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Women in data science at Prince Sultan University (WiDS PSU), International Conference of

作者： Fatima AlShannaq Sawsan Alhalawani Heba Al-Jarrah Mohammad Shehab Artificial Intelligence and Data Analytics Lab (AIDA) CCIS Prince Sultan University Riyadh Saudi Arabia College of Computer Science and Information Systems Prince Sultan University Riyadh Saudi Arabia Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan College of Computer Sciences and Informatics Amman Arab University Amman Jordan

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Cyberbullying is a growing concern in the digital age, particularly on social media platforms like X (formerly Twitter). This study presents a deep learning-based approach for detecting cyberbullying in Arabic tweets using the Arabic Cyberbullying Corpus (ArCybC). Leveraging the Arabic-BERT model, we conducted binary and multi-class classification tasks to identify cyberbullying content and its associated categories or domains. Our methodology incorporated advanced data-balancing techniques, including oversampling and undersam-pling, to address class imbalance challenges. The study aims to answer the research questions: (1) How effectively can deep learning models detect cyberbullying in Arabic tweets? (2) How do data balancing techniques impact classification performance? (3) Can Arabic-BERT outperform traditional classifiers in cy-berbullying detection? These questions are addressed through our methodology, where we employ Arabic-BERT, implement rebalancing techniques, and compare our model's performance with baseline classifiers. Experimental results demonstrate significant improvements in F1-scores, with 89.1% achieved in binary classification and 91.6% in category-level classification after oversampling. These findings highlight the effectiveness of fine-tuned deep learning models in tackling the linguistic complexity of Arabic and the nuanced nature of cyberbullying detection. This study provides a foundation for future research in addressing online harassment in Arabic-speaking digital communities.

关键词： Deep learning Blogs Cyberbullying Linguistics data science information age data models Complexity theory

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Path optimization method for the sign problem caused by fermion determinant

arXiv

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arXiv 2025年

作者： Hisayoshi, Kazuki Kashiwa, Kouji Namekawa, Yusuke Takase, Hayato Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka811-0295 Japan Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima730-0053 Japan

The path optimization method with machine learning is applied to the one-dimensional massive lattice Thirring model, which has the sign problem caused by the fermion determinant. This study aims to investigate how the path optimization method works for the sign problem. We show that the path optimization method successfully reduces statistical errors and reproduces the analytic results. We also examine an approximation of the Jacobian calculation in the learning process and show that it gives consistent results with those without an approximation. Copyright © 2025, The Authors. All rights reserved.

关键词： Fermions

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Detection of ARP Spoofing Attack in Healthcare Networks Using a LSTM Model

Detection of ARP Spoofing Attack in Healthcare Networks Usin...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Salam Al-E'mari Yousef Sanjalawe Ghader Kurdi Budoor Allehyani Information Security Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (UJ) Amman Jordan Department of Data Science College of Computing Umm Al-Qura University (UQU) Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University (UQU) Makkah Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Healthcare networks face significant cybersecurity threats due to their extensive interconnectivity, with Address Resolution Protocol (ARP) spoofing posing a major risk to the confidentiality and integrity of medical data. ARP spoofing is a Man-in-the-Middle (MITM) attack where an attacker sends falsified ARP messages to associate their MAC address with the IP address of a legitimate device, enabling unauthorized interception or manipulation of network traffic. In this paper, we propose an LSTM-based detection model designed to effectively identify ARP spoofing attacks in healthcare environments. Unlike traditional methods that rely on static markers at fixed time intervals, the LSTM model leverages temporal dependencies in network traffic, enhancing detection accuracy and adaptability to evolving attack patterns. Experimental results based on the CICIoMT dataset 2024 demonstrate that the proposed model achieves a detection accuracy of 98.5%, significantly outperforming conventional approaches. These findings highlight the effectiveness of LSTM networks in mitigating ARP spoofing threats, offering a scalable solution for securing healthcare networks.

关键词： Adaptation models Protocols Accuracy Computational modeling Intrusion detection Telecommunication traffic Internet of Medical Things IP networks Long short term memory Faces

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Attention-based unsupervised prompt learning for SAM in leaf disease segmentation

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Knowledge-Based Systems 2025年 320卷

作者： Tian, Luda Yuan, Yingchun En, Qing Ma, Wei Zhang, Guanghui Liang, Fangfang School of Information Science and Technology Hebei Agricultural University Baoding071001 China Hebei Key Laboratory of Agricultural Big Data Baoding071001 China Hebei Engineering Research Center for Agricultural Remote Sensing Application Baoding071001 China Lab School of Computer Science Carleton University OttawaK1S 5B6 Canada Faculty of Information Technology Beijing University of Technology Beijing100124 China

