Fractured bone detection and categorization is currently receiving research attention in computer aided diagnosis system because of the ease it has brought to doctors in classification and interpretation of X-ray images.  The choice of an efficient algorithm or combination of algorithms is paramount to accurately detect and categorize fractures in X-ray images, which is the first stage of diagnosis in treatment and correction of damaged bones for patients. This is what this research seeks to address. The research design involves data collection, preprocessing, segmentation, feature extraction, classification and evaluation of the proposed method. The sample dataset were x-ray images collected from the Department of Radiology, National Orthopedic Hospital, Igbobi-Lagos, Nigeria as well as Open Access Medical Image Repositories. The image preprocessing involves the conversion of images in RGB format to grayscale, sharpening and smoothing using Unsharp Masking Tool.  The segmentation of the preprocessed image was carried out by adopting the Entropy method in the first stage and Canny edge method in the second stage while feature extraction was performed using Hough Transformation. Detection and classification of fracture image employed a combination of two algorithms;  K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) for detecting fracture locations based on four classification types: (normal, comminute, oblique and transverse).Two performance assessment methods were employed to evaluate the developed system. The first evaluation was based on confusion matrix which evaluates fracture and non-fracture on the basis of TP (True Positive), TN (True negative), FP (False Positive) and FN (False Negative). The second appraisal was based on Kappa Statistics which evaluates the type of fracture by determining the accuracy of the categorized fracture bone type. The result of first assessment for fracture detection shows that 26 out of 40 preprocessed images were fractured, resulting to the following three values of performance metrics: accuracy value of 90%, sensitivity of 87% and specificity of 100%. The Kappa coefficient error assessment produced accuracy of 83% during classification. The proposed method can find suitable use in categorization of fracture types on different bone images based on the results obtained from the experiment.

The January 2022 edition of webometric ranking placed Yaba College of Technology as number one from 152 polytechnics in Nigeria. The ranking weight is 66 for country ranking, 8162 world ranking, Impact, Openness, and Excellence of 9698, 4558, and 7190 respectively. The negative variation and low webometric ranking of Yaba College of Technology that happened to be the first higher institution of learning in Nigeria with the slogan the first and still the best is a point of concern and motivates this research work. This research work collected data to evaluate the indicators for webometric ranking among the students and staff of Yaba College of Technology, a total of 346 were sampled students 44.51 % and Staff 55.49 %. The discussion and analysis of data obtained revealed that the poor webometric ranking is due to inadequacy of the necessary ICT infrastructure to encourage robust web presence; non-availability of up-to-date and scanty content on the Polytechnics website; Non-frequent usage of the Polytechnic website by the staff and students of the Polytechnic; the inadequate number of external networks (subnets) links with Polytechnic website; insufficient number of the top-cited publications in high impact Journals from the staff of the Yaba College of Technology; and Scanty number of the profile of staff from the Polytechnic on Google Scholar and ResearchGate, etc. among others. This research work opined that low webometric ranking could result in the following negative impact on the polytechnics lowering the esteem of the Polytechnic in the eyes of stakeholders, potential students and funding agencies, academic exchange with reputable institutions from other parts of the world for teaching, learning and research may writhe. The consequence of our findings recommendations was made to improve webometric ranking in future.

Machine learning algorithms have aided health workers (including doctors) in the processing, analysis, and diagnosis of medical problems, as well as the detection of disease patterns and other patient data. Diabetes mellitus (DM), commonly referred to as diabetes, is a gathering of a syndrome issue that is portrayed by high glucose levels in the blood over a drawn-out period. It is a long-term illness that is a great threat to humanity and causes death. Most of the existing machine learning algorithms used for the classification and prediction of diabetes suffer from embodying redundant or inessential medical procedures that cause complications and wastage of time and resources. The absence of a correct diagnosis scheme, deficiency of economic means, and a general lack of awareness represent the main reasons for these negative effects. Hence, preventing the sickness altogether through early detection may doubtless cut back a considerable burden on the economy and aid the patient in diabetes management. This study developed diabetes classification using machine learning techniques that will minimize the aforementioned drawbacks in the prediction of diabetes systems. Decision tree classifiers, logistic regression, random forest, and support vector machines are all examples of predictive algorithms that were tested in this paper. 1009 records of data set were obtained from the Diabetes dataset of Abelvikas, Data World. We used a confusion matrix to visualize the performance evaluation of the classifiers. The experimental result shows that the four machine learning algorithms perform well. However, Random Forest outperforms the other three, with a prediction accuracy of 100% and has a better prediction level when compared with others and existing work.

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