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LowResIR-Net: Lightweight Residual Network for ADL Recognition Using Low-Resolution Infrared Sensor Data

Procedia computer Science 2025年 256卷 1358-1366页

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao 56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand

Detecting activities of daily living (ADL) is crucial for supported living and medical monitoring. Traditional approaches rely on high-resolution sensors and computationally intensive algorithms, limiting their scalability and practical use in real-world settings. This research introduces LowResIR-Net, a compact deep residual network architecture specifically tailored for recognizing ADL utilizing low-resolution infrared sensor data. LowResIR-Net efficiently extracts key features from low-dimensional data by leveraging the advantages of infrared sensing, such as privacy protection and light resilience. Its architecture combines residual connections with depth-wise convolutions, enhancing feature learning while maintaining a compact model size. The model’s performance is evaluated using the Coventry-2018 dataset, containing low-resolution infrared data from three residential sensor locations. Our tests have shown that in all three cases, LowResIR-Net consistently achieves superior performance compared to other cutting-edge deep learning models, such as CNN, LSTM, BiLSTM, GRU, and BiGRU. LowResIR-Net attains the most excellent accuracies of 90.18%, 93.75%, and 93.30%, and F1-scores of 81.81%, 89.55%, and 88.92% for scenarios I, II, and III, respectively. Furthermore, it achieves these results with fewer parameters than most other models. LowResIR-Net’s strong performance highlights its ability to accurately capture complex patterns in low-resolution infrared data for ADL detection. This study demonstrates the potential of lightweight deep learning and low-resolution infrared sensors in developing privacy-focused and computationally efficient wellness monitoring systems.

关键词： Activities of daily living low-resolution infrared sensors deep residual networks lightweight architecture healthcare monitoring

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Dynamical behaviors of optical solitons, and Jacobi elliptic wave solutions of fractional chiral (2+1) dimensional NLSE in physics

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Physics Letters, Section A: General, Atomic and Solid State Physics 2025年 550卷

作者： Roshid, Md Mamunur Bashar, Md. Habibul Rezapour, Shahram Inc, Mustafa Department of Mathematics Hamdard University Bangladesh Munshiganj Gazaria1510 Bangladesh Department of Computer Science and Engineering Stamford University Bangladesh Dhaka 1217 Bangladesh Department of Medical Research China Medical University Hospital Taichung Taiwan Department of Mathematics Firat University Elazig23119 Turkey Department of Computer Engineering Biruni University Istanbul34010 Turkey

Various complex nonlinear evolution equations are used to illustrate the inner characteristics of various complex processes that occur in real-life events. In this framework, we use extended Jacobian elliptic function expansion (JEFET) and extended hyperbolic function techniques (EHFT) to analyze optical soliton solutions (SSs) of fractional chiral (2 + 1) dimensional nonlinear Schrödinger's equation ((2 + 1)-D CNLSE) in field of physics and in the field of fluid sciences. The suggested methods provide insights into optical soliton in a variety of technical fields, including quantum mechanics, plasma physics, nonlinear optics, and optical communications. While nonlinearity produces distortions, dispersion causes signals to disperse and deteriorate over distance. These methods allow us to produce some optical soliton solutions that may be analytically expressed in terms of rational, hyperbolic, trigonometric, and elliptic functions. Double periodic wave (PW), PW with lump wave SSs, breather wave with PW, various kinky PW, periodic breather wave by using the extended JEFET, and double PW, kink-PW, periodic breather wave, double PW patterns by using the EHFT are the numerical forms of the obtained solution that are studied with three and two-dimensional diagrams in figures 1 through 10. We demonstrate the impact of truncated M-fractional parameters (MFP) for [s = 0.1,0.5,0.9] on a two-dimensional graph. The gathered results could help to clarify and better understand the physical properties of waves traveling through a dispersive material. As a result, the previously discussed applied techniques may be a useful tool for producing distinct, accurate SSs for a variety of applications, which are essential to engineering, nonlinear optics, and fluid. © 2025 Elsevier B.V.

