The main objective of the study is to compare the Newton–Cotes methods such as the Trapezium rule, Simpson 1/3 rule and Simpson 3/8 rule to estimate the area under the Lorenz curve and Gini coefficient of income using polynomial function with degree 5. Comparing the Gini coefficients of income computed from the Polynomial function with degree 5 for the Trapezium, Simpson 1/3 and Simpson 3/8 methods using the relative errors showed that the trapezium rule, Simpson’s 1/3 rule and Simpson’s 3/8 rule show negative biases with the Simpson 1/3 rule yielding the lowest absolute relative true error of 4.230711 %.

Introduction: Retinal diseases, a significant global health concern, often lead to severe vision impairment and blindness, resulting in substantial functional and social limitations. This study explored a novel goal of developing and comparing the performance of multiple state-of-the-art convolutional neural network (CNN) models for the automated detection and classification of retinal diseases using optical coherence tomography (OCT) images. Method: The study utilized several models, including DenseNet121, ResNet50, Inception V3, MobileNet, and OCT images obtained from the WATBORG Eye Clinic, to detect and classify multiple retinal diseases such as glaucoma, macular edema, posterior vitreous detachment (PVD), and normal eye cases. The preprocessing techniques employed included data augmentation, resizing, and one-hot encoding. We also used the Gaussian Process-based Bayesian Optimization (GPBBO) approach to fine-tune the hyperparameters. Model performance was evaluated using the F1-Score, precision, recall, and area under the curve (AUC). Result: All the CNN models evaluated in this study demonstrated a strong capability to detect and classify various retinal diseases with high accuracy. MobileNet achieved the highest accuracy at 96% and AUC of 0.975, closely followed by DenseNet121, which had 95% accuracy and an AUC of 0.963. Inception V3 and ResNet50, while not as high in accuracy, showed potential in specific contexts, with 83% and 79% accuracy, respectively. Conclusion: These results underscore the potential of advanced CNN models for diagnosing retinal diseases. With the exception of ResNet50, the other CNN models displayed accuracy levels that are comparable to other state-of-the-art deep learning models. Notably, MobileNet and DenseNet121 showed considerable promise for use in clinical settings, enabling healthcare practitioners to make rapid and accurate diagnoses of retinal diseases. Future research should focus on expanding datasets, integrating multi-modal data, exploring hybrid models, and validating these models in clinical environments to further enhance their performance and real-world applicability.

Gathering public opinions on the Internet and Internet-based applications like Twitter has become popular in recent times, as it provides decision-makers with uncensored public views on products, government policies, and programs. Through natural language processing and machine learning techniques, unstructured data forms from these sources can be analyzed using traditional statistical learning. The challenge encountered in machine learning method-based sentiment classification still remains the abundant amount of data available, which makes it difficult to train the learning algorithms in feasible time. This eventually degrades the classification accuracy of the algorithms. From this assertion, the effect of training data sizes in classification tasks cannot be overemphasized. This study statistically assessed the performance of Naive Bayes, support vector machine (SVM), and random forest algorithms on sentiment text classification task. The research also investigated the optimal conditions such as varying data sizes, trees, and kernel types under which each of the respective algorithms performed best. The study collected Twitter data from Ghanaian users which contained sentiments about the Ghanaian Government. The data was preprocessed, manually labeled by the researcher, and then trained using the aforementioned algorithms. These algorithms are three of the most popular learning algorithms which have had lots of success in diverse fields. The Naive Bayes classifier was adjudged the best algorithm for the task as it outperformed the other two machine learning algorithms with an accuracy of 99%, F1 score of 86.51%, and Matthews correlation coefficient of 0.9906. The algorithm also performed well with increasing data sizes. The Naive Bayes classifier is recommended as viable for sentiment text classification, especially for text classification systems which work with Big Data.

Background. Annually, 5.4 million children under five face mortality, with 2.5 million deaths in the first month, 1.6 million between one and eleven months, and 1.3 million aged one to four. Despite global strides, sub-Saharan Africa, including Ghana, grapples with persistent high child mortality. This study employs statistical methods to pinpoint factors driving under-five mortality in the Greater Accra Regional Hospital. Methods. The data was acquired from Greater Accra Regional Hospital, Ghana, spanning January to December 2020. The data comprised all under-five deaths recorded in the hospital in 2020. The statistical tools employed were the chi-square test of association and the multinomial logistic regression model. Results. In 2020, there were 238 cases of under-five mortality recorded in the hospital, with males constituting the majority (55). About 85 of these cases occurred within the first month of birth, primarily attributed to respiratory distress, prematurity, and sepsis. Notably, meconium aspiration was the least common among grouped diagnoses. The test of association and multinomial logistic model emphasised the childs age, birth type, and weight at birth as significant factors influencing child mortality. Conversely, attributes like sex, marital status, and mothers age displayed no notable association with the diagnosis of death. Conclusion. The study on child mortality at the Greater Accra Regional Hospital unveils key factors shaping child health outcomes, emphasising the role of age, birth type, and weight. While specific demographics show no significant association, identified predictors are vital for targeted interventions. Proposed strategies encompass education programs, improved care, birthing practices, and data-driven policies.

[This corrects the article DOI: 10.1186/s40064-016-2868-z.].

Retinal diseases pose a significant global health challenge due to their potential to cause severe visual impairment and blindness. This study aimed to develop a robust deep learning ensemble framework for the automated detection and classification of retinal diseases from optical coherence tomography (OCT) images. This study used OCT images from WATBORG Eye Services in Ghana, including glaucoma, macular edema, posterior vitreous detachment (PVD), and healthy eyes. The data preprocessing steps included augmentation, resizing, and one‐hot encoding. The dataset was divided into training (56%), validation (14%), and testing (30%) sets using stratified sampling. Six convolutional neural network (CNN) architectures, Visual Geometry Group (VGG)‐16, VGG19, DenseNet121, MobileNet, ResNet50, and InceptionV3, were evaluated as base models. To optimize hyperparameters for each model, Gaussian process–based Bayesian optimization was used. In addition to analyzing individual architectures, two ensemble learning strategies, soft ensemble and stacked ensemble, were implemented to combine the predictions from the models. Among the six base learners, VGG16, VGG19, InceptionV3, DenseNet121, MobileNet, and ResNet50‐VGG19 achieved the highest individual accuracy of 97.74%, followed by MobileNet (93.99%), InceptionV3 (93.61%), DenseNet121 (93.23%), VGG16 (91.73%), and ResNet50 (60.53%). The ensemble strategies outperformed most single models. The soft ensemble achieved an accuracy of 95.11% with high precision, recall, and specificity, indicating improved stability and reduced variance across disease categories. The stacked ensemble yielded the best overall results, achieving an accuracy of 97.37%, a precision of 97.78%, a recall of 97.93%, an F 1‐score of 97.85%, and a ROC–AUC of 0.9993. Bootstrap resampling was incorporated to assess the stability and reliability of the reported performance. This study demonstrates that integrating Bayesian‐optimized CNN architectures with ensemble learning significantly enhances retinal disease classification performance. By applying these methods to a Ghanaian dataset—an underrepresented population in retinal artificial intelligence (AI) research—this work highlights the potential of AI to enhance diagnostic accuracy, optimize resource allocation, and promote health equity in ophthalmic care. Future research should focus on scaling datasets, integrating multimodal data, and conducting clinical validations to support the real‐world deployment of these models.