The Nature of Intelligent Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

Development and Analysis of Virtual Laboratory as an Assistive Tool for Teaching Grade 8 Physical Science Classes

This chapter discusses the development of a virtual laboratory (VL) named “EduPhysics,” an assistive software tailored around the Namibian Physical Science textbook for Grade 8 learners, and examines the viability of implementing VL in education. It further presented reviews on the role of computer simulations in science education and teachers' perspective on the use of EduPhysics in physical science classrooms. The chapter adopted a mixed method with an experimental research design and used questionnaires and interviews as data collection tools in high school physical science classes. The analysis found that there are limited resources in most physical science laboratories. Computer laboratories, however, are well equipped and have computing capacities to support the implementation of VL. It was concluded that virtual laboratories could be an alternative approach to hands-on practical work that is currently undertaken in resource-constrained physical science labs. For future work, augmented reality and logs will be incorporated within EduPhysics.

On the Similarity Search With Hamming Space Sketches

This chapter focuses on data searching, which is nowadays mostly based on similarity. The similarity search is challenging due to its computational complexity, and also the fact that similarity is subjective and context dependent. The authors assume the metric space model of similarity, defined by the domain of objects and the metric function that measures the dissimilarity of object pairs. The volume of contemporary data is large, and the time efficiency of similarity query executions is essential. This chapter investigates transformations of metric space to Hamming space to decrease the memory and computational complexity of the search. Various challenges of the similarity search with sketches in the Hamming space are addressed, including the definition of sketching transformation and efficient search algorithms that exploit sketches to speed-up searching. The indexing of Hamming space and a heuristic to facilitate the selection of a suitable sketching technique for any given application are also considered.

Only Can AI Understand Me?

This chapter addresses whether AI can understand me. A framework for regulating AI systems that draws on Strawson's moral philosophy and concepts drawn from jurisprudence and theories on regulation is used. This chapter proposes that, as AI algorithms increasingly draw inferences following repeated exposure to big datasets, they have become more sophisticated and rival human reasoning. Their regulation requires that AI systems have agency and are subject to the rulings of courts. Humans sponsor the AI systems for registration with regulatory agencies. This enables judges to make moral culpability decisions by taking the AI system's explanation into account along with the full social context of the misdemeanor. The proposed approach might facilitate the research and development of intelligent analytics, intelligent big data analytics, multiagent systems, artificial intelligence, and data science.

Face Recognition and Face Detection Using Open Computer Vision Classifiers and Python

Identification of a person by looking at the image is really a topic of interest in this modern world. There are many different ways by which this can be achieved. This research work describes various technologies available in the open-computer-vision (OpenCV) library and methodology to implement them using Python. To detect the face Haar Cascade are used, and for the recognition of face eigenfaces, fisherfaces, and local binary pattern, histograms has been used. Also, the results shown are followed by a discussion of encountered challenges and also the solution of the challenges.

Big Data Analytics for Smart Airport Management

Smart airport management has drawn increasing attention worldwide for improving airport operational efficiency. Big data analytics is an emerging computing paradigm and enabler for smart airport management in the age of big data, analytics, and artificial intelligence (AI). This chapter will explore big data analytics for smart airport management from a perspective of PNG Jackson's International Airport. More specifically, this chapter first provides an overview of big data analytics and smart airport management and then looks at the impact of big data analytics on smart airport management. The chapter discusses how to apply big data analytics and smart airport management to upgrade PNG Jackson's International Airport in terms of safety and security, optimizing operational effectiveness, service enhancements, and customer experience. The approach proposed in this chapter might facilitate research and development of intelligent big data analytics, smart airport management, and customer relationship management.

A Big Data Analysis of the Factors Influencing Movie Box Office in China

A movie's economic revenue comes mainly from the movie box office, while the influencing factors of the movie box office are complex and numerous. This research explores the influencing factors of China's commercial movie box office by analyzing the top 100 box office movies released in Mainland China between 2013-2016, with a total of 400 movies. The authors analyzed the data collected using correlation analysis and decision tree analysis using RapidMiner, respectively. Based on the analysis results, they put forward suggestions for improving the box office of the movie industry.

Connectivity Management in Drone Networks

Drone technologies have attracted the attention of many researchers in recent years due to their potential opportunities. Fleets of drones integrated with widely available relatively short-range communication technologies have various application areas such as wildlife monitoring, disaster relief, and military surveillance. One of the major problems in this manner is maintaining the connectivity of the drone network. In this chapter, the authors study the connectivity management issues in drone networks. Firstly, movement, communication, and channel models are described by the authors, along with the problem definition. The hardness of the problem is investigated by proving its NP-Hardness. Various algorithms proposed to solve the connectivity management problem and their variants are evaluated in detail. Lastly, for future directions, the authors present mathematical methods to solve the emerging problem in drone networks.

AI-Driven Big Healthcare Analytics

With the increased development of technology in healthcare, a huge amount of data is collected from healthcare organizations and stored in distributed medical data centers. In this context, such data quantities, called medical big data, which include different types of digital contents such as text, image, and video, have become an interesting topic tending to change the way we describe, manage, process, analyze, and visualize data in healthcare industry. Artificial intelligence (AI) is one of the sub-fields of computer science enabling us to analyze and solve more complex problems in many areas, including healthcare. AI-driven big healthcare analytics have the potential to predict patients at risk, spread of viruses like SARS-CoV-2, spread of new coronavirus, diseases, and new potential drugs. This chapter presents the AI-driven big healthcare analytics as well as discusses the benefits and the challenges. It is expected that the chapter helps researchers and practitioners to apply AI and big data to improve healthcare.

Data Science and Big Data Practice Using Apache Spark and Python

Data science and big data analytics are still at the center of computer science and information technology. Students and researchers not in computer science often found difficulties in real data analytics using programming languages such as Python and Scala, especially when they attempt to use Apache-Spark in cloud computing environments-Spark Scala and PySpark. At the same time, students in information technology could find it difficult to deal with the mathematical background of data science algorithms. To overcome these difficulties, this chapter will provide a practical guideline to different users in this area. The authors cover the main algorithms for data science and machine learning including principal component analysis (PCA), support vector machine (SVM), k-means, k-nearest neighbors (kNN), regression, neural networks, and decision trees. A brief description of these algorithms will be explained, and the related code will be selected to fit simple data sets and real data sets. Some visualization methods including 2D and 3D displays will be also presented in this chapter.