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Fulltext Problem characterization for visual analytics in MOOC learner's support monitoring: A case of Malaysian MOOC

Asli MF, Hamzah M, Ibrahim AAA, Ayub E

Heliyon, 2020 Dec;6(12):e05733.
PMID: 33426320 DOI: 10.1016/j.heliyon.2020.e05733

Malaysia and many other developing countries progressively adopting massively open online course (MOOC) in their national higher education approach. We have observed an increasing need for facilitating MOOC monitoring that is associated with the rising adoption of MOOCs. Our observation suggests that recent adoption cases led analyst and instructors to focus on monitoring enrolment and learning activities. Visual analytics in MOOC support education analysts in analyzing MOOC data via interactive visualization. Existing literature on MOOC visualization focuses on enabling visual analysis on MOOC data from forum and course material. We found limited studies that investigate and characterize domain problems or design requirements of visual analytics for MOOC. This paper aims to present the empirical problem characterization and abstraction for visual analytics in MOOC learner's support monitoring. Detailed characterization and abstraction of the domain problem help visualization designer to derive design requirements in generating appropriate visualization solution. We examined the literature and conducted a case study to elicit a problem abstraction based on data, users, and tasks. We interviewed five Malaysian MOOC experts from three higher education institutes using semi-structured questions. Our case study reveals the priority of enabling MOOC analysis on learner's progression and course completion. There is an association between design and analysis priority with the pedagogical type of implemented MOOC and users. The characterized domain problems and requirements offer a design foundation for visual analytics in MOOC monitoring analysis.
CYP3A4*1B and CYP3A5*3 SNPs significantly impact the response of Egyptian candidates to high-intensity statin therapy to atorvastatin

Maslub MG, Daud NAA, Radwan MA, Sha'aban A, Ibrahim AG

Eur J Med Res, 2024 Nov 10;29(1):539.
PMID: 39523378 DOI: 10.1186/s40001-024-02109-7

BACKGROUND: A single nucleotide polymorphism (SNP) is a variation in the DNA sequence that results from the alteration of a single nucleotide in the genome. Atorvastatin is used to treat hypercholesterolemia. It belongs to a class of drugs called statins, which lower elevated levels of total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C). Research findings on the associations between the response to atorvastatin and genetic polymorphisms in CYP3A4 and CYP3A5 are inconclusive. The effects of CYP3A4*1B (rs2740574 C/T) and CYP3A5*3 (rs776746 T/C) on atorvastatin therapy have not been previously studied among Egyptians.
OBJECTIVE: This research aimed to investigate the effects of the genetic polymorphisms CYP3A4*1B and CYP3A5*3 on atorvastatin treatment in Egyptians.
METHODS: In this prospective cohort study, 100 subjects were genotyped for these SNPs. All participants were screened for serum lipid profiles, liver enzymes, total bilirubin (TB), and creatine kinase (CK) before and after 40 mg postatorvastatin therapy. Atorvastatin plasma levels were assessed posttreatment; atorvastatin pharmacokinetics were evaluated in five carriers of the CYP3A4*1B (T/T) and CYP3A5*3 (C/C) genotypes.
RESULTS: The allele frequencies of the CYP3A4*1B and CYP3A5*3 SNPs were 86% and 83%, respectively. The CYP3A4*1B (T/T) and CYP3A5*3 (C/C) genotypes significantly improved the serum triglyceride (TG) level (P
Artificial Neural Networks to Solve the Singular Model with Neumann-Robin, Dirichlet and Neumann Boundary Conditions

Nisar K, Sabir Z, Asif Zahoor Raja M, Ag Ibrahim AA, J P C Rodrigues J, Refahy Mahmoud S, et al.

Sensors (Basel), 2021 Sep 29;21(19).
PMID: 34640818 DOI: 10.3390/s21196498

The aim of this work is to solve the case study singular model involving the Neumann-Robin, Dirichlet, and Neumann boundary conditions using a novel computing framework that is based on the artificial neural network (ANN), global search genetic algorithm (GA), and local search sequential quadratic programming method (SQPM), i.e., ANN-GA-SQPM. The inspiration to present this numerical framework comes through the objective of introducing a reliable structure that associates the operative ANNs features using the optimization procedures of soft computing to deal with such stimulating systems. Four different problems that are based on the singular equations involving Neumann-Robin, Dirichlet, and Neumann boundary conditions have been occupied to scrutinize the robustness, stability, and proficiency of the designed ANN-GA-SQPM. The proposed results through ANN-GA-SQPM have been compared with the exact results to check the efficiency of the scheme through the statistical performances for taking fifty independent trials. Moreover, the study of the neuron analysis based on three and 15 neurons is also performed to check the authenticity of the proposed ANN-GA-SQPM.
Numerical Study of the Environmental and Economic System through the Computational Heuristic Based on Artificial Neural Networks

Nisar K, Sabir Z, Zahoor Raja MA, Ibrahim AAA, Mahmoud SR, Balubaid M, et al.

Sensors (Basel), 2021 Sep 30;21(19).
PMID: 34640887 DOI: 10.3390/s21196567

In this study, the numerical computation heuristic of the environmental and economic system using the artificial neural networks (ANNs) structure together with the capabilities of the heuristic global search genetic algorithm (GA) and the quick local search interior-point algorithm (IPA), i.e., ANN-GA-IPA. The environmental and economic system is dependent of three categories, execution cost of control standards and new technical diagnostics elimination costs of emergencies values and the competence of the system of industrial elements. These three elements form a nonlinear differential environmental and economic system. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions.