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Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach

Neutrosophic Sets and Systems 2025年 81卷 479-502页

作者： Abdo, Doaa A. Salama, A.A. Abdelmegaly, Alaa A. Mahmoud, Hanan Khadari Mahdi Applied statistics and insurance department faculty of commerce Mansoura university Mansoura Egypt Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Higher Institute of Advanced Management Sciences and Computers Al-Buhayrah Egypt Depatrment of applied statistics at the Nile Higher Institute of commercial sciences and Computer Technology in Mansoura Egypt

This study tackles the problem of missing data in migrant datasets by introducing a new framework that combines machine learning techniques with neutrosophic sets. These sets, which can represent uncertainty and ambiguity, are well-suited for managing the complex nature of missing information in sensitive fields like migration research. We test the effectiveness of KNN, SVM, decision tree, random forest, and Ada Boost algorithms on a migrant dataset, comparing their results using different imputation methods (mean/mode, model-based imputer (simple tree), and random values). Our research showed that our proposed approach, which used neutrosophic sets, improved imputation accuracy and strengthened model reliability. Our results underscored the potential of neutrosophic set-based machine learning for addressing missing data issues across various fields. © 2025, University of New Mexico. All rights reserved.

关键词： Adversarial machine learning

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Data Augmentation for Messenger Phishing Detection Using Large Language Models

Data Augmentation for Messenger Phishing Detection Using Lar...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Noh, Keonwoong Oh, Seokjin Kim, Sunyub An, Dohyung Jung, Woohwan Hanyang University Dept. of Applied Artificial Intelligence Seoul Korea Republic of Hanyang University Dept. of Computer Science Ansan Korea Republic of

ISBN: (纸本)9798331510756

In this study, we introduce a new dataset specifically designed for detecting messenger phishing, an increasingly significant issue in cybercrime. To overcome the scarcity of labeled phishing data, we employ large language models (LLMs) to generate synthetic data, thereby expanding the dataset and improving detection capabilities. Our experimental results show that a model trained exclusively on synthetic data performs comparably to those trained with labeled data. Furthermore, combining synthetic data with labeled data achieves superior F1 and accuracy scores compared to using labeled data only while reducing misclassification errors. © 2025 IEEE.

关键词： Phishing

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Improving Messenger Phishing Detection Using Heterogeneous Phishing Data

Improving Messenger Phishing Detection Using Heterogeneous P...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Oh, Seokjin Noh, Keonwoong Kim, Sunyub An, Dohyung Jung, Woohwan Hanyang University Dept. of Applied Artificial Intelligence Seoul Korea Republic of Hanyang University Dept. of Computer Science Ansan Korea Republic of

ISBN: (纸本)9798331510756

Recently, as messenger phishing has been occurring more frequently, the need for its detection has increased;however, datasets for messenger phishing detection are publicly unavailable. In this paper, we address the data scarcity problem of the newly collected messenger phishing dataset by leveraging various types of pre-existing auxiliary phishing data. Experimental results demonstrate that the error rate decreased by up to 1.81 % and the F1 score improved by 7.35% when smishing and voice phishing data are used. These findings confirm that integrating heterogeneous phishing data can mitigate the data scarcity problem and enhance messenger phishing detection performance. © 2025 IEEE.

关键词： Phishing

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Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques 22

Automating Neural Model Selection in Spiking Neural Networks...

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22nd International Learning and Technology Conference, L and T 2025

作者： Sanaullah Honda, Hirotada Roy, Kaushik Schneider, Axel Wasmuth, Joachim Jungeblut, Thorsten University of Applied Sciences and Arts Dept. of Mathematics and Engineering Bielefeld Germany Toyo University Dept. of Information Networking Tokyo Japan A&T State University Dept. of Computer Science NC United States

ISBN: (纸本)9798331532215

The selection of optimal neural models in Spiking Neural Networks (SNNs) traditionally depends on a trial-and-error approach, which is both time-consuming and sometimes tends to suboptimal selection of the neural model. This research study explores the integration of Automated Machine Learning (AutoML) techniques into SNNs to simplify the process of designing an SNN model by automatically selecting the most optimal neural model. For example, in traditional neural networks, AutoML proves that it has the potential to significantly reduce the time and effort required to identify the best-performing architectures, by automating the selection and updating of loss functions and hyper-parametric values. To this end, we present a stand-alone framework for applying AutoML in SNNs, highlighting how this approach can improve accuracy, efficiency, and reliability in the selection process of optimal neural models, as these neural models are the core principle of designing SNN architecture. The proposed method not only accelerates the development of SNN models but also enhances their performance by systematically identifying optimal configurations that might be overlooked through manual methods. Therefore, to validate our approach, the proposed architecture was tested using different well-known benchmarks with different sets of neurons, ranging from 100 to 3000 neurons, and demonstrated state-of-the-art results in image classification tasks. © 2025 IEEE.

关键词： Neural network models

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Hysteresis Model of Permafrost Thermal State Variation with Air Temperature in Utqiaġvik, Alaska, Based on Distributed Temperature Sensing

Hysteresis Model of Permafrost Thermal State Variation with ...

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Geotechnical Frontiers 2025: Geotechnics of Natural Hazards

作者： Ji, Xiaohang Xiao, Ming Martin, Eileen Dept. of Civil and Environmental Engineering Pennsylvania State Univ. University ParkPA United States Dept. of Geophysics Dept. of Applied Math and Statistics Hydrologic Science and Engineering Program Colorado School of Mines GoldenCO United States

ISBN: (数字)9780784485996

Increasing air temperatures are driving permafrost warming across the Arctic and sub-Arctic. This in turn degrades the geomechanical properties of soils, disrupts the natural environment and infrastructure systems, and results in long-lasting societal impacts. Understanding the thermal state of degrading permafrost in the Arctic is crucial for maintaining resilient infrastructure in the changing Arctic. This study employs Distributed Temperature Sensing (DTS) to monitor permafrost temperature near Utqiaġvik, Alaska, from 2021 to 2024. Fiber-optic DTS sensors have unique and attractive characteristics that allow their deployment in the Arctic for low-maintenance, cost-effective, and high-resolution data. This paper presents the temperature recordings of a 2-km DTS system on the tundra near Utqiaġvik, Alaska, by the Elson Lagoon. A hysteresis model is developed to link permafrost temperature with air temperature, incorporating meteorological data and ground surface conditions. The study also explores the impact of civil infrastructure on permafrost thermal dynamics. © ASCE.

关键词： Permafrost

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Machine Learning Models for Foreign Worker Prediction in Thailand: A Study of Linear Regression and Decision Tree

Machine Learning Models for Foreign Worker Prediction in Tha...

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2024 International Conference on Measurement, Communication, and Virtual Reality, MCVR 2024

作者： Jomsri, P. Prangchumpol, D. Eamthanakul, B. Poonsilp, K. Pudprommarat, C. Dept. of Information Technology Suan Sunandha Rajabhat University Dusit Bangkok10300 Thailand Dept. of Computer Science Suan Sunandha Rajabhat University Dusit Bangkok10300 Thailand Dept. of Applied Science Faculty of Science and Technology Suan Sunandha Rajabhat University Bangkok Thailand

ISBN: (纸本)9781510691773

This study proposed to predict the immigration patterns of foreign workers in Thailand using machine learning techniques. The primary focus is to analyze how factors such as the year, month, area, and legal immigration type (MOU) influence the number of foreign workers entering the country. MOU represents the Memorandum of Understanding, a legal framework for foreign migration. By utilizing two distinct machine learning models—Linear Regression and Decision Tree—this research seeks to identify the most effective method for forecasting immigration trends. This study analyzes foreign worker immigration in Thailand using data collected from 2560 to 2561 B.E. The data used in this study includes key variables that are indicative of immigration movements, providing a comprehensive view of the factors influencing foreign migration. The findings reveal that the Decision Tree model outperforms Linear Regression in terms of accuracy at 0.99. This analysis is crucial for policymakers and stakeholders as it provides insights into the patterns of foreign worker immigration, enabling better planning and management of foreign worker resources. Understanding these patterns is essential for developing effective immigration policies and ensuring that foreign worker market needs are met in a regulated and orderly manner. The comparative analysis demonstrates the importance of selecting appropriate machine learning models for accurate predictions in the domain of foreign immigration. © 2025 SPIE.

关键词： Linear regression

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Faster R-CNN in Healthcare and Disease Detection: A Comprehensive Review

Faster R-CNN in Healthcare and Disease Detection: A Comprehe...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Tian, Jiawei Lee, Seungho Kang, Kyungtae Major in Bio Artificial Intelligence Hanyang University Dept. of Computer Science and Engineering Ansan-si Korea Republic of Hanyang University Dept. of Applied Artificial Intelligence Ansan-si Korea Republic of Hanyang University Dept. of Artificial Intelligence Ansan-si Korea Republic of

ISBN: (纸本)9798331510756

This review examines the applications, challenges, and prospects of Faster Region-based Convolutional Neural Networks (Faster R-CNN) in healthcare and disease detection. Through a meta-analysis of Web of science literature from 2017 to 2024, we provide insights into the evolving landscape of Faster R-CNN in medical contexts. The algorithm can be applied to medical image analysis across various modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, microscopy, and endoscopy. Its applications extend to optical RGB images for dermatological and surgical uses, as well as broader healthcare areas such as posture detection, medication recognition, and assistive technologies. Despite its success, Faster R-CNN faces challenges in handling subtle abnormalities, addressing class imbalance in medical datasets, ensuring result interpretability, and managing patient privacy. Our analysis reveals dominant application areas, technological advancements, and integration trends with other artificial intelligence technologies. The review highlights Faster R-CNN's significant impact on improving diagnostic accuracy and healthcare delivery while acknowledging the need for continued research to address limitations. Emerging directions include real-time disease detection and advancements in personalized medicine, underscoring Faster R-CNN's potential to further transform healthcare practices and patient outcomes. © 2025 IEEE.

