Seasonal rainfall forecasting is crucial for agricultural planning and water resource management in Delta State, Nigeria, as the region's economy is highly dependent on climate. This study investigates the trend and appropriate models for forecasting seasonal rainfall patterns in the region. We employed a range of methods, including traditional time series techniques like Holt's Winter exponential smoothing and the Seasonal Autoregressive Integrated Moving Average (SARIMA), alongside more advanced machine learning and deep learning models. Critical data properties such as stationarity and normality of error terms were first assessed. Model performance was then evaluated using standard metrics, including root mean square error, mean absolute error, mean absolute percentage error, and mean square error. The data was found to have a stationary pattern, and among the models explored, the Holt's Winter exponential smoothing model was identified as the best performing.

This study evaluates a hybrid model that integrates Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) to predict stock prices. The model leverages two datasets: historical Google stock data and sentiment data from Reddit comments. Sentiment analysis was performed using VADER from NLTK, which classified comments as negative, neutral, or positive, while a CNN model was trained to predict sentiment scores. Separately, an LSTM model was built using ten years of Google stock data from Yahoo Finance, with features scaled using MinMax normalization to improve learning and a dropout layer added to prevent overfitting. Model performance was evaluated using Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). The LSTM model performed well on test data but showed lower accuracy on unseen data during forecasting. The hybrid model successfully combined the outputs of both the CNN and LSTM, demonstrating superior performance with lower RMSE and higher classification accuracy compared to the standalone models. This highlights the potential of integrating sentiment analysis with traditional stock prediction. The study acknowledges challenges in classifying neutral sentiments, suggesting that more comprehensive sentiment data is needed for future research.

This study applies machine learning to investigate gendered power dynamics and women's socio-economic and political engagement in Nigeria (1991-2023, World Bank, UNDP, INEC data). We trained Random Forest, Support Vector Machine (SVM), and Neural Network models with k-fold cross-validation, evaluating performance with R2, RMSE, and MSE. The SVM model demonstrated superior performance (R2 = 0.998). Feature analysis revealed that women's industry participation positively correlates with population share and education, while rural residence diminishes their likelihood of being employers. Additionally, K-means clustering of 2023 voting data uncovered regional variations in women's political representation. This research highlights enduring socio-economic and spatial barriers, demonstrating how AI-based evidence can inform gender-sensitive policies for inclusive representation in Nigeria.

This study introduces and rigorously investigates the novel three-parameter Type II Exponentiated Half-Logistic Exponential (TIIEHLEx) distribution, designed to offer enhanced flexibility for modeling positive-valued data in fields such as hydrology and risk analysis. We first establish the model's theoretical foundation, demonstrating that its probability density function (PDF) is versatile and that its hazard rate function can capture non-monotone risk patterns (bathtub, inverted bathtub, IHR, and DHR), positioning it favorably against models with restricted hazard shapes. A comprehensive simulation study evaluating fourteen non-Bayesian estimation methods confirmed that the Minimum Spacing Absolute Distance Estimator (MSADE) consistently provided the most accurate and reliable parameter estimates (lowest Bias, MSE, and MRE). Applying the TIIEHLEx model to two distinct real-world datasets—March Precipitation and Annual Insurance and Financial Services data—we demonstrated its empirical superiority, as evidenced by the highest Kolmogorov-Smirnov p-values and lowest information criteria compared to several competitor models. This work validates the TIIEHLEx distribution as a highly versatile and robust tool for reliable modeling and parameter estimation of complex data structures.

Malaria is a huge public health problem in sub-Saharan Africa, but it is not clear to what extent national income, health expenditure and access to sanitation reduce the burden of malaria. In this study, we analyze these relationships using a balanced panel of 31 sub-Saharan African countries during the period 2000–2023 ($N = 744$ country-years). To explore the dispersion of the count data and cross-sectional associations, pooled Poisson and Negative Binomial models were estimated, and then a Two-Way Fixed Effects (TWFE) Negative Binomial model was employed to isolate within-country relationships. Potential endogeneity was checked using a Two-Stage Residual Inclusion (2SRI) control-function approach with a lagged trade-weighted commodity price index . The pooled Poisson model showed extreme overdispersion (Pearson dispersion ratio $\approx 1.85 \times 10^{6}$), in favor of the Negative Binomial specification ($\hat{\alpha} = 0.362$). In the pooled Negative Binomial model, malaria burden was positively associated with GDP per capita ($\text{IRR} = 4.889, p < 0.0001$). However, this association disappeared after controlling for country and year fixed effects ($\text{LR }\chi^2 = 1218.6, p < 0.0001$), with the TWFE model yielding a non-significant protective within-country GDP association ($\text{IRR} = 0.671, p = 0.6798$). Health expenditure (IRR=1.048, p=0.7144) and sanitation access (IRR=0.927, p=0.6919) were also not significant under TWFE. The proposed commodity-price instrument was weakly relevant under fixed effects ($F=0.0758$), causing serious variance propagation in the 2SRI control-function model ($\beta_{\hat{v}}=6.8548,p=0.9392$).These results suggest that the positive relationships between pooled GDP and malaria are confounded by cross-sectional scale, rather than by economic drivers within countries. From a methodological point of view, the results indicate the relevance of controlling for overdispersion and structural country heterogeneity in macro-health models. From a policy standpoint, targeted, malaria-specific interventions and investments in the health system cannot be substituted by overall economic growth.