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The purpose of this paper is to investigate students’ attitude based on affective, behavioural and cognitive components. It will ascertain whether there is a link between the three components of attitude, which leads the possible classification of the elective courses.

The current study considers the students of the International Business Administration Department from Rustaq College of Applied Sciences, Ministry of Higher Education, Sultanate of Oman, during the academic year 2016–2017. The list of the elective courses was obtained from the existing study plan. A total of 101 students assessed elective courses’ affective and cognitive learning with the use of a web-based survey instrument.

An empirical analysis of the selection criterion was performed employing fuzzy set qualitative comparative analysis. The results of this study found that students rated 17 elective courses into 8 different configurations (triodes) based on various degrees assigned to attitudinal variables.

The present study explores the interaction between affective and cognitive factors in determining the selection behaviour of students. It is an investigation into the context of student choices regarding elective courses, especially the decision to select or not to select available courses.

The world of feelings and beliefs is always open to learning and self-development for the students. Students are continuously involved in taking charge of high-stakes decisions; one of them is the selection of elective courses. However, the critical components into the overall evaluations of their selection behaviour, such as feelings and beliefs, are not well studied.

The present paper is an attempt to study Education 4.0 supported by Industry 4.0 tools and techniques. The main purpose of the study is to examine the acceptance and use of one of the internet of things (IoT)-based learning management systems, i.e. videoconferencing application (Google Meet, Microsoft Teams, Zoom, GoToMeeting, WebEx), by academicians of higher education using the unified theory of acceptance and use of technology (UTAUT) model.

The study comprises 218 responses of academicians associated with higher education in the Sultanate of Oman. Descriptive and factor analysis of the collected data are employed using SPSS-26. Further, using Amos-21, the fit and validity indices of the measurement model are computed. Various relationships of the UTAUT structural model along with moderation effects of gender and nationality are tested.

The results suggest that performance expectancy, effort expectancy and social influence significantly predict behavioral intention. In turn, behavioral intention and facilitating conditions also significantly predict the use behavior of academicians for videoconferencing in higher education. Finally, gender moderates two out of four UTAUT relations, but nationality does not moderate any of these relations.

A lot of prior studies investigate several models to use technology-enabled pedagogy from educators' or students' perspectives. There are very limited studies that examine IoT-based learning tools within the UTAUT environment. Additionally, no study is available that considers UTAUT relations for the use of videoconferencing in higher education. Also, in the present study, one more moderator, i.e. nationality, is tested.

Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.

The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.

The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.

To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).

This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

The case deals with Arihant Retail, a family business firms located at Chennai in Tamil Nadu, India. It is a small scale firm, with a turnover of ‘340 million in 2009–10. Mr. Vishal Surana, the young Chief Executive of Arihant, dreams of making this into a ‘3 billion store by 2015. He has a concept named “Hot Male”, a chain of stores stocking trendy fashionwear targeted at the “funky” young generation belonging to the SEC (Socio Economic Classification) “B” group. He is excited about it, and thinks he can build a whole new concept and grow based mainly on these “Hot Male” stores. Being a family firm, however, he has to take into consideration the views of his family members (they do not seem to interfere in any way) and family friends of long standing, who have their own views. The case outlines the broad options available to Vishal taking into account the business logic, the family logic, and the top management aspirations.

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

Recent research advances in artificial intelligence, machine learning, and neural networks are becoming essential tools for building a wide range of intelligent applications. Moreover, machine learning helps to automate analytical model building. Machine learning based frameworks and approaches allow making well-informed and intelligent choices for improving daily eating habits and extension of healthy lifestyle. This book chapter presents a new machine learning approach for meal classification and assessment of nutrients values based on weather conditions along with new and innovative ideas for further study and research on health care-related applications.

The rapid increase in analytics is playing an essential role in enlarging various practices related to the health sector. Big Data Analytics (BDA) provides multiple tools to store, maintain, and analyze large sets of data provided by different systems of health. It is essential to manage and analyze these data to get meaningful information. Pharmaceutical companies are accumulating their data in the medical databases, whereas the payers are digitalizing the records of patients. Biomedical research generates a significant amount of data. There has been a continuous improvement in the health sector for past decades. They have become more advanced by recording the patient’s data on the Internet of Things devices, Electronic Health Records efficiently. BD is undoubtedly going to enhance the productivity and performance of organizations in various fields. Still, there are several challenges associated with BD, such as storing, capturing, and analyzing data, and their subsequent application to a practical health sector.

Big Data is one of the most promising area where it can be applied to make a change is health care. Healthcare analytics have the potential to reduce the treatment costs, forecast outbreaks of epidemics, avoid preventable diseases, and improve the quality of life. In general, the lifetime of human is increasing along world population, which poses new experiments to today’s treatment delivery methods. Health professionals are skillful of gathering enormous volumes of data and look for best approaches to use these numbers. Big data analytics has helped the healthcare area by providing personalized medicine and prescriptive analytics, medical risk interference and predictive analytics, computerized external and internal reporting of patient data, homogeneous medical terms and patient registries, and fragmented point solutions. The data generated level within healthcare systems is significant. This includes electronic health record data, imaging data, patient-generated data, etc. While widespread information in health care is now mostly electronic and fits under the big data as most is unstructured and difficult to use. The use of big data in health care has raised substantial ethical challenges ranging from risks for specific rights, privacy and autonomy, to transparency and trust.

A detailed description will be provided of all the classification algorithms that have been widely used in the domain of medical science. The foundation will be laid by giving a comprehensive overview pertaining to the background and history of the classification algorithms. This will be followed by an extensive discussion regarding various techniques of classification algorithm in machine learning (ML) hence concluding with their relevant applications in data analysis in medical science and health care. To begin with, the initials of this chapter will deal with the basic fundamentals required for a profound understanding of the classification techniques in ML which will comprise of the underlying differences between Unsupervised and Supervised Learning followed by the basic terminologies of classification and its history. Further, it will include the types of classification algorithms ranging from linear classifiers like Logistic Regression, Naïve Bayes to Nearest Neighbour, Support Vector Machine, Tree-based Classifiers, and Neural Networks, and their respective mathematics. Ensemble algorithms such as Majority Voting, Boosting, Bagging, Stacking will also be discussed at great length along with their relevant applications. Furthermore, this chapter will also incorporate comprehensive elucidation regarding the areas of application of such classification algorithms in the field of biomedicine and health care and their contribution to decision-making systems and predictive analysis. To conclude, this chapter will devote highly in the field of research and development as it will provide a thorough insight to the classification algorithms and their relevant applications used in the cases of the healthcare development sector.