Kadhim Hayawi
Biographic Data
| ID | 1836565 |
|---|---|
| NAME | Kadhim Hayawi |
| GIVEN NAMES | Kadhim |
| FAMILY NAME | Hayawi |
| SIGNATURE | HAYAWI K |
| AFFILIATIONS | Zayed University |
| ORCID | 0000-0002-8092-4590 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 1 |
DeeProBot
Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one's opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to class…
Hybrid feature selection approach to identify optimal features of profile metadata to detect social bots in Twitter
The last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we …
DeeProBot
Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one's opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to class…
Hybrid feature selection approach to identify optimal features of profile metadata to detect social bots in Twitter
The last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we …
DeeProBot
Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one's opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to class…
Artificial Intelligence (2 works) · Artificial neural network (2 works) · Computer Science (2 works) · Data mining (2 works) · Machine learning (2 works) · Misinformation and Its Impacts (2 works) · Spam and Phishing Detection (2 works) · World Wide Web (2 works) · Advanced Malware Detection Techniques (1 work) · F1 score (1 work)