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Photons Plus Ultrasound: Imaging and Sensing 2025

作者： Barratov, Farrukh Rajendran, Praveenbalaji Singh, Mithun Kuniyil Ajith Francis, Kalloor Joseph Awasthi, Navchetan Department of Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Harvard University United States Cyberdyne Inc. Netherlands Erasmus MC Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam Netherlands

ISBN: (纸本)9781510683860

Photoacoustic imaging (PAI) is an emerging medical imaging technique with applications in blood oxygen imaging and tumor detection. LED-based PAI offers a cost-effective and accessible alternative but faces challenges in high-frame-rate applications due to significant noise, necessitating extensive signal averaging. In this work, we investigate the use of deep learning techniques, specifically a conditional Denoising Diffusion Probabilistic Model (cDDPM), for denoising photoacoustic images obtained from an LED-based system. Our study evaluates the effectiveness of cDDPM in reconstructing image features and explores optimization through scheduler modifications. We implement a cosine scheduler to reduce redundant denoising steps, significantly improving inference efficiency while maintaining high image quality. These results demonstrate the potential of diffusion models for enhancing low-frame-averaged photoacoustic images. © 2025 SPIE.

关键词： Image enhancement

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Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach

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Scientific Reports 2025年第1期15卷 1-33页

作者： Hassan, Osama F. Ibrahim, Ahmed F. Gomaa, Ahmed Makhlouf, M.A. Hafiz, B. Information Systems Department Faculty of Computers and Informatics Suez Canal University Ismailia 41522 Egypt Artificial Intelligence Department Faculty of Computer Science and Engineering King Salman International University (KSIU) South Sinai 46511 Egypt Computer Science and Engineering Department Egypt Japan University of Science and Technology (E-JUST) Alexandria 21934 Egypt Computer Science and Engineering Department National Research Institute of Astronomy and Geophysics (NRIAG) Helwan 11731 Egypt

Driver drowsiness is a leading cause of road accidents, resulting in significant societal, economic, and emotional losses. This paper introduces a novel and robust deep learning-based framework for real-time driver drowsiness detection, leveraging state-of-the-art transformer architectures and transfer learning models to achieve unprecedented accuracy and reliability. The proposed methodology addresses key challenges in drowsiness detection by integrating advanced data preprocessing techniques, including image normalization, augmentation, and region-of-interest selection using Haar Cascade classifiers. We employ the MRL Eye Dataset to classify eye states into “Open-Eyes” and “Close-Eyes,” evaluating a range of models, including Vision Transformer (ViT), Swin Transformer, and fine-tuned transfer learning models such as VGG19, DenseNet169, ResNet50V2, InceptionResNetV2, InceptionV3, and MobileNet. The ViT and Swin Transformer models achieved groundbreaking accuracy rates of 99.15% and 99.03%, respectively, outperforming all other models in precision, recall, and F1-score. To ensure the generalization and robustness of the proposed models, we also evaluate their performance on the NTHU-DDD and CEW datasets, which provide diverse real-world scenarios and challenging conditions. This represents a significant advancement over existing methods, demonstrating the effectiveness of transformer-based architectures in capturing complex spatial dependencies and extracting relevant features for drowsiness detection. The proposed system also incorporates a real-time drowsiness scoring mechanism, which triggers alarms when prolonged eye closure is detected, ensuring timely intervention to prevent accidents. A key novelty of this work lies in the integration of Class Activation Mapping (CAM) for enhanced model interpretability, allowing the system to focus on critical eye regions and improve decision-making transparency. The system was rigorously tested under varying lighting condit

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A novel real-time noise-resilient zeroing neural network and its applications to matrix problem solving

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Mathematics and computers in Simulation 2025年

作者： Yang, Yiguo Wu, Pin Katsikis, Vasilios N. Li, Shuai Feng, Weibing The School of Computer Engineering and Science Shanghai University Shanghai200444 China The Department of Economics Division of Mathematics-Informatics and Statistics-Econometrics National and Kapodistrian University of Athens Athens10559 Greece The Faculty of Information Technology and Electrical Engineering University of Oulu Oulu90570 Finland

Given the critical role of zeroing neural networks (ZNN) in various fields and the practical demand for models in effectively resisting real-time noise, this study introduces a novel anti-noise integral zeroing neural network (AN-IZNN) model alongside its enhanced counterpart (EAN-IZNN), for the applications of matrix problem solving. Theoretical analysis demonstrates their ability to achieve convergence even under different noise conditions. Both theoretical analyses and simulation validations highlight the superior performance of the proposed models over existing neural network models. Notably, the root mean square error of the proposed AN-IZNN and EAN-IZNN models is reduced by 92.6249% and 91.4178%, respectively, compared to scenarios without the proposed method, demonstrating the effectiveness of the solution. © 2025

关键词： Neural network models

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Supply Chain Sustainability in Disruption: SC Intelligence, Customer Knowledge Transfer, and the Mediating Role of IS/IT

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IEEE Engineering Management Review 2025年

