Generic data warehousing for consumer electronics retail industry

The dynamic and highly competitive nature of the consumer electronics retail industry means that businesses in this industry are experiencing different decision making challenges in relation to pricing, inventory control, consumer satisfaction and product offerings. To overcome the challenges facing retailers and create opportunities, we propose a generic data warehousing solution which can be applied to a wide range of consumer electronics retailers with a minimum configuration. The solution includes a dimensional data model, a template SQL script, a high level architectural descriptions, ETL tool developed using C#, a set of APIs, and data access tools. It has been successfully applied by ASK Outlets Ltd UK resulting in improved productivity and enhanced sales growth

Snap-drift neural computing for intelligent diagnostic feedback

Information and communication technologies have been playing a crucial role in improving the efficiency and effectiveness of learning and teaching in higher education. Two decades ago, research studies were focused on how to use artificial intelligence techniques to imitate teachers or tutors in delivering learning sessions. Machine learning techniques have been applied in several research studies to construct a student model in the context of intelligent tutoring systems. However, the usage of intelligent tutoring systems has been very limited in higher education as most educational institutions are in favour of using virtual learning environments (VLEs). VLEs are computer-based systems that support all aspects of teaching and learning from provision of course materials to managing coursework. In this research study, the emphasis is on the assessment aspect of VLEs. A literature review revealed that existing computer-based formative assessments have never utilised unsupervised machine learning to improve their feedback mechanisms. Machine learning techniques have been applied to construct student models, which is represented as categories of knowledge levels such as beginning, intermediate and advanced. The student model does not specify what concepts are understood, the gap of understanding and misconceptions. Previously, a snap-drift modal learning neural network has been applied to improve the feedback mechanisms of computer-based formative assessments. This study investigated the application of snap-drift modal learning neural network for analysing student responses to a set of multiple choice questions to identify student groups. This research study builds on this previous study and its aim is to improve the effectiveness of the application of snap-drift modal learning neural network in modelling student responses to a set of multiple choice questions and to extend its application in modelling student responses gathered from object-oriented programming exercises. A novel method was proposed and evaluated using trials that improves the effectiveness of snap-drift modal learning neural network in identifying useful student group profiles, representing them to facilitate generation of diagnostic feedback and assigning an appropriate diagnostic feedback automatically based on a given student response. Based on the insight gained into the use of this novel method, we extend it to identify useful student group profiles that represent different programming abilities for writing an object-oriented class. The purpose of identifying student group profiles is to facilitate construction of diagnostic feedback that improves the development of basic object-oriented programming abilities. Overall, the main objectives of this research project were addressed successfully. New insights are gained into the application of unsupervised learning in general and snap-drift modal learning in particular. The proposed methods are capable of improving the feedback mechanisms of existing computer-based formative assessment tools. The improved computer-based formative assessments could have a huge impact on students in improving conceptual understanding of topics and development of basic object-oriented programming abilities

Research Assignment TU Delft Contents Table Contents Table..................................................................................................................2

ID No. 103102

Identifying student group profiles for diagnostic feedback using snap-drift modal learning neural network

The aim of this paper is to propose a novel method for identifying student group profiles based on student responses to a set of multiple choice questions for the purpose of constructing diagnostic feedback using snap-drift modal learning neural network. The proposed method is capable of supporting tutors without the knowledge of machine learning in identifying useful student groups and constructing diagnostic feedback. Trials were conducted and analysis of the result showed that the snap-drift modal learning neural network was able to identify distinct student groups and represented student group profiles were helpful in revealing gaps of understanding and misconceptions that facilitate construction of diagnostic feedback. Moreover, the result showed that all student responses gathered were assigned to their appropriate student group profiles and the diagnostic feedback constructed based on the identified student group profiles had a positive impact on improving the learning performance of the students

Self-medication with over the counter drugs, prevalence of risky practice and its associated factors in pharmacy outlets of Asmara, Eritrea

Abstract Background Although over the counter (OTC) drugs are believed to be relatively safe, their inappropriate use could have serious implications. The aim of the study was to assess the practice of self-medication, prevalence of risky practice and its associated factors in pharmacy outlets of Asmara, Eritrea. Methods A descriptive cross-sectional study was conducted among 609 customers in 20 pharmacy outlets in Asmara between August and September, 2017. Two-stage cluster sampling was employed and data were collected using a structured questionnaire through face to face exit interviews. Descriptive statistics and multivariate logistic regression were performed using SPSS (version 22). Results Of the 609 customers, 93.7% had practiced self-medication with OTC drugs; of which 81.8% were at risky practice. On average, each participant was using OTC drugs at least once a month (Median = 1, IQR = 3.67). Educational level (p < 0.0001), religion (p = 0.047), occupation (p = 0.027) and knowledge regarding OTC drugs (p = 0.019) were significantly associated with risky practice. Respondents with elementary and below educational level were fifteen times (AOR = 15.49, CI: 1.97, 121.80) at higher risk compared to those with higher education, and students were almost three times (AOR = 2.96, CI: 1.13, 7.73) at higher risk than governmental employees. Furthermore, respondents with below average score in knowledge were more likely to be engaged in risky practice (AOR = 1.83, CI: 1.11, 3.04) compared to those with above average score. The most frequently preferred OTC drug group was analgesics (34.3%) followed by antipyretics (15.7%) and cough and cold preparations (14.2%). About 14% of the respondents admitted that they had taken more than the recommended dose and 6.9% had experienced drug related problems following the consumption of OTC drugs. Always, 35% of the respondents read package insert(s) and 73.9% check expiry dates while purchasing OTC drugs. Refrigerating OTC drugs, where it is not recommended, was also one of the prominent risky practices. Conclusions This study revealed that inappropriate self-medication practice with OTC drugs was prevalent requiring early intervention to minimize the risks