Browsing by Author "SHAALAN, KHALED"

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    A Survey on Opinion Reason Mining and Interpreting Sentiment Variations
    (IEEE, 2021) ALATTAR, FUAD; SHAALAN, KHALED
    Tracking social media sentiment on a desired target is certainly an important query for many decision-makers in fields like services, politics, entertainment, manufacturing, etc. As a result, there has been a lot of focus on Sentiment Analysis. Moreover, some studies took one step ahead by analyzing subjective texts further to understand possible motives behind extracted sentiments. Few other studies took several steps ahead by attempting to automatically interpret sentiment variations. Learning reasons from sentiment variations is indeed valuable, to either take necessary actions in a timely manner or learn lessons from archived data. However, machines are still immature to carry out the full Sentiment Variations’ Reasoning task perfectly due to various technical hurdles. This paper attempts to explore main approaches to Opinion Reason Mining, with focus on Interpreting Sentiment Variations. Our objectives are investigating various methods for solving the Sentiment Variations’ Reasoning problem and identifying some empirical research gaps. To identify these gaps, a real-life Twitter dataset is analyzed, and key hypothesis for interpreting public sentiment variations are examined.
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    A Survey on Using Blockchain in Trade Supply Chain Solutions
    (IEEE, 2019) JUMA, HUSSAM; SHAALAN, KHALED; KAMEL, ANDIBRAHIM
    Blockchain has emerged as a promising technology to ensure trust between parties. By using this technology, we can establish a secure communication paradigm, where data integrity and immutability can be guaranteed. These inherited features underline blockchain as a suitable technology to optimise the adoptedprocessingmodelinseveraldomains,suchashealth,tradesupplychainandfoodsafety.Inthispaper, we present a detailed overview of the use of blockchain technology in (international) trade supply chains. Furthermore, the discussed proposals have been classi ed based on the target application scenarios. Our goal is to clarify the bene ts of applying this technology to the trading domain and highlight the challenges that are associated with applying this technology to optimise the trading domain. Accordingly, we underline several issues that occur during the designing of the blockchain solution to optimise the (international) trade supply chain.
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    A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques
    (IEEE, 2022) SALLOUM, SAID; GABER, TAREK; VADERA, SUNIL; SHAALAN, KHALED
    Every year, phishing results in losses of billions of dollars and is a major threat to the Internet economy. Phishing attacks are now most often carried out by email. To better comprehend the existing research trend of phishing email detection, several review studies have been performed. However, it is important to assess this issue from different perspectives. None of the surveys have ever comprehensively studied the use of Natural Language Processing (NLP) techniques for detection of phishing except one that shed light on the use of NLP techniques for classification and training purposes, while exploring a few alternatives. To bridge the gap, this study aims to systematically review and synthesise research on the use of NLP for detecting phishing emails. Based on specific predefined criteria, a total of 100 research articles published between 2006 and 2022 were identified and analysed. We study the key research areas in phishing email detection using NLP, machine learning algorithms used in phishing detection email, text features in phishing emails, datasets and resources that have been used in phishing emails, and the evaluation criteria. The findings include that the main research area in phishing detection studies is feature extraction and selection, followed by methods for classifying and optimizing the detection of phishing emails. Amongst the range of classification algorithms, support vector machines (SVMs) are heavily utilised for detecting phishing emails. The most frequently used NLP techniques are found to be TF-IDF and word embeddings. Furthermore, the most commonly used datasets for benchmarking phishing email detection methods is the Nazario phishing corpus. Also, Python is the most commonly used one for phishing email detection. It is expected that the findings of this paper can be helpful for the scientific community, especially in the field of NLP application in cybersecurity problems. This survey also is unique in the sense that it relates works to their openly available tools and resources. The analysis of the presented works revealed that not much work had been performed on Arabic language phishing emails using NLP techniques. Therefore, many open issues are associated with Arabic phishing email detection.
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    Speech Recognition Using Deep Neural Networks: A Systematic Review
    (Speech recognition, deep neural network, systematic review, 2019) BOU NASSIF, ALI; SHAHIN, ISMAIL; ATTILI, IMTINAN; AZZEH, MOHAMMAD; SHAALAN, KHALED
    Over the past decades, a tremendous amount of research has been done on the use of machine learning for speech processing applications, especially speech recognition. However, in the past few years, research has focused on utilizing deep learning for speech-related applications. This new area of machine learning has yielded far better results when compared to others in a variety of applications including speech, and thus became a very attractive area of research. This paper provides a thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications. A thorough statistical analysis is provided in this review which was conducted by extracting specific information from 174 papers published between the years 2006 and 2018. The results provided in this paper shed light on the trends of research in this area as well as bring focus to new research topics.
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    Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media
    (IEEE, 2021) ALATTAR , FUAD; SHAALAN, KHALED
    Sentiment Analysis tools allow decision-makers to monitor changes of opinions on social media towards entities, events, products, solutions, and services. These tools provide dashboards for tracking positive, negative, and neutral sentiments for platforms like Twitter where millions of users express their opinions on various topics. However, so far, these tools do not automatically extract reasons for sentiment variations, and that makes it difficult to conclude necessary actions by decision-makers. In this paper, we first compare performance of various Sentiment Analysis classifiers for short texts to select the top performer. Then we present a Filtered-LDA framework that significantly outperformed existing methods of interpreting sentiment variations on Twitter. The framework utilizes cascaded LDA Models with multiple settings of hyperparameters to capture candidate reasons that cause sentiment changes. Then it applies a filter to remove tweets that discuss old topics, followed by a Topic Model with a high Coherence Score to extract Emerging Topics that are interpretable by a human. Finally, a novel Twitter’s sentiment reasoning dashboard is introduced to display the most representative tweet for each candidate reason.