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Full-Text Articles in Computer Sciences
Predictive Analysis Of Students’ Learning Performance Using Data Mining Techniques: A Comparative Study Of Feature Selection Methods, S. M. F. D. Syed Mustapha
Predictive Analysis Of Students’ Learning Performance Using Data Mining Techniques: A Comparative Study Of Feature Selection Methods, S. M. F. D. Syed Mustapha
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The utilization of data mining techniques for the prompt prediction of academic success has gained significant importance in the current era. There is an increasing interest in utilizing these methodologies to forecast the academic performance of students, thereby facilitating educators to intervene and furnish suitable assistance when required. The purpose of this study was to determine the optimal methods for feature engineering and selection in the context of regression and classification tasks. This study compared the Boruta algorithm and Lasso regression for regression, and Recursive Feature Elimination (RFE) and Random Forest Importance (RFI) for classification. According to the findings, Gradient …
Hybrid Feature Selection Based On Principal Component Analysis And Grey Wolf Optimizer Algorithm For Arabic News Article Classification, Osama Ahmad Alomari, Ashraf Elnagar, Imad Afyouni, Ismail Shahin, Ali Bou Nassif, Ibrahim Abaker Hashem, Mohammad Tubishat
Hybrid Feature Selection Based On Principal Component Analysis And Grey Wolf Optimizer Algorithm For Arabic News Article Classification, Osama Ahmad Alomari, Ashraf Elnagar, Imad Afyouni, Ismail Shahin, Ali Bou Nassif, Ibrahim Abaker Hashem, Mohammad Tubishat
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The rapid growth of electronic documents has resulted from the expansion and development of internet technologies. Text-documents classification is a key task in natural language processing that converts unstructured data into structured form and then extract knowledge from it. This conversion generates a high dimensional data that needs further analusis using data mining techniques like feature extraction, feature selection, and classification to derive meaningful insights from the data. Feature selection is a technique used for reducing dimensionality in order to prune the feature space and, as a result, lowering the computational cost and enhancing classification accuracy. This work presents a …
Using Deep Learning To Detect Social Media ‘Trolls’, Áine Macdermott, Michal Motylinski, Farkhund Iqbal, Kellyann Stamp, Mohammed Hussain, Andrew Marrington
Using Deep Learning To Detect Social Media ‘Trolls’, Áine Macdermott, Michal Motylinski, Farkhund Iqbal, Kellyann Stamp, Mohammed Hussain, Andrew Marrington
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Detecting criminal activity online is not a new concept but how it can occur is changing. Technology and the influx of social media applications and platforms has a vital part to play in this changing landscape. As such, we observe an increasing problem with cyber abuse and ‘trolling’/toxicity amongst social media platforms sharing stories, posts, memes sharing content. In this paper we present our work into the application of deep learning techniques for the detection of ‘trolls’ and toxic content shared on social media platforms. We propose a machine learning solution for the detection of toxic images based on embedded …
Tweet-To-Act: Towards Tweet-Mining Framework For Extracting Terrorist Attack-Related Information And Reporting, Farkhund Iqbal, Rabia Batool, Benjamin C. M. Fung, Saiqa Aleem, Ahmed Abbasi, Abdul Rehman Javed
Tweet-To-Act: Towards Tweet-Mining Framework For Extracting Terrorist Attack-Related Information And Reporting, Farkhund Iqbal, Rabia Batool, Benjamin C. M. Fung, Saiqa Aleem, Ahmed Abbasi, Abdul Rehman Javed
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The widespread popularity of social networking is leading to the adoption of Twitter as an information dissemination tool. Existing research has shown that information dissemination over Twitter has a much broader reach than traditional media and can be used for effective post-incident measures. People use informal language on Twitter, including acronyms, misspelled words, synonyms, transliteration, and ambiguous terms. This makes incident-related information extraction a non-trivial task. However, this information can be valuable for public safety organizations that need to respond in an emergency. This paper proposes an early event-related information extraction and reporting framework that monitors Twitter streams, synthesizes event-specific …
