Department Of Computer Science

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https://ir.unilag.edu.ng/handle/123456789/3381
The overall philosophy of Computer Sciences degree programmes in the Faculty of Science is to produce graduates with fully integrated science knowledge so that they can have a sound background required to fully understand the theoretical base of Computer Science.

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Browsing Department Of Computer Science by Author "Adewole, Adetunji Philip"

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    Open Access
    Artificial Neural Network Model for Forecasting Foreign Exchange Rate
    (World of Computer Science and Information Technology Journal, 2011) Adewole, Adetunji Philip; Akinwale, Adio Taofiki; Akintomide, Ayo Bidemi
    The present statistical models used for forecasting cannot effectively handle uncertainty and instability nature of foreign exchange data. In this work, an artificial neural network foreign exchange rate forecasting model (AFERFM) was designed for foreign exchange rate forecasting to correct some of these problems. The design was divided into two phases, namely: training and forecasting. In the training phase, back propagation algorithm was used to train the foreign exchange rates and learn how to approximate input. Sigmoid Activation Function (SAF) was used to transform the input into a standard range [0, 1]. The learning weights were randomly assigned in the range [-0.1, 0.1] to obtain the output consistent with the training. SAF was depicted using a hyperbolic tangent in order to increase the learning rate and make learning efficient. Feed forward Network was used to improve the efficiency of the back propagation. Multilayer Perceptron Network was designed for forecasting. The datasets from oanda website were used as input in the back propagation for the evaluation and forecasting of foreign exchange rates. The design was implemented using matlab7.6 and visual studio because of their supports for implementing forecasting system. The system was tested using mean square error and standard deviation with learning rate of 0.10, an input layer, 3 hidden layers and an output layer. The best known related work, Hidden Markov foreign exchange rate forecasting model (HFERFM) showed an accuracy of 69.9% as against 81.2% accuracy of AFERFM. This shows that the new approach provided an improved technique for carrying out foreign exchange rate forecasting.
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    Open Access
    COMPARISON OF NEURAL NETWORK AND MULTIVARIATE DISCRIMINANT ANALYSIS IN SELECTING NEW COWPEA VARIETY
    (International Journal of Computer Science and Information Security (IJCSIS), 2010-07) Adewole, Adetunji Philip; Sofoluwe, A. B.; Agwuegbo, Samuel Obi-Nnamdi
    In this study, neural networks (NN) algorithm and multivariate discriminant (MDA) based model were developed to classify ten (10) varieties of cowpea which were widely planted in Kano. . In order to demonstrate the validity of our model, we use the case study to build a neural network model using Multilayer Feedforward Neural Network, and compare its classification performance against the Multivariate discriminant analysis. Two groups of data (Spray and Nospray) were used. Twenty kernels were used as training data set and test data to classify cowpea seed varieties. The neural network classified the new cowpea seed varieties based on the information it is trained with. At the end both methods were compared for their strength and weakness. It is noted that NN performed better than MDA, so that NN could be considered as a support tool in the process of selection of new cowpea varieties.