关键词： computerized tomography

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InterACT: Enhancing Human-AI Interaction 2

InterACT: Enhancing Human-AI Interaction

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Katta, Jithendra Kodumuru, Nikhil Reddy Katta, Swathi School of Engineering and Applied Sciences The George Washington University WashingtonDC United States Vignana Bharathi Institute of Technology Computer Science & Engineering dept. Hyderabad India

ISBN: (纸本)9798331505745

The rapid advancement of large language models and computer vision systems has opened new frontiers in artificial intelligence. This paper introduces InterACT, a novel cross-modal system that integrates leading language and vision models to enable more intuitive and context-aware human AI interactions. By leveraging the strengths of both modalities, InterACT processes natural language queries to guide object detection in visual environments, bridging the gap between linguistic and visual understanding. We evaluate the performance of three language models of varying sizes in conjunction with an advanced object detection model, exploring the balance between computational efficiency and interaction quality. Our experiments demonstrate InterACT's potential across diverse real-world scenarios, revealing promising results in maintaining contextual relevance and adaptability. This research contributes to the growing field of cross-modal AI systems and lays the groundwork for more sophisticated human AI interactions, with implications for applications ranging from assistive technologies to educational tools. © 2025 IEEE.

关键词： Object detection

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Asynchronous Traffic Shaping and Redundancy: Avoiding Unbounded Latencies in In-Car Networks

arXiv

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arXiv 2025年

作者： Lübeck, Teresa Meyer, Philipp Häckel, Timo Korf, Franz Schmidt, Thomas C. Dept. Computer Science Hamburg University of Applied Sciences Germany

Time-Sensitive Networking (TSN) enhances Ethernet-based In-Vehicle Networks (IVNs) with real-time capabilities. Different traffic shaping algorithms have been proposed for time-critical communication, of which the Asynchronous Traffic Shaper (ATS) is an upcoming candidate. However, recent research has shown that ATS can introduce unbounded latencies when shaping traffic from non-FIFO systems. This impacts the applicability of ATS in IVNs, as these networks often use redundancy mechanisms that can cause non-FIFO behaviour. In this paper, we approach the problem of accumulated delays from ATS by analyzing the scenarios that generate latency and by devising placement and configurations of ATS schedulers to prevent this behavior. Our solution successfully mitigates problematic preconditions that lead to unbounded delays, which we evaluate in simulations. Through a realistic IVN simulation case study, we demonstrate the occurrence of unbounded latencies and validate the effectiveness of our approach in avoiding them. Copyright © 2025, The Authors. All rights reserved.

关键词： Time division multiple access

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Assessment of Super-Resolution Ultrasound Imaging Using the Erythrocytes Through Comparison with Micro-CT

Assessment of Super-Resolution Ultrasound Imaging Using the ...

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Medical Imaging 2025: Ultrasonic Imaging and Tomography

作者： Hansen, Lauge Naur Ráth, Andre Amin-Naji, Mostafa McDermott, Amy Sørensen, Charlotte Mehlin Kjer, Hans-Martin Gundlach, Carsten Dahl, Anders Bjorholm Jensen, Jørgen Arendt Center for Fast Ultrasound Dept. of Health Technology Technical University of Denmark Kgs. Lyngby Denmark Dept. of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark Dept. of Physics Technical University of Denmark Kgs. Lyngby Denmark Dept. of Biomedical Sciences University of Copenhagen Copenhagen Denmark

ISBN: (纸本)9781510686021

This work presents a dual-sample validation study of Super-Resolution Ultrasound imaging using the Erythrocytes (SURE) and state-of-the-art laboratory micro-CT data of rat kidneys. The study hypothesizes that SURE images reliably depict cortical radial blood vessels in rat kidneys, assessed by comparing the depicted blood vessels and vessel diameter estimates from registered micro-CT data. Previous qualitative comparisons between SURE images and micro-CT were either made at an insufficient micro-CT voxel size or in a small region of a rat kidney cortex scanned at a higher resolution. Here, micro-CT data of entire rat kidneys scanned at a voxel size of 5 µm is compared to images from the SURE pipeline with peak detection done after blood flow separation. The separation is achieved using Hankel singular value decomposition before peak detection (SURE-Hankel). The analysis shows a consistent depiction of cortical radial vessels in the entire rat kidney using SURE-Hankel and that these blood vessels have estimated diameters around 50 µm to 100 µm. However, discrepancies in the registered images highlight the need for more accurate CT-to-ultrasound image registration techniques. © 2025 SPIE.

关键词： Echocardiography

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