Research studies on educational data mining are on the increase due to the benefits obtained from the knowledge acquired from machine learning processes which help to improve decision making processes in higher institutions of learning. In this study, predictive analysis was carried out to determine the extent to which the fifth year and final Cumulative Grade Point Average (CGPA) of engineering students in a Nigerian University can be determined using the program of study, the year of entry and the Grade Point Average (GPA) for the first three years of study as inputs into a Konstanz Information Miner (KNIME) based data mining model. Six data mining algorithms were considered, and a maximum accuracy of 89.15% was achieved. The result was verified using both linear and pure quadratic regression models, and R² values of 0.955 and 0.957 were recorded for both cases. This creates an opportunity for identifying students that may graduate with poor results or may not graduate at all, so that early intervention may be deployed.

Internet traffic measurement and analysis generate dataset that are indicators of usage trends, and such dataset can be used for traffic prediction via various statistical analyses. In this study, an extensive analysis was carried out on the daily internet traffic data generated from January to December, 2017 in a smart university in Nigeria. The dataset analysed contains seven key features: the month, the week, the day of the week, the daily IP traffic for the previous day, the average daily IP traffic for the two previous days, the traffic status classification (TSC) for the download and the TSC for the upload internet traffic data. The data mining analysis was performed using four learning algorithms: the Decision Tree, the Tree Ensemble, the Random Forest, and the Naïve Bayes Algorithm on KNIME (Konstanz Information Miner) data mining application and kNN, Neural Network, Random Forest, Naïve Bayes and CN2 Rule Inducer algorithms on the Orange platform. A comparative performance analysis for the models is presented using the confusion matrix, Cohen’s Kappa value, the accuracy of each model, Area under ROC Curve, etc. A minimum accuracy of 55.66% was observed for both the upload and the download IP data on the KNIME platform while minimum accuracies of 57.3% and 51.4% respectively were observed on the Orange platform.

Liquefied Petroleum Gas (LPG) is a major fuel for cooking in Nigeria. LPG is dispensed into cylinders which are in various sizes and storage capacities. Multiple cases of household fires, injuries and fatalities due to LPG leakage and explosion have been recorded in Nigeria. This undesirable trend persists majorly because households in Nigeria don't have gas leakage detection systems, and at best only smoke detectors are available in selected high class homes. The proactive safety approach is to detect gas leakage and evacuate the gas before it is ignited. In this study, a model gas leakage detector and evacuation system are presented. The implemented, microcontroller-based system, activates a buzzer when a gas leak is detected, it shuts the gas supply solenoid valve to stop the gas flow, and also, it evacuates the gas by switching on evacuator fans. To ensure that the house occupant is adequately notified when there is a gas leakage; the system sends a text via short message service (SMS) to a stored mobile number. The status of the system can be remotely determined by the user, by sending codes to prompt the device via SMS.

Energy is indispensable to human existence, and its need is on the increase daily due to technological advancement. In recent times, there is sporadic increase in the development of modern buildings, and these buildings require a cost-effective and sustainable source of energy. Nigeria and some other African countries have major energy shortage issues due to the insufficient power generation and distribution facilities. This energy poverty creates a challenge for the growing population, and as such, it is vital to ensure that the energy supplied to a building is duly optimized and delivered cost effectively. In this study, an energy audit of an eight-floor multipurpose business complex was performed to determine the nature and type of loads within the building. Based on the load profile and energy consumption over a 10-year period, three alternative energy sources (national grid, diesel generator and photovoltaic [PV] system) were considered using various load-sharing ratios. The result reveals that though PV solar system is a renewable energy source that reduces the production of greenhouse gases generated by burning diesel, its continuous application in low–middle-income country like Nigeria may be challenged by the initial start-up capital which raises the cost of its unit energy.