Breast cancer is the second most commonly diagnosed cancer in women throughout the world. It is on the rise, especially in developing countries, where the majority of cases are discovered late. Breast cancer develops when cancerous tumors form on the surface of the breast cells. The absence of accurate prognostic models to assist physicians recognize symptoms early makes it difficult to develop a treatment plan that would help patients live longer. However, machine learning techniques have recently been used to improve the accuracy and speed of breast cancer diagnosis. If the accuracy is flawless, the model will be more efficient, and the solution to breast cancer diagnosis will be better. Nevertheless, the primary difficulty for systems developed to detect breast cancer using machine-learning models is attaining the greatest classification accuracy and picking the most predictive feature useful for increasing accuracy. As a result, breast cancer prognosis remains a difficulty in today's society. This research seeks to address a flaw in an existing technique that is unable to enhance classification of continuous-valued data, particularly its accuracy and the selection of optimal features for breast cancer prediction. In order to address these issues, this study examines the impact of outliers and feature reduction on the Wisconsin Diagnostic Breast Cancer Dataset, which was tested using seven different machine learning algorithms. The results show that Logistic Regression, Random Forest, and Adaboost classifiers achieved the greatest accuracy of 99.12%, on removal of outliers from the dataset. Also, this filtered dataset with feature selection, on the other hand, has the greatest accuracy of 100% and 99.12% with Random Forest and Gradient boost classifiers, respectively. When compared to other state-of-the-art approaches, the two suggested strategies outperformed the unfiltered data in terms of accuracy. The suggested architecture might be a useful tool for radiologists to reduce the number of false negatives and positives. As a result, the efficiency of breast cancer diagnosis analysis will be increased.

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.

Globally, air-conditioning systems consume over 40% of the total energy consumed by buildings. This accounts for one-third of global greenhouse gas (GHG) emissions, which significantly contribute to climate change. Building designs must take climate into account at all times. This study sought to evaluate passive methods of making residential buildings more energy efficient, hence lowering energy usage. The study concentrated on residential buildings in Abuja, Nigeria. Using a quantitative method, a survey was utilised to collect data from randomly selected respondents. A total of 121 questionnaires were distributed to study participants, and the weighted mean of replies was sorted ordinally from 107 respondents. Although the results indicated that the residential buildings were designed and supervised by architects and engineers, it was surprising that features that could make the buildings more energy efficient were not fully considered in the design because the respondents were unsure of the availability of passive design measures due to their inadequacy. The study emphasises the necessity of considering the micro climatic condition of the building environment, as well as passive design features that could lessen reliance on mechanical means and so use energy efficiently. The paper builds on the findings by arguing for energy-conscious residential building design that takes into account natural dynamics. This might be accomplished by implementing passive design features appropriate for the climatic environment, which could result in a reduction in energy demand for cooling the indoor environment.

Despite the popularity and utility of most machine learning techniques, expert knowledge is required in guiding choices about the suitable technique and settings that are good for solving a specific problem. The lack of expert information renders the procedures vulnerable to poor parameter settings. Several of these machine learning techniques configurations are offered under default settings. However, since different classification problems required suitable machine learning techniques, selecting the appropriate technique and tuning its settings are vital works that will rightly improve predictions in terms of reliability and accuracy. This study aims to perform grid search parameters tuning on 5-selected machine learning techniques on hepatitis disease. Comparative performance is drawn side-by-side with the default settings. The experimental results of the five tuning techniques show that using the configurations suggested in our work yield predictions of a greatly sophisticated quality than choice under its default settings. The result proves that tuning parameters of Support Vector Machine via grid search yields the best accuracy outcomes of 90% and has a competitive performance relative towards criteria of precision, recall, accuracy and Area Under the Curve. Present combinations of parameter settings for each of the techniques by identifying ranges of values for each setting that give good Hepatitis disease outcomes

The role of sudden application or removal of a porous material on generalized Couette flow in a horizontal channel is carried out. The governing momentum equation is obtained and solved with the necessary initial and boundary conditions. The well-known Laplace transform technique is employed to transform the PDEs into ODEs and then solved exactly in the Laplace domain. A numerical approximation based on the Riemann-sum is employed to transform the solutions obtained from the Laplace domain to the time domain. Based on the simulated results, it is found that the time taken to attain steady-state skin-friction and volumetric flow rate is strictly affected by the sudden application/withdrawal of porous medium. Also, despite the sudden application/withdrawal of porous medium, the velocities, skin-friction and volumetric flow-rates still attain steady state values.