Название: Handbook of Research on Ai and Knowledge Engineering for Real-time Business IntelligenceАвтор: Kamal Kant Hiran, K. Hemachandran
Издательство: IGI Global
Год: 2023
Страниц: 359
Язык: английский
Формат: epub (true)
Размер: 31.1 MB
Artificial Intelligence (AI) is influencing the future of almost every sector and human being. AI has been the primary driving force behind emerging technologies such as Big Data, blockchain, robots, and the Internet of Things (IoT), and it will continue to be a technological innovator for the foreseeable future. New algorithms in AI are changing business processes and deploying AI-based applications in various sectors. The Handbook of Research on AI and Knowledge Engineering for Real-Time Business Intelligence is a comprehensive reference that presents cases and best practices of AI and knowledge engineering applications on business intelligence. Covering topics such as Deep Learning methods, face recognition, and sentiment analysis, this major reference work is a dynamic resource for business leaders and executives, IT managers, AI scientists, students and educators of higher education, librarians, researchers, and academicians.
Artificial Intelligence (AI) has rapidly transformed from a niche concept to a mainstream technology in recent years. It has the potential to revolutionize every aspect of our lives, from the way we work and communicate to how we make decisions and interact with the world around us. One of the more significant areas where AI has already shown tremendous promise is in improving decision-making processes and Predictions. By processing vast amounts of data and using advanced Algorithms, AI can identify patterns and insights that would be impossible for humans to discern. This capability has led to the development of AI-Powered Systems that can help us to make better decisions, anticipate future events, and event prevent disasters. A variety of data sources can be integrated using AI algorithms to create reliable, repeatable business operations. Business Intelligence (BI) is the term used to describe the procedural and technical framework that gathers, organises, and evaluates the data generated by a company's operations. Organizations may extract critical insights from previously untapped data using AI-enabled Business intelligence. Additionally, AI enables Business Intelligence to benefit from technologies like Machine Learning, predictive analytics, and Natural Language Processing (NLP) in order to expand the scope of the information shown. Before learning about AI, you must have the basic knowledge of computer programming language such as C, C++, Java, Python, etc. along with this knowledge of essential mathematics such as derivatives, probability theory, etc.
Chapter 1: To That of Artificial Intelligence, Passing Through Business Intelligence
Given the ever-growing number of data and the resulting overload, business intelligence (BI) is no longer sufficient to manage the daily operations of any firm. We need a more advanced intelligence system, which we currently refer to as Artificial Intelligence, given the sophisticated malware of today and the increasing relevance of the Internet of Things (IoT) (AI). AI and its two key subdivisions, Machine Learning (ML) and Deep Learning, have improved our chances of surviving a cyberattack (DL).
Chapter 2: A Systematic Literature Review of Reinforcement Algorithms in Machine Learning
Reinforcement learning (RL) is the process of picking up information from interactions with the environment in order to achieve long-term goals related to the environmental state. The goal of the study was to develop a successful multi-agent reinforcement learning algorithm that can be used for robotics, network packet routing, energy distribution, and other applications.
Chapter 3: An Introduction to Data Visualisation – An Overview of Visualizing Data
Making interactive renderings of data to investigate patterns and variances and draw insightful conclusions from the data is known as data visualisation. Data visualisation is typically used to communicate business outcomes to key stakeholders as well as for data cleansing and verification.
Chapter 4: A Breakthrough With Machine Learning in Real-Time Environments
The chapter attempts to provide a quick overview of Machine Learning algorithms and to highlight the numerous fields and applications where ML has helped to develop ground-breaking solutions.
Chapter 5: Machine Learning-Based Data Analytics With Privacy: Privacy-Preserving Data analytics
From Data collection to analytics and data storage, this chapter explores privacy-preserving strategies. In order to grasp Machine Learning data analytics, the methodologies for data classification are also presented. Unresolved privacy-related issues are also highlighted in the conclusion.
Chapter 6: An Overview of Data Science Process and Data Analytics Within Organisations
This paper's goal is to give an overview of data analytics and to suggest a conceptual framework for illustrating the Data Science methodology and some of the most well-known and practical aspects of the field.
Chapter 19: RezFind – NLP-Based Resume Shortlisting
A typical job posting on any job-hunting portal like Linkedin, Naukri, Indeed, etc., will receive many resumes. Screening a resume manually is a tedious process involving huge costs. Screening resumes also consumes a lot of time for the hiring managers. Sometimes, because of the massive numbers, a few qualified resumes don't get noticed, leading to considerable loss to both the company and a loss of opportunity for the applicant. This study uses advanced natural language processing to automate the resume screening process. It also describes a data mining method to extract relevant information like the eligible applicant's name, contact, and email. The GUI for the project is created using the Streamlit library in Python. Streamlit is an open-source framework used to create webapps in minutes.
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