Toward Tweet-Mining Framework For Extracting Terrorist Attack-Related Information And Reporting, Farkhund Iqbal, Rabia Batool, Benjamin C. M. Fung, Saiqa Aleem, Ahmed Abbasi, Abdul Rehman Javed
Toward Tweet-Mining Framework For Extracting Terrorist Attack-Related Information And Reporting, Farkhund Iqbal, Rabia Batool, Benjamin C. M. Fung, Saiqa Aleem, Ahmed Abbasi, Abdul Rehman Javed
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The widespread popularity of social networking is leading to the adoption of Twitter as an information dissemination tool. Existing research has shown that information dissemination over Twitter has a much broader reach than traditional media and can be used for effective post-incident measures. People use informal language on Twitter, including acronyms, misspelled words, synonyms, transliteration, and ambiguous terms. This makes incident-related information extraction a non-trivial task. However, this information can be valuable for public safety organizations that need to respond in an emergency. This paper proposes an early event-related information extraction and reporting framework that monitors Twitter streams synthesizes event-specific …
A Framework For Online Social Network Volatile Data Analysis: A Case For The Fast Fashion Industry, Anoud Bani-Hani, Feras Al-Obeidat, Elhadj Benkhelifa, Oluwasegun Adedugbe
A Framework For Online Social Network Volatile Data Analysis: A Case For The Fast Fashion Industry, Anoud Bani-Hani, Feras Al-Obeidat, Elhadj Benkhelifa, Oluwasegun Adedugbe
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Consumer satisfaction is an important part for any business as it has been shown to be a major factor for consumer loyalty. Identifying satisfaction in products is also important as it allows businesses alter production plans based on the level of consumer satisfaction for a product. With consumer satisfaction data being very volatile for some products due to a short requirement period for such products, current consumer satisfaction must be identified within a shorter period before the data becomes obsolete. The fast fashion industry, which is part of the fashion industry, is adopted as a case study in this research. …
Wordnet-Based Criminal Networks Mining For Cybercrime Investigation, Farkhund Iqbal, Benjamin C.M. Fung, Mourad Debbabi, Rabia Batool, Andrew Marrington
Wordnet-Based Criminal Networks Mining For Cybercrime Investigation, Farkhund Iqbal, Benjamin C.M. Fung, Mourad Debbabi, Rabia Batool, Andrew Marrington
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© 2019 IEEE. Cybercriminals exploit the opportunities provided by the information revolution and social media to communicate and conduct underground illicit activities, such as online fraudulence, cyber predation, cyberbullying, hacking, blackmailing, and drug smuggling. To combat the increasing number of criminal activities, structure and content analysis of criminal communities can provide insight and facilitate cybercrime forensics. In this paper, we propose a framework to analyze chat logs for crime investigation using data mining and natural language processing techniques. The proposed framework extracts the social network from chat logs and summarizes conversation into topics. The crime investigator can use information visualizer …
Os2: Oblivious Similarity Based Searching For Encrypted Data Outsourced To An Untrusted Domain, Zeeshan Pervez, Mahmood Ahmad, Asad Masood Khattak, Naeem Ramzan, Wajahat Ali Khan
Os2: Oblivious Similarity Based Searching For Encrypted Data Outsourced To An Untrusted Domain, Zeeshan Pervez, Mahmood Ahmad, Asad Masood Khattak, Naeem Ramzan, Wajahat Ali Khan
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© 2017 Pervez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by …
E-Mail Authorship Attribution Using Customized Associative Classification, Michael R. Schmid, Farkhund Iqbal, Benjamin C.M. Fung
E-Mail Authorship Attribution Using Customized Associative Classification, Michael R. Schmid, Farkhund Iqbal, Benjamin C.M. Fung
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E-mail communication is often abused for conducting social engineering attacks including spamming, phishing, identity theft and for distributing malware. This is largely attributed to the problem of anonymity inherent in the standard electronic mail protocol. In the literature, authorship attribution is studied as a text categorization problem where the writing styles of individuals are modeled based on their previously written sample documents. The developed model is employed to identify the most plausible writer of the text. Unfortunately, most existing studies focus solely on improving predictive accuracy and not on the inherent value of the evidence collected. In this study, we …