Yazar "Ali, Seyid Amjad" seçeneğine göre listele

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    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 (Ceratophyllum demersum L.)
    (Springer, 2024) Ali, Seyid Amjad; Gümüş, Numan Emre; Aasim, Muhammad
    Nano-phytoremediation is a novel green technique to remove toxic pollutants from the environment. In vitro regenerated Ceratophyllum demersum (L.) plants were exposed to different concentrations of chromium (Cr) and exposure times in the presence of titania nanoparticles (TiO2NPs). Response surface methodology was used for multiple statistical analyses like regression analysis and optimizing plots. The supplementation of NPs significantly impacted Cr in water and Cr removal (%), whereas NP × exposure time (T) statistically regulated all output parameters. The Firefly metaheuristic algorithm and the random forest (Firefly-RF) machine learning algorithms were coalesced to optimize hyperparameters, aiming to achieve the highest level of accuracy in predicted models. The R2 scores were recorded as 0.956 for Cr in water, 0.987 for Cr in the plant, 0.992 for bioconcentration factor (BCF), and 0.957 for Cr removal through the Firefly-RF model. The findings illustrated superior prediction performance from the random forest models when compared to the response surface methodology. The conclusion is drawn that metal-based nanoparticles (NPs) can effectively be utilized for nano-phytoremediation of heavy metals. This study has uncovered a promising outlook for the utilization of nanoparticles in nano-phytoremediation. This study is expected to pave the way for future research on the topic, facilitating further exploration of various nanoparticles and a thorough evaluation of their potential in aquatic ecosystems. Graphical Abstract: (Figure presented.) © The Author(s) 2024.
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    Genetic algorithms assisted machine learning algorithms to optimize nano-phytoremediation of cadmium designed by response surface methodology
    (Taylor and Francis Ltd., 2025) Baş, Serpil; Aasim, Muhammad; Gümüş, Numan Emre; Katırcı, Ramazan; Ali, Seyid Amjad; Karataş, Mehmet
    Advancements in nanotechnology and artificial intelligence can enhance phytoremediation efficacy, particularly in removing hazardous contaminants like cadmium (Cd). Experiment was conducted by using different concentrations of Cd and titanium dioxide (TiO2) NPs for different time periods, designed by design of experiment of with a total of 20 combinations. Response Surface Regression Analysis was used for data analysis to identify optimal input factors. Results revealed that TiO2 nanoparticles significantly improved the efficiency of phytoremediation by increasing Cd uptake. Cd absorption rates were predicted using machine learning models, and their performance was evaluated using R2 and MSE metrics. Moreover, the Genetic Algorithm (GA) was employed to minimize MSE between predicted and actual Cd absorption values. Ceratophyllum demersum showed an absorption capacity of 99.58%, with a remaining Cd concentration as low as 0.0199 mg/L. The Gaussian Process Regressor (GPR) was the most accurate predictive model with an R2 of 0.99 and MSE of 0.07. The Genetic Algorithm (GA) further optimized the process, identifying optimal NP concentration, Cd concentration, and treatment time. It was concluded that computational models exhibited enhanced Cd absorption due to a synergetic relationship between Cd concentration and treatment time, and absorption efficiency was further enhanced by the supplementation of TiO2 nanoparticles.
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    Machine learning modeling and response surface methodology driven antioxidant and anticancer activities of chitosan nanoparticle-mediated extracts of bacopa monnieri
    (Elsevier, 2025) Bulut, Şeyma; Aasim, Muhammad; Emsen, Buğrahan; Ali, Seyid Amjad; Aşkın, Hakan; Karataş, Mehmet
    This study investigates the potential of chitosan nanoparticles (CNPs) in enhancing the bioavailability and efficacy of Bacopa monnieri extracts, known for their neuroprotective, antioxidant, and anticancer properties. Different concentrations of CNPs were added to the culture medium for in vitro shoot regeneration. Antioxidant activity (DPPH free radical scavenging and H2O2 removal assays) and cytotoxicity assay (LDH release and XTT viability) were performed. The results demonstrated the highest DPPH radical scavenging activity of 95.60 % at 125 mu g/mL CNPs from methanol extract. Whereas, H2O2 scavenging activity increased with higher extract concentrations, and the maximum was recorded from methanol extract when used at 1000 mu g/mL. Cytotoxicity assays revealed a dose-dependent increase in LDH activity and XTT reduction, and water-based extracts demonstrated the strongest cytotoxic effects. IC50 analysis indicated that CNP-enriched methanol and water extracts were significantly more cytotoxic to HeLa cells as compared to ethanol extracts. Response surface regression analysis and ML models confirmed the reliability of the experimental data, with the multilayer perceptron (MLP) model exhibiting the best predictive accuracy, followed by the random forest (RF) model. It can be concluded that CNP enrichment significantly improved the antioxidant and anticancer properties of B. monnieri extracts, highlighting the potential of CNP-based formulations for future studies.
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    Machine learning modeling and response surface methodology driven antioxidant and anticancer activities of chitosan nanoparticle-mediated extracts of Bacopa monnieri (vol 310, 143470, 2025)
    (Elsevier B.V., 2025) Bulut, Şeyma; Aasim, Muhammad; Emsen, Buğrahan; Ali, Seyid Amjad; Aşkın, Hakan; Karataş, Mehmet
    The authors regret that the affiliations of Bugrahan Emsen and Mehmet Karatas were published wrong in the above-mentioned article. The correct affiliations are: Bugrahan Emsen – Department of Biology, Kamil Ozdag Faculty of Science, Karamanoglu Mehmetbey University, 70200 Karaman, Turkey; Mehmet Karatas – Department of Biotechnology, Faculty of Science, Necmettin Erbakan University, 42090 Konya, Turkey. The authors would like to apologise for any inconvenience caused.