Medhat Mohiuddin Khan
Biographic Data
| ID | 10298640 |
|---|---|
| NAME | Medhat Mohiuddin Khan |
| GIVEN NAMES | Medhat Mohiuddin |
| FAMILY NAME | Khan |
| SIGNATURE | KHAN M M |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Detecting Cryptocurrency Scams in the USA
The exponential growth of cryptocurrency implementation in the USA has brought with it a surge in correlated risks, particularly in the form of scams that exploit the relative novelty and complexity of digital currencies. The primary objective of this study was to develop machine algorithms for identifying fraud trends in cryptocurrency transactions. By employing complex analysis, this research project attempted to identify certain trends and beh…
Understanding Social Media Behavior in the USA
The swift advancement in social networking platforms has radically shifted the patterns and nature of how people connect with services and brands in America. The utmost objective of this research project was to implement artificial intelligence together with machine learning approaches for creating predictive models that forecast digital pattern development as well as user commitment through social media interactions within the United States. The…
Leveraging Machine Learning for Insights and Predictions in Synthetic E-commerce Data in the USA
The primary purpose of this research was to leverage the potential of machine learning to extract meaningful information out of synthetic e-commerce data to overcome the limitations of traditional analysis. This research focused on the consumption patterns and trends of the US e-commerce market with specific knowledge of its idiosyncrasies and challenges. The synthetic e-commerce dataset comprises a comprehensive collection of simulated transacti…
AI-Driven Sentiment Analysis for Bitcoin Market Trends
Cryptocurrency markets in the USA, especially that of Bitcoin, are plagued by extreme volatility fueled by a dynamic intersection of macroeconomic forces, speculator behavior, and sentiment of investors. Conventional financial models cannot keep pace with the high-frequency price changes typical of digital assets, prompting the need for novel methodologies that can better account for unstructured data like social media sentiment, news reports, an…
No prominent works on this page.
Detecting Cryptocurrency Scams in the USA
The exponential growth of cryptocurrency implementation in the USA has brought with it a surge in correlated risks, particularly in the form of scams that exploit the relative novelty and complexity of digital currencies. The primary objective of this study was to develop machine algorithms for identifying fraud trends in cryptocurrency transactions. By employing complex analysis, this research project attempted to identify certain trends and beh…
Understanding Social Media Behavior in the USA
The swift advancement in social networking platforms has radically shifted the patterns and nature of how people connect with services and brands in America. The utmost objective of this research project was to implement artificial intelligence together with machine learning approaches for creating predictive models that forecast digital pattern development as well as user commitment through social media interactions within the United States. The…
Leveraging Machine Learning for Insights and Predictions in Synthetic E-commerce Data in the USA
The primary purpose of this research was to leverage the potential of machine learning to extract meaningful information out of synthetic e-commerce data to overcome the limitations of traditional analysis. This research focused on the consumption patterns and trends of the US e-commerce market with specific knowledge of its idiosyncrasies and challenges. The synthetic e-commerce dataset comprises a comprehensive collection of simulated transacti…
AI-Driven Sentiment Analysis for Bitcoin Market Trends
Cryptocurrency markets in the USA, especially that of Bitcoin, are plagued by extreme volatility fueled by a dynamic intersection of macroeconomic forces, speculator behavior, and sentiment of investors. Conventional financial models cannot keep pace with the high-frequency price changes typical of digital assets, prompting the need for novel methodologies that can better account for unstructured data like social media sentiment, news reports, an…
Computer Science (4 works) · Artificial Intelligence (3 works) · Blockchain Technology Applications and Security (2 works) · Computer security (2 works) · Cryptocurrency (2 works) · Data science (2 works) · Machine learning (2 works) · Big Data and Business Intelligence (1 work) · Digital Marketing and Social Media (1 work) · Econometrics (1 work)