Precision propagation by integrating response surface methodology and machine learning for (Benth) Hook. F

Aasim M, Yıldırım B, Say A, Ali SA, Aytaç S, Nadeem MA (2024) Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H2O2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.). Plant Mol Biol 114:33

Article  CAS  PubMed  Google Scholar 

Abbasi Z, Hooshyar S, Bagherieh-Najjar MB (2016) Improvement of callus production and shoot regeneration using various organs of soybean (Glycine max L. Merr) by response surface methodology. In Vitro Cell Dev Biol - Plant 52:537–545

Article  CAS  Google Scholar 

Afzal S, Ziapour BM, Shokri A, Shakibi H, Sobhani B (2023) Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms. Energy 282:128446

Article  Google Scholar 

Aghayeh RNM, Abedy B, Balandari A, Samiei L, Tehranifar A (2020) Use of response surface methodology for optimizing the media of establishment and proliferation phases of Iranian seedless barberry. Plant Cell Tiss Org Cult 141:87–101

Article  CAS  Google Scholar 

Ahmadpour R, MalekiZanjani B, Garoosi G, Haddad R, Farjaminezhad R (2023) Prediction of the concentration of plant growth regulators for somatic embryogenesis and regeneration of Hyoscyamusniger using Box-Behnken design of response surface methodology. Plant Cell Tiss Org Cult 154:55–71

Article  CAS  Google Scholar 

Akbari B, Najafi F, Bahmaei M, Mahmoodi NM, Sherman JH (2023) Modeling and optimization of malondialdehyde (MDA) absorbance behavior through response surface methodology (RSM) and artificial intelligence network (AIN): An endeavor to estimate lipid peroxidation by determination of MDA. J Chemom 37:e3468

Article  CAS  Google Scholar 

Ali SA, Aasim M (2024) Response surface methodology and artificial intelligence modeling for in vitro regeneration of Brazilian micro sword (Lilaeopsisbrasiliensis). Plant Cell Tiss Org Cult 157:1–13

Article  Google Scholar 

Ali SA, Gümüş NE, Aasim M (2024) A unified framework of response surface methodology and coalescing of Firefly with random forest algorithm for enhancing nano-phytoremediation efficiency of chromium via in vitro regenerated aquatic macrophyte coontail (Ceratophyllumdemersum L.). Environ Sci Pollut Res 31:42185–42201

Article  CAS  Google Scholar 

Anderson MJ (2017) DOE simplified: practical tools for effective experimentation. CRC Press

Book  Google Scholar 

Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA (2008) Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76:965–977

Article  CAS  PubMed  Google Scholar 

Chaari M, Elhadef K, Akermi S, Ben HH, Fourati M, ChakchoukMtibaa A, Sarkar T, Shariati MA, Rebezov M, D’Amore T (2022) Multiobjective response and chemometric approaches to enhance the phytochemicals and biological activities of beetroot leaves: an unexploited organic waste. Biomass Convers Biorefinery 13:15067–15081

Article  Google Scholar 

Chakraborty D, Bandyopadhyay A, Bandopadhyay S, Gupta K, Chatterjee A (2010) Use of response surface methodology for optimization of a shoot regeneration protocol in Basilicumpolystachyon. In Vitro Cell Dev Biol - Plant 46:451–459

Article  Google Scholar 

Chantarangsi W, Liuz W, Bretz F, Kiatsupaibul S, Hayter AJ (2016) QQ plots with confidence for testing Weibull and exponential distributions. Hacettepe J Math Stat 45:887–904

Google Scholar 

Chhalgri MA, Khan MT, Nizamani GS, Yasmeen S, Khan IA, Aslam MM, Rajpar AA, Tayyaba T, Nizamani F, Nizamani MR (2020) Effect of plant growth hormones on shoot and root regeneration in rose under in vitro conditions. Adv Life Sci 8:93–97

CAS  Google Scholar 

Connelly LM (2021) Introduction to Analysis of Variance (ANOVA). Medsurg Nurs 30:218

Article  Google Scholar 

Cui X-H, Murthy HN, Wu C-H, Paek K-Y (2010) Sucrose-induced osmotic stress affects biomass, metabolite, and antioxidant levels in root suspension cultures of Hypericumperforatum L. Plant Cell Tiss Org Cult 103:7–14

Article  CAS  Google Scholar 

Custódio L, Charles G, Magné C, Barba-Espín G, Piqueras A, Hernández JA, Ben Hamed K, Castañeda-Loaiza V, Fernandes E, Rodrigues MJ (2022) Application of in vitro plant tissue culture techniques to halophyte species: A review. Plants 12:126

Article  PubMed  PubMed Central  Google Scholar 

Goldstein BA, Hubbard AE, Cutler A, Barcellos LF (2010) An application of Random Forests to a genome-wide association dataset: methodological considerations & new findings. BMC Genet 11:1–13

Article  Google Scholar 

Gómez-Montes EO, Oliver-Salvador C, Durán-Figueroa N, Badillo-Corona JA, Salas CE (2015) Optimization of direct shoot regeneration using cotyledonary explants and true leaves from lettuce cv. Romaine (Lactuca sativa L.) by surface response methodology. Plant Growth Regul 77:327–334

Article  Google Scholar 

Gutiérrez-Miceli FA, Arias L, Juarez-Rodríguez N, Abud-Archila M, Amaro-Reyes A, Dendooven L (2010) Optimization of growth regulators and silver nitrate for micropropagation of Dianthus caryophyllus L. with the aid of a response surface experimental design. In Vitro Cell Dev Biol - Plant 46:57–63

