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The polarimetric Synthetic Aperture Radar (SAR) data sets have been widely exploited for land use land cover (LULC) classification due to their sensitivity to the structural and dielectric properties of the imaging target. In this study, the potential of fully polarimetric L‐ and S‐band Airborne SAR (LS‐ASAR) data sets were explored for the machine‐learning‐based classification of Urban, Vegetation, Waterbody, and Open Ground. This work was done by utilizing dual‐frequency L‐ and S‐band airborne data of Santa Barbara, California, USA, acquired under the Airborne SAR (LS ASAR) campaign, a precursor airborne mission to the space‐borne NASA‐ISRO (NISAR) mission. The LS‐ASAR polarimetric information was utilized for LULC classification using the SVM classifier. The roll‐invariant Barnes, and eigenvalue/eigenvector‐based Cloude and H/A/Alpha decomposition were implemented to retrieve the scattering parameters. The backscatter response of classes was studied, and separability analysis was done to reduce the misclassification error between six class pairs‐ Vegetation—Urban, Vegetation—Waterbody, Vegetation—Open Ground, Urban—Waterbody, Urban—Open Ground, and Water—Open Ground. The decomposition models failed to achieve the desirable separability index for all six class pairs; consequently, the classification of Barnes, Cloude, H/A/Alpha decomposition showed misclassification between vegetation‐urban class, and waterbody‐open ground class for both L‐ and S‐band data sets. The effort was made toward improving the classification accuracy by integrating the roll‐invariant and eigenvalue‐eigenvector scattering parameters of the multifrequency L‐ and S‐band data set. This method presented the desirable separability index for all class‐pair; eventually highest classification accuracy was achieved i.e. 93.35% (kc ${k}_{c}$ = 0.91) by significantly reducing the misclassification error between class pairs. Key Points: Implementation of Barnes, Cloude & H/A/Alpha polarimetric decomposition models on dual‐frequency polarimetry SAR L‐ and S‐band data setsSupport Vector Machine classification of scattering parameters and generation of confusion matrices for Accuracy AssessmentImproving the classification accuracy by integrating the polarimetric scattering components of L‐ and S‐band data sets [ABSTRACT FROM AUTHOR]

Analysis of protein thermostability is vital in protein science to aid the understanding of evolutionary aspects of organic life as well as in protein engineering for modern day industrial applications. In the present study, supervised machine learning (ML) algorithms are employed to unravel potential patterns behind protein thermostability. This computational analysis conclusively shows inverse gamma turns, VIII turns, and the propensity of cysteine (Cys) to be the most important biophysical–biochemical attributes responsible for protein thermostability. From the propensity analysis of amino acids, polar residues, specifically glutamine (Gln) and serine (Ser), and charged residues, specifically glutamic acid (Glu) and lysine (Lys), are found to favor the enhancement of protein thermostability. The study demonstrates the feasibility of assigning quantifiable descriptors of thermostability which is expected to aid protein engineering. [ABSTRACT FROM AUTHOR]

The potential of single date fully Polarimetric RADARSAT‐2 data in retrieving crop biophysical parameters using Machine Learning techniques was investigated. Various polarimetric parameters along with coherent and incoherent decomposition techniques were assessed for its sensitivity toward crop parameters like Wet and Dry Biomass, Crop Height, Leaf Area Index and Vegetation Water Content. A set of 39 polarimetric observables extracted from the Quad‐Pol data were used for regression analysis. In this study two Machine Learning techniques Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were assessed for the prediction of Wet Biomass (gm−2) and Height (cm). The most significant (6 out of 39) variables were applied for prediction. The results revealed that RFR algorithm performed better than MLR. The coefficient of determination (R2) and root‐mean‐square‐error of estimating wet biomass and height were 0.646, 655.65 (gm−2) and 0.71, 14.5 (cm) respectively in RFR and 0.566, 683.86 (gm−2) and 0.65, 16.14 (cm) respectively in MLR. Thus this study explored the effective application of quad‐pol data for assessing sensitivity and accurate retrieval of parameters using optimum PolSAR observables. Key Points: Important polarimetric observables and their sensitivity toward biophysical parameters were analyzedEffective retrieval of crop parameters from a single‐date polarimetric data using Random Forest and Multiple Linear Regression was explored [ABSTRACT FROM AUTHOR]