作者： Ratna, Silvia Saide, Saide Rater, Salya Svyazhanovna, Baizakova Saule Herzavina, Herzavina Faculty of Information Technology Department of Informatics Engineering and Department of Information Systems Banjarmasin Indonesia State Islamic University of Sultan Syarif Kasim Riau Universitas Islam Negeri Sultan Syarif Kasim Riau-UIN SUSKA Riau Department of Information Systems Faculty of Science and Technology Indonesia PRO Knowledge Indonesia Pekanbaru Indonesia Universitas Pahlawan Tuanku Tambusai Indonesia Lembaga Enreach UIN Suska Riau Indonesia Politeknik Aceh Selatan Department of Informatics Engineering Aceh Indonesia Information Communication Technologies Robotics in Arkalyk Pedagogical Institute Master of Natural Sciences Educational Program for the Informatics Computer Science Kazakhstan Universitas Pahlawan Tuanku Tambusai Department of Informatics Engineering Indonesia National Taiwan University of Science and Technology Taiwan

Knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover from disruption (e.g., COVID-19). Questionnaire-based surveys was adopted and received feedback from sixty (160) Indonesian companies or about 28.07% of response rate. © 1973-2011 IEEE.

关键词： customer knowledge transfer disruption information system/technology (IS/IT) responding knowledge management resource-based view (RBV) Supply chain (SC) sustainability supply chain knowledge transfer

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Using the Hurwicz Criterion to Optimize Selection Queries Under Partial Ignorance

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International Journal of Semantic Computing 2025年

作者： Helmer, Sven Wood, Peter T. Stuefer, Manuel Department of Informatics University of Zurich Zurich8050 Switzerland School of Computing and Mathematical Sciences Birkbeck University of London LondonWC1E 7HX United Kingdom Faculty of Computer Science Free University of Bozen-Bolzano Bolzano39100 Italy

Optimizing queries in real-world situations under imperfect conditions is still a problem that has not been fully solved. We consider finding the optimal order in which to execute a given set of selection operators under partial ignorance of their selectivities. The selectivities are modeled as intervals rather than exact values and we apply various methods from decision theory in order to measure optimality. On the one hand, we evaluate the different techniques theoretically, showing which axioms of decision theory they satisfy and for which cases they efficiently provide an optimal solution. On the other hand, we also conduct an empirical evaluation, illustrating the differences of the approaches in terms of the relative error to the optimal solution. We demonstrate that a hybrid algorithm combining optimistic and pessimistic aspects performs best. © World Scientific Publishing Company.

关键词： Database query optimization Hurwicz criterion selection ordering

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The role of moral intention and moral obligation in predicting attitudes toward avoiding AIgiarism: A protection motivation theory perspective

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computers in Human Behavior Reports 2025年 18卷

作者： Ali, Ismail Mohamed Arpaci, Ibrahim Faculty of Computing SIMAD University Somalia Department of Software Engineering Faculty of Engineering and Natural Sciences Bandirma Onyedi Eylul University Balikesir Turkey Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea

Traditional methods of combating plagiarism have proven ineffective due to the dual use of AI chatbots to plagiarize and avoid detection. Therefore, there is a growing rationale to focus on moral responsibility towards protecting academic integrity. This study investigated the impact of students' moral intentions and moral obligations on their attitudes toward avoiding AI-assisted plagiarism (AIgiarism). Data was collected from a representative group of 263 students and analyzed using “partial least squares structural equation modeling” (PLS-SEM) and “artificial neural network” (ANN). The results revealed that students' moral intentions and obligations positively influenced their attitudes towards avoiding AIgarism. “Protection motivation theory” (PMT) constructs positively predicted students' moral intentions. Moreover, the results of the ANN analysis showed that moral obligation and intention were the most critical factors influencing students’ attitudes toward avoiding plagiarism. This study will help educational institutions develop AI-supported anti-plagiarism solutions in areas such as identification, instilling moral responsibility, and creating an environment of trust and support. © 2025 The Authors

关键词： AIgiarism Moral intention Moral obligation Plagiarism Protection motivation theory

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Data Dams: A Novel Framework for Regulating and Managing Data Flow in Large-Scale Systems

arXiv

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arXiv 2025年

作者： Bouke, Mohamed Aly Abdullah, Azizol Cengiz, Korhan Ivković, Nikola Mihaljević, Ivan Mohamud, Mudathir Ahmed Kowrina, Ahmed Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia Department of Electrical-Electronics Engineering Biruni University Istanbul Turkey Faculty of Organization and Informatics University of Zagreb Pavlinska 2 Varaždin42000 Croatia Faculty of Computing SIMAD University Mogadishu252 Somalia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang43400 Malaysia Département d’Informatique et Mathématiques Faculté des Sciences et Techniques Université de Nouakchott Al Aasriya Nouakchott880 Mauritania

In the era of big data, managing dynamic data flows efficiently is crucial as traditional storage models struggle with real-time regulation and risk overflow. This paper introduces Data Dams, a novel framework designed to optimize data inflow, storage, and outflow by dynamically adjusting flow rates to prevent congestion while maximizing resource utilization. Inspired by physical dam mechanisms, the framework employs intelligent sluice controls and predictive analytics to regulate data flow based on system conditions such as bandwidth availability, processing capacity, and security constraints. Simulation results demonstrate that the Data Dam significantly reduces average storage levels (371.68 vs. 426.27 units) and increases total outflow (7999.99 vs. 7748.76 units) compared to static baseline models. By ensuring stable and adaptive outflow rates under fluctuating data loads, this approach enhances system efficiency, mitigates overflow risks, and outperforms existing static flow control strategies. The proposed framework presents a scalable solution for dynamic data management in large-scale distributed systems, paving the way for more resilient and efficient real-time processing architectures. © 2025, CC BY.

