This study carried out a usability evaluation of some selected Nigerian universities websites. A total number of ten randomly selected universities though; mostly first and second generations universities were taken into account. This was done by making use of automated tools such as web page analyzer and HTML toolbox for data collection. The internal attributes that were taken into consideration embodied Total number of html files, Total html page size, Total size of images, Total number of images, Total number of external files, Total size of external files, as well as Load time, HTML check and repair, Browsers compatibility, Pages with bad links respectively and the various values were collected and analysed and presented in the graphical form using bar charts. The results showed that some of universities' websites adhered to the laid down threshold values of these attributes while some are still very much lacking. These include University of Calabar, Nnmadi Azikiwe University and University of Ibadan. Generally there it was also observed that no single university adhered to the threshold values as stipulated by the two automated tools used. A conclusion was made and some necessary suggestions were also proffered so as to enhance the usability of the stated universities' websites.

Background: Cardiovascular diseases are recognized generally to be among the number one illnesscausing death across the globe. Predicting heart disease using a computer-aided technique makes it easier for medical practitioners to diagnose and thereby savinglives andreducingcosts. Feature selection has become an essential component for developing Machinelearning models. It chooses the most relevant features from the available dataset,thereby shortening the training period, making the model easier to train, improving generalization and decreasing overfitting without necessarily compromising the system’s accuracy. Aim:The purpose of this work is to design and build an optimal model forthe prediction of heart diseases,especially at an early stage by considering certain features that are most relevant forthe prediction without compromising the system’s accuracy. Method: The Cleveland UCI dataset with 303 instances wereused in trainingthe model and the findings showthat selectKBest is an effective tool in improving the prediction of heart diseases. The performance metrics Accuracy, Sensitivity, Precision were measured.Results: the study found that when hybridizing k-Nearest Neighbor Bagging, Decision TreeBagging, Gradient Boosting generated the highest accuracy of 90%, 85% and 88% respectively.