Marwa Abd Elghany

Bio: Marwa Abd Elghany is an academic researcher. The author has contributed to research in topics: Higher education & Supply chain management. The author has an hindex of 3, co-authored 8 publications receiving 23 citations.

Modelling the Effect on Quality of Information and Communications Technology (ICT) facilities in Higher Education: Case Study—Egyptian Universities

Abstract: This article contends that to modernise the nation, the higher education system must fulfil the community expectation of perceived quality and have a distinguished balance in graduate output relative to labour market requirements. Better finding strategy for information and communication technology (ICT) facilities should be adopted to enhance the performance of the HE institutions. The availability of (ICT) facilities act as the vital factor in choosing to apply to a certain university in accordance to students. Hence, these facilities in Egyptian Universities were synthesised to establish the effect of the resources provided by Egyptian HE institutions in the context of the quality of the education through the enrichment of student experience.

Investigating Game Developers’ Guilt Emotions Using Sentiment Analysis

Abstract: Game Development is one of the most important emerging fields in software engineering era. Game addiction is the nowadays disease which is combined with playing computer and videogames. Shame is a negative feeling about self evaluationas well as guilt that is considered as a negative evaluation of the transgressing behaviour, both are associated withadaptive and concealing responses. Sentiment analysis demonstrates a huge progression towards the understanding of web users’ opinions. In this paper, the sentiments of game developers are examined to measure their guilt’s emotions when working in this career. The sentiment analysis model is implementedthrough the following steps: sentiment collector, sentiment pre-processing, and then machine learning methods were used. The model classifies sentiments into guilt or no guilt and is trained with 1000 Reddit website sentiment. Results have shown that Support Vector Machine (SVM) approach is more accurate incomparison to Naive Bayes (NV) and Decision Tree.

Exploratory research on digitalization transformation practices within supply chain management context in developing countries specifically Egypt in the MENA region

Abstract: With no doubt, the adoption of Artificial Intelligence applications in customer service reduces the time to market. Nevertheless, the question remains whether or not its adoption in production and ...

Quality improvement and infrastructure activity costs in: software development: A longitudinal analysis

Abstract: This study draws upon theories of task interdependence and organizational inertia to analyze the effect of quality improvement on infrastructure activity costs in software development. Although increasing evidence indicates that quality improvement reduces software development costs, the impact on infrastructure activities is not known. Infrastructure activities include services like computer operations, data integration, and configuration management that support software development. Because infrastructure costs represent a substantial portion of firms' information technology budgets, it is important to identify innovations that yield significant cost savings in infrastructure activities. We evaluate quality and cost data collected in nine infrastructure activity centers over 10 years of product development in a major software firm undergoing a quality transformation. Findings indicate that infrastructure activities do benefit from quality improvement. The greatest marginal cost savings are realized in infrastructure activities that are highly interdependent with development and that occur later in the software development life cycle. Organizational inertia influences the rapidity with which the infrastructure activities benefit from higher product quality, especially for the more specialized activities. Finally, our findings suggest that although the savings in infrastructure from quality improvement are substantial, there are diminishing returns to quality improvement in infrastructure activities.

Machine Learning-Based Sentiment Analysis for Analyzing the Travelers Reviews on Egyptian Hotels

Abstract: Tourism affects the economy of any country; actually, it is the foundation of the country on the economic side. Egyptian Government is giving a big concern in developing the tourism sector. Hotel companies are using E-commerce technology for online booking and online reviewing. Travelers choose hotels based on their prices, facilities and other traveler’s review. Sentiment analysis is a very important topic that can be used to analyze the opinion of online users. Different websites are classifying the traveler reviews such as Tripadvisor, Expedia. The research aims to propose a Traveler Review Sentiment Classifier that will analyze the traveler’s reviews on Egyptian Hotels and provide a classification of each sentiment based on hotel features. Travelers Sentiment about five hotels located in Aswan in Egypt with a total of 11458 reviews were collected and analyzed. Sentiment model uses three classification techniques: Support Vector Machine, Naive Bayes and Decision Tree. Results had shown that Naive Bayes has the highest accuracy level.

Egyptian Student Sentiment Analysis Using Word2vec During the Coronavirus (Covid-19) Pandemic

Abstract: Education field is affected by the COVID-19 pandemic which also affects how universities, schools, companies and communities function. One area that has been significantly affected is education at all levels, including both undergraduate and graduate. COVID-19 pandemic emphasis the psychological status of the students since they changed their learning environment. E-learning process focuses on electronic means of communication and online support communities, however social networking sites help students manage their emotional and social needs during pandemic period which allow them to express their opinions without controls. The paper will propose a Sentiment Analysis Model that will analyze the sentiments of students in the learning process with in their pandemic using Word2vec technique and Machine Learning techniques.The sentiment analysis model will start with the processing process on the student's sentiment and selects the features through word embedding then uses three Machine Learning classifies which are Naive Bayes, SVM and Decision Tree. Results including precision, recall and accuracy of all these classifiers are described in this paper. The paper helps understand the Egyptian student's opinion on learning process during COVID-19 pandemic.