An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms

Indian classical dance (ICD) classification is an interesting subject because of its complex body posture. It provides a stage to experiment with various computer vision and deep learning concepts. With a change in learning styles, automated teaching solutions have become inevitable in every field, from traditional to online platforms. Additionally, ICD forms an essential part of a rich cultural and intangible heritage, which at all costs must be modernized and preserved. In this paper, we have attempted an exhaustive classification of dance forms into eight categories. For classification, we have proposed a deep convolutional neural network (DCNN) model using ResNet50, which outperforms various state-of-the-art approaches.Additionally, to our surprise, the proposed model also surpassed a few recently published works in terms of performance evaluation. The input to the proposed network is initially pre-processed using image thresholding and sampling. Next, a truncated DCNN based on ResNet50 is applied to the pre-processed samples. The proposed model gives an accuracy score of 0.911

The Paradox of Regulation: An Analysis of the Legislation Surrounding the Sex Trade in India.

This project aims to look at existing national laws and policies regarding prostitution and trafficking for prostitution. What role do laws against prostitution aim to do? How do they encompass and provide for women who are a part of this trade? What information do they give us on the perceptions of women involved in sex work

Understanding the role of resective osseous surgical procedure in chronic periodontitis patients: A review article

Variety of treatment approaches have been developed and used to treat periodontal diseases associated with attachment loss. While the goal of periodontal surgical treatments is to access the root surfaces for proper debridement, the decision to remove or reshape the supporting bone has been controversial. Osseous resective surgery necessitates following the use of strict guidelines for proper recontouring of the alveolar bone and proper management and positioning of the gingival tissues so that the results from osseous resective surgery are highly predictable

Oxidative DNA Damage and Carotid Intima Media Thickness as Predictors of Cardiovascular Disease in Prediabetic Subjects

Prediabetes is considered as a risk factor for the development of diabetes mellitus and cardiovascular disease. The present study was conducted with the aim of finding out the relationship between oxidative DNA damage and carotid intima media thickness for the prediction of cardiovascular disease in prediabetic subjects. The study included 100 prediabetic subjects and 100 normal individuals as controls. In both cases and controls, 8-OHdG was measured by ELISA, and CIMT was measured by B mode ultrasonography. Both 8-OHdG and CIMT were significantly higher in subjects with prediabetes as compared to controls (185.80 ± 10.72 pg/mL vs. 126.13 ± 16.01 pg/mL, p < 0.001 and 0.70 ± 0.04 mm vs. 0.57 ± 0.03 mm, p < 0.001, respectively). There was significant and positive correlation of IGT with 8-OHdG (r = 0.783; p < 0.001) and CIMT (r = 0.787; p < 0.001) in prediabetic subjects. Moreover, 8-OHdG showed significant positive correlation with CIMT (r = 0.704; p < 0.001) in prediabetic subjects. In conclusion, increased 8-OHdG and CIMT in prediabetic subjects indicate that biochemical changes of atherosclerosis start even before the onset of diabetes mellitus. Hence, 8-OHdG and CIMT could be used as indicators of cardiovascular disease risk in these subjects

Relationship between Atherogenic Indices and Carotid Intima-Media Thickness in Prediabetes: A Cross-Sectional Study from Central India

Prediabetes is the precursor stage of diabetes mellitus and is also considered to be a risk factor for the development of cardiovascular disease. Atherogenic indices have been used for assessment of risk for cardiovascular disease development. To date, there is no data on evaluating the relationship between atherogenic indices (cardiac risk ratio (CRR), atherogenic coefficient (AC), and atherogenic index of plasma (AIP)) and carotid intima-media thickness (CIMT) in prediabetes. Hence, we aimed to determine atherogenic indices (CRR, AC, and AIP) and CIMT in prediabetic subjects and then sought to evaluate the relationship between them. A total of 400 human subjects were included in the present study, out of which 200 were prediabetic subjects and 200 were normal healthy control subjects. For each subject, CRR, AC, and AIP were calculated from routine lipid parameters and carotid intima-media thickness was measured as well. Atherogenic indices, that is, CRR, AC, and AIP, were significantly increased in prediabetic subjects as compared to the controls (5.87 ± 0.87 vs. 4.23 ± 0.50, p < 0.001; 4.87 ± 0.87 vs. 3.23 ± 0.50, p < 0.001; and 0.29 ± 0.07 vs. 0.09 ± 0.09, p < 0.001, respectively). Moreover, a significant and positive correlation was observed between CIMT and AIP (r = 0.529, p < 0.01), CRR (r = 0.495, p < 0.01), and AC (r = 0.495, p < 0.01). Prediabetic subjects present abnormalities in atherogenic indices and CIMT, which indicate a greater propensity of prediabetes for the development of cardiovascular disease. Hence, atherogenic indices can be used in addition to routine lipid parameters for the better assessment of subclinical atherosclerosis in prediabetic subjects

An Emotion Care Model using Multimodal Textual Analysis on COVID-19

At the dawn of the year 2020, the world was hit by a significant pandemic COVID-19, that traumatized the entire planet. The infectious spread grew in leaps and bounds and forced the policymakers and governments to move towards lockdown. The lockdown further compelled people to stay under house arrest, which further resulted in an outbreak of emotions on social media platforms. Perceiving people\u27s emotional state during these times becomes critically and strategically important for the government and the policymakers. In this regard, a novel emotion care scheme has been proposed in this paper to analyze multimodal textual data contained in real-time tweets related to COVID-19. Moreover, this paper studies 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) over multiple categories such as nature, lockdown, health, education, market, and politics. This is the first of its kind linguistic analysis on multiple modes pertaining to the pandemic to the best of our understanding. Taking India as a case study, we inferred from this textual analysis that ‘joy’ has been lesser towards everything (~9-15%) but nature (~17%) due to the apparent fact of lessened pollution. The education system entailed more trust (~29%) due to teachers\u27 fraternity\u27s consistent efforts. The health sector witnessed sadness (~16%) and fear (~18%) as the dominant emotions among the masses as human lives were at stake. Additionally, the state-wise and emotion-wise depiction is also provided. An interactive internet application has also been developed for the same

Prediction modelling of COVID using machine learning methods from B-cell dataset

Coronavirus is a pandemic that has become a concern for the whole world. This disease has stepped out to its greatest extent and is expanding day by day. Coronavirus, termed as a worldwide disease, has caused more than 8 lakh deaths worldwide. The foremost cause of the spread of coronavirus is SARS-CoV and SARS-CoV-2, which are part of the coronavirus family. Thus, predicting the patients suffering from such pandemic diseases would help to formulate the difference in inaccurate and infeasible time duration. This paper mainly focuses on the prediction of SARS-CoV and SARS-CoV-2 using the B-cells dataset. The paper also proposes different ensemble learning strategies that came out to be beneficial while making predictions. The predictions are made using various machine learning models. The numerous machine learning models, such as SVM, Naïve Bayes, K-nearest neighbors, AdaBoost, Gradient boosting, XGBoost, Random forest, ensembles, and neural networks are used in predicting and analyzing the dataset. The most accurate result was obtained using the proposed algorithm with 0.919 AUC score and 87.248% validation accuracy for predicting SARS-CoV and 0.923 AUC and 87.7934% validation accuracy for predicting SARS-CoV-2 virus

Metadata record for: HIT-COVID, a global database tracking public health interventions to COVID-19

This dataset contains key characteristics about the data described in the Data Descriptor HIT-COVID, a global database tracking public health interventions to COVID-19. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON forma