A B S T R A C TA three parameter probability model, the so called Weibull-exponential distribution was proposed using the Weibull Generalized family of distributions. Some important models in the literature were found to be sub models of the new model. Explicit expressions for some of its basic mathematical properties like moments, moment generating function, reliability analysis, limiting behavior and order statistics were derived. The method of maximum likelihood estimation was proposed in estimating its parameters and real life applications were provided to illustrate its flexibility and potentiality over the exponential distribution.

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In this research, we aggregated students log data such as Class Test Score (CTS), Assignment Completed (ASC), Class Lab Work (CLW) and Class Attendance (CATT) from the Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria. Similarly, we employed data mining techniques such as ID3 & J48 Decision Tree Algorithms to analyze these data. We compared these algorithms on 239 classification instances. The experimental results show that the J48 algorithm has higher accuracy in the classification task compared to the ID3 algorithm. The important feature attributes such as Information Gain and Gain Ratio feature evaluators were also compared. Both the methods applied were able to rank search method and the experimental results confirmed that the two methods derived the same set of attributes with a slight deviation in the ranking. From the results analyzed, we discovered that 67.36 percent failed the course titled Introduction to Computer Programming, while 32.64 percent passed the course. Since the CATT has the highest gain value from our analysis; we concluded that it is largely responsible for the success or failure of the students.

This study uses the quantitative research approach to examine the connection between students’ teamwork experience, self-regulated learning, technology self-efficacy, and performance in an online educational technology course. Sixty-three (63) students participated in this study. The study data were collected through an online questionnaire that included background information, course satisfaction, motivation strategies for learning, and online technology self-efficacy, to study the variables’ interactions using quantitative research. To realize this study’s aims, multivariate regression and correlation approaches were employed to analyze the online students’ data. The multivariate regression analysis results show a relationship between self-regulated learning, the online course level, and the number of online courses that the students have completed. Right self-regulated learning strategies in online courses motivate students to strive for a good teamwork experience, leading to increased interest in online learning. In addition, the results also show that there is a relationship between satisfaction and the level of the online course. Achieving good grades makes the student more satisfied and improves the level of technology use. Finally, this study established a relationship between the students’ motivation and the online course level. Therefore, teachers and course designers should implement learning objects that promote students’ engagement and motivation in online learning environments.

Malnutrition among children is an important public health problem in Pakistan. Conventional indicators (stunting, wasting and underweight) are well known. However, there is a need for aggregate indicators in this perspective. The goal of this study is to assess the prevalence and trends of malnutrition among Pakistani children under the age of five using the so-called composite index of anthropometric failure (CIAF), a tool for calculating the whole aggregate burden of malnutrition. The data were extracted from the Pakistan Demographic and Health Survey 2012–2013. Mothers’ education and socioeconomic statuses (SES) were assessed as important factors in malnutrition. Chi-squared analysis was used to check the bivariate association, and multiple logistic regression was used to identify the significant correlates of child malnutrition. Moreover, multiple correspondence analysis (MCA) was applied to strengthen the use of CIAF as an outcome variable. The study looked at 3071 children under the age of five, with 52.2% of them falling into the CIAF. Children of educated mothers had 43% fewer odds of being malnourished (OR (Odd Ratio) = 0.57, 95% CI (Confidence Interval) = 0.44–0.73). Additionally, a decreasing trend in malnutrition was found with increasing SES. There is a need to improve maternal education. Such programs focusing on increasing women’s autonomy in making home decisions should be established. Furthermore, long-term interventions for improving home SES and effective nutritional methods should be examined. For policymakers, the use of CIAF is suggested since it provides an estimate of the entire burden of undernutrition.

A new two-parameter weighted exponential distribution which has more mild algebraic properties than the existing weighted exponential distribution was studied. Explicit expressions for some of its basic statistical properties including moments, reliability analysis, quantile function and order statistics were derived. Its parameters were estimated using the method of maximum likelihood estimation. The new probability model was applied to four real data sets to assess its flexibility over the existing weighted exponential distribution.

Background: Modeling with the complex random phenomena that are frequently observed in reliability engineering, hydrology, ecology, medical science, and agricultural sciences was once thought to be an enigma. Scientists and practitioners agree that an appropriate but simple model is the best choice for this investigation. To address these issues, scientists have previously discussed a variety of bounded and unbounded, simple to complex lifetime models. Methods: We discussed a modified Lehmann type II (ML-II) model as a better approach to modeling bathtub-shaped and asymmetric random phenomena. A number of complementary mathematical and reliability measures were developed and discussed. Furthermore, explicit expressions for the moments, quantile function, and order statistics were developed. Then, we discussed the various shapes of the density and reliability functions over various model parameter choices. The maximum likelihood estimation (MLE) method was used to estimate the unknown model parameters, and a simulation study was carried out to evaluate the MLEs' asymptotic behavior. Results: We demonstrated ML- II's dominance over well-known competitors by modeling anxiety in women and electronic data.

In this paper, we focus on providing a narrative review of healthcare services in which artificial intelligence (AI) based services are used as part of the operations and analyze key elements to create successful AI-based services for healthcare. The benefits of AI in healthcare are measured by how AI is improving the healthcare outcomes, help caregivers in work, and reducing healthcare costs. AI market in healthcare sector have also a high market potential with 28% global compound annual growth rate. This paper will collect outcomes from multiple perspectives of healthcare sector including financial, health improvement, and care outcome as well as provide proposals and key factors for successful implementation of AI methods in healthcare. It is shown in this paper that AI implementation in healthcare can provide cost reduction and same time provide better health outcome for all.