With the increasingly broadening adoption of Electronic Health Record (EHR) worldwide, there is a growing need to widen the use of EHR to support clinical decision making and research particularly in radiology. A number of studies on generation, analysis and presentation of chest x-ray reports from digital images to detect abnormalities have been well documented in the literature but studies on automatic analysis of chest x-ray reports have not been well represented. Interestingly, there is a large amount of unstructured electronic chest x-ray notes that need to be organized and processed in such a way that it can be automated for the purpose of giving urgent attention to abnormal radiographs in clinical findings to allow for quicker report analysis and decision making. This study developed a system to automate this analysis in order to prioritize findings from chest x-rays using support vector machine and Lagrange Multiplier for the constraint optimization. The classification model was implemented using Python programming language and Django framework. The developed system was evaluated based on precision, recall, f1-score, negative predictive value (NPV). Expert’s knowledge was also used as gold standard and comparison with the existing system. The result showed a precision of 96.04%, recall of 95.10%, f1-score of 95.57%, specificity of 86.21%, negative predictive value of 83.33% and an accuracy of 93.13%. The study revealed that a limited but important number of relevant attributes provided an effective and efficient model for the detection of cardiomegaly in clinical chest x-ray reports. From the evaluation result, it is evident that this system can help the clinicians to quickly prioritize findings from chest x-ray reports, thereby reducing the delay in attending to patients. Hence, the developed system could be used for the analysis of chest x-ray reports with the purpose of diagnosing the patient for cardiomegaly. Chest X-ray reports are usually textual, therefore, further studies can introduce spell checker to the system to provide higher sensitivity.

Customer service is an important area in the success of a system or a service. For services that have a relatively large customer base, the efficiency with which complaints are attended to becomes an issue. The Computer Centre of the Obafemi Awolowo University attends to students with various complaints majorly in relation to their e-portal accounts. Although efforts are in place to manage the crowd, there is still a major need for the complaint management service to save time and energy. The need for a system that can handle the enormous request and complaints of the undergraduate students of the institution is the thesis of this work. Design and implementation was done using the range of tools provided by the Microsoft Bot Framework. C# Programming language was used to implement the decision algorithm. Online web services were used to handle natural language understanding and the Bot Connector to implement the Web Canvas. Microsoft Azure Service was used to host the web after which evaluations were drawn through surveys. Thus, this study projected an easier flow of operations involving logging of complaints by students.

This study compared two neural network models (Multilayer Perceptron and Generalized Regression Neural Network) with a view to identifying the best model for predicting students’ academic performance based on single performance factor. Only academic factor (students’ results) was considered as the single performance factor of the study. One cohort of graduated students’ academic data was collected from the Computer Science and Engineering Department of Obafemi Awolowo University, Nigeria using documents and records technique. The models were simulated using MATLAB version 2015a and evaluated using mean square error, receiver operating characteristics and accuracy as the performance metrics. The results obtained show that although Multilayer Perceptron had prediction accuracy of 75%, Generalized Regression Neural Network had a better accuracy. The response time of Generalized Regression Neural Network (0.016sec) was faster than Multilayer Perceptron (0.03sec) and its memory consumption size (5kb) lower than that of Multilayer Perceptron (8kb). The simulated models were further compared with t-test method using a confidence interval of 95%. The attained t-test result from p-value (0.6854) suggests acceptance of null hypothesis, which shows that there is no significant difference between the predicted Grade Point Average and the actual Grade Point Average. The findings therefore reveal that the overall performance of Generalized Regression Neural Network outperforms the Multilayer Perceptron model with an accuracy of 95%. The study concluded that Generalized Regression Neural Network model which was simulated and with 95 % accuracy could be deployed by educationists to predict students’ academic performance using single performance factor.