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 Subject "Artificial Neural Network"

Now showing 1 - 2 of 2
  • Item
    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.
  • Item
    Open Access
    Artificial neural network-based learning analytics technique for employability and self-sustenance
    (The journal of computer science and its applications, 2018-12) Adewole, P.; Okewu, E.
    The growing global rate of unemployment buttresses the quest for functional and all-inclusive education as canvassed by the Sustainable Development Goal 4 (SDG 4). Employability of graduates and entrepreneurial skills for self-sustenance can be fostered through Learning Analytics (LA) - a pedagogical paradigm that inculcates data analytics and team work skills in learners. LA measures the learning process by collecting learning-related data, analyzing same, and reporting trends to stakeholders for adaptive learning solutions that improve learning experience and learning outcomes. Though there are many data science techniques for enhancing LA, artificial neural network stands out as a highly predictive data mining tool and machine learning technique. An Artificial Neural Network-based Learning Analytics (ANN-based LA) system uses regression analysis, pattern recognition, and predictive analytics to elicit robust information from learner’s data for informed decision making by education stakeholders. However, there are open issues confronting ANN-based LA systems such as system quality issues, prolonged time of training neural network, and the huge memory space requirements. This paper proposes an n-tier layered software architecture for tackling the quality indicator concerns while hoping that upcoming researchers will resolve the others. This way, ANN-based LA will be repositioned for delivering functional education that promotes graduate employment through the impartation of industry-relevant skills like data analytics and team work.