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37 results on '"Mohasseb, Alaa"'

2. A Prediction Analysis for the Case of a Korean Police Dataset

Author

Aziz, Benjamin, Mohasseb, Alaa, Jung, Jeyong, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Macintyre, John, editor, Avlonitis, Markos, editor, and Papaleonidas, Antonios, editor

Published
2024
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3. A Comparative Analysis of GPT-3 and BERT Models for Text-based Emotion Recognition: Performance, Efficiency, and Robustness

Author

Boitel, Enguerrand, Mohasseb, Alaa, Haig, Ella, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Naik, Nitin, editor, Jenkins, Paul, editor, Grace, Paul, editor, Yang, Longzhi, editor, and Prajapat, Shaligram, editor

Published
2024
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4. Epistemology for Cyber Security: A Controlled Natural Language Approach

Author

Chase, Leigh, Mohasseb, Alaa, Aziz, Benjamin, van der Aalst, Wil, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Guizzardi, Giancarlo, Series Editor, Saad, Inès, editor, Rosenthal-Sabroux, Camille, editor, Gargouri, Faiez, editor, Chakhar, Salem, editor, Williams, Nigel, editor, and Haig, Ella, editor

Published
2024
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8. A customizable grammar-based framework for user-intent text classification

Author

Mohasseb, Alaa, Bader-El-Den, Mohamed, Cocea, Mihaela, and Hadikin, Glenn Stewart

Subjects
004
Abstract

In real-life classification problems, prior information about the problem and expert knowledge about the domain are often used to obtain reliable and consistent solutions. This is especially true in fields where the data is ambiguous, such as text, in which the same words can be used in seemingly similar texts but have a different meaning. Many of the proposed approaches rely on the bag-of-words representation, which loses the information about the structure of the text. In this thesis, a literature review of related works in text classification is provided which includes an overview of text classification methods. In addition, detailed review of related works of two text classification domains; search engines and question answering systems. The core contribution is divided into three main parts. The first contribution is the Customizable Grammar Framework for user-intent text classification (CGF) which employs a formal grammar approach and exploits domain-related information in a new way to represent text as a series of syntactic categories forming syntactic patterns. In addition, the proposed framework has been applied to different domains which resulted in the second and third contribution. The second contribution is the Grammar-Based Framework for Query Classification (GQC) which helped in the improvement of query identification and classification. The third contribution is the Grammar-Based Framework for Question Categorization and Classification (GQCC) which helped in the enhancement of question identification and classification. In addition, using different machine learning algorithms the overall results show that the proposed approach outperforms previous ones in terms of classification performance for query and question classifications. Finally, comparison of the classification performance with the state-of-the-art approaches has been conducted, results validate that the proposed approach improves the classification accuracy and the identification of the different types of queries and questions.

Published
2018

13. Incremental Learning for Large Scale Classification Systems

Author

Alexopoulos, Athanasios, Kanavos, Andreas, Giotopoulos, Konstantinos, Mohasseb, Alaa, Bader-El-Den, Mohamed, Tsakalidis, Athanasios, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Series Editor, Goedicke, Michael, Series Editor, Tatnall, Arthur, Series Editor, Neuhold, Erich J., Series Editor, Pras, Aiko, Series Editor, Tröltzsch, Fredi, Series Editor, Pries-Heje, Jan, Series Editor, Whitehouse, Diane, Series Editor, Reis, Ricardo, Series Editor, Furnell, Steven, Series Editor, Furbach, Ulrich, Series Editor, Winckler, Marco, Series Editor, Rauterberg, Matthias, Series Editor, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, and Plagianakos, Vassilis, editor

Published
2018
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14. Detecting Question Intention Using a K-Nearest Neighbor Based Approach

Author

Mohasseb, Alaa, Bader-El-Den, Mohamed, Cocea, Mihaela, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Series Editor, Goedicke, Michael, Series Editor, Tatnall, Arthur, Series Editor, Neuhold, Erich J., Series Editor, Pras, Aiko, Series Editor, Tröltzsch, Fredi, Series Editor, Pries-Heje, Jan, Series Editor, Whitehouse, Diane, Series Editor, Reis, Ricardo, Series Editor, Furnell, Steven, Series Editor, Furbach, Ulrich, Series Editor, Winckler, Marco, Series Editor, Rauterberg, Matthias, Series Editor, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, and Plagianakos, Vassilis, editor

