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Investigation into the Identification of AI-Generated Short Dialectal Arabic Texts

IEEE Access 2025年 13卷 85131-85138页

作者： Alharthi, Haifa Saudi Electronic University College of Computing and Informatics Riyadh Saudi Arabia

Identifying AI-generated text (AIGT) has become crucial in addressing the potential misuse of generative AI tools and their impact on trust, fairness, and authenticity of content. This study investigates the detection of AIGT in short dialectal Arabic texts, an area that, to our knowledge, has not been previously explored. Two datasets were constructed featuring texts that were rephrased or generated to match the topics and length of MADAR CORPUS, encompassing dialectical human-written texts (HWTs) originating from five major Arabic cities. Among several AIGT detection methods, the pre-trained language models AraELECTRA and AraBERT outperformed both machine learning and feature based approaches. Moreover, we analyzed the key features that significantly influenced the accuracy of the model. © 2013 IEEE.

关键词： Natural language processing systems

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On detecting stock price manipulation attacks: a comprehensive systematic literature review

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Multimedia Tools and Applications 2025年 1-78页

作者： Alfajeer, Amal Altaweel, Ala Bouridane, Ahmed Mouheb, Djedjiga Aslam, Sidra College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Stock market is pivotal to economical systems. It is a platform where listed stocks of companies can be bought/sold by market participants to gain profit. In 2020, the total capitalization of all markets reached $95 trillion. Within such profiting platforms, malicious attacks like stock price/market manipulation are conducted. Stock price manipulation attack refers to the strategic act of trading either a stock, a future (derivative financial contracts), or a security (financial instrument) to increase or decrease their prices. Hence, making significant profits. Manipulation types vary by sources and/or exploited methods. To detect and deter such attacks/practices, extensive research have been conducted. To further assist the future studies, in this paper, we conduct a systematic literature review to extract research articles relevant to stock price/market manipulation attacks and their state-of-art detection systems/solutions. To the best of our knowledge, this is the first systematic literature review that is based on a predefined search methodology and systematically collects the relevant articles that were published between 2011 and 2021. After applying the systematic search methodology, we extracted and surveyed 66 articles and classified them into four main detection approaches. Namely, statistical-based, Machine Learning based, system-based/artefacts, and visualization-based/observation-based. We provide a thorough comparison among the detection systems in terms of utilized datasets, leveraged features, employed optimization techniques, and applied performance measures. Our survey also identifies and discusses a set of existing research gaps such as generality of detection approaches, near/real-time detection capability, validity of real/synthesised datasets, etc. We concluded our survey by proposing several research topics and recommendations to be investigated and addressed by future works. © The Author(s), under exclusive licence to Springer Science+Busine

关键词： Profitability

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Enhancing IoT Security: A Comparative Study of CNN and RNN-Based Anomaly Detection Using the CICIoT2023 Dataset

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第5期52卷 1424-1441页

作者： Al-Ghamdi, Amal M. Alansari, Marwah M. at College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Information Technology Department College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The rapid growth of Internet of Things (IoT) devices has resulted in numerous security challenges. Anomaly detection has become a crucial aspect of IoT security, as it helps identify unusual behaviors and potential threats within IoT networks. Deep learning techniques have shown considerable potential in recognizing anomalies across various domains. However, the dynamic and continuously developing nature of cyberattacks complicates the process of monitoring and identifying among different types of attacks effectively. This study aims to enhance IoT security by conducting a comparative analysis of two deep learning algorithms: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We deploy these models on the new CICIoT2023 dataset to detect large-scale attacks targeting IoT devices. Our findings reveal that the CNN model outperforms others with an accuracy of 98%, while the BiLSTM model achieves 85%, and the hybrid CNN-BiLSTM model reaches 94%. These results show that CNNs are better at extracting spatial features and that hybrid models might be able to use both spatial and temporal dependencies. By providing insights into model selection and optimization, this research contributes to the development of robust intrusion detection systems for IoT security. © (2025), (International Association of Engineers). All rights reserved.

关键词： Recurrent neural networks

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Identifying the Barriers to Human-Centered Design in the Workplace: Perspectives from UX Professionals

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Proceedings of the ACM on Human-Computer Interaction 2025年第2期9卷 1-25页

作者： Gorichanaz, Tim College of Computing & Informatics Drexel University PhiladelphiaPA United States

Human-centered design, a theoretical ideal, is sometimes compromised in industry practice. Technology firms juggle competing priorities, such as adopting new technologies and generating shareholder returns, which may conflict with human-centered design values. This study sought to identify the types of workplace situations that present barriers for human-centered design, going beyond the views and behaviors of individual professionals. Q methodology was used to analyze the experiences of 14 UX professionals based in the United States. Five factors were identified, representing workplace situations in which human-centered design is inhibited, despite the involvement of UX professionals: Single-Minded Arrogance, Competing Visions, Moving Fast and Breaking Things, Pragmatically Getting By, and Sidestepping Responsibility. Underpinning these five factors are the dimensions of speed and clarity of vision. This paper demonstrates connections between the literature on UX ethics and human-centered design practice, and its findings point toward opportunities for education and intervention to better enable human-centered and ethical design in practice. © 2025 Owner/Author.

