Investigating the Performance of Selected Weka Classifiers for Knowledge Discovery in Mining Educational Data

In the analyzed students’ educational data several parameters such as True Postive Rate, False Positive Rate and Classification Error were used as a yard stick in measuring the performance of both Kstar and BayeNet algorithms in mining the educational data. The performance investigation of the applied classifiers revealed hidden knowledge in the data set which was helpful in the re-calibration of the model to yield a higher precision of each of the classifier with minimal classification error. Keywords: Data Mining, Educational Data Mining, Knowledge Discovery, Student,   Classifiers, Performance, Investigation

Using Optimized Features for Modified Optical Backpropagation Neural Network Model in Online Handwritten Character Recognition System

One major problem encountered by researchers in developing character recognition system is selection of efficient features (optimal features). In this paper, Particle Swarm Optimization (PSO) is proposed for feature selection. However, backpropagation algorithm has been reported to be an effective and most widely used supervised training algorithm for multi-layered feedforward neural networks but has the shortcomings of longer training time and entrapment into a local minimal. Several research works have been proposed to improve this algorithm but some of these research works were based on ‘learning parameter’ which in some cases slowed down the training process. Hence, this paper has focused on alleviating the problem of standard backpropagation algorithm based on ‘error adjustment’. To this effect, PSO is integrated with a ‘Modified Optical Backpropagation (MOBP)’ neural network to enhancement the performance of the classifier in terms of recognition accuracy and recognition time.  Experiments were conducted on MOBP neural network and PSO-based MOBP classifiers using 6,200 handwritten character samples (uppercase (A-Z), lowercase (a-z) English alphabet and 10 digits (0-9)) collected from 100 subjects using G-Pen 450 digitizer and the system was tested with 100 character samples written by people who did not participate in the initial data acquisition process. Experimental results show promising results for the PSO-based MOBP classifier in terms of the performance measures. Keywords: Artificial Neural Network, Feature Extraction, Feature Selection, Particle Swarm Optimization, Modified Optical Backpropagation

TELEMEDICINE ACCEPTABILITY IN SOUTH WESTERN NIGERIA: ITS PROSPECTS AND CHALLENGES

The use of advanced technology to deliver healthcare services at a distance has proven to be one of the defining medical revolutions of the 21st century. With the unwieldy healthcare system under scrutiny, telemedicine can be one of the answers for increasing access and at the same time decreasing the cost of healthcare service delivery. Nevertheless, employing the services of telemedicine comes with a cost though its benefits transcend any cost that may be accrued. This paper presents a report of an evaluation carried out in the south western states of Nigeria, the report reveals the extent to which medical facilities in south western Nigeria has embraced telemedicine, its prospect and challenges. Structured questionnaires and interviews were used to elicit information from medical professionals who were recruited into the study using a convenience sampling method. Results obtained after analyzing the evaluation results showed that despite the awareness of telemedicine by medical practitioners in south western Nigeria, only few medical facilities have integrated telemedicine into their services. Keywords: e-health, Telemedicin

Web Document Classification Using Naïve Bayes

World Wide Web has become a huge collection of documents and the amount of documents available is increasing on a daily basis. How to correctly classify the vast documents into a particular category and locate any document of interest easily has become a challenge researchers have been trying to solve for decades and different researchers have attempted different algorithms using different platform to achieve this aim. In this paper, a University web site was used as a case study and a machine learning workbench called WEKA (Waikato Environment for Knowledge Analysis) which provides a general-purpose environment for automatic classification, regression, clustering and feature selection was used as a machine learning platform. Running Naïve Bayes with 10-fold cross validation on the selected web data gives a 77% correctly classified instances in zero second with relative absolute error of 68.9937%. This shows the ability of Naïve Bayes algorithm to accurately classify vast amount of web document in a short time

Wild vegetable Rumex acetosa Linn.: Its ethnobotany, pharmacology and phytochemistry – A review

Rumex acetosa Linn belongs to Polygonaceae, the species is distributed worldwide (African, Asian, American and European countries). Rumex acetosa is used traditionally as vegetables and for its medicinal uses. Its diverse uses in traditional and cultural applications, have geared much research towards its phytochemical and pharmacological activities. This review intends to offer and give up-to-date knowledge cum research on its ethnopharmacology, chemical constituents, pharmacological activities, reactions with other drugs, and safety concerns, so that the medicinal uses and new research trends can be recognized. Methodology The literature related to the title i.e. R. acetosa Linn was effected by a search using the keywords like “Rumex acetosa, sorrel, garden sorrel and biological activity of R. acetosa” in “Google Scholar,” “PubMed,” “ScienceDirect” “Scopus” and “Web of Science” databases. Plant taxonomy was queried by the databases “The Plant List,” and Many publications' sites were queried like Springer, Elsevier, and dissertation search engines like Open-thesis, OATD, ProQuest and EthOs were put to use. Results This study argues the huge advantages that this plant species possesses and further highlights the up-to-date knowledge of chemistry, the invitro and invivo biological studies, phytochemistry and scientific basis for the use of the plant R. acetosa. Many cultures around the world uses the leaves and aerial parts as vegetables, other parts of this medicinal plant are employed in the management of a number of ailments such as constipation, diarrhea, jaundice, mild diabetes and as an analgesic, antihypertensive, against gallbladder, liver and skin disorders and inflammation. The phytochemistry of this wild vegetable showed that over 60 compounds were reportedly isolated, these includes anthraquinones, naphthalene, polyphenols and other compounds. Furthermore, this manuscript depicts the high level of oxalic acid in R. acetosa though cooking reduces oxalic acid concentration to negligible amount