Artificial neural network and decision tree–based models for prediction and validation of in vitro organogenesis of two hydrophytes—Hemianthus callitrichoides and Riccia fluitans

Author:

Özcan Esra,Atar Hasan Hüseyin,Ali Seyid Amjad,Aasim MuhammadORCID

Funder

Ankara Universitesi

Publisher

Springer Science and Business Media LLC

Subject

Plant Science,Biotechnology

Reference65 articles.

1. Aasim M, Akin F, Ali SA et al (2023a) Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Cicer arietinum L). Physiol Mol Biol Plants 29:1–16

2. Aasim M, Ali SA, Bekiş P, Nadeem MA (2022a) Light-emitting diodes induced in vitro regeneration of Alternanthera reineckii mini and validation via machine learning algorithms. Vitr Cell Dev Biol 58:1–10

3. Aasim M, Ayhan A, Katırcı R et al (2023b) Computing artificial neural network and genetic algorithm for the feature optimization of basal salts and cytokinin-auxin for in vitro organogenesis of royal purple (Cotinus coggygria Scop). Ind Crops Prod 199:116718

4. Aasim M, Katırcı R, Akgur O et al (2022b) Machine learning (ML) algorithms and artificial neural network for optimizing in vitro germination and growth indices of industrial hemp (Cannabis sativa L.). Ind Crops. Prod 181:114801

5. Aasim M, Khawar KM, Ahmed SI, Karataş M (2019a) Multiple uses of some important aquatic and semiaquatic medicinal plants. In: Plant and human health, vol 2. Springer, pp 541–577

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