Browsing by Author "Darweesh, Dirar"

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    A Classifier to Detect Informational vs. Non-Informational Heart Attack Tweets
    Karajeh, Ola; Darweesh, Dirar; Darwish, Omar; Abu-El-Rub, Noor; Alsinglawi, Belal; Alsaedi, Nasser (MDPI, 2021-01-16)
    Social media sites are considered one of the most important sources of data in many fields, such as health, education, and politics. While surveys provide explicit answers to specific questions, posts in social media have the same answers implicitly occurring in the text. This research aims to develop a method for extracting implicit answers from large tweet collections, and to demonstrate this method for an important concern: the problem of heart attacks. The approach is to collect tweets containing “heart attack” and then select from those the ones with useful information. Informational tweets are those which express real heart attack issues, e.g., “Yesterday morning, my grandfather had a heart attack while he was walking around the garden.” On the other hand, there are non-informational tweets such as “Dropped my iPhone for the first time and almost had a heart attack.” The starting point was to manually classify around 7000 tweets as either informational (11%) or non-informational (89%), thus yielding a labeled dataset to use in devising a machine learning classifier that can be applied to our large collection of over 20 million tweets. Tweets were cleaned and converted to a vector representation, suitable to be fed into different machine-learning algorithms: Deep neural networks, support vector machine (SVM), J48 decision tree and naïve Bayes. Our experimentation aimed to find the best algorithm to use to build a high-quality classifier. This involved splitting the labeled dataset, with 2/3 used to train the classifier and 1/3 used for evaluation besides cross-validation methods. The deep neural network (DNN) classifier obtained the highest accuracy (95.2%). In addition, it obtained the highest F1-scores with (73.6%) and (97.4%) for informational and non-informational classes, respectively.
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    A Notional Understanding of the Relationship between Code Readability and Software Complexity
    Tashtoush, Yahya; Abu-El-Rub, Noor; Darwish, Omar; Al-Eidi, Shorouq; Darweesh, Dirar; Karajeh, Ola (MDPI, 2023-01-31)
    Code readability and software complexity are considered essential components of software quality. They significantly impact software metrics, such as reusability and maintenance. The maintainability process consumes a high percentage of the software lifecycle cost, which is considered a very costly phase and should be given more focus and attention. For this reason, the importance of code readability and software complexity is addressed by considering the most time-consuming component in all software maintenance activities. This paper empirically studies the relationship between code readability and software complexity using various readability and complexity metrics and machine learning algorithms. The results are derived from an analysis dataset containing roughly 12,180 Java files, 25 readability features, and several complexity metric variables. Our study empirically shows how these two attributes affect each other. The code readability affects software complexity with 90.15% effectiveness using a decision tree classifier. In addition, the impact of software complexity on the readability of code using the decision tree classifier has a 90.01% prediction accuracy.
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    Survey on authentication and security protocols and schemes over 5G networks
    Tashtoush, Yahya; Darweesh, Dirar; Karajeh, Ola; Darwish, Omar; Maabreh, Majdi; Swedat, Safa'; Koraysh, Rawan; Almousa, Omar; Alsaedi, Nasser (Sage, 2022-10)
    The emergence of fifth generation networks opens the doors for Internet of Things environment to spread widely. The number of connected devices to fifth generation networks is expected to increase to more than 1.7 billion users by 2025. Each year, millions of modern devices go online at the beginning of the school year and after the holidays, and you can even notice the publicity of Internet of Things devices swinging with the seasons. Nowadays, these devices are considered to be very important to our daily life. That is because they provide power to our homes, organize our work operations and let communications more suitable. As a result of the increasing number of connected devices to fifth generation networks, the necessity to protect these Internet of Things devices against different types of cyber-attacks is also increased. For this reason, many researchers proposed different protocols and schemes to achieve the security of the Internet of Things devices. In this article, we introduce a survey of some protocols proposed by researchers in different domains and make a comparative study between them in terms of their category, authentication process, evaluation methodology, advantages, target, development year and applications within Internet of Things environment. The objective of this survey is to provide researchers with rich information about these protocols and their uses within Internet of Things systems, whether they can be used for cloud radio access networks, Internet of Things general purposes, telecommunications systems, e-healthcare systems or drone delivery service systems. It can also assist them in choosing the proper protocol to be used according to the type of their Internet of Things system.