关键词： Nonlinear optics

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Risks and Challenges Associated with the Chain of Custody of Cryptographic Assets in Law Enforcement Operations 6th

Risks and Challenges Associated with the Chain of Custod...

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6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024

作者： Abad, Jesús Martín Guardeño, David Arroyo Casino, Fran Universidad Autónoma de Madrid Madrid Spain Madrid Spain Department of Computer Engineering and Mathematics Rovira i Virgili University Tarragona Spain Information Management Systems Institute of Athena Research Center Artemidos 6 Marousi151 25 Greece

ISBN: (纸本)9783031819278

The emergence of cryptocurrencies represents a radical change in the transmission of value in a disintermediated manner, which, in many cases, means that they are beyond the control of regulators and law enforcement bodies. This circumstance, together with the growing training in the use of crypto-asset management resources, has led to an increase in their popularity, and it is becoming increasingly common to find these crypto-assets in criminal operations. From a law enforcement perspective, the use of crypto-currency poses a challenge in terms of the procedure for investigating crimes involving the use of crypto-assets. Indeed, in addition to the problem of tracking and attribution, there is the complex issue of interception, custody and, if the investigation concludes that there is no criminal activity, the refund of the cryptographic asset to its owner. This paper analyses the challenges facing law enforcement investigation of criminal events involving cryptocurrencies that have been intercepted. In particular, it addresses the difficulties in achieving adequate protection to the chain of custody of crypto-assets, all by the inalienable criterion of the presumption of innocence that must govern all criminal proceedings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cybersecurity

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A Deep Multi-Task Learning Network for Activity Recognition and User Identification Using Smartphone Sensors

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Procedia computer Science 2025年 256卷 1350-1357页

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-than-ideal performance and restricted applicability. This research presents a novel deep multi-task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-term memory (BiLSTM) layers to extract features from raw sensor data. The CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. These features are processed by fully connected layers for both activity recognition and user identification. A multi-task loss function is proposed to optimize both tasks based on their complexity and importance. The approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-the-art methods. This demonstrates the effectiveness of the multi-task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks.

关键词： human activity recognition user identification multi-task learning deep learning smartphone sensors

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An AI-powered Public Health Automated Kiosk System for Personalized Care: An Experimental Pilot Study

arXiv

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arXiv 2025年

作者： Falahati, Sonya Alizadeh, Morteza Safahi, Zhino Khaledian, Navid Meybodi, Mohsen Alambardar Salmanpour, Mohammad R. VancouverBC Canada Electrical and Computer Engineering Department Nooshirvani University of Technology Babol Iran Department of Mathematics University of Isfahan Isfahan Iran Social & Biological Network Laboratory Department of Computer Engineering University of Kurdistan Sanandaj Iran University of Luxembourg Esch-sur-Alzette Luxembourg Department of Applied Mathematics and Computer Science University of Isfahan Isfahan Iran Department of Integrative Oncology BC Cancer Research Institute Vancouver Canada

Background: The HERMES Kiosk (Healthcare Enhanced Recommendations through Artificial Intelligence & Expertise System) is designed to provide personalized Over-the-Counter (OTC) medication recommendations, addressing the limitations of traditional health kiosks. It integrates an advanced GAMENet model enhanced with Graph Attention Networks (GAT) and Multi-Head Cross-Attention (MHCA) while ensuring user privacy through federated learning. This paper outlines the conceptual design and architecture of HERMES, with a focus on deployment in high-traffic public areas. Methods: HERMES analyzes self-reported symptoms and anonymized medical histories using AI algorithms to generate context-aware OTC medication recommendations. The system was initially trained using Electronic Health Records (EHR) from the MIMIC-III dataset (6,350 patients) and Drug-Drug Interaction (DDI) data from the TWOSIDES database, incorporating the top 90 severity DDI types. Real-time DDI checks and ATC-mapped drug codes further improve safety. The kiosk is designed for accessibility, offering multilingual support, large fonts, voice commands, and Braille compatibility. A built-in health education library promotes preventive care and health literacy. A survey was conducted among 10 medical professionals to evaluate its potential applications in medicine. Results: Preliminary results show that the enhanced GAMENet model achieved a Precision-Recall AUC (PRAUC) of 0.74, outperforming the original model. These findings suggest a strong potential for delivering accurate and secure healthcare recommendations in public settings. Conclusion: HERMES demonstrates how AI-driven, privacy-preserving kiosks can enhance public health access, empower users, and alleviate burdens on healthcare systems. Future work will focus on real-world deployment, usability testing, and scalability for broader adoption. © 2025, CC BY-NC-ND.