Published
2018
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16. Web Queries Classification Based on the Syntactical Patterns of Search Types

Author

Mohasseb, Alaa, Bader-El-Den, Mohamed, Kanavos, Andreas, Cocea, Mihaela, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Karpov, Alexey, editor, Potapova, Rodmonga, editor, and Mporas, Iosif, editor

Published
2017
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18. Exploring Clustering Techniques for Analyzing User Engagement Patterns in Twitter Data.

Author

Kanavos, Andreas, Karamitsos, Ioannis, and Mohasseb, Alaa

Subjects
SOCIAL media, MICROBLOGS, SOCIAL networks, INFORMATION sharing
Abstract

Social media platforms have revolutionized information exchange and socialization in today's world. Twitter, as one of the prominent platforms, enables users to connect with others and express their opinions. This study focuses on analyzing user engagement levels on Twitter using graph mining and clustering techniques. We measure user engagement based on various tweet attributes, including retweets, replies, and more. Specifically, we explore the strength of user connections in Twitter networks by examining the diversity of edges. Our approach incorporates graph mining models that assign different weights to evaluate the significance of each connection. Additionally, clustering techniques are employed to group users based on their engagement patterns and behaviors. Statistical analysis was conducted to assess the similarity between user profiles, as well as attributes, such as friendship, followings, and interactions within the Twitter social network. The findings highlight the discovery of closely linked user groups and the identification of distinct clusters based on engagement levels. This research emphasizes the importance of understanding both individual and group behaviors in comprehending user engagement dynamics on Twitter. [ABSTRACT FROM AUTHOR]

Published
2023
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27. Towards Context-Aware Syntax Parsing and Tagging

Author

Mohasseb, Alaa, Bader-El-Den, Mohamed, Cocea, Mihaela, Pirovano, Edoardo, and Graversen, Eva

Subjects
000 Computer science, knowledge, general works, Computer Science
Abstract

Information retrieval (IR) has become one of the most popular Natural Language Processing (NLP) applications. Part of speech (PoS) parsing and tagging plays an important role in IR systems. A broad range of PoS parsers and taggers tools have been proposed with the aim of helping to find a solution for the information retrieval problems, but most of these are tools based on generic NLP tags which do not capture domain-related information. In this research, we present a domain-specific parsing and tagging approach that uses not only generic PoS tags but also domain-specific PoS tags, grammatical rules, and domain knowledge. Experimental results show that our approach has a good level of accuracy when applying it to different domains.

Published
2019
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37. Manipulating the perception of paragraph breaks : a new theoretical model of textual steganography using paragraphs

Author

Bukhelli, Aysha Abdulmonem Abdulla Erhama, Aziz, Benjamin Yowell Yousif, Mohasseb, Alaa Ali Ghareeb, and Khusainov, Rinat

Abstract

The ancient technique of information hiding known as text steganography has enjoyed much research in recent years due to the rising popularity of social media platforms, and the abundant availability of online literature and other text as cover media for steganography. Whilst the majority of the research approaches have focused on manipulating or replacing text, in some form or another, to embed secret information, the utilisation of the structure of the document itself for such embedding has rarely been researched. Subsequently, research in the field of written English paragraph structure and the related error analysis is outdated. Therefore, a new approach in embedding secret messages in textual documents based on the splitting, merging, and resizing of paragraph text is proposed. The size comparison between adjacent paragraphs embeds one bit of information. We outline a basic and advanced model, and define the syntax and semantics of the embedding language. We also propose and applied two analysis applications, one using Machine Learning by classifying text based on their attributes, such as; words per paragraph, paragraph proportion based on sentences, and other written English data points. The other analysis technique utilised is the Chi-squared method, here the distribution of paragraphs sizes is analysed to see if it can statistically detect between clean and embedded text when and if the manipulation is applied. The embedding model showed to be resilient against the analysis techniques. It detected around 50% of the bad corpus. However, we concluded that it extremely difficult to detect an embedding model that manipulates paragraphs and structure of novel texts.

Published
2023

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