In this work, a novel data mining model based on Support Vector Machines (SVM) for the identification of gasoline types has been investigated and developed. Detection and correct identification of gasoline types during Arson and Fuel Spill Investigation are very important in forensic science. As the number of arson and spillage becomes a common place, it becomes more important to have an accurate means of detecting and classifying gasoline found at such sites of incidence. However, currently only a very few number of classification models have been explored in this germane field of forensic science, particularly as relates to gasoline identification. Thus, we have developed Support Vector Machines (SVM) based identification model for identifying gasoline types. The model was constructed using gas chromatography-mass spectrometry (GC-MS) spectral data obtained from gasoline sold in Canada over one calendar year. Prediction accuracy of the model was evaluated and compared with earlier used methods on the same datasets. Empirical results from simulation showed that SVM based model produced accurate and promising results better than the best among the other earlier implemented Artificial Neural Network and Principal Component Analysis methods on the same datasets.
Cloud computing is a technology that represents a shift from the traditional ownership of infrastructure and other resources to a more scalable pattern in which computer resources are rented online to organizations on either as a pay-as-you-use basis or by subscription. The researcher is motivated to undertake this study due to cloud computing growing awareness which has led to the increasing adoption of the technology by organizations in Nigeria including educational sector. However, the challenges that comes with the technology slow down the rate of its adoption by many universities in Nigeria and making them unconvinced to entrust into the cloud technology. This work investigates the use of cloud computing technology for effective administration in Nigerian universities. From analysis, the research reveals the strengths, weaknesses, opportunities and threats in the adoption of cloud technology and points out the concerns and challenges impeding its adoption. It further discusses the enormous benefits the universities stand to gain for adopting the cloud technology and proffers threat mitigation techniques to prevent the occurrence of the impediment towards the cloud adoption. The finding also recommends some cloud platforms as a means of IT delivery services for effective university administration.
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