FACULTY OF SCIENCE
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Item Characterisation of academic journal publications using text mining techniques(Science and Education Publishing, 2017) Ojo, A. K.; Adeyemo, A. B.The ever-growing volume of published academic journals and the implicit knowledge that can be derived from them has not fully enhanced knowledge development but rather resulted into information and cognitive overload. However, publication data are textual, unstructured and anomalous. Analysing such high dimensional data manually is time consuming and this has limited the ability to make projections and trends derivable from the patterns hidden in various publications. This study was designed to develop and use intelligent text mining techniques to characterise academic journal publications. Journals Scoring Criteria by nineteen rankers from 2001 to 2013 of 50th edition of Journal Quality List (JQL) were used as criteria for selecting the highly rated journals. The text-miner software developed was used to crawl and download the abstracts of papers and their bibliometric information from the articles selected from these journal articles. The datasets were transformed into structured data and cleaned using filtering and stemming algorithms. Thereafter, the data were grouped into series of word features based on bag of words document representation. The highly rated journals were clustered using Self-Organising Maps (SOM) method with attribute weights in each cluster.Item Trend analysis in academic journals in computer science using text mining(IJCSIS Publication, 2015-04) Ojo, A. K.; Adeyemo, A. B.Text mining is the process of discovering new, hidden information from texts- structured, semi-structured and unstructured. There are so many benefits, valuable insights, discoveries and useful information that can be derived from unstructured or semi- unstructured data. In this study, text mining techniques were used to identify trends of different topics that exist in the text and how they change over time. Keywords were crawled from the abstracts in Journal of Computer Science and Technology (JCST), one of the ISI indexed journals in the field of Computer Science from 1993 to 2013. Results of our analysis clearly showed a varying trend in the representation of various subfields in a Computer Science journal from decade to decade. It was discovered that the research direction was changing from pure mathematical foundations, Theory of Computation to Applied Computing, Artificial Intelligence in form of Robotics and Embedded Systems.Item Improving information acquisition via text mining for efficient e-governance(2015-03) Adeyemo, A. B.; Ojo, A. K.In this paper we proposed a framework for integrating text mining with E-Governance. We suggested that the users of electronic governance can use the text terms to describe their interest which can be processed for clustering and term extraction. The words thus expressed by users are tracked and subjected to processing wherein it is possible to generate content. We have provided the framework and tested it in a few web sites. We have used the clustering and pre-processing for the content management. The results are encouraging and it is possible to extent such exercises for other text minging processes.Item A comparison of the predictive capabilities of artificial neural networks and regression models for knowledge discovery(2013) Ojo, A. K.; Adeyemo, A. B.In this paper, Artificial Neural Networks (ANN) and Regression Analysis models were considered to determine which of them performs better. Prediction was done using one hidden layer and three processing elements in the ANN model. Furthermore, prediction was done using regression analysis. The parameters of regression model were estimated using Least Square method. To determine the better prediction, mean square errors (MSE) attached to ANN and regression models were used. Seven real series were fitted and predicted with in both models. It was found out that the mean square error attached to ANN model was smaller than regression model which made ANN a better model in prediction.Item Knowledge discovery in academic electronic resources using text mining(2013-02) Ojo, A. K.; Adeyemo, A. B.Academic resources documents contain important knowledge and research results. They have highly quality information. However, they are lengthy and have much noisy results such that it takes a lot of human efforts to analyse. Text mining could be used to analyse these textual documents and extract useful information from large amount of documents quickly and automatically. In this paper, abstracts of electronic publications from African Journal of Computing and ICTs, an IEEE Nigerian Computer Chapter Publication were analysed using text mining techniques. A text mining model was developed and was used to analyse the abstracts collected. The texts were transformed into structured data in frequency form, cleaned up and the documents split into series of word features (adjectives, verbs, adverbs, nouns) and the necessary words were extracted from the documents. The corpus collected had 1637 words. The word features were then analysed by classifying and clustering them. The text mining model developed is capable of mining texts from academic electronic resources thereby identifying the weak and strong issues in those publications.