Browsing by Subject "Multilayer perceptron"

Now showing 1 - 2 of 2

Open Access
Artificial neural network and decision tree facilitated prediction and validation of cytokinin‑auxin induced in vitro organogenesis of sorghum (Sorghum bicolor L.)
(Springer Dordrecht, 2023-04-05) Aasim, M.; Ali, Seyid Amjad; Altaf, M. T.; Ali, A.; Nadeem, M. A.; Baloch, F. S.
In this study, in vitro regeneration protocol of sorghum (Sorghum bicolor) was successfully established by using direct organogenesis from a mature zygotic embryo explant. The used basal medium encompassed Murashige and Skoog medium (MS) supplemented with 2–4 mg/L Benzylaminopurine (BAP) alone or with 0.25 mg/L Indole butyric acid (IBA) or Naphthalene acetic acid (NAA). Results demonstrated a significant impact of cytokinin-auxin on shoot count (1.24–3.46) and shoot length (2.80–3.47 cm). Maximum shoot count (3.46) and shoot length (3.97 cm) were achieved on the MS medium enriched with 2 mg/L BAP + 0.25 mg/L NAA and 2.0 mg/L BAP, respectively. To ascertain the impact of BAP alone, BAP + IBA, and BAP + NAA, the data were also analyzed by using a factorial regression model. Pareto chart and normal plots were used to check either the positive or negative impact of input variables on output variables. To further explore the association between BAP + IBA and BAP + NAA on shoot count and shoot length, contour and surface plots were also built. Three different artificial intelligence-based models along with four different performance metrics were utilized to validate the predicted results. Multilayer perceptron (MLP) model performed more efficiently (R2 = 0.799 for shoot count and R2 = 0.831 for shoot length) as compared to the decision tree-based algorithms of random forest (RF) – (R2 = 0.779 for shoot count and R2 = 0.786 for shoot length) and extreme gradient boost (XGBoost) – (R2 = 0.768 for shoot count and R2 = 0.781 for shoot length). As plant tissue culture protocol is a powerful tool for genetic engineering and genome editing of crops, integration of different artificial intelligence-based models can lead to improvement of sorghum with the aid of biotechnological tools.
Embargo
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)
(Elsevier BV, 2023-09-01) Aasim, Muhammad; Ayhan, Ayşe; Katırcı, Ramazan; Acar, Alpaslan Şevket; Ali, Seyid Amjad
This study presents the in vitro regneration protocol for Royal purple [(Cotinus coggygria Scop. (syn.: Rhus cotinus L.)] from nodal segment explants followed by optimizing the input variable combinations with the aid of PyTorch ANN and Genetic Algorithm (GA). The Murashige and Skoog (MS) culture medium yielded relatively higher regeneration frequency (91.52 %) and shoot count (1.96) as compared to woody plant medium (WPM), which yielded 84.58 % regeneration and shoot count (1.61) per explant. The supplementation of plant growth regulators (PGRs) + MS medium yielded 80.0–100.0 % shoot regeneration and 1.48–3.25 shoot counts compared to 60.0–100.0 % shoot regeneration and 1.00–2.37 shoots from the combination of PGRs + WPM. In order to predict the shoot count and regeneration with the aid of a mathematical model, the machine learning algorithms of Multilayer Perceptron (MLP), Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and Random Forest (RF) models were utilized. The highest R2 values for both output variables were acquired using MLP model in PyTorch platform. The R2 scores for regeneration and shoot counting were recorded as 0.69 and 0.71 respectively. NSGA-II algorithm revealed the 1.25 mg/L BAP (6-Benzylaminopurine), 0.02 mg/L NAA (Naphthalene acetic acid), and 0.03 mg/L IBA (Indole butyric acid) in WPM medium as an optimum combination for 100 % regeneration. On the other hand, the algorithm suggested multiple combination in MS medium for maximum shoot counting.