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Your search keyword '"Nadeem, Qazi"' showing total 8 results
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8 results on '"Nadeem, Qazi"'

6. Global impact of COVID-19 on surgeons and team members (GlobalCOST): a cross-sectional study

Author

Zahra Jaffry, Luis Carlos Uta Nakano, Peter A Brennan, Hassan Abdalla, Francesco Pata, Mohamed A Imam, Anshul Sobti, Siddarth Raj, April Camilla Roslani, Alexis P Arnaud, Harish Neelamraju Lakshmi, Abdulrasheed A Nasir, Stephen Lyman, Muhammed Elhadi, Adrian Jones, Bijayendra Singh, Asser Sallam, Ahmed Negida, Chi Fung Antony Yiu, Nelson Bua, Richard E Field, Rawad Hammad, Nadeem Qazi, Amr Hussein, Ali Narvani, Ghayur Abbas, Ademola Adetoyese Adeyeye, Ahmad Nayef Althaher, Firas Arnaout, Ana Vega Carreiro de Freitas, Krishna Kumar Govindarajan, Sachin Y Kale, Chandramohan Ravichandran, Edmund Leung-Kai Yau, and Luiz Fernando Santetti Zanin

Subjects
Medicine
Abstract

Objectives To investigate the impact of COVID-19 on the well-being of surgeons and allied health professionals as well as the support provided by their institutions.Design This cross-sectional study involved distributing an online survey through medical organisations, social media platforms and collaborators.Setting It included all staff based in an operating theatre environment around the world.Participants 1590 complete responses were received from 54 countries between 15 July and 15 December 2020. The average age of participants was 30–40 years old, 64.9% were men and 32.5% of a white ethnic background. 79.5% were surgeons with the remainder being nurses, assistants, anaesthetists, operating department practitioners or classified other.Main outcome measures Participants that had experienced any physical illness, changes in mental health, salary or time with family since the start of the pandemic as well as support available based on published recommendations.Results 32.0% reported becoming physically ill. This was more likely in those with reduced access to personal protective equipment (OR 4.62; CI 2.82 to 7.56; p

Published
2022
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8. Stock market prediction based on machine learning and social sentiment analysis

Author

Ali Javed, Amin Karimi, Nadeem Qazi, Sin Wee Lee, Madiha Anwar, Mustansar Ali Ghazanfar, and Noor Zaman

Abstract

Precise stock market prediction is crucial for investors, but the volatility of the stock market is influenced by multiple factors such as public sentiments, business news, and related product volatility. While several algorithms have been proposed to predict the stock exchange index based on historical data, they are not ideal as external factors play a critical role in market volatility. To address this issue, we proposed a machine learning model that incorporates historical data with external factors such as social media sentiments, oil and gold trends, and financial news data to enhance prediction accuracy. Our study used HPQ, IBM, ORCL, and MSFT stock market datasets to validate the effectiveness of the proposed model, including an analysis of the impact of Covid19 on companies. Our experimental results showed the highest accuracy of 87.2% using oil and sentiment datasets. Additionally, we identified that social media significantly affects IBM stocks, and the GBM (Gradient Boosting Classifier) classifier produced consistent results.

Published
2023
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