State-of-the-art biosynthesis of tin oxide nanoparticles by chemical precipitation method towards photocatalytic application.

Tin oxide (SnO2) with versatile properties is of substantial standing for practical application, and improved features of the material are demonstrated in the current issue through the integration of nanotechnology with bio-resources leading to what is termed as biosynthesis of SnO2 nanoparticles (NPs). This review reveals the recent advances in biosynthesis of SnO2 NPs by chemical precipitation method focused on distinct methodologies, characterization, and reaction mechanism along with a photocatalytic application for dye degradation. According to available literature reviews, numerous bio-based precursors selectively extracted from biological substrates have effectively been applied as capping or reducing agents to achieve the metal oxide NPs. The major precursor obtained from the aqueous extract of root barks of Catunaregam spinosa is found to be 7-hydroxy-6-methoxy-2H-chromen-2-one that has been proposed as a model compound for the reduction of metal ions into nanoparticles due to having highly active functional groups, being abundant in plants (67.475 wt%), easy to extract, and eco benign. In addition, the photocatalytic activity of SnO2 NPs for the degradation of organic dyes, pharmaceuticals, and agricultural contaminants has been discussed in the context of a promising bio-reduction mechanism of the synthesis. The final properties are supposed to depend exclusively upon a number of factors, e.g., particle size (< 50 nm), bandgap (< 3.6 eV), crystal defects, and catalysts dosage. With this contribution, it has been perceived not only to provide an overview of recent advances in the biosynthesis of SnO2 NPs but also to indicate the main issues in need aiming to show vision towards innovative outcomes.

Data-driven diagnosis of spinal abnormalities using feature selection and machine learning algorithms.

This paper focuses on the application of machine learning algorithms for predicting spinal abnormalities. As a data preprocessing step, univariate feature selection as a filter based feature selection, and principal component analysis (PCA) as a feature extraction algorithm are considered. A number of machine learning approaches namely support vector machine (SVM), logistic regression (LR), bagging ensemble methods are considered for the diagnosis of spinal abnormality. The SVM, LR, bagging SVM and bagging LR models are applied on a dataset of 310 samples publicly available in Kaggle repository. The performance of classification of abnormal and normal spinal patients is evaluated in terms of a number of factors including training and testing accuracy, recall, and miss rate. The classifier models are also evaluated by optimizing the kernel parameters, and by using the results of receiver operating characteristic (ROC) and precision-recall curves. Results indicate that when 78% data are used for training, the observed training accuracies for SVM, LR, bagging SVM and bagging LR are 86.30%, 85.47%, 86.72% and 85.06%, respectively. On the other hand, the accuracies for the test dataset for SVM, LR, bagging SVM and bagging LR are the same being 86.96%. However, bagging SVM is the most attractive as it has a higher recall value and a lower miss rate compared to others. Hence, bagging SVM is suitable for the classification of spinal patients when applied on the most five important features of spinal samples.