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.).

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.

Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines.

Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due to time-consuming processes and the need for expert knowledge. The purpose of this article is to employ convolutional neural networks (CNNs) and support vector machines (SVMs) to improve classification accuracy and efficiency. By employing advanced computational techniques, including MobileNet and GoogleNet models, alongside SVM classification, the study demonstrates significant advancements over conventional identification methods. In the classification of a dataset consisting of 7820 images using four different SVM kernel functions, the linear kernel achieved the highest success rate at 98.79 %. It is followed by the RBF kernel at 98.73 %, the polynomial kernel at 97.84 %, and the sigmoid kernel at 97.20 %. This research not only provides a methodological framework for future studies in marine biodiversity monitoring but also highlights the potential for real-time applications in ecological conservation and understanding mucilage dynamics amidst climate change and environmental pollution.

Deep learning-based classification of microalgae using light and scanning electron microscopy images.

Microalgae possess diverse applications, such as food production, animal feed, cosmetics, plastics manufacturing, and renewable energy sources. However, uncontrolled proliferation, known as algal bloom, can detrimentally impact ecosystems. Therefore, the accurate detection, monitoring, identification, and tracking of algae are imperative, albeit demanding considerable time, effort, and expertise, as well as financial resources. Deep learning, employing image pattern recognition, emerges as a practical and promising approach for rapid and precise microalgae cell counting and identification. In this study, we processed light microscopy (LM) and scanning electron microscopy (SEM) images of two Cyanobacteria species and three Chlorophyta species to classify them, utilizing state-of-the-art Convolutional Neural Network (CNN) models, including VGG16, MobileNet V2, Xception, NasnetMobile, and EfficientNetV2. In contrast to prior deep learning based identification studies limited to LM images, we, for the first time, incorporated SEM images of microalgae in our analysis. Both LM and SEM microalgae images achieved an exceptional classification accuracy of 99%, representing the highest accuracy attained by the VGG16 and EfficientNetV2 models to date. While NasnetMobile exhibited the lowest accuracy of 87% with SEM images, the remaining models achieved classification accuracies surpassing 93%. Notably, the VGG16 and EfficientNetV2 models achieved the highest accuracy of 99%. Intriguingly, our findings indicate that algal identification using optical microscopes, which are more cost-effective, outperformed electron microscopy techniques.