 
								Information Accessibility and Utilization: The Panacea to Job Performance and Productivity of Academic StaffIn the Faculties of Agricultural Sciences: A Case Study
								
									
										
											
											
												Familusi E. B.,
											
										
											
											
												N. A. Ajayi
											
										
									
								 
								
									
										Issue:
										Volume 4, Issue 6, December 2015
									
									
										Pages:
										95-100
									
								 
								
									Received:
										21 June 2015
									
									Accepted:
										19 August 2015
									
									Published:
										19 November 2015
									
								 
								
								
								
									
									
										Abstract: This study probes into accessibility and utilization of information resources by academic staff in the Faculties of Agricultural Sciences of selected universities in the Southwest, Nigeria. The objectives of this study were to determine the level of information access and utilization, identify the factors responsible for low productivity, and investigate challenges confronting information access and utilization. This study adopted a descriptive survey design to describe information accessibility and utilization. The researchers made use of questionnaires which were administered among two hundred (200) out of which 182 (91.9percent) were found useful Academic Staff randomly selected from Faculties of Agricultural Sciences. The study found that majority of the respondents (51.3%), frequently used virtually all the resources, and e-resources most especially internet/CD-ROM and databases were perceived to be most accessible of all the resources (index of 3.9), followed by textbook (index of 3.7), while the least accessible information resources was electronic board. Low productivity was caused by high no of students assigned to each academic staff for teaching and supervision (11.8%) followed by lack of internet facilities (11.1%), while inadequate workspace did not significantly contribute to their low productivity ( 6%). Challenges faced by academic staff in information resources accessibility and utilization include epileptic power supply, poor ICT maintenance, poor funding, internet connectivity and, computer illiteracy. It was recommended that the Federal government of Nigeria should collaborate with the universities administrators to accord priority to Nigerian Universities in financial commitment, provision of regular supply of electricity in campuses and others to enhance high productivity of the university academic staff.
										Abstract: This study probes into accessibility and utilization of information resources by academic staff in the Faculties of Agricultural Sciences of selected universities in the Southwest, Nigeria. The objectives of this study were to determine the level of information access and utilization, identify the factors responsible for low productivity, and inves...
										Show More
									
								
								
							
							
								 
								An XCS-Based Algorithm for Classifying Imbalanced Datasets
								
									
										
											
											
												Hooman Sanatkar,
											
										
											
											
												Saman Haratizadeh
											
										
									
								 
								
									
										Issue:
										Volume 4, Issue 6, December 2015
									
									
										Pages:
										101-105
									
								 
								
									Received:
										5 November 2015
									
									Accepted:
										22 November 2015
									
									Published:
										14 December 2015
									
								 
								
								
								
									
									
										Abstract: Imbalanced datasets are datasets with different samples distribution in which the distribution of samples in one class is scientifically more than other class samples. Learning a classification model for such imbalanced data has been shown to be a tricky task. In this paper we will focus on learning classifier systems, and will suggest a new XCS-based approach for learning classification models from imbalanced data sets. The main idea behind the suggested approach is to update the important parameters of the learning method based on the information gathered in each step of learning, in order to provide a fair situation for the minor class, to contribute in building the final model. We have also evaluated our approach by testing it with real-world known imbalanced datasets. The results show that our new algorithm has a high detection rate and a low false positive rate.
										Abstract: Imbalanced datasets are datasets with different samples distribution in which the distribution of samples in one class is scientifically more than other class samples. Learning a classification model for such imbalanced data has been shown to be a tricky task. In this paper we will focus on learning classifier systems, and will suggest a new XCS-ba...
										Show More