Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.

Malaria is a mosquito-borne infectious disease that affects humans and poses a severe public health problem. Nigeria has the highest number of global cases. Geospatial technology has been widely used to study the risks and factors associated with malaria hazards. The present study is conducted in Ibadan, Oyo State, Nigeria. The objective of this study is to map out areas that are at high risk of the prevalence of malaria by considering a good number of factors as criteria that determine the spread of malaria within Ibadan using open-source and Landsat remote sensing data and further analysis in GIS-based multi-criteria evaluation (MCE). This study considered factors like climate, environmental, socio-economic, and proximity to health centers as criteria for mapping malaria risk. The MCE used a weighted overlay of the factors to produce an element at-risk map, a malaria hazard map, and a vulnerability map. These maps were overlaid to produce the final malaria risk map, which showed that 72% of Ibadan has a risk of malaria prevalence. Identification and delineation of risk areas in Ibadan would help policymakers and decision-makers mitigate the hazards and improve the health status of the state.

In this study, the effect of roasting and boiling on the yield and oxidative stability of soya bean oil was investigated. The oil was soxhlet extracted and the oxidative stability was determined by the free fatty acid value, acid value and peroxide value. The results showed that the oil yield, free fatty acid value, acid value and peroxide value were significantly affected by roasting, boiling, and the thermal treatment time. The percentage oil yield in the control oil sample was 18.51%, which increased to 20.24% and 20.73% after boiling and roasting respectively, at 40mins. The corresponding free fatty acid and the peroxide value of the control oil sample were 0.14% and 2.04 meqO₂/kg, which increased to 0.82% and 6.60 meqO₂/kg by roasting, and 0.47% and 5.62 meqO₂/kg by boiling respectively. Thus the oil yield, free fatty acid value, peroxide value, and acid value increased with increasing roasting and boiling time.
The results indicate that roasting provides a higher oil yield than boiling, but boiled oil has higher oxidative stability than roasted oil.