In modern agriculture, leaf disease segmentation is crucial for crop disease management and yield improvement. Since most deep learning-based segmentation models require extensive annotations, pursuing unsupervised methods becomes a practical solution. Although advanced models like the Segment Anything Model (SAM) can produce precise class-agnostic masks without annotations, their reliance on human-interactive prompts limits their utility to unsupervised disease segmentation tasks. To address this limitation, we present UPLS, a novel three-stage framework that leverages unsupervised prompt learning to enable SAM for leaf disease segmentation. This method employs unsupervised contrastive learning to progressively segment leaf and disease areas and automatically generate disease prompts for SAM. Concretely, for fine and intricate lesions, we utilize an improved high-frequency attention mechanism to extract high-frequency features from the leaf area, and construct contrastive losses between global priors and foreground/background features. Furthermore, we devise a strategy to automatically generate disease-related prompts from the segmented leaf and initial lesion regions, enabling SAM to refine disease boundaries without human intervention. Experiments on three public plant disease segmentation datasets show that UPLS outperforms existing non-fully supervised segmentation methods in accuracy and robustness. Source code is available at https://***/Tianluda/UPLS. © 2025 Elsevier B.V.

关键词： Unsupervised learning

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Back to the Future: Challenges of Sparse and Irregular Medical Image Time Series

Back to the Future: Challenges of Sparse and Irregular M...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Disch, Nico Albert Peretzke, Robin Roy, Saikat Ulrich, Constantin Zimmerer, David Stiefelhagen, Rainer Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Karlsruhe Institute of Technology Karlsruhe Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany

ISBN: (纸本)9783031845246

In longitudinal medical image analysis, most work focuses on regularly sampled images, or on tasks like regression or classification. However, in the clinical context, images are frequently generated irregularly due to factors such as cost constraints. This work tackles the problem of irregularly sampled longitudinal medical imaging by evaluating methods in segmentation and reconstruction tasks. We examine the reconstruction for real-life MRI from patients with Alzheimer’s Disease (AD), where the model predicts future MRI scans from an arbitrary number of input images sampled at irregular intervals. However, experiments show that most models cannot surpass the performance of using an image from the same patient at a different time point as a baseline. Therefore, we conducted experiments on a synthetic dataset to better isolate effects in temporal learning. These experiments offer insights into model behavior and serve as a benchmark for validating models’ longitudinal learning capability, thus providing a straightforward method to assess temporal understanding in a controlled environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Towards A Hybrid Quantum Differential Privacy

arXiv

引用

arXiv 2025年

作者： Song, Baobao Pokhrel, Shiva Raj Vasilakos, Athanasios V. Zhu, Tianqing Li, Gang The Faculty of Engineering and IT University of Technology Sydney Sydney Australia The Centre for Cyber Security Research and Innovation Deakin University Geelong Australia The Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia University of Agder Grimstad Norway The Faculty of Data Science City University of Macau China

Quantum computing offers unparalleled processing power but raises significant data privacy challenges. Quantum Differential Privacy (QDP) leverages inherent quantum noise to safeguard privacy, surpassing traditional DP. This paper develops comprehensive noise profiles, identifies noise types beneficial for QDP, and highlights the need for practical implementations beyond theoretical models. Existing QDP mechanisms, limited to single noise sources, fail to reflect the multi-source noise reality of quantum systems. We propose a resilient hybrid QDP mechanism utilizing channel and measurement noise, optimizing privacy budgets to balance privacy and utility. Additionally, we introduce Lifted Quantum Differential Privacy, offering enhanced randomness for improved privacy audits and quantum algorithm evaluation. © 2025, CC BY.

关键词： Quantum noise

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Park4U: A Blockchain-Based Parking Reservation and Management for Smart Cities

Park4U: A Blockchain-Based Parking Reservation and Managemen...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Amjad Aldweesh Mohammad Alauthman Ahmad Al-Qerem Mohammad Al Khaldy Saad Alateef Someah Alangari College of computing and IT Shaqra University Shaqra Saudi Arabia Faculty of Information Technology University of Petra Amman Jordan Faculty of Information Technology Zarqa University Zarqa Jordan Department of Business Intelligence and Data Analytics University of Petra Amman Jordan School of Computing Science Newcastle University Newcastle UK College of Computing and IT Shaqra University Aldawadmi Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Urbanization and increasing vehicles in smart cities have created challenges in managing parking efficiently. Traditional systems face issues like lack of transparency, security vulnerabilities, and poor stakeholder coordination. This paper introduces Park4U, a blockchain-based parking reservation and management system for smart cities. It takes advantage of blockchain's decentralized, secure, and transparent nature to improve efficiency, accessibility, and reliability of parking services. Park4U integrates IoT sensors, smart contracts, and a mobile application facilitating seamless reservation, payment, and management. The system aims to enhance resource utilization, reduce congestion, and provide a secure and transparent platform for stakeholders: drivers, operators, and authorities. An evaluation of performance and comparison with traditional systems are presented, showcasing blockchain-based approach's benefits. It also explores potential challenges and future research directions.

