Global Journal of Artificial Intelligence

  • Transputers Epoch

    The transputer (Transistor Computer) was an innovative computer design of the 1980s from INMOS, a British semiconductor company based in Bristol. The transputer was conceived of as a building block for electronic systems comprising a processor, memory and a communication system. The transputer was unique in that each processor had a built-in simple operating system, memory and four high speed (20 Mbit/s full duplex) bi-directional serial links. The transputer is essentially a computer system on a chip. The links on the transputer allow connection to up to four other transputers or peripherals such as video graphics, floppy and hard disc drives, Ethernet networking and standard RS-232 serial ports. In this paper discusses the original purpose of the transputer, the architectural and the network design. It also lay emphasis on the factors that birth the dead end of the tranputer technology and the restoration project.

  • Suggestive Solution to Security and Short range problems using RF/OWC Hybridization

    As the knowledge of wireless technology keeps growing exponentially in the field of telecommunication, new ideas spring up over time to address and proffer solutions to generational wireless communication issues. In this term paper, the reasons for wireless technology growth was explained, and a detailed information on emerging wireless technology was highlighted. The paper highlights the idealistic of heterogeneous networks, how security and short range within the network can be solved through a suggested solution of hybridizing RF and OW.

  • Understanding the Literature Review

    Research has over time played a pivotal role in mankind’s quest for knowledge and technological advancement. In all spheres of human existence, research and its further application have over time been able to show the obvious, and yet sometimes hidden unity of science and the philosophical and sociological settings in which everything operates. Essentially, research has helped man to explore once thought of as bizarre phenomena and afforded man the opportunity to draw a fine line between opinions and facts towards gaining maximum benefits from the research’s orientation (Williams, 2007). Too frequently, research is viewed as a formalized process of applying a rigid sequence of steps to the solution to a problem but in actual fact, research in itself entails flexibility in order to maximize scientific methods. This paper explains the concept of literature review in research and how a literature review is done in other to enhance the quality of the research work produced.

  • Neuro-Fuzzy Approach to River Sediment Yield Prediction

    This work is motivated by the critical role that sediment yield prediction plays in preventing natural and economic disasters. Methods based on regression techniques have been used to solve the problem but they are generally inadequate in predicting river sediment yield because of the inherent complexity of the problem. This work uses the Adaptive Neuro-Fuzzy Inference System (ANFIS) to solve the problem. The ANFIS model accepts four input data namely temperature, rainfall, water stage and water discharge and gives on output data that represents the sediment yield. The ANFIS model was developed and simulated with MATLAB 7.0 using the Levenberg-Marquardt optimization method and trained with a maximum of 1500 epochs at a learning rate of 0.5. the results obtained was compared with the ones obtained with the Artificial Neural Network (ANN) model and it was found that the ANFIS model performs better than the ANN model.

  • Artificial Neural Network Approach to Football Score Prediction

    Sport betting companies and participants can maximize their profit in the sports betting business if they are able to accurately predict the outcome of football matches. This work seeks to develop such a football match prediction system with Manchester United football club as a case study. The developed system is based on an Artificial Neural Network (ANN) model. Scores from previous matches played by Manchester United were used to train and validate the network. The system has prediction accuracies of 73.72% and 113.5% for goals scored by, and against Manchester United respectively. The performance of the model is reasonably good but it can be improved by training the model with more football scores.