A5093017803- Advanced Database Systems and Queries
- Data Stream Mining Techniques
- Data Management and Algorithms
- Software System Performance and Reliability
- Biometric Identification and Security
- Advanced Steganography and Watermarking Techniques
- Multimodal Machine Learning Applications
- Network Security and Intrusion Detection
- Digital and Cyber Forensics
- Advanced Malware Detection Techniques
- Sentiment Analysis and Opinion Mining
- Video Analysis and Summarization
- Text and Document Classification Technologies
- Advanced Image and Video Retrieval Techniques
- Advanced Text Analysis Techniques
Nile University
2023-2024
The exploration of sentiment analysis in multilingual contexts, particularly through the integration deep learning techniques and knowledge graphs, represents a significant advance language processing research. This study specifically concentrates on Arabic language, addressing challenges presented by its morphological complexity. While primary focus is Arabic, research also includes comprehensive review related work other languages such as Bangla Chinese. contextualizes solutions found...
This paper presents a comprehensive methodology for gender detection using hand palm images, leveraging image processing techniques and PySpark scalable efficient processing. The approach encompasses meticulous preprocessing pipeline, incorporating essential stages like grayscale conversion, the application of Difference Gaussians (DoG) filter, adaptive histogram equalization. not only refines features but also ensures scalability, accommodating large datasets seamlessly. After VGG19 model...
This study introduces a novel approach for detecting malware using cutting-edge machine Learning with PySpark, which targets the increasingly complex cyber threats that conventional security measures struggle to counter. Our research aims create highly accurate system harmful network activities by examining traffic patterns, including duration and frequency of connections, amount data transferred, involved endpoints. We utilized comprehensive dataset from Kaggle performed thorough...
Big Data Stream processing engines such as Apache Flink use windowing techniques to handle unbounded streams of events. Gathering all pertinent input within a window is crucial for event-time since it affects how accurate results are. A significant part this process played by watermarks, which are unique timestamps that show the passage events in time. However, current watermark generation method Flink, works at level stream, tends favor faster sub-streams, resulting dropped from slower...
Abstract Big Data Stream processing engines, exemplified by tools like Apache Flink, employ windowing techniques to manage unbounded streams of events. Aggregating relevant data within Windows is important for event-time due its impact on result accuracy. A pivotal role in this process attributed watermarks, unique timestamps signifying event progression time. Nonetheless, the existing watermark generation method operating at input stream level, exhibits a bias towards faster sub-streams,...