Modular Feed Forward Networks to Predict Sugar Diffusivity from Date Pulp Part I. Model Validation

Trigui, Maher; Gabsi, Karim; Amri, Inès El; Helal, Ahmed Noureddine; Barrington, Suzelle

doi:10.1080/10942910903191609

Cited by 13 publications

(10 citation statements)

References 9 publications

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“…Furthermore, Table 1 is a compilation of only the major food produce. Tunisia offers a good example of food losses as a result of aesthetic reasons, where some 45,000 tonne of dates are not harvested, on an annual production of 113,000 tonnes (25% wastage), because of poor colour and appearance (Trigui et al, 2010). A world compilation of food production and wastage (FAO, 2013b) indicates that the highest losses are associated with cereals at 26%, followed by vegetables at 24%, starchy roots at 18%, fruits at 16%, milk and eggs at 17.5%, meat at 4% and finally oilcrops and pulses at 3%.…”

Section: Food Wastagementioning

confidence: 99%

Production vs. consumption management for sustainable agricultural resources

Barrington¹,

Adhikari²

2018

IJISD

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At 35%, world food production wastage is jeopardising the sustainability of global resources and the future of food security, especially considering that agriculture uses 60% and 37% of the world water and land resources. Ironically, rural populations are most exposed to poverty and hunger when they feed the world. Through a literature review, this paper will demonstrate that supply management, based on consumption, is a solution to food wastage and can in parallel, improve rural wealth, introduce sustainable agricultural practices, resolve major urban pressures and bring about global climate change adaptation. The concepts presented in this paper confront modern world agricultural policies as production management systems are being abolished throughout the world, including Canada.

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Section: Food Wastagementioning

confidence: 99%

Production vs. consumption management for sustainable agricultural resources

Barrington¹,

Adhikari²

2018

IJISD

Self Cite

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“…Water 2021, 13, 3615 2 of 17 have been performed using meta-heuristic algorithms and artificial neural network (ANN) methods [2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Estimating Yield and Water Productivity of Tomato Using a Novel Hybrid Approach

Dehghanisanij

Emami

Achite

et al. 2021

Water

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Water productivity (WP) of crops is affected by water–fertilizer management in interaction with climatic factors. This study aimed to evaluate the efficiency of a hybrid method of season optimization algorithm (SO) and support vector regression (SVR) in estimating the yield and WP of tomato crops based on climatic factors, irrigation–fertilizer under the drip irrigation, and plastic mulch. To approve the proposed method, 160 field data including water consumption during the growing season, fertilizers, climatic variables, and crop variety were applied. Two types of treatments, namely drip irrigation (DI) and drip irrigation with plastic mulch (PMDI), were considered. Seven different input combinations were used to estimate yield and WP. R2, RMSE, NSE, SI, and σ criteria were utilized to assess the proposed hybrid method. A good agreement was presented between the observed (field monitoring data) and estimated (calculated with SO–SVR method) values (R2 = 0.982). The irrigation–-fertilizer parameters (PMDI, F) and crop variety (V) are the most effective in estimating the yield and WP of tomato crops. Statistical analysis of the obtained results showed that the SO–SVR hybrid method has high efficiency in estimating WP and yield. In general, intelligent hybrid methods can enable the optimal and economical use of water and fertilizer resources.

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“…Recently, artificial intelligence technologies such as artificial neural network ( ANN ), adaptive neuro-fuzzy inference system ( ANFIS ), and support vector machine ( SVM ) have drawn researchers’ attention because of their high capability, and flexibility in different applications including system classifications, predictive processes, and control systems [ 4 , 20 ] that can be applied to a vast range of systems to predict the behavior of experimental systems [ 21 , 22 , 23 , 24 , 25 ]. To estimate diffusivity, different types of intelligent predictive tools have been reported [ 4 , 26 , 27 , 28 , 29 ]. Based on the paper of Abbasi and et.…”

Section: Introductionmentioning

confidence: 99%

Hybrid intelligence methods for modeling the diffusivity of light hydrocarbons in bitumen

Kuyakhi

Zarenia

Boldaji

2020

Heliyon

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The solvent diffusivity is considered as a key factor in the design of solvent assisted processes in the bitumen field. In this study, a novel Adaptive neuro-fuzzy interference system ( ANFIS ) is employed to evaluate the diffusivity of the light hydrocarbons in the bitumen system. The particle swarm optimization ( PSO ) and genetic algorithm ( GA ) are adopted to promote ANFIS efficiency. The proposed models are established by a prepared dataset from multiple papers in the literature. Temperature (T), pressure (P) and molecular weight of alkanes (Mw) were considered as the input variables and on the other hand, Statistical parameters and graphical methods were used to appraise ANFIS , ANFIS-PSO , and ANFIS-GA performance. The results demonstrated that the highest correlation coefficient is related to ANFIS-PSO with R 2 = 0.991 and 0.987 for train and test data, respectively. In the end, the results indicated that the ANFIS-PSO model has a higher level of desirability based on statistical parameters.

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Modular Feed Forward Networks to Predict Sugar Diffusivity from Date Pulp Part I. Model Validation

Cited by 13 publications

References 9 publications

Production vs. consumption management for sustainable agricultural resources

Production vs. consumption management for sustainable agricultural resources

Estimating Yield and Water Productivity of Tomato Using a Novel Hybrid Approach

Hybrid intelligence methods for modeling the diffusivity of light hydrocarbons in bitumen

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