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- Academic journals are an important outlet for dissemination of academic research. In this study, Neural Networks model was used in the prediction of abstracts from The Institute of Electrical and Electronics Engineers (IEEE) Transactions on Computers. Simulation of results was done using the Polynomial Neural Networks algorithm. This algorithm, which is based on Group Method of Data Handling (GMDH) method, utilizes a class of polynomials such as linear, quadratic and modified quadratic. The prediction was done for a period of twenty-four months using a predictive model of three layers and two coefficients. The performance measures used in this study were mean square errors, mean absolute error and root mean square error. 1 results 1
- CROPWAT-8 Model, 1 results 1
- Climate change 1 results 1
- Computer Engiineering 1 results 1
- Computer Science 1 results 1
- Earth Sciences 1 results 1
- Effects of climate change on the yield reduction and prediction of vegetable crop was carried out using CROPWAT 8 with irrigation scheduling conditions; critical depletion at a water application depth of 2 mm, definite intervals of 3 days at a depth of 2 mm and no irrigation. The model was run with 9 year weather records for Ibadan, Nigeria spanning 2000 to 2008, the yearly weather records was divided into quarterly records depicting vegetable crop growth period from planting to harvesting. Quarterly growth season of January- April (1), April- July(II), July- October (III), October- January(IV) for the vegetable crop with an increase in temperature rise at each 1C. Simulation results analyses for 2000-2008 under critical depletion reveals that each 1C temperature rises from ambient condition to 3 C, yield reduction for season I ranges from 4.3% to 27.1%, 0%-0.2% for season II, 0% for season III and increasing to 7.1% to 15.9% for season IV. Also, from the prediction analysis (2009-2013) obtained from SPSS a statistical tool and the method of Least Square Deviation (LSD), for ambient weather condition of the study location there are higher yield reduction from 9% to 11.68% for season I, 0.1% to 0.77% for season II, 0% to 0.76% for season III and 12.2% to 12.0% for season IV respectively. Hence, climate change has impacted negatively on higher predicted yield reductions of three out of the four seasons considered from year 2009 to 2013. 1 results 1
- Equilibrium Moisture Content 1 results 1
- GMDH 1 results 1
- IEEE 1 results 1
- Long Short-term Memory 1 results 1
- Mean absolute error 1 results 1
- Mean square errors 1 results 1
- Polynomial Neural Networks 1 results 1
- Prediction in data mining, is a technique used in predicting results or outcomes of future occurrence in reference to existing information. Several predictive models have been developed for different fields of study. In solar kiln drying experiment, as a result of dependence on nature for its operation, outcomes of drying process is unstable and varies with weather variability. Although predictive models have been developed for wood drying experiments, there is very limited information on the use of Neural Networks for predicting outcomes in solar kiln drying of wood. In this work, Long Short-term Memory model, a special type of Recurrent Neural Network was adopted for prediction in solar kiln drying of wood. Data collected on external (atmospheric) and internal conditions of a solar kiln sited at from Forestry Research Institute of Nigeria was used for this study. Daily ambient and internal temperature and relative humidity were used as input data. The closeness of relationship between the experimental and predicted values (Mean Square Error, MSE = 0.97; 30.4) and (Squared Correlation, R2=0.68, 0.85) for Temperature and Relative Humidity respectively revealed that the model had a good agreement with data. The Equilibrium Moisture Content (EMC) of internal solar kiln environment which influences the outcome of drying was considered. The EMC of internal solar kiln environment was predicted for the next 730 days and suitability of the model for prediction was examined giving an MSE value of 0.2 and r2 value of 0.87. The findings of this study suggest a viable model for predicting drying outcomes under varying weather conditions. 1 results 1
- Root mean square error 1 results 1
- Temperature 1 results 1
- Vegetable crop, 1 results 1
- Yeild reduction, 1 results 1
- Yield prediction, 1 results 1
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