关键词： Professional aspects

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Aspect-based sentiment analysis in MOOCs: a systematic literature review introducing the MASC-MEF framework

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Journal of King Saud University - Computer and Information Sciences 2025年第1期37卷 1-33页

作者： Awadh, Wed Akeel Sulaiman, Rosnafisah Bte Mahmoud, Moamin A. Institute of Informatics and Computing in Energy University Tenaga Nasional Kajang Malaysia Department of Computer Information Systems University of Basrah Basrah Iraq Institute of Informatics and Computing in Energy Department of Informatics Universiti Tenaga Nasional Jalan IKRAM UNITEN Selangor Kajang Malaysia Institute of Informatics and Computing in Energy Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Selangor Kajang Malaysia

Aspect-based sentiment analysis (ABSA) in massive open online courses (MOOCs) offers a powerful tool to understand student experiences and improve the quality of online education. As MOOCs grow in popularity, there is an increasing need to analyze student feedback at a granular level, focusing on specific course aspects rather than just overall sentiment. Despite ABSA’s potential in MOOCs, research in this specific domain remains limited. Thus, this review of the literature seeks to fill this gap by systematically examining ABSA within the realm of MOOCs. We utilized a PRISMA framework to guide the search process. Consequently, this review covered 57 studies conducted between 2019 and 2024, retrieved from eight electronic research databases. The studies included in this analysis were examined on the basis of their datasets, data preprocessing, ABSA tasks in MOOCs, evaluation aspects in online courses, fusion approach in ABSA, techniques of word representations, and evaluation criteria utilized to evaluate the proposed models. Additionally, this review introduces the multiaspect sentiment classification using multiembedding and fusion (MASC-MEF) framework, a novel approach that uniquely incorporates multiword embeddings and multifeature extraction along with advanced classification techniques. Results shed light on several critical issues and various limitations, pointing towards future directions for ABSA research within the realm of MOOCs. This review underscores significant contributions to improving ABSA in educational technology, with a focus on course effectiveness and advancements in online learning, guiding researchers towards innovative techniques to tackle this challenge in the future. © The Author(s) 2025.

关键词： Aspect-based sentiment analysis Massive open online courses Multiaspect sentiment classification Online education technology Systematic literature review

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Designing Computerized Support for Delay Awareness During Time-Critical Teamwork 25

Designing Computerized Support for Delay Awareness During Ti...

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2025 ACM International Conference on Supporting Group Work, GROUP Companion 2025

作者： Zellner, Katherine Ann College of Computing and Informatics Drexel University PhiladelphiaPA United States

ISBN: (纸本)9798400711879

In emergency medicine, where delays can result in dire consequences, technological interventions have the potential to significantly improve outcomes. This research focuses on understanding trauma resuscitation teams’ experiences with delays and designing an alerting system to improve delay awareness during time-critical teamwork. This research has three aims: (1) identify critical delays and examine delay awareness, (2) define design requirements for delay alert systems, and (3) evaluate the impact of visual alerts on delay awareness. Through iterative user research, we developed a prototype alert system. Current and future work involves designing an ambient alert system and evaluating its effects through live simulations. © 2025 Copyright held by the owner/author(s).

关键词： Resuscitation

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Toward Scalable Security: Intrusion Detection with Spark-Based Classifiers on Hadoop YARN 5th

Toward Scalable Security: Intrusion Detection with Spark-Bas...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Alhammadi, Omar Elnagar, Ashraf College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9789819771776

In the dynamic field of cybersecurity, the development of effective, scalable intrusion detection systems is crucial. Utilizing distributed computing frameworks like Apache Spark on Hadoop YARN offers significant scalability and performance benefits, essential for modern intrusion detection tasks. This study evaluates the efficacy of various machine learning classifiers—Decision Trees, Random Forests, SVMs, Logistic Regression, GBT, MLP, and Naive Bayes—within such an environment. Our findings highlight notable disparities in performance metrics like accuracy, precision, recall, F1-score, and AUC among these classifiers. Notably, the GBT and chi-squared optimized random forest classifiers excelled in accuracy, F1-scores, and AUC. We also examined the impact of chi-squared feature selection on model performance, observing marked improvements in accuracy and computational efficiency. Further, the study underscores the significance of balancing false negatives and positives in NIDS, emphasizing comprehensive pre-deployment attack evaluation. Real-world simulations on a Spark-Hadoop cluster affirmed the system’s scalability and robustness, showing consistent performance across various log sizes while maintaining high accuracy. Looking ahead, we aim to leverage GPU support in Spark 3 for enhancing MLP models and integrate a real-time firewall log parser into the Hadoop ecosystem to bolster threat detection capabilities. This research underscores the value of distributed computing in fortifying cybersecurity, offering insights and advancements to counteract cyberthreats effectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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Privacy-Aware Federated Learning Framework for IoT Security Using Chameleon Swarm Optimization and Self-Attentive Variational Autoencoder

