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Articles by E.O. Omidiora
Total Records ( 4 ) for E.O. Omidiora
  E.O. Omidiora , G.O. Oyediran , S.O. Olabiyisi and O.T. Arulogun
  This research work explores soil classification using soil of central Western Nigeria as a case study. This study uses Neural Networks and rule extraction and decision table for the classification of the soils. Soil classification was made more accurate, cheaper and easier with the implementation of Neural Network trained model. A well-developed database capable of generating outputs from a non-linear inputs based on its universal approximation properties was employed, thus, reducing the cost, time, mistakes and labour associated with practical fieldwork. However, the method takes into account the topography and parent material (configuration) of a certain soil sample (location). A generalized Neural Network for soil classification was obtained using the Matlab 6.5 Tool box to build, simulate and test the network. The Feed forward back propagation was used. A GUI (Graphical User Interface) was designed to achieve a high predictive accuracy and enables users most especially a lay user to directly experience an interesting and simulated environment via a friendly user interface of the network. A simplified and explanatory representation of the inputs and outputs was employed. The performance goal was met at 28 epochs using Leveberg Marguartt training algorithm. The neural network achieved an accuracy of 98.20%, which suggested quite a stable network.
  O.O. Adeosun , E.R. Adagunodo , I.A. Adetunde , T.H. Adeosun and E.O. Omidiora
  The network is a highly dynamic environment where even subtle changes can now have a major, unforeseen impact on application performance and availability causing Internet failures. A system with faults/failures may continue to provide its services (i.e., not fail). Such a system is said to be fault tolerant. This is our focus in this research, to provide Internet services to clients without any interruption even at the presence of faults, through the deployment and redeployment of replicated proxy servers using Markov Reward Model. To achieve this, this study considered different replication techniques that can be used for Internet connectivity and finally suggested the best technique for use.
  S.O. Olabiyisi , R.O. Ayeni and E.O. Omidiora
  In this study, we apply different software complexity measures to Quick sort algorithm. Our intention is to study what kind of new information about the algorithm the complexity measures (Halstead’s volume and Cyclomatic number) are able to give and to study which software complexity measures are the most useful ones in algorithm comparison. The results explicitly show that Quick sort has the least Halstead’s Volume, Program Difficulty and Program Effort when programmed in Assembly language.
  E.O. Omidiora , A.O. Fakolujo , R.O. Ayeni and I.A. Adeyanju
  A face recognition system is one of the most desirable biometric identification such as computerized access control, document control and database retrieval. Although, several researches have been done on face recognition, most (if not all) have made use of non-black faces or very few numbers of black faces in their experiments. This study presents results of experiments based on black African faces (with and without tribal marks) using the optimized Fisher Discriminant Analysis. In the experiment, different sizes of gray scale images were used for recognition performance accuracy of between 88 and 99% were obtained. Also, taking into consideration was the rate of identifying an image using the same number of images to test the face recognition system. While, a completely robust real-time face recognition system is still under heavy investigation and development, the implemented system serves as an extendable foundation for future research.
 
 
 
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