关键词： Smart cities Smart contracts Blockchains Mobile applications Stakeholders Security Reliability Intelligent sensors Faces Vehicles

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Enhancing Financial Stability: Machine Learning Models for Bank Loan Risk Prediction

Enhancing Financial Stability: Machine Learning Models for B...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Shadi Nashwan Issam Jebreen Ahmad Al-Qerem Mohammad A Al Khaldy Mohammed Rajab Faculty of Information Technology Middle East University Jordan Computer Science Department Zarqa University Jordan Business Intelligence Data Analytics University of Petra Amman Jordan University Headquarter University Of Anbar Ramadi Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The efficient prediction of loan risk in banking is pivotal for maintaining profitability and financial stability. This paper tackles two main objectives: (1) identifying the most suitable classifier among twenty distinct algorithms (encompassing six learning strategies) to detect loan risk, and (2) evaluating two widely adopted feature selection methods to pinpoint the most influential predictors within the loan dataset. By integrating advanced data mining techniques, including decision trees, ensemble approaches, Bayesian methods, and neural networks, we analyzed a comprehensive set of borrower attributes and loan characteristics. The best-performing classifier (Decision Table) achieved high accuracy, precision, recall, and Fl-score, establishing its reliability for loan risk modeling. Furthermore, we found GainRatioAttributeEval to be the most effective feature selection technique, significantly enhancing predictive performance by highlighting relevant variables. These findings underscore how coupling an optimal classifier with a targeted feature selection method can substantially improve a bank's risk management framework and operational decision-making. In practice, the proposed data-driven approach can bolster financial institutions' resilience, reduce default rates, and ensure the more strategic allocation of capital resources.

关键词： Accuracy Banking Predictive models Feature extraction Stability analysis data models data mining Risk management Resource management Resilience

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GPT-Based Wasm Instruction Analysis for Program Language Processing 5th

GPT-Based Wasm Instruction Analysis for Program Language Pro...

引用

5th International Conference on Emerging information Security and Applications, EISA 2024

作者： Deng, Liangjun Zhong, Qi Lei, Hang Qiu, Yao Chen, Jingxue School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Faculty of Data Science City University of Macau China Ningbo Polytechnic College Zhejiang Ningbo315800 China School of Mathematical and Physical Sciences University of Technology Sydney SydneyNSW2007 Australia

ISBN: (纸本)9783031804182

Capturing the logical structure of programming languages poses a significant challenge for program analysis. Given the complex syntax rules, subjective code vulnerabilities, irrelevant statements, code annotations, and intricate structural information, related studies have explored various semantic comprehension and intermediate representation approaches to extract precise information for program analysis. However, most research in the generic domain ignores defective and non-defective program code, putting them in the same category. In this paper, we introduce a new program analysis method that combines WebAssembly (Wasm) instructions with a 20-billion-parameter transformer model and natural language processing. This approach aims to advance the capabilities of program analysis tools in computer science by jointly embedding Wasm instructions and natural language for more effective program analysis. Our experiments demonstrate that this fused embedding approach achieves state-of-the-art performance, and the accuracy reaches approximately 98 percent, better than traditional small-scale weight models based on intricate conversion tasks such as abstract syntax trees(AST). Moreover, it is more valuable to classify potential vulnerable and non-vulnerable programs in the formal verification special field. Our exploration enhances traditional program classification methods in software security and introduces the application of GPT to offer a more straightforward, convenient, and high-performance approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Formal verification

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Vegetable oil oxidation: Mechanisms, impacts on quality, and approaches to enhance shelf life

引用

Food Chemistry: X 2025年 28卷

作者： Gharby, Saïd Asbbane, Abderrahim Nid Ahmed, Moussa Gagour, Jamila Hallouch, Otmane Oubannin, Samira Bijla, Laila Goh, Khan Wen Bouyahya, Abdelhakim Ibourki, Mohamed Biotechnology Analytical Sciences and Quality Control Team Polydisciplinary Faculty of Taroudant Ibn Zohr University Agadir Morocco Faculty of Data Science and Information Technology INTI International University Nilai Malaysia Laboratory of Human Pathologies Biology Faculty of Sciences Mohammed V University in Rabat Rabat10106 Morocco

Vegetable oils (VOs) are essential components of the human diet and serve as the primary source of dietary fats. However, lipids in food are highly sensitive to environmental factors such as exposure to light and high temperatures, which promote the formation of free radicals and accelerate lipid oxidation, leading to an unstable food system. Their susceptibility to oxidation poses a significant challenge to both food quality and human health. In fact, oil oxidation is a major concern for the food industry as it leads to loss of nutritional value, formation of undesirable off-flavors and changes in the functional quality and chemical composition of oils, reducing consumer acceptance. In addition, oxidation produces potentially toxic compounds that pose significant health risks. Considering these concerns, this critical review examines various aspects of oil oxidation, including its impact on oil quality, underlying mechanisms, recent measurement techniques, shelf-life prediction and strategies to prevent and mitigate oxidation. The selection of appropriate methods, adapted to specific food matrices, is crucial for the accurate assessment of lipid oxidation and antioxidant capacity. © 2025 The Authors

关键词： Food preservatives

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