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Articles by S. Issa
Total Records ( 3 ) for S. Issa
  Kwari, I.D , S.S. Diarra , J.U. Igwebuike , I. Nkama , S. Issa , B.R. Hamaker , J.D. Hancock , M. Jauro , O.A. Seriki and I. Murphy
  The decrease in the production coupled with the numerous industrial uses make maize an expensive energy ingredient for poultry feeding, thus the need for research into cheaper alternatives. A 9-week experiment was conducted to assess the effects of feeding low tannin sorghum grain as a replacement for maize on the growth, haematology and carcass measurements of broiler chickens at the Poultry Unit of University of Maiduguri Livestock Research Farm, Maiduguri, Nigeria. A total of 300 day-old broiler chicks were randomly allotted to 5 dietary treatments containing 3 replications of 20 chicks each. The diets contained sorghum grains at 0.00, 25.00, 50.00, 75.00, and 100% respectively as a replacement for maize. The starter and finisher diets were formulated to contain 23% and 20% crude protein respectively. The results of growth performance showed no superiority of maize over sorghum grain in terms of weight gain and feed conversion ratio, during both phases of growth (starter and finisher). Feeding sorghum grain had no adverse effect on the haematological parameters analyzed. The yields of carcass and cut-up parts were not adversely affected by the level of sorghum grain in the diet. Similarly, there was no adverse effect of feeding sorghum grain on the weight of vital organs (heart, liver and spleen). It was concluded that low tannin sorghum can completely replace maize in broiler chickens diets without compromising the growth, meat yield or the health of the birds. The substitution is beneficial as it reduces competition between poultry and man for the already scarce maize grain.
  N. Brah , F.M. Houndonougbo and S. Issa
  Objective: This study was conducted to evaluate the bioeconomic performance of grasshopper meal (GM) when used to replace fish meal (FM) in broiler diets during a period of 49 days. Materials and Methods: A total of 360 one-day-old broiler chicks (Cobb 500) were used in this experiment. The FM was replaced with GM on a kg per kg basis at 0% (control), 25% (25% GM+75% FM), 50% (50% GM+50% FM), 75% (75% GM+25% FM) and 100% (100% GM+0% FM) in broiler diets. Treatments (G0, G25, G50, G75 and G100) were randomly distributed into 20 pens of 18 birds each with 4 replications (4 pens/treatment). Data were analyzed in R 3.2 using ANOVA and regression was executed in Microsoft Excel 2013. Results: At the end of the experiment, the daily feed intake, body weight and weight gain linearly and significantly decreased (p<0.05) with increasing substitution rates of fish meal with grasshopper meal. Also, the results showed that feed conversion ratios linearly increased and were significantly affected by the treatments (p<0.05), with the highest performance observed in broilers fed the control diet. Carcass characteristics also significantly decreased (p<0.05) with increasing levels of grasshopper meal in broiler diets. However, the substitution did not significantly affect feed efficacy of broilers during the growing phase (p>0.05). In addition, during the 49 days of experimentation, the body weight, feed conversion ratio, economic feed efficiency and carcass yield of broilers fed G0, G25 and G50 were similar (p>0.05). Conclusion: Therefore, in Niger, fish meal may be substituted with up to 50% grasshopper meal in broiler feed.
  M.A. Alabi , S. Issa and R.B. Afolayan
  The research paper deals with credit scoring in banking system which compares most commonly statistical predictive model for credit scoring, Artificial intelligent Neural Network (ANN) and discriminant analyses. It is very clear from the classification outcomes of this research that neural network compares well with linear discriminant model. It gives slight better results than discriminant analysis. However, it is noteworthy that a bad accepted generates much high costs than a good rejected and neural network acquires less amount of bad accepted than the discriminant predictive model. It achieves less cost of misclassification for the data set use in the research. Furthermore, if the final section of this research, an optimization algorithm (genetic algorithm) is proposed in order to obtain better classification accuracy through the configuration of the neural network architecture. On the contrary, it is important to note that the success of the predictive model largely depends on the predictor variables selection to be used as inputs of the model.
 
 
 
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