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Computer Modeling in Engineering & Sciences 2025年第4期143卷 849-873页

作者： Saad Alahmari Abdulwhab Alkharashi Department of Computer Science Applied CollegeNorthern Border UniversityArar91431Saudi Arabia Department of Computer Science College of Computing and InformaticsSaudi Electronic UniversityRiyadh11673Saudi Arabia

The Internet of Things(IoT)is emerging as an innovative phenomenon concerned with the development of numerous vital *** the development of IoT devices,huge amounts of information,including users’private data,are *** systems face major security and data privacy challenges owing to their integral features such as scalability,resource constraints,and *** challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data,creating an attractive opportunity for *** address these challenges,artificial intelligence(AI)techniques,such as machine learning(ML)and deep learning(DL),are utilized to build an intrusion detection system(IDS)that helps to secure IoT *** learning(FL)is a decentralized technique that can help to improve information privacy and performance by training the IDS on discrete linked *** delivers an effectual tool to defend user confidentiality,mainly in the field of IoT,where IoT devices often obtain privacy-sensitive personal *** study develops a Privacy-Enhanced Federated Learning for Intrusion Detection using the Chameleon Swarm Algorithm and Artificial Intelligence(PEFLID-CSAAI)*** main aim of the PEFLID-CSAAI method is to recognize the existence of attack behavior in IoT ***,the PEFLIDCSAAI technique involves data preprocessing using Z-score normalization to transformthe input data into a beneficial ***,the PEFLID-CSAAI method uses the Osprey Optimization Algorithm(OOA)for the feature selection(FS)*** the classification of intrusion detection attacks,the Self-Attentive Variational Autoencoder(SA-VAE)technique can be ***,the Chameleon Swarm Algorithm(CSA)is applied for the hyperparameter finetuning process that is involved in the SA-VAE model.A wide range of experiments were conducted to validate the execution of the PEFLID-CSAAI *** simulated outcomes demonstrated that the PEFLID-CSAAI

关键词： Federated learning internet of things artificial intelligence chameleon swarm algorithm intrusion detection system healthcare IoT devices

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The Transparency Challenge in Blockchain-Enabled Sustainable Development Goals Applications: Exploring Privacy-Preserving Techniques and Emerging Platforms

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IEEE Access 2025年 13卷 81769-81793页

作者： Bounceur, Ahcene Berkani, Ahmed-Sami Moumen, Hamouma Benharzallah, Saber University of Sharjah College of Computing and Informatics Sharjah United Arab Emirates University of Batna 2 Computer Science Department Batna05078 Algeria

Blockchain technology has emerged as a key enabler for achieving the United Nations' Sustainable Development Goals (SDGs) by offering transparent, decentralized solutions across multiple sectors. However, its inherent transparency introduces significant privacy challenges, particularly in sensitive areas such as healthcare, energy, finance, and supply chains, where confidentiality is critical. To address this tension, this paper explores a range of emerging privacy-preserving techniques, including Ring Signatures, Zero-Knowledge Proofs (ZKPs), Secure Multi-Party Computation (SMPC), Trusted Execution Environments (TEEs), and mixers, analyzing their implementation, effectiveness, and limitations. Furthermore, we conduct a detailed technical study of next-generation blockchain platforms that integrate these techniques to balance privacy, scalability, and interoperability. Our contributions include a deep dive into privacy challenges in blockchain applications for SDGs, a comparative analysis of privacy-preserving solutions, a comprehensive evaluation of emerging platforms, and a review of existing research that leverages these privacy techniques in SDG-related domains. This work aims to provide critical insights into how blockchain technology can be effectively adapted to support sustainable development while safeguarding sensitive data. © 2013 IEEE.

关键词： Privacy by design

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End-to-end optimized ensemble model for Alzheimer’s disease detection 19

End-to-end optimized ensemble model for Alzheimer’s disease...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Hassan, Abdelrahman Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities—demographics, cognitive scores, neuropsychological assessments, and MRI data—via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management. © 2025 IEEE.

关键词： Adversarial machine learning

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