Karunya University

Abstract We present the habitability and stability of the proposed planets around the young stellar system HD 21841 based on the estimated astrophysical parameters of its components. Our study utilizes an orbital analysis of the system, employing Tokovinin's Method and incorporating 12 new speckle interferometric measurements that collectively span approximately 90 degrees of the orbit. Additionally, we use Al-Wardat's method for analyzing binary and multiple stellar systems in conjunction with Gaia DR3 astrometry and spectral data for a comprehensive spectrophotometric assessment of the components. This approach utilizes Kurucz’s model stellar atmospheres and available photometric data to develop synthetic spectral energy distributions (SEDs) for each component and subsequently the entire system. We can identify the physical parameters and the optimal solution by comparing the synthetic SED of the entire system with the observational data from Gaia. The analysis successfully determined the stellar parameters of the system for the first time, revealing the following: $ T_{\rm eff.}=5945\pm80\,K$, $R=1.018\pm0.05\, R_\odot$ , $\mathcal{M}=1.12\pm0.10\, \mathcal{M_\odot}$ for the primary component and $T_{\rm eff.}=5700\pm80\,K$, $R=0.975\pm0.06\, R_\odot$ , $\mathcal{M}=1.05\pm0.09\, \mathcal{M_\odot}$ for the secondary component. Both components are found to have solar metallicity and are approximately 1 billion years old. The combined spectrophotometric and dynamical analysis resulted in precise stellar masses and a new dynamical parallax of $23.756\pm0.001$ mas, consistent with the Hipparcos 1997 measurement ($23.76\pm1.07$) and intermediate between that of Gaia DR2 ($23.51\pm0.28$) and Gaia DR3 ($24.31\pm0.19$). In addition to studying the habitability and stability of the exoplanets within the system, we explored the processes of its formation and evolution.

High-level screening technologies have generated a vast volume of drug-sensitivity data for a panel of cancer cell lines and hundreds of chemicals. By identifying molecular genetic factors of drug sensitivity and developing novel anticancer medicines, computational approaches to analysing these data can assist in the development of anticancer therapies. Conventional deep learning models lack the ability to select the best imputation strategy or to handle missing values. This may compromise the dataset’s originality and introduce data sensitivity issues. To address these issues, we introduce the Explainable Drug Graph Attention Transformer (EDrGAT), which proposes task-specific integration of feature-level graph attention and engineered temporal pharmacodynamics features to predict drug sensitivity. To make the proposed model more data-sensitive, lag, rolling mean, and Exponential mean are computed. The cat boost regressor model performs best for imputation and for data with genomic features such as cell line, drug name, and drug concentration (IC50), and is trained using EDrGAT. We demonstrate the model’s performance using metrics such as RMSE, R2, and training and validation loss/accuracy. The proposed model achieves an R2 of 93% by outperforming previous state-of-the-art models.

Social media websites are popular, and it is now possible to collect a significant amount of behavioral data in real time. This has made it possible to conduct research on mental health. This study proposes a machine learning framework to identify and predict mental health issues, such as emotion and cognition, related to social media with respect to depression, anxiety, and bipolar disorder. In this study, three social media datasets were used, each with more than 1.6 million posts: Sentiment140, Twitter Depression Dataset, and Facebook Sentiment. The data was tokenized and lemmatized, and stopwords were removed. We used BERT, a transformer model, to view the data using emotional traits. This was done using the sentimental polarity score and emotions such as grief, anger, joy, fear, and insignificance. We used Latent Dirichlet Allocation (LDA) and Linguistic Inquiry and Word Count (LIWC) to infer cognitive distortions and psychological indices. We used Recursive Feature Elimination (RFE) for feature selection and a Random Forest classifier to predict mental health across multiple classes. The proposed method is more effective than deep learning and machine learning techniques, achieving an accuracy of 95.0%, precision of 94.0%, recall of 93.7%, F1-score of 94.8%, and AUC-ROC of 98.9%. The sentiment polarity score improved the prediction by 28%, the emotional scores improved the prediction by 35%, the cognitive themes improved the prediction by 22%, and the LIWC features improved the prediction by 15%. The results indicate that the use of cognitive linguistics and emotional representation facilitates the identification of early symptoms of severe depression and bipolar disorder.

Fruits are the most vital items of global diets because of their rich nutritional value, thereby providing very high demand and agricultural revenues to the economy. Among the fruit crops, pomegranate is a valuable one due to its highest antioxidant potential. However, most crops of pomegranate suffer from diseases, which greatly reduce agricultural yield and productivity. Thus, along with the increasing demand of the fruit, early detection as well as classification of diseases will prove very crucial in boosting the yield and taking appropriate measures for prevention. We propose a segmentation-based model using deep learning in this paper to conduct disease identification in pomegranates The process begins with pre-processing images that is primarily an activity of cropping and resizing of the images, followed by enhanced Wiener filtering, which eliminates noise and enhances the clarity of the images The preprocessed images are then further segmented using a CA_YV5GC algorithm, (Channel Attentive YOLOv5-based Grab Cut), which isolates diseased regions from the images. Then the optimized ResNet-152 network is applied to acquire the fundamental features embedding the texture along with the shape characteristics which could identify ailments related symptoms. Coati Optimization is applied to choose the most dominant features in the lower dimensional representation of the extracted information for the classification of the disease. Ultimately, classification is performed using a Deep Capsule Canonical Auto-encoder (DC_CAENet) to classify the disease type with higher accuracy. Adaptive Osprey Optimization is used to optimize the parameters of the model. The existing methods are compared with that results proved this technique to be more accurate and efficient as compared to traditional techniques.