Improving the Dependability of Destination Recommendations using Information on Social Aspects

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process.Content-based filtering; Recommender Systems; Ontology; Social Attributes, Destination recommendation

ANALYSIS OF THE EFFECT OF CLASS ATTENDANCE ON STUDENTS'ACADEMIC PERFORMANCE USING ASSOCIATION RULE MINING TECHNIQUE

Many institutions of learning encourage students to have good lecture attendance records. The belief is that an above average attendance rate will enhance students’ academic performance. However, very few studies have attempted to answer questions that relate to: i) the actual impact of good attendance record on students' academic performance; ii) the extent, in quantitative terms, of the effect of good attendance record on students’ academic performance. This paper reports the findings from an experimental analysis of students’ attendance record and corresponding academic performance results using Association Rule Mining. Over the years, Association Rule Mining has proved to be effective in analysing relationship between variables in transactional databases. The result of the case study provides useful information for the managements of higher institutions of learning on appropriate perspective to adopt on class attendance policies

Improving the Dependability of Destination Recommendations using Information on Social Aspects

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process

Improving Rural Healthcare Delivery in Nigeria using Distributed Expert System Technology

Provision of adequate healthcare for the citizens is the responsibility of governments. This involves recruiting qualified medical personnel, and providing quality medical services nationwide. Theratio of medical doctorsto patients in Nigeria is 1:6,800, which means the citizens are grossly underserved in terms of medical services. Hence, there is need for new strategies that will ensure that more citizens access healthcare services, particularly people in the rural areas. In this paper, a framework for an SMS based expert system for rural healthcare delivery is proposed, which takes advantage of the wide coverage of telephony services in the rural areas in Nigeria. A preliminary evaluation of the expert system for pulmonary heart disease that was developed reveals that it emulates human expert capability at a reasonable level. This makes it suitable for deployment on a national scale to cater for the shortage of medical practitioners particularly in the rural area

Improving the Dependability of Destination Recommendations using Information on Social Aspects

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process

ASSESSING THE IMPACT OF CLASS ATTENDANCE ON STUDENTS'ACADEMIC PERFORMANCE USING DATA MINING

Many institutions of learning encourage students to have good lecture attendance records. The belief is that an above average attendance rate will enhance students’ academic performance. However, very few studies have attempted to answer questions that relate to: i) the actual impact of good attendance record on students' academic performance; ii) the extent, in quantitative terms, of the effect of good attendance record on students’ academic performance. This paper reports the findings from an experimental analysis of students’ attendance record and corresponding academic performance results using Association Rule Mining. Based on the extracted patterns in rules from the five course assessed, it was discovered that the impact of class attendance on academic performance is very low. A student with greater than 70 % attendance score, can still fall into any grade between “A-F”. This indicates that class attendance is not the major factor that determines student academic performance but other key factors such as the student participation in the class, personal study, and group study. The result of this case study and the recommendations is expected to provide useful information for the managements of higher institutions of learning on appropriate perspective to adopt on class attendance policies and good motivation for distance and online learning programmes

Carbon Emissions, Agricultural Output and Life Expectancy in West Africa

Carbon emissions are basically gaseous substances that are generated from human activities such as the burning of fossil fuels, into the atmosphere, and these emissions affect agricultural output and human health. The rising level of carbon emissions into the atmosphere has become a problem worldwide. Thus, this study examined the effect of carbon emissions on agricultural output and life expectancy in West Africa using data that spanned the period between 2000 and 2018. The study employed the two stage least squares econometric technique. The findings from the study revealed that a 1% increase in carbon emissions bring about a 3.818% reduction in agricultural output, that is, carbon emissions adversely affect agricultural output in West Africa. Also, a 1% increase in carbon emissions bring about a 0.123% increase in life expectancy, that is, carbon emissions boost life expectancy in West Africa. Therefore, this study recommends that the governments of the West African countries should formulate environmental policies that will help mitigate the adverse impact of carbon dioxide emissions on the agricultural sector, and also improve on healthcare delivery in the hospitals so as to reduce the mortality rate, this will help increase life expectancy in West Africa

