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.

Diabetes is a chronic disease related to the rise of levels of blood glucose. The disease that leads to serious damage to the heart, blood vessels, eyes, kidneys, and nerves is one of the reasons of death among the people in the world. There are two main types of diabetes: Type 1 and Type 2. The former is a chronic condition in which the pancreas produces little or no insulin by itself. The latter usually in adults, occurs when insulin level is insufficient. Classification of diabetes mellitus data which is one of the reasons of death among the people in the world is important. This study which successfully distinguishes diabetes or normal persons contains two major steps. In the first step, the feature selection or weighting methods are analyzed to find the most effective attributes for this disease. In the further step, the performances of AdaBoost, Gradient Boosted Trees and Random Forest ensemble learning algorithms are evaluated. According to experimental results, the prediction accuracy of the combination of Stability Selection method and AdaBoost learning algorithm is a little better than other algorithms with the classification accuracy by 73.88%.

In this work, we have developed a research grant management system in Haramaya University. The main aim of this project was to automate the manual working system in order to facilitate grant application process, improve time efficiency, save manual cost, improve the flow of information among researchers, and eliminate work delay. In the process of developing the system, researchers conducted a survey research which helps to identify the stakeholders and experts view. Delphi technique was used to identify the view of stakeholders. Whereas a questionnaire was used to collect data from purposely selected researchers to undertake user acceptance test. Finally, we adopted an iterative and incremental method. For the design, we used the UML Modeling language and PHP, JavaScript, Jquery, Json, Bootstrap and CSS used for the implementation. The user acceptance test found the system is acceptable with an average of 94.45%. Through all this process the system was successfully developed, tested and deployed.

Cloud computing is distinctively marked by its capability of providing on demand virtualized IT resources in a pay as you go fashion. Due to its popularity, the cloud computing users are increasing day by day which has become an important challenge for cloud providers. They need to serve their users in a best possible manner. The providers should not only provide their users a secure access to resources but also need to maintain a proper balance of QOS parameters like throughput, end-to-end delay, packet delivery ratio, jitter, response time, etc. The paper proposes an approach of using a meta-heuristic algorithm called Genetic Algorithm (GA) to optimize QOS parameters like packet delivery ratio and end to end delay in cloud networks. The intelligent optimization algorithms address several shortcomings of existing protocols by improving QOS parameters in an optimum manner. The results are simulated through MATLAB based simulator and the simulated results of proposed approach exhibit optimized parameters when compared to conventional method of shortest path cloud routing approach.

Automated Vehicular System has become a necessity in the current technological revolution. Real Traffic sign detection and recognition is a vital part of that system that will find roadside traffic signs to warn the automated system or driver beforehand of the physical conditions of roads. Mostly, researchers based on Traffic sign detection face problems such as locating the sign, classifying it and distinguishing one sign from another. The most common approach for locating and detecting traffic signs is the color information extraction method. The accuracy of color information extraction is dependent upon the selection of a proper color space and its capability to be robust enough to provide color analysis data. Techniques ranging from template matching to critical Machine Learning algorithms are used in the recognition process. The main purpose of this research is to give a review based on methods and framework of Traffic Sign Detection and Recognition solution and discuss also the current challenges of the whole solution.

In this paper, we propose an approach to extract ontological structures from datasets generated by health care users of social networking sites. The objective of this approach is to exploit the user generated implicit semantics as a complement to more formalized knowledge representations. We aim for this latter to leverage the adoption level of the Electronic Health Record systems that are complaining from the shortage in standards and controlled vocabularies.

The problems that exist in the school decline in student achievement ahead of class III, especially before approaching the national exam. If the learning achievement of third-grade students can be known earlier then the school can perform the actions necessary for students to achieve good learning achievement.
This research uses two methods of data mining, Neural Network Model Multilayer Perceptron, and Decision Tree. For comparison, this study also uses t-statistic test, t-test and to compare precision/recall using Roc Curve.
Neural Network Model Multilayer Perceptron Positive performance vector accuracy: 88.64% and Negative: 14.07%, precision (positive guidance class) positive 88.00% and negative 16.88%, recall (class: Ordinary guidance) positive 84.50%, and negative 21.73%. Decision Tree Positive performance vector accuracy: 84.82% and Negative: 15.24%, precision (positive guidance class) positive 86.55% and negative 18.52%, recall (class: ordinary guidance) positive 84.00% and negative 23.85%
Experiments conducted in this study aims to prove that data mining can predict student achievement by finding the best data mining method between the multilayer perceptron neural network and Decision tree to be implemented into integrated information system between student motivation data, student learning interest, and intelligence emotional students.