processes, ultimately leading to better outcomes. The clear and structured chapters facilitate understanding, starting with the basics and progressing to advanced machine learning algorithms, equipping readers with hands-on skills for future practical applications. Key topics covered include neural networks, deep learning fundamentals, and convolutional and recurrent networks. The book illustrates this essential knowledge with real-world applications for solving complex business problems. Furthermore, it delves into Natural Language Processing (NLP) methods, sentiment analysis, text classification, and document analytics within the context of business cases like customer feedback analysis and market intelligence. By actively engaging with the material, the reader will learn and understand the tools for both predictive and prescriptive analytics, gaining valuable competitive insights and enhanced strategic decision-making capabilities. This comprehensive approach ensures readers can confidently apply AI to drive tangible business improvements.This book is targeted at upper-level undergraduates and graduates in Business Analytics, Data Science, AI in Business, MBA in Analytics, and Digital Transformation. Industry professionals like business analysts, data analysts, data scientists, AI specialists, and decision-makers seeking strategic AI analytics implementation will also benefit.
R 2,674.00
Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making
This book comprehensively discusses nature-inspired algorithms, deep learning methods, applications of mathematical programming and artificial intelligence techniques. It will further cover important topic such as linking green supply chain management practices with competitiveness, industry 4.0 and social responsibility.This book:Addresses solving practical problems such as supply chain management, take-off, and healthcare analytics using intelligent computing. Presents a comparative analysis of machine learning algorithms for the power consumption prediction. Discusses machine learning-based multi-objective optimization technique for load balancing in an integrated fog cloud environment. Illustrates a data-driven optimization concept for modeling environmental and economic sustainability. Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals.The text is primarily written for graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, mathematics and statistics, computer science and engineering.
R 5,633.33
Predictive Analytics
"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer "The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections.How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics(aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession.Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves.Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights.Five reasons why organizations predict death — including one health insurance company.How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual.Why the NSA wants all your data: machine learning supercomputers to fight terrorism.How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy!How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job.How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison.182 examples from Airbnb, the BBC, Citibank,
R 680.00
Lockwood Analytical Method for Prediction (LAMP)
The Lockwood Analytical Method for Prediction (LAMP) is a systematic technique for predicting short-term, unique behaviors. Using primarily qualitative empirical data, LAMP allows the analyst to predict the most likely outcomes for specific research questions across a wide range of intelligence problems, such as cyber threats in the U.S., the possibility of an Al Qaeda attack, the likelihood of Iran providing nuclear capability to terrorist groups, or the future actions of the Mexican drug cartel. LAMP offers an innovative and powerful method for organizing all available information based on the perceptions of the national actors, using it to make relevant predictions as to which alternate future is most likely to occur at a given moment in time. Its transparent structure enables anyone to see how an analyst gets from point A to point B to produce an intelligence estimate. LAMP differs from other analytical techniques in that it is based on determining the relative probability of a range of alternate futures, rather than attempting to determine the quantitative probability of their occurrence. After explaining its theoretical framework, the text leads the reader through the process of predictive analysis before providing practical case studies showing how LAMP is applied against real world problems, such as the possible responses of Israel, the U.S., and Lebanon to the behavior of Hezbollah or the competing visions of the future of Afghanistan. Evaluation of the method is provided with the case studies to show the effectiveness of the LAMP predictions over time. The book is complemented by a website with downloadable software for use by students of intelligence in conducting their own predictive analysis. It will be an essential tool for the analyst and the student, not only for national security issues but also for competitive intelligence.
R 5,885.00
Applied Learning Algorithms for Intelligent IoT
This book vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devices Cyber physical systems (CPS) The Internet of Things (IoT) and industrial use cases Industry 4.0 for smarter manufacturing Predictive and prescriptive insights for smarter systems Machine vision and intelligence Natural interfaces K-means clustering algorithm Support vector machine (SVM) algorithm A priori algorithms Linear and logistic regression Applied Learning Algorithms for Intelligent IoT clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now, with the emergence of machine learning algorithms, the field of data analytics is bound to reach new heights.This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges, and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book’s detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research.
R 2,812.15
Heuristics in Analytics
Employ heuristic adjustments for truly accurate analysis Heuristics in Analytics presents an approach to analysis that accounts for the randomness of business and the competitive marketplace, creating a model that more accurately reflects the scenario at hand. With an emphasis on the importance of proper analytical tools, the book describes the analytical process from exploratory analysis through model developments, to deployments and possible outcomes. Beginning with an introduction to heuristic concepts, readers will find heuristics applied to statistics and probability, mathematics, stochastic, and artificial intelligence models, ending with the knowledge applications that solve business problems. Case studies illustrate the everyday application and implication of the techniques presented, while the heuristic approach is integrated into analytical modeling, graph analysis, text analytics, and more. Robust analytics has become crucial in the corporate environment, and randomness plays an enormous role in business and the competitive marketplace. Failing to account for randomness can steer a model in an entirely wrong direction, negatively affecting the final outcome and potentially devastating the bottom line. Heuristics in Analytics describes how the heuristic characteristics of analysis can be overcome with problem design, math and statistics, helping readers to: Realize just how random the world is, and how unplanned events can affect analysisIntegrate heuristic and analytical approaches to modeling and problem solvingDiscover how graph analysis is applied in real-world scenarios around the globeApply analytical knowledge to customer behavior, insolvency prevention, fraud detection, and moreUnderstand how text analytics can be applied to increase the business knowledge Every single factor, no matter how large or how small, must be taken into account when modeling a scenario or event—even the unknowns. The presence or absence of even a single detail can dramatically alter eventual outcomes. From raw data to final report, Heuristics in Analytics contains the information analysts need to improve accuracy, and ultimately, predictive, and descriptive power.
R 1,139.00
