Cointegration and causality relationship of Indian stock market with selected world markets.

The purpose of this study is to explore the trends and causes of established and emerging nations' stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States.

Modelling time-varying volatility using GARCH models: evidence from the Indian stock market.

In this study, we examined the volatility of the Indian stock market from 2008 to 2021. Owing to the financial crisis, volatility forecasting of the Indian stock market has become crucial for economic and financial analysts. An empirical study of the returns of the NSE indices revealed an autoregressive conditional heteroskedastic trend in the Indian stock market.

Are YouTube videos on complete arch fixed implant-supported prostheses useful for patient education?

Statement Of Problem: Complete arch fixed implant-supported prostheses have gained substantial popularity as a treatment option for patients with edentulism or a failing dentition. While YouTube is a leading source of health-related information videos, the usefulness and quality of videos on YouTube about complete arch fixed implant-supported prostheses are unclear.

Purpose: The purpose of this study was to evaluate the quality of videos on complete arch fixed implant-supported prostheses and assess their usefulness for the general population as an educational resource.

Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study.

Efficiently implementing remote sensing image classification with high spatial resolution imagery can provide significant value in land use and land cover (LULC) classification. The new advances in remote sensing and deep learning technologies have facilitated the extraction of spatiotemporal information for LULC classification. Moreover, diverse disciplines of science, including remote sensing, have utilised tremendous improvements in image classification involving convolutional neural networks (CNNs) with transfer learning.