Article  Google Scholar 

Hassani F, Kouhkord A, Golshani A, Amirmahani M, Moghanlou FS, Naserifar N, Beris AT (2024) Micro-electro-mechanical acoustofluidic mixing system: A response surface-metaheuristic machine learning fusion framework. Expert Syst Appl 249:123638

Article  Google Scholar 

Hesami M, Jones AMP (2020) Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture. Appl Microbiol Biotechnol 104:9449–9485

Article  CAS  PubMed  Google Scholar 

Hooker JD (1853) The botany of the Antarctic voyage of H.M. discovery ships Erebus and Terror. II. Flora Novae-Zelandiae 1:189–190

Hooker JD (1864) Handbook of the New Zealand flora. Lovell Reeve, London

Hou L, Wang Y, Cui Y, Pang X, Li Y (2018) Optimisation of a highly efficient shoot regeneration system using leaf explants of Chinese jujube (Ziziphusjujuba Mill.) by response surface methodology. J Hortic Sci Biotechnol 93:289–295

Article  CAS  Google Scholar 

Huang X-Y, Ao T-J, Zhang X, Li K, Zhao X-Q, Champreda V, Runguphan W, Sakdaronnarong C, Liu C-G, Bai F-W (2023) Developing high-dimensional machine learning models to improve generalization ability and overcome data insufficiency for mixed sugar fermentation simulation. Bioresour Technol 385:129375

Article  CAS  PubMed  Google Scholar 

Ishrat F, Afrasiab H, Chaudhury FA (2022) Effects of salinity stress on growth and physio-biochemical parameters of three pea (Pisumsativum l.) cultivars of different maturity duration. Adv Life Sci 9:380–391

CAS  Google Scholar 

Jaichandran R, Krishna TM, Arigela SH, Raman R, Dharani N, Kumar A (2022) Light gbm algorithm based crop recommendation by weather detection and acquired soil nutrients. International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India. https://doi.org/10.1109/ICPECTS56089.2022.10047765

Javed MA, Habib I, Jamil MW, Anwer MJ, Nazir S, Iqbal MZ (2019) Protocol optimization for efficient in vitro micro-propagation of Stevia rebaudiana Bertoni. Adv Life Sci 6:100–105

CAS  Google Scholar 

Jha AK, Sit N (2021) Comparison of response surface methodology (RSM) and artificial neural network (ANN) modelling for supercritical fluid extraction of phytochemicals from Terminalia chebula pulp and optimization using RSM coupled with desirability function (DF) and genetic algorithm (GA) and ANN with GA. Ind Crops Prod 170:113769

Article  CAS  Google Scholar 

Kadhim ZS, Abdullah HS, Ghathwan KI (2022) Artificial Neural Network Hyperparameters Optimization: A Survey. Int J Online Biomed Eng 18:59–87

Article  Google Scholar 

Kasman M, Riyanti A, Salmariza S, Aslamia RTSS (2019) Response surface methodology approach for analysis of phytoremediation process of Pb (II) from aqueous solution using Echinodoruspalaefolius. IOP Conf Ser: Mater Sci Eng 546:22009

Article  CAS  Google Scholar 

Katırcı R (2015) Statistical optimisation of trivalent chromium bath and characterisation of coating defects. Surf Eng 31:465–471

Article  Google Scholar 

Katirci R, Aktas H, Zontul M (2021) The prediction of the ZnNi thickness and Ni % of ZnNi alloy electroplating using a machine learning method. Trans Inst Met Finish 99:162–168

Article  CAS  Google Scholar 

Katırcı R, Soomro SN, Soomro SR, Aasim M (2024) Optimization of in vitro regeneration of Cowpea ( Vigna unguiculata L.) using comput models. J Glob Innov Agric Sci 12:285–291

Article  Google Scholar 

Keijok WJ, Pereira RHA, Alvarez LAC, Prado AR, da Silva AR, Ribeiro J, de Oliveira JP, Guimarães MCC (2019) Controlled biosynthesis of gold nanoparticles with Coffea arabica using factorial design. Sci Rep 9:1–10

Article  CAS  Google Scholar 

Khan MHU, Wang S, Wang J, Ahmar S, Saeed S, Khan SU, Xu X, Chen H, Bhat JA, Feng X (2022) Applications of artificial intelligence in climate-resilient smart-crop breeding. Intl J Mol Sci 23:11156

Article  Google Scholar 

Khashei M, Hamadani AZ, Bijari M (2012) A novel hybrid classification model of artificial neural networks and multiple linear regression models. Expert Syst Appl 39:2606–2620

Article  Google Scholar 

Koç B, Kaymak-Ertekin F (2010) Response surface methodology and food processing applications. GIDA-J Food 35:63–70

Google Scholar 

Kumar V, Singh J, Kumar P (2018) Response surface methodology based optimization of cadmium and lead remediation from aqueous solution by water hyacinth (Eichhorniacrassipes [Mart.] Solms) and its anatomical study. Arch Agric Env Sci 3:163–173

Article  Google Scholar 

Lamidi S, Olaleye N, Bankole Y, Obalola A, Aribike E, Adigun I (2022) Applications of response surface methodology (RSM) in product design, development, and process optimization. IntechOpen. https://doi.org/10.5772

Comments (0)

No login
gif