Department of Information Systems and Technologies

Permanent URI for this communityhttps://hdl.handle.net/11693/115471

Browse

Browsing Department of Information Systems and Technologies by Author "Aasim, M."

Filter results by typing the first few letters
Now showing 1 - 5 of 5
  • Thumbnail Image
    ItemOpen Access
    Artifcial intelligence–based approaches to evaluate and optimize phytoremediation potential of in vitro regenerated aquatic macrophyte Ceratophyllum demersum L.
    (2023-01-06) Aasim, M.; Ali, Seyid Amjad; Aydin, S.; Bakhsh, A.; Sogukpinar, C.; Karatas, M.; Khawar, K.M.; Aydin, M.E.
    Water bodies or aquatic ecosystem are susceptible to heavy metal accumulation and can adversely afect the environment and human health especially in underdeveloped nations. Phytoremediation techniques of water bodies using aquatic plants or macrophytes are well established and are recognized as eco-friendly world over. Phytoremediation of heavy metals and other pollutants in aquatic environments can be achieved by using Ceratophyllum demersum L. — a well-known foating macrophyte. In vitro regenerated plants of C. demersum (7.5 g/L) were exposed to 24, 72, and 120 h to 0, 0.5, 1.0, 2.0, and 4.0 mg/L of cadmium (CdSO4·8H2O) in water. Results revealed signifcantly diferent relationship in terms of Cd in water, Cd uptake by plants, bioconcentration factor (BCF), and Cd removal (%) from water. The study showed that Cd uptake by plants and BCF values increased signifcantly with exposure time. The highest BCF value (3776.50) was recorded for plant samples exposed to 2 mg/L Cd for 72 h. Application of all Cd concentrations and various exposure duration yielded Cd removal (%) between the ranges of 93.8 and 98.7%. These results were predicted through artifcial intelligence–based models, namely, random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). The tested models predicted the results accurately, and the attained results were further validated via three diferent performance metrics. The optimal regression coefcient (R2) for the models was recorded as 0.7970 (Cd water, mg/L), 0.9661 (Cd plants, mg/kg), 0.9797 bioconcentration factor (BCF), and 0.9996 (Cd removal, %), respectively. These achieved results suggest that in vitro regenerated C. demersum can be efcaciously used for phytoremediation of Cd-contaminated aquatic environments. Likewise, the proposed modeling of phytoremediation studies can further be employed more comprehensively in future studies aimed at data prediction and optimization.
  • Thumbnail Image
    ItemOpen 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.
  • Thumbnail Image
    ItemOpen Access
    Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Cicer arietinum L)
    (Springer (India) Private Ltd., 2023-01-30) Aasim, M.; Akin, F.; Ali, Seyid Amjad; Taskin, M.B.; Colak, M.S.
    Salt stress is one of the most critical abiotic stresses having significant contribution in global agriculture production. Chickpea is sensitive to salt stress at various growth stages and a better knowledge of salt tolerance in chickpea would enable breeding of salt tolerant varieties. During present investigation, in vitro screening of desi chickpea by continuous exposure of seeds to NaCl-containing medium was performed. NaCl was applied in the MS medium at the rate of 6.25, 12.50, 25, 50, 75, 100, and 125 mM. Different germination indices and growth indices of roots and shoots were recorded. Mean germination (%) of roots and shoots ranged from 52.08 to 100%, and 41.67–100%, respectively. The mean germination time (MGT) of roots and shoots ranged from 2.40 to 4.78 d and 3.23–7.05 d. The coefficient of variation of the germination time (CVt) was recorded as 20.91–53.43% for roots, and 14.53–44.17% for shoots. The mean germination rate (MR) of roots was better than shoots. The uncertainty (U) values were tabulated as 0.43–1.59 (roots) and 0.92–2.33 (shoots). The synchronization index (Z) reflected the negative impact of elevated salinity levels on both root and shoot emergence. Application of NaCl exerted a negative impact on all growth indices compared to control and decreased gradually with elevated NaCl concentration. Results on salt tolerance index (STI) also revealed the reduced STI with elevated NaCl concentration and STI of roots was less than shoot. Elemental analysis revealed more Na and Cl accumulation with respective elevated NaCl concentrations. The In vitro growth parameters and STI values validated and predicted by multilayer perceptron (MLP) model revealed the relatively high R2 values of all growth indices and STI. Findings of this study will be helpful to broaden the understanding about the salinity tolerance level of desi chickpea seeds under in vitro conditions using various germination indices and seedling growth indices.
