BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

288 related articles for article (PubMed ID: 35178628)

  • 1. 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]  

  • 2. Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel.
    Wong YJ; Arumugasamy SK; Chung CH; Selvarajoo A; Sethu V
    Environ Monit Assess; 2020 Jun; 192(7):439. PubMed ID: 32556670
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Performance evaluation of artificial intelligence paradigms-artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction.
    Tabbussum R; Dar AQ
    Environ Sci Pollut Res Int; 2021 May; 28(20):25265-25282. PubMed ID: 33453033
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems.
    Almaraashi M
    PLoS One; 2017; 12(8):e0182429. PubMed ID: 28806754
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Improving one-dimensional pollution dispersion modeling in rivers using ANFIS and ANN-based GA optimized models.
    Seifi A; Riahi-Madvar H
    Environ Sci Pollut Res Int; 2019 Jan; 26(1):867-885. PubMed ID: 30415370
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review.
    Nawab F; Abd Hamid AS; Ibrahim A; Sopian K; Fazlizan A; Fauzan MF
    Heliyon; 2023 Jun; 9(6):e17038. PubMed ID: 37484325
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization.
    Teferra DM; Ngoo LMH; Nyakoe GN
    Heliyon; 2023 Jan; 9(1):e12802. PubMed ID: 36704286
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Comparison of different heuristic and decomposition techniques for river stage modeling.
    Seo Y; Kim S; Singh VP
    Environ Monit Assess; 2018 Jun; 190(7):392. PubMed ID: 29892912
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Prediction of Attendance Demand in European Football Games: Comparison of ANFIS, Fuzzy Logic, and ANN.
    Şahin M; Erol R
    Comput Intell Neurosci; 2018; 2018():5714872. PubMed ID: 30158960
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Prediction of autistic disorder using neuro fuzzy system by applying ANN technique.
    Arthi K; Tamilarasi A
    Int J Dev Neurosci; 2008 Nov; 26(7):699-704. PubMed ID: 18706991
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation.
    Tahmasebi P; Hezarkhani A
    Comput Geosci; 2012 May; 42():18-27. PubMed ID: 25540468
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review.
    Ling JYX; Chan YJ; Chen JW; Chong DJS; Tan ALL; Arumugasamy SK; Lau PL
    Environ Sci Pollut Res Int; 2024 Mar; 31(13):19085-19104. PubMed ID: 38376778
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Inhibition activity prediction for a dataset of candidates' drug by combining fuzzy logic with MLR/ANN QSAR models.
    Abdolmaleki A; Ghasemi JB
    Chem Biol Drug Des; 2019 Jun; 93(6):1139-1157. PubMed ID: 31343121
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and adaptive network-based fuzzy inference system.
    Hosseinzadeh A; Zhou JL; Altaee A; Baziar M; Li D
    Bioresour Technol; 2020 Nov; 316():123967. PubMed ID: 32777721
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Modelling monthly mean air temperature using artificial neural network, adaptive neuro-fuzzy inference system and support vector regression methods: A case of study for Turkey.
    Yakut E; Süzülmüş S
    Network; 2020; 31(1-4):1-36. PubMed ID: 32397767
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Mapping of solar energy potential in Fiji using an artificial neural network approach.
    Oyewola OM; Ismail OS; Olasinde MO; Ajide OO
    Heliyon; 2022 Jul; 8(7):e09961. PubMed ID: 35874079
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Intelligent algorithms-aided modeling and optimization of the deturbidization of abattoir wastewater by electrocoagulation using aluminium electrodes.
    Obi CC; Nwabanne JT; Igwegbe CA; Abonyi MN; Umembamalu CJ; Kamuche TT
    J Environ Manage; 2024 Feb; 353():120161. PubMed ID: 38290261
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.