Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
Articles by A.O. Ajayi
Total Records ( 3 ) for A.O. Ajayi
  S.A. Adesoji , A.J. Farinde and A.O. Ajayi
  The study determined factors influencing the training needs of fadama farmers and drew implications for extension work in Osun State, Nigeria. Primary data were collected from 150 fadama farmers through a multi-staged random sampling technique. The data were analysed using descriptive statistics, regression and factor analytical techniques. Six factors were isolated from the twenty-six variables with 66.22% contributions to training needs of fadama farmers. These include socio-economic, informational, credit, resources, culture and training related factors. While training related factors had the lowest contribution (5.01%), socio-economic factor contributed the highest (21.48%) to the training needs among the factors. Also two important variables, level of education (b = 1.701) and formal training attended (b = 1.57) were positively significant at p<0.05 to the training needs of fadama farmers.
  A.O. Afolabi , B.O. Olatunji and A.O. Ajayi
  Load forecasting is an essential part of an efficient power system planning and operation. This research work is on short term electricity load forecasting using Artificial Neural Network (ANN) and Ogbomoso a city in Nigeria is considered as a case study. Input variables considered are past loads history, hours of the day and days of the week, while the output is the forecasted load for 24 h ahead. The training tool Neurosolution was employed in simulating and designing the feed forward back propagation forecasting network. Result obtained shows that electricity load can be predicted ahead of time also, it can also be inferred from this research that, load forecast using neural network is somewhat intelligent in that it gives real values even when the past load history is of zero value. This shows that areas without constant supply of electricity can still forecast future loads with a reasonable error margin so as to help in better load distribution and effective load shedding planning.
  A.O. Afolabi and A.O. Ajayi
  A database management system (DBMS) is a system, usually automated and computerized, for the management of any collection of compatible and ideally normalized data. The focus of this research is to compare 2 database technologies for software developers to have the advantage of selecting the best technology in terms of Execution time, Resource consumption and Space and data capacity. In the course of this research work the following database management systems (DBMS) were considered, MySQL Server and Borland Interbase Server. Selecting from diverse existing software development tools, Microsoft Visual Basic 6.0 was used in designing the front-end engine for the entire database system analysis and evaluation. It was found out that for large quantities of data that needs to be tracked and also seek or make use of selection, update and delete transaction will request more of the interbase server, because of its high speed in performing those transaction, while for insertion transaction MySQL server performed better.
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility