Efficiency and precision in prediction of Chlorophyll-a using this model is still a pandemic among researchers, due to the natural conditions in ocean water systems itself, which involved chemical, biological and phys...Efficiency and precision in prediction of Chlorophyll-a using this model is still a pandemic among researchers, due to the natural conditions in ocean water systems itself, which involved chemical, biological and physical processes and interaction among them may affect the model performance drastically. Thus, to overcome this problem as well as to improve the strength of MLR, we proposed a hybrid approach, i.e., an Artificial Neural Network to the MLR coins as Artificial Neural Network-Multiple Linear Regression (ANN-MLR). To investigate the performance of the proposed model, we compared Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and proposed hybrid Artificial Neural Network and Multiple Linear Regression (ANN-MLR) in the prediction of chlorophyll-a (chl-a) concentration by statistical measurement which are MSE and MAE. Achieving our objectives of study, we used 4 parameters, i.e. temperature (°C), pH, salinity (ppt), DO (ppm) at the Offshore Kuala Terengganu, Terengganu, Malaysia. The results showed that our proposed model can improve the performance of the model as compared to ANN and MLR due to small errors generated, error reduced, and increased the correlation coefficient for all parameters in both MSE and MAE, respectively. Thus, this result indicated that our proposed model is efficient, precise and almost perfect correlation as compared to ANN and MLR.展开更多
In the present scenario,computational modeling has gained much importance for the prediction of the properties of concrete.This paper depicts that how computational intelligence can be applied for the prediction of co...In the present scenario,computational modeling has gained much importance for the prediction of the properties of concrete.This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete(SCC).Three models,namely,Extreme Learning Machine(ELM),Adaptive Neuro Fuzzy Inference System(ANFIS)and Multi Adaptive Regression Spline(MARS)have been employed in the present study for the prediction of compressive strength of self compacting concrete.The contents of cement(c),sand(s),coarse aggregate(a),fly ash(f),water/powder(w/p)ratio and superplasticizer(sp)dosage have been taken as inputs and 28 days compressive strength(fck)as output for ELM,ANFIS and MARS models.A relatively large set of data including 80 normalized data available in the literature has been taken for the study.A comparison is made between the results obtained from all the above-mentioned models and the model which provides best fit is established.The experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete.展开更多
Sensing the content of macronutrients in the agricultural soil is an essential task in precision agriculture.It helps the farmers in the optimal use of fertilizers.It reduces the cost of food production and also the n...Sensing the content of macronutrients in the agricultural soil is an essential task in precision agriculture.It helps the farmers in the optimal use of fertilizers.It reduces the cost of food production and also the negative environmentalimpacts on atmosphere and water bodies due to indiscriminate dosageof fertilizers.The traditional chemical-based laboratory soil analysis methodsdo not serve the purpose as they are hardly suitable for site specific soil management.Moreover,the spectral range used in the chemical-based laboratory soil analysismay be of 350-2500 nm,which leads to redundancy and confusion.Developing sensors based on the discovery of spectral wavebands that respondto soil macronutrient concentrations,on the other hand,is an innovative and successfultechnology since the results are dependable and timely.The goal of thisarticle is to use a supervised neuro-fuzzy based dimensionality reduction approachin the sensor development process to determine sensitive wavebands of soilmacronutrients.Accordingly,the spectral signatures of the soil are collected inan outdoor environment and mapped with its macronutrient concentrations.In thisspectral analysis,the spectral reflectance of 424 wavelengths has been obtainedand these wavelengths are evaluated through combined and individual modesas well.Appropriate wavelengths are selected in each case by minimizing the fuzzy reflectance assessment index.The effectiveness of these selected wavelengthsin each mode is validated by modeling the relation between the reduced reflectancespace and each macronutrient concentration using Partial Least Squares Multi Variable Regression(PLS-MVR)method.Set of optimal wavebands areidentified and the results are compared with the existing systems.展开更多
文摘Efficiency and precision in prediction of Chlorophyll-a using this model is still a pandemic among researchers, due to the natural conditions in ocean water systems itself, which involved chemical, biological and physical processes and interaction among them may affect the model performance drastically. Thus, to overcome this problem as well as to improve the strength of MLR, we proposed a hybrid approach, i.e., an Artificial Neural Network to the MLR coins as Artificial Neural Network-Multiple Linear Regression (ANN-MLR). To investigate the performance of the proposed model, we compared Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and proposed hybrid Artificial Neural Network and Multiple Linear Regression (ANN-MLR) in the prediction of chlorophyll-a (chl-a) concentration by statistical measurement which are MSE and MAE. Achieving our objectives of study, we used 4 parameters, i.e. temperature (°C), pH, salinity (ppt), DO (ppm) at the Offshore Kuala Terengganu, Terengganu, Malaysia. The results showed that our proposed model can improve the performance of the model as compared to ANN and MLR due to small errors generated, error reduced, and increased the correlation coefficient for all parameters in both MSE and MAE, respectively. Thus, this result indicated that our proposed model is efficient, precise and almost perfect correlation as compared to ANN and MLR.
文摘In the present scenario,computational modeling has gained much importance for the prediction of the properties of concrete.This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete(SCC).Three models,namely,Extreme Learning Machine(ELM),Adaptive Neuro Fuzzy Inference System(ANFIS)and Multi Adaptive Regression Spline(MARS)have been employed in the present study for the prediction of compressive strength of self compacting concrete.The contents of cement(c),sand(s),coarse aggregate(a),fly ash(f),water/powder(w/p)ratio and superplasticizer(sp)dosage have been taken as inputs and 28 days compressive strength(fck)as output for ELM,ANFIS and MARS models.A relatively large set of data including 80 normalized data available in the literature has been taken for the study.A comparison is made between the results obtained from all the above-mentioned models and the model which provides best fit is established.The experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete.
文摘Sensing the content of macronutrients in the agricultural soil is an essential task in precision agriculture.It helps the farmers in the optimal use of fertilizers.It reduces the cost of food production and also the negative environmentalimpacts on atmosphere and water bodies due to indiscriminate dosageof fertilizers.The traditional chemical-based laboratory soil analysis methodsdo not serve the purpose as they are hardly suitable for site specific soil management.Moreover,the spectral range used in the chemical-based laboratory soil analysismay be of 350-2500 nm,which leads to redundancy and confusion.Developing sensors based on the discovery of spectral wavebands that respondto soil macronutrient concentrations,on the other hand,is an innovative and successfultechnology since the results are dependable and timely.The goal of thisarticle is to use a supervised neuro-fuzzy based dimensionality reduction approachin the sensor development process to determine sensitive wavebands of soilmacronutrients.Accordingly,the spectral signatures of the soil are collected inan outdoor environment and mapped with its macronutrient concentrations.In thisspectral analysis,the spectral reflectance of 424 wavelengths has been obtainedand these wavelengths are evaluated through combined and individual modesas well.Appropriate wavelengths are selected in each case by minimizing the fuzzy reflectance assessment index.The effectiveness of these selected wavelengthsin each mode is validated by modeling the relation between the reduced reflectancespace and each macronutrient concentration using Partial Least Squares Multi Variable Regression(PLS-MVR)method.Set of optimal wavebands areidentified and the results are compared with the existing systems.
