Prediction of chemical exergy of organic substances using artificial neural network-multi layer perceptron

Shan, Jie; Wang, Yaya; Gao, Wei

doi:10.1080/15567036.2018.1486924

Cited by 3 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A variety of ML classifiers could be adopted. Considering the data feature including continuity and high dimensionality, and to ensure the universality and accessibility of the developed models, after the data pre-processing and organization steps, six supervised ML classifiers including LR-L1 and LR-L2 [ 27 ], RF [ 28 ], GBDT [ 29 ], SVM [ 29 ] and MLP [ 30 ] were selected and employed. The standard procedure for evaluating classifier performance was k-fold cross-validation [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Nontargeted metabolomics-based multiple machine learning modeling boosts early accurate detection for citrus Huanglongbing

Wang

Niu

Vashisth

et al. 2022

Horticulture Research

View full text Add to dashboard Cite

Early accurate detection of crop disease is extremely important for timely disease management. Huanglongbing (HLB), one of the most destructive citrus diseases, has brought about severe economic losses for the global citrus industry. The direct strategies for HLB identification, such as quantitative real-time polymerase chain reaction (qPCR) and chemical staining, are robust for the symptomatic plants but powerless for the asymptomatic ones at the early stage of affection. Thus, it is very necessary to develop a practical method used for the early detection of HLB. In this study, a novel method combining ultra-high performance liquid chromatography/mass spectrometry (UHPLC/MS)-based nontargeted metabolomics and machine learning (ML) was developed for conducting the early detection of HLB for the first time. Six ML algorithms were selected to build the classifiers. Regularized logistic regression (LR-L2) and gradient-boosted decision tree (GBDT) outperformed with the highest average accuracy of 95.83% to not only classify healthy and infected plants but identify significant features. The proposed method proved to be practical for early detection of HLB, which tackled the shortcomings of low sensitivity in the conventional methods and avoid the problems such as lighting condition interference in spectrum/image recognition-based ML methods. Additionally, the discovered biomarkers were verified by the metabolic pathway analysis and content change analysis, which was remarkably consistent with the previous reports.

show abstract

Section: Methodsmentioning

confidence: 99%

Nontargeted metabolomics-based multiple machine learning modeling boosts early accurate detection for citrus Huanglongbing

Wang

Niu

Vashisth

et al. 2022

Horticulture Research

View full text Add to dashboard Cite

show abstract

“…MLP contains several fully connected layers of nodes in which a non-linear activation function is considered for each node, except at the input layer. MLP employs back-propagation for training ( Breiman, 2001 ) and has shown to be a highly applicable network, thus a popular choice among researchers ( Shan et al, 2018 ). Two hidden layers of size 10 and 4 and Adam optimization were considered in this work.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

et al. 2020

View full text Add to dashboard Cite

Early prediction of pathogen infestation is a key factor to reduce the disease spread in plants. Macrophomina phaseolina (Tassi) Goid, as one of the main causes of charcoal rot disease, suppresses the plant productivity significantly. Charcoal rot disease is one of the most severe threats to soybean productivity. Prediction of this disease in soybeans is very tedious and non-practical using traditional approaches. Machine learning (ML) techniques have recently gained substantial traction across numerous domains. ML methods can be applied to detect plant diseases, prior to the full appearance of symptoms. In this paper, several ML techniques were developed and examined for prediction of charcoal rot disease in soybean for a cohort of 2,000 healthy and infected plants. A hybrid set of physiological and morphological features were suggested as inputs to the ML models. All developed ML models were performed better than 90% in terms of accuracy. Gradient Tree Boosting (GBT) was the best performing classifier which obtained 96.25% and 97.33% in terms of sensitivity and specificity. Our findings supported the applicability of ML especially GBT for charcoal rot disease prediction in a real environment. Moreover, our analysis demonstrated the importance of including physiological featured in the learning. The collected dataset and source code can be found in https://github.com/Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-code.

show abstract

Determining biomass chemical exergy using a novel hybrid intelligent approach to promote biomass-based biorefineries

Aghbashlo

Tabatabaei

Nadian

et al. 2020

Journal of Cleaner Production

View full text Add to dashboard Cite

Prediction of chemical exergy of organic substances using artificial neural network-multi layer perceptron

Cited by 3 publications

References 9 publications

Nontargeted metabolomics-based multiple machine learning modeling boosts early accurate detection for citrus Huanglongbing

Nontargeted metabolomics-based multiple machine learning modeling boosts early accurate detection for citrus Huanglongbing

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

Determining biomass chemical exergy using a novel hybrid intelligent approach to promote biomass-based biorefineries

Contact Info

Product

Resources

About