Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

https://www.healio.com/psychiatry/journals/psycann/1976-12-6-12/{806b8887-c02e-40c8-a228-57f1cdb2bdba}/the-stress-of-life.pdf

The stress of life

It is considered by many that for industry to prosper into the 1990s it must adopt the principles of total quality control. These principles offer the opportunity to tap previously unused brainpower within an organisation, and to achieve what our potential overseas customers expect of us, that is, a guaranteed 100 per cent good product. This paper presents one company's ideas and methods of implementation. Tqc is very much a people thing, and requires employees at all levels to become familiar with, and practise basic charting and problem solving techniques. Economics do not allow us to load up our organisations with service departments. Quality must be controlled by the people at the workface (a).

Total quality control

The provision and usage of online and e-learning system is becoming the main challenge for many universities during COVID-19 pandemic. E-learning system such as Blackboard has several fantastic features that would be valuable for use during this COVID-19 pandemic. However, the successful usage of e-learning system relies on understanding the adoption factors as well as the main challenges that face the current e-learning systems. There is lack of agreement about the critical challenges and factors that shape the successful usage of e-learning system during COVID-19 pandemic; hence, a clear gap has been identified in the knowledge on the critical challenges and factors of e-learning usage during this pandemic. Therefore, this study aims to explore the critical challenges that face the current e-learning systems and investigate the main factors that support the usage of e-learning system during COVID-19 pandemic. This study employed the interview method using thematic analysis through NVivo software. The interview was conducted with 30 students and 31 experts in e-learning systems at six universities from Jordan and Saudi Arabia. The findings of this study offer useful suggestions for policy-makers, designers, developers and researchers, which will enable them to get better acquainted with the key aspects of the e-learning system usage successfully during COVID-19 pandemic.

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Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic.

The growing popularity of social media sites has generated a massive amount of data that attracted researchers, decision-makers, and companies to investigate people’s opinions and thoughts in various fields. Sentiment analysis is considered an emerging topic recently. Decision-makers, companies, and service providers as well-considered sentiment analysis as a valuable tool for improvement. This research paper aims to obtain a dataset of tweets and apply different machine learning algorithms to analyze and classify texts. This research paper explored text classification accuracy while using different classifiers for classifying balanced and unbalanced datasets. It was found that the performance of different classifiers varied depending on the size of the dataset. The results also revealed that the Naive Byes and ID3 gave a better accuracy level than other classifiers, and the performance was better with the balanced datasets. The different classifiers (K-NN, Decision Tree, Random Forest, and Random Tree) gave a better performance with the unbalanced datasets.

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Sentiment analysis in English texts

Healthcare is considered a pillar for keeping humans alive, thus managing their health through diagnosing and treating the disease or illness is a must, in addition to preventing it Knowledge management (KM) in healthcare domain plays a substantial role in the implementation of different processes to ensure the existence of good and outstanding healthcare systems In this study, a systematic review is carried out among previous studies to observe KM processes studies with respect to healthcare Based on a search strategy, the systematic review offers a broad analysis of 10 quality assessed research articles published in peer-reviewed journals from 2009 till 2019 The results provide findings regarding the most KM processes studied, which are knowledge sharing and applications, and the least KM processes studied, which are knowledge acquisition and protection Surveys and interviews were used as research methods to avail information in the context of KM processes Taiwan, Finland, United States, Ghana, and Pakistan were the most active countries in the surveyed studies Furthermore, the contributors to these studies were healthcare staff This review study attempts to determine and feature KM processes research as an increase considering healthcare systems

Critical Review of Knowledge Management in Healthcare

The study conducted aims to form a conceptual model to calculate the pupils’ acceptance of social media in education and its factors. The study is carried out by extending the Technology Acceptance Model (TAM) using social influence factors. Alongside this, the collected data is evaluated through Machine learning approaches and the partial least squares-structural equation modeling (PLS-SEM). A total of 350 students enrolled at highly regarded universities in the United Arab Emirates (UAE) filled out questionnaire surveys, then analyzed, and results are stated. This research suggests that students’ intention to adopt social media networks in learning is significant social influence, perceived usefulness, and ease of use.

The Acceptance of Social Media Sites: An Empirical Study Using PLS-SEM and ML Approaches

This study aimed to analyze and discover the relation of using digital media sites (Facebook) on promoting medical tourism destinations in Jordan, and its impact on the behavior of tourists through the technologies provided by these means. Away from the traditional methods in marketing, the researchers used the survey methodology for a sample of 560 tourists distributed at central of Jordan in Dead Sea area to realize the study objective, a new framework was suggested to show the impact of Facebook on the behavior of tourists through: demographic variables, Facebook features, advertising, by using the TAM model in adoption of social media technology in tourism marketing for tourist destinations in Jordan. The proposed data were analyzed using the Smart PLS system by modeling structural equations (SEM). The outcome of the study showed that the advantages of Facebook, advertising and demographic variables have a favorable effect on the (PEOU) of the tourist and the PU in the adoption of tourism behavior, in addition to the (PU) and (PEOU) (ATT), which led to the adoption of behavior around therapeutic tourism destinations in Jordan. By determining the impact of Facebook in marketing tourism in Jordan, it would be useful to conduct further research to provide better proposals for marketing tourist therapeutic destinations in Jordan.

The Relationship Between Digital Media and Marketing Medical Tourism Destinations in Jordan: Facebook Perspective

This paper explores the world of energy drinks and its negative effects on the youth and how to de-market it, the study uses a quantitative method to evaluate the impacts of consuming energy drinks among minors and the youth, in this paper we distributed surveys on the targeted sample who are the youth of Jordan and got back some very interesting results, the results of the surveys were analyzed thoroughly using the latest mathematical analyzing methods and software to give out a clear big picture of what is happening in reality with the consumers of energy drinks from the youth. the results that we arrived to showed some trends that are worrying which require immediate actions before the issues go out of hand, and for the sake of countering and solving these issues we made a list of applicable recommendations that we found best at the end of the paper to act as a starting point to apply measures to solve the issues at hand in order to save the society from a danger they are unaware of, and help preserve the health of youth.

Said A. Salloum

Papers

Sentiment analysis in English texts

Critical Review of Knowledge Management in Healthcare

The Acceptance of Social Media Sites: An Empirical Study Using PLS-SEM and ML Approaches

The Relationship Between Digital Media and Marketing Medical Tourism Destinations in Jordan: Facebook Perspective

The Impact of De-marketing in Reducing Jordanian Youth Consumption of Energy Drinks