Most of the home automation wireless technologies on the internet of things are based on ZigBee, Z-Wave, Wi-Fi, and Bluetooth wireless technologies. While these solutions are good enough, users of smart homes are facing challenges of selecting the best technology. This paper compares the performance of ZigBee, Z-Wave Wi-Fi and Bluetooth technologies from the user's perspective. Power consumption, range, cost, ease of use, scalability, and interoperability are the six indices developed as guidelines to potential users, and hence, the results of this study. With this knowledge, the users can make the appropriate choice of wireless technology solution to use in their home automation systems.

Nigeria’s agricultural sector relies heavily on rainfall, but insecurity in various regions poses significant challenges. This study aims to address this issue by identifying secure, rain-rich areas in northern Nigeria to support sustainable agriculture. Two models, one integrating classical statistical methods (polynomial and Fourier series fittings) and another using a hybrid approach (artificial neural networks, polynomial, and Fourier series fittings), were employed to analyze historical rainfall data from 1981 to 2021 in the selected districts (Kano, Zaria, Bida, Nguru, and Yelwa) known for their rainfall levels and security stability. The study demonstrates that the machine learning-classical hybrid model outperforms existing models, including the classical-classical hybrid and benchmark models like Iwok’s (2016) model, Fourier series, and SARIMA models. Multi-step ahead forecasting with this hybrid model reveals potential changes in rainfall patterns. Notably, Kano, Zaria, Bida, and Yelwa are expected to experience increased rainfall from 2022 to 2026, while Nguru may initially witness decreased rainfall, with improvement in the final year (2026). In conclusion, this study introduces an effective approach for rainfall modeling and forecasting, facilitating the identification of secure agricultural regions in northern Nigeria. These findings carry implications for crop production and agricultural development, contributing to climate resilience efforts and assisting stakeholders in strategic decision-making for regional agricultural investments.

Data mining and regression techniques are important methods that we can use to predict students' performance to inform decision making. This study uses five regression techniques to analyse students' first-year cumulative grade point average (CGPA) and predict their final graduation CGPA. The data set used in this study is that of programming and software development students at Kano Informatics Institute. The results and the grades obtained by 163 students forms the sample data used in the study. The forecast error, mean forecast error and mean absolute forecast error are all calculated. Dickey-Fuller's stationary t-test is performed for all the regressions analysis values using the Python programming language to determine the mean and if the data is centred on the mean. We use the stationary t-test to test the null and alternative Dickey-Fuller's hypotheses to compare our P-values and critical values for all regressions analyses done. The results show that the P-values obtained for all the regressions are small and less than the critical value. However, linear regression is the model with the mean closest to zero, and, according to Dickey-Fuller's statistics, it is the model that best fits our data.