Application of Fizzy Logic in Decision Making on Student’s academic performance.

Decision making is a knowledge is a knowledge discovery in Fuzzy logic application. Therefore, this paper conceptually defined, explained, and implemented fuzzy logic to the model to system performance, specifically, students’ performance model is studied and the various results generated and the performance chart obtained from overall performance for each year for the consecutive eight years in making decisions for future academic performance are also obtained

The design and implementation of online medical record system (OMRS)

Access to appropriate and credible medical information is essential. It is however saddening that many developing countries, especially in sub-Saharan Africa, have low or no access to information on personal health status. The Online Medical Record System (OMRS) is a departure from the traditional paper-based medical record system of healthcare practices to an Internet based medical record storage system. In this paper, we implemented OMRS software that has successfully been able to store, update and modify the patients\' medical history records. It also creates an appointment scheduler system and a platform for online consultation. OMRS allows patients control their own records while allowing doctors access when they need it. OMRS provides a way for doctors and patients to replace thick medical charts and swap information without the need for costly and time-consuming office visits. The advent of internet has made it possible for OMRS to come up with a way in which the problem of computerizing medical records effectively and sharing it can be solved. The OMRS would serve important national interests and it is believed that implementation of the OMRS will have a dramatic impact on the overall quality of healthcare delivery in developing countrie

Reducing the Time Requirement of k-Means Algorithm

Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d. In k-means clustering, we are given a set of n data points in ddimensional space Rd and an integer k. The problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this work, we develop a novel k-means algorithm, which is simple but more efficient than the traditional k-means and the recent enhanced k-means. Our new algorithm is based on the recently established relationship between principal component analysis and the k-means clustering. We provided the correctness proof for this algorithm. Results obtained from testing the algorithm on three biological data and six non-biological data (three of these data are real, while the other three are simulated) also indicate that our algorithm is empirically faster than other known k-means algorithms. We assessed the quality of our algorithm clusters against the clusters of a known structure using the Hubert-Arabie Adjusted Rand index (ARIHA). We found that when k is close to d, the quality is good (ARIHA.0.8) and when k is not close to d, the quality of our new k-means algorithm is excellent (ARIHA.0.9). In this paper, emphases are on the reduction of the time requirement of the k-means algorithm and its application to microarray data due to the desire to create a tool for clustering and malaria research. However, the new clustering algorithm can be used for other clustering needs as long as an appropriate measure of distance between the centroids and the members is used. This has been demonstrated in this work on six non-biological data