A SOA-based framework for e-procurement in multi-organisations

The lack of standard platform for application-to-application interaction between the procurement systems of subsidiary organisations of a multi-organisation limits transparency of procurement procedures, and uniformity in the procurement patterns and practices, even when there are cross-cutting concerns. In this paper, an SOA-based e-procurement framework is proposed for effective e-procurement in a multi-organisational context. The e-procurement framework leverages SOA's inherent capability for addressing problems of heterogeneity, interoperability and dynamic requirements. An empirical case study showed that the framework is effective for achieving the corporate goal of promoting transparency, and enhancing uniformity of corporate procurement management in a multi-organisational context.business-to-business; B2B; e-procurement; service-oriented architecture; web services; multi-organisation; e-finance; goal question metrics; SOA evaluation; interoperability; procurement management; electronic procurement; online procurement; electronic finance; transparency; uniformity.

Improving Rural Healthcare Delivery in Nigeria using Distributed Expert System Technology

Provision of adequate healthcare for the citizens is the responsibility of governments. This involves recruiting qualified medical personnel, and providing quality medical services nationwide. The ratio of medical doctors to patients in Nigeria is 1:6,800, which means the citizens are grossly underserved in terms of medical services. Hence, there is need for new strategies that will ensure that more citizens access healthcare services, particularly people in the rural areas. In this paper, a framework for an SMS-based expert system for rural healthcare delivery is proposed, which takes advantage of the wide coverage of telephony services in the rural areas in Nigeria. A preliminary evaluation of the expert system for pulmonary heart disease that was developed reveals that it emulates human expert capability at a reasonable level. This makes it suitable for deployment on a national scale to cater for the shortage of medical practitioners particularly in the rural area

Detecting the most critical clinical variables of COVID-19 breakthrough infection in vaccinated persons using machine learning

Background COVID-19 vaccines offer different levels of immune protection but do not provide 100% protection. Vaccinated persons with pre-existing comorbidities may be at an increased risk of SARS-CoV-2 breakthrough infection or reinfection. The aim of this study is to identify the critical variables associated with a higher probability of SARS-CoV-2 breakthrough infection using machine learning. Methods A dataset comprising symptoms and feedback from 257 persons, of whom 203 were vaccinated and 54 unvaccinated, was used for the investigation. Three machine learning algorithms – Deep Multilayer Perceptron (Deep MLP), XGBoost, and Logistic Regression – were trained with the original (imbalanced) dataset and the balanced dataset created by using the Random Oversampling Technique (ROT), and the Synthetic Minority Oversampling Technique (SMOTE). We compared the performance of the classification algorithms when the features highly correlated with breakthrough infection were used and when all features in the dataset were used. Result The results show that when highly correlated features were considered as predictors, with Random Oversampling to address data imbalance, the XGBoost classifier has the best performance (F1 = 0.96; accuracy = 0.96; AUC = 0.98; G-Mean = 0.98; MCC = 0.88). The Deep MLP had the second best performance (F1 = 0.94; accuracy = 0.94; AUC = 0.92; G-Mean = 0.70; MCC = 0.42), while Logistic Regression had less accurate performance (F1 = 0.89; accuracy = 0.88; AUC = 0.89; G-Mean = 0.89; MCC = 0.68). We also used Shapley Additive Explanations (SHAP) to investigate the interpretability of the models. We found that body temperature, total cholesterol, glucose level, blood pressure, waist circumference, body weight, body mass index (BMI), haemoglobin level, and physical activity per week are the most critical variables indicating a higher risk of breakthrough infection. Conclusion These results, evident from our unique data source derived from apparently healthy volunteers with cardiovascular risk factors, follow the expected pattern of positive or negative correlations previously reported in the literature. This information strengthens the body of knowledge currently applied in public health guidelines and may also be used by medical practitioners in the future to reduce the risk of SARS-CoV-2 breakthrough infection