  • Thumbnail Image
    ItemOpen Access
    Light-emitting diodes induced in vitro regeneration of Alternanthera reineckii mini and validation via machine learning algorithms
    (Springer, 2022-10-22) Aasim, M.; Ali, Seyid Amjad
    Optimization of in vitro regeneration protocol using multiple input variables is highly significant, and can be achieved by validating the data using machine learning algorithms. Shoot tip and nodal segment explants of Alternanthera reineckii mini were inoculated on Murashige and Skoog (MS) medium enriched with different concentrations of benzylaminopurine (BAP), and cultured under five different monochromic light-emitting diodes (LEDs). The attained results were validated through the application of four different supervised machine learning models (RF, XGBoost, KNN, and GP). The prediction of the data were validated by using regression coefficient (R2), mean squared error (MSE), and mean absolute percentage error (MAPE) performance metrics. Results revealed R2 values of 0.61 and 0.59 for shoot counts and shoot length, respectively. The results of MSE were registered between 3.48–5.42 for shoot count and 0.40–0.74 for shoot length, whereas, 28.9–35.1% and 13.2–18.4% MAPE values were recorded for both shoot count and shoot length. Among the utilized models, the RF model validated and predicted the results more accurately, followed by the XGBoost model for both output variables. The results confirm that ML models can be used for data validation, and opens a new era of employing ML modeling in plant tissue culture of other economically important plants. Graphical abstract: Schematic structure presenting input features and outputs together with ML models, used validation and performance metrics [Figure not available: see fulltext.]. © 2022, The Society for In Vitro Biology.
  • Thumbnail Image
    ItemOpen Access
    Machine learning and artificial neural networks-based approach to model and optimize ethyl methanesulfonate and sodium azide induced in vitro regeneration and morphogenic traits of water hyssops (Bacopa monnieri L.)
    (2022-09-10) Mirza, K.; Aasim, M.; Katırcı, R.; Karataş, M.; Ali, Seyid Amjad
    Application of chemical mutagens is used for artificially induced in vitro mutation to develop new cultivars with elite characteristics. However, the optimization of selecting proper mutagen, its concentration, and exposure time is of utmost importance, especially for plants containing noteworthy secondary metabolites. In this study, the effect of sodium azide (NaN3) and ethyl methanesulfonate (EMS) in different concentrations (0.025, 0.05, 0.1, and 0.2 mg l−1), and treatment time (30, 60, and 120 min) was investigated on Bacopa monnieri; an important medicinal plant. The maximum shoot counts (57.0) were achieved from the combination of 0.10 mg l−1 EMS × 60 min. Whereas, maximum shoot length (4.07 cm), node numbers (4.97) and leaf numbers (12,23) were achieved from the combination of 0.20 mg l−1 EMS × 120 min, respectively. Combination of 0.025 mg l−1 NaN3 × 120 mg/l yielded maximum shoot counts (52.30), shoot length (3.23 cm), node numbers (6.07) and leaf numbers (12.13). The trained model to predict the outputs were designed and calibrated with machine learning (ML) algorithms. Support Vector Classifier (SVC), Gaussian Process (GP), Extreme Gradient Boosting (XGBoost), Random Forest (RF) models, and Multilayer Perceptron (MLP) neural network algorithms were used to discover the best models and their hyperparameters. The RF model gave exceptional results in the prediction of the outputs. F1 scores of the RF were acquired in the range of 0.98–1.00 for different outputs. The other models’ F1 scores varied in the range of 0.65 and 0.85. The present work opens the new era of applying ML and artificial neural network (ANN) models in plant tissue culture with the possibility of application for other economic crops.