417 related articles for article (PubMed ID: 29892912)
21. Evaluating the potential of artificial neural network and neuro-fuzzy techniques for estimating antioxidant activity and anthocyanin content of sweet cherry during ripening by using image processing.
Taghadomi-Saberi S; Omid M; Emam-Djomeh Z; Ahmadi H
J Sci Food Agric; 2014 Jan; 94(1):95-101. PubMed ID: 23633396
[TBL] [Abstract][Full Text] [Related]
22. Least square support vector machine-based variational mode decomposition: a new hybrid model for daily river water temperature modeling.
Heddam S; Ptak M; Sojka M; Kim S; Malik A; Kisi O; Zounemat-Kermani M
Environ Sci Pollut Res Int; 2022 Oct; 29(47):71555-71582. PubMed ID: 35604598
[TBL] [Abstract][Full Text] [Related]
23. Granular computing-neural network model for prediction of longitudinal dispersion coefficients in rivers.
Ghiasi B; Sheikhian H; Zeynolabedin A; Niksokhan MH
Water Sci Technol; 2019 Nov; 80(10):1880-1892. PubMed ID: 32144220
[TBL] [Abstract][Full Text] [Related]
24. Research on air pollutant concentration prediction method based on self-adaptive neuro-fuzzy weighted extreme learning machine.
Li Y; Jiang P; She Q; Lin G
Environ Pollut; 2018 Oct; 241():1115-1127. PubMed ID: 30029320
[TBL] [Abstract][Full Text] [Related]
25. Application of wavelet theory to enhance the performance of machine learning techniques in estimating water quality parameters (case study: Gao-Ping River).
Chen TC
Water Sci Technol; 2023 Mar; 87(5):1294-1315. PubMed ID: 36919749
[TBL] [Abstract][Full Text] [Related]
26. Prediction of oxidation parameters of purified Kilka fish oil including gallic acid and methyl gallate by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network.
Asnaashari M; Farhoosh R; Farahmandfar R
J Sci Food Agric; 2016 Oct; 96(13):4594-602. PubMed ID: 26909668
[TBL] [Abstract][Full Text] [Related]
27. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients.
Güler I; Ubeyli ED
J Neurosci Methods; 2005 Oct; 148(2):113-21. PubMed ID: 16054702
[TBL] [Abstract][Full Text] [Related]
28. Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysis.
Chang FJ; Chung CH; Chen PA; Liu CW; Coynel A; Vachaud G
Sci Total Environ; 2014 Oct; 494-495():202-10. PubMed ID: 25046611
[TBL] [Abstract][Full Text] [Related]
29. Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh.
Jamei M; Karbasi M; Malik A; Abualigah L; Islam ARMT; Yaseen ZM
Sci Rep; 2022 Jul; 12(1):11165. PubMed ID: 35778436
[TBL] [Abstract][Full Text] [Related]
30. Artificial intelligence modeling to predict transmembrane pressure in anaerobic membrane bioreactor-sequencing batch reactor during biohydrogen production.
Taheri E; Amin MM; Fatehizadeh A; Rezakazemi M; Aminabhavi TM
J Environ Manage; 2021 Aug; 292():112759. PubMed ID: 33984638
[TBL] [Abstract][Full Text] [Related]
31. Evaluation of a new neutron energy spectrum unfolding code based on an Adaptive Neuro-Fuzzy Inference System (ANFIS).
Hosseini SA; Esmaili Paeen Afrakoti I
J Radiat Res; 2018 Jul; 59(4):436-441. PubMed ID: 29351656
[TBL] [Abstract][Full Text] [Related]
32. Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring.
Najah A; El-Shafie A; Karim OA; El-Shafie AH
Environ Sci Pollut Res Int; 2014 Feb; 21(3):1658-1670. PubMed ID: 23949111
[TBL] [Abstract][Full Text] [Related]
33. Research on Ship-Radiated Noise Denoising Using Secondary Variational Mode Decomposition and Correlation Coefficient.
Li Y; Li Y; Chen X; Yu J
Sensors (Basel); 2017 Dec; 18(1):. PubMed ID: 29278380
[TBL] [Abstract][Full Text] [Related]
34. Solar radiation and solar energy estimation using ANN and Fuzzy logic concept: A comprehensive and systematic study.
Patel D; Patel S; Patel P; Shah M
Environ Sci Pollut Res Int; 2022 May; 29(22):32428-32442. PubMed ID: 35178628
[TBL] [Abstract][Full Text] [Related]
35. A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network.
Zhou Q; Jiang H; Wang J; Zhou J
Sci Total Environ; 2014 Oct; 496():264-274. PubMed ID: 25089688
[TBL] [Abstract][Full Text] [Related]
36. Comparison of adaptive neuro-fuzzy inference system and artificial neural networks for estimation of oxidation parameters of sunflower oil added with some natural byproduct extracts.
Karaman S; Ozturk I; Yalcin H; Kayacier A; Sagdic O
J Sci Food Agric; 2012 Jan; 92(1):49-58. PubMed ID: 21769881
[TBL] [Abstract][Full Text] [Related]
37. Leuconostoc mesenteroides growth in food products: prediction and sensitivity analysis by adaptive-network-based fuzzy inference systems.
Wang HY; Wen CF; Chiu YH; Lee IN; Kao HY; Lee IC; Ho WH
PLoS One; 2013; 8(5):e64995. PubMed ID: 23705023
[TBL] [Abstract][Full Text] [Related]
38. Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods.
Najafzadeh M; Ghaemi A
Environ Monit Assess; 2019 May; 191(6):380. PubMed ID: 31104155
[TBL] [Abstract][Full Text] [Related]
39. Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest.
Uzun Ozsahin D; Duwa BB; Ozsahin I; Uzun B
Diagnostics (Basel); 2024 Feb; 14(4):. PubMed ID: 38396424
[TBL] [Abstract][Full Text] [Related]
40. Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators.
You H; Ma Z; Tang Y; Wang Y; Yan J; Ni M; Cen K; Huang Q
Waste Manag; 2017 Oct; 68():186-197. PubMed ID: 28408281
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