关键词： Queueing theory

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LEARNING SURROGATE EQUATIONS FOR THE ANALYSIS OF AN AGENT-BASED CANCER MODEL

arXiv

引用

arXiv 2025年

作者： Burrage, Kevin Burrage, Pamela Kreikemeyer, Justin N. Uhrmacher, Adelinde M. Weerasinghe, Hasitha N. School of Mathematical Sciences Queensland University of Technology BrisbaneQLD Australia Department of Computer Science University of Oxford United Kingdom Modelling and Simulation Institute for Visual and Analytic Computing Faculty of Informatics and Electrical Engineering University of Rostock Rostock Germany

In this paper, we adapt a two species agent-based cancer model that describes the interaction between cancer cells and healthy cells on a uniform grid to include the interaction with a third species – namely immune cells. We run six different scenarios to explore the competition between cancer and immune cells and the initial concentration of the immune cells on cancer dynamics. We then use coupled equation learning to construct a population-based reaction model for each scenario. We show how they can be unified into a single surrogate population-based reaction model, whose underlying three coupled ordinary differential equations are much easier to analyse than the original agent-based model. As an example, by finding the single steady state of the cancer concentration, we are able to find a linear relationship between this concentration and the initial concentration of the immune cells. This then enables us to estimate suitable values for the competition and initial concentration to reduce the cancer substantially without performing additional complex and expensive simulations from an agent-based stochastic model. The work shows the importance of performing equation learning from agent-based stochastic data for gaining key insights about the behaviour of complex cellular dynamics. © 2025, CC BY.

关键词： Lung cancer

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Early Detection of Breast Cancer with Microcalcifications on Mammography Using Deep Learning

Early Detection of Breast Cancer with Microcalcifications on...

引用

Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Ashraf Osman Ibrahim Hafia Mamoun Ismail Abuharaz Mohammed A Saleh Razan Alharith Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia Depeartment of Computer Science Al Neelain University Khartoum Sudan DaTA Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia School of Computing and Artificial Intelligence Southwest Jiaotong University

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Breast cancer is among the leading causes of death in women globally, making early detection critical for improving survival rates. Microcalcifications (MCs) on mammographic images are important markers for early detection of breast cancer; yet, identifying and interpreting them can be difficult. Conventional diagnostic procedures sometimes face obstacles due to the complexity and nuance of MC patterns, resulting in higher percentages of missed diagnoses and false positives. This paper develops a deep convolutional neural network (CNN) model to increase the detection and classification accuracy of microcalcifications (MCs) in mammographic images. Dataset of 1,093 mammography images used, the proposed model reaches a remarkable training accuracy of 99.98% and testing accuracy of 90.37%. The model's excellent accuracy and low overfitting highlight its potential to assist radiologists in the early detect and diagnose of breast cancer, thereby improving patient outcomes. This study's contribution is the innovative use of advanced deep learning algorithms to a major issue in medical imaging, which represents a significant improvement over current diagnostic approaches.

关键词： Deep learning Training Accuracy Biological system modeling Breast cancer Mammography Data models Convolutional neural networks Testing Overfitting

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Supervised methods of machine learning for email classification: a literature survey

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Systems science and Control Engineering 2025年第1期13卷

作者： AlShaikh, Muath Alrajeh, Yasser Alamri, Sultan Melhem, Suhib Abu-Khadrah, Ahmed Cybersecurity Department College of Engineering Al Ain University Abu Dhabi United Arab Emirates Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Electrical Engineering Al-Balqa Applied University Faculty of Engineering As-Salt Jordan

In today’s digital landscape, email is acknowledged as a critical conduit for global data exchanges. With a surge in data volume, malefactors exploit user identities, leading to data misuse. Cybercriminals employ electronic transgressions such as phishing and spam to orchestrate security infractions. Machine learning counters these breaches using myriad techniques, demonstrating significant efficiency in identifying phishing emails. We can divide machine learning into two types: supervised and unsupervised. Supervised learning requires pre-training the model on labelled datasets, amalgamating classification, and regression learning. Notably, supervised methodologies such as support vector machines (SVMs), naive Bayes, decision trees, neural networks, random forests, and deep learning have been exploited for spam filtering. This review delves into issues concerning spam filtering and email classification through supervised machine learning techniques, offering a comprehensive evaluation of strategies, methods, performance indicators, and the benefits and drawbacks of different research. This information allows researchers to assess the efficiency and effectiveness of supervised learning algorithms, laying the foundation for advanced email categorization techniques. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Support vector regression

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