关键词： Personalized medicine

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Educational data mining: a 10-year review

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Discover Computing 2025年第1期28卷 1-25页

作者： Kalita, Emi Oyelere, Solomon Sunday Gaftandzhieva, Silvia Rajesh, Kandala N. V. P. S. Jagatheesaperumal, Senthil Kumar Mohamed, Asmaa Elbarawy, Yomna M. Desuky, Abeer S. Hussain, Sadiq Cifci, Mehmet Akif Theodorou, Paraskevi Hilčenko, Slavoljub Hazarika, Jiten Ali, Tazid Centre for Computer Science and Applications Dibrugarh University Dibrugarh 786004 India Department of Computer Science Electrical and Space Engineering Luleå University of Technology Skellefteå 931 87 Sweden Department of Computer Science Faculty of Environment Science and Economy University of Exeter Exeter United Kingdom Research Group On Data Artificial Intelligence and Innovations for Digital Transformation Johannesburg Business School (JBS) University of Johannesburg Johannesburg South Africa Faculty of Mathematics and Informatics University of Plovdiv “Paisii Hilendarski” Plovdiv Bulgaria School of Electronics Engineering VIT-AP University Vijayawada India Department of Electronics & Communication Engineering Mepco Schlenk Engineering College Sivakasi 626005 India Department of Mathematics Faculty of Science Al-Azhar University (Girls Branch) Cairo 11754 Egypt Department of Computer Engineering Bandirma Onyedi Eylul University Balikesir 10200 Turkey Engineering and Informatics Department Klaipėdos Valstybinė Kolegija/Higher Education Institution Klaipeda 92294 Lithuania Department of Digital Systems University of Piraeus Piraeus Greece College for Vocational Education of Preschool Teachers and Coaches Subotica Serbia Department of Statistics Dibrugarh University Dibrugarh 786004 India

This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations. © The Author(s) 2025.

关键词： Artificial intelligence in education Education data mining Explainability in education Multimodal learning analytics

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Coarse graining and reduced order models for plume ejection dynamics

arXiv

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arXiv 2025年

作者： Salas, Ike Griss Ebers, Megan R. Stevens-Haas, Jake Kutz, J. Nathan Department of Applied Mathematics University of Washington SeattleWA98195 United States Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States

Monitoring the atmospheric dispersion of pollutants is increasingly critical for environmental impact assessments. High-fidelity computational models are often employed to simulate plume dynamics, guiding decision-making and prioritizing resource deployment. However, such models can be prohibitively expensive to simulate, as they require resolving turbulent flows at fine spatial and temporal resolutions. Moreover, there are at least two distinct dynamical regimes of interest in the plume: (i) the initial ejection of the plume where turbulent mixing is generated by the shear-driven Kelvin-Helmholtz instability, and (ii) the ensuing turbulent diffusion and advection which is often modeled by the Gaussian plume model. We address the challenge of modeling the initial plume generation. Specifically, we propose a data-driven framework that identifies a reduced-order analytical model for plume dynamics – directly from video data. We extract a time series of plume center and edge points from video snapshots and evaluate different regressions based to their extrapolation performance to generate a time series of coefficients that characterize the plume’s overall direction and spread. We regress to a sinusoidal model inspired by the Kelvin-Helmholtz instability for the edge points in order to identify the plume’s dispersion and vorticity. Overall, this reduced-order modeling framework provides a data-driven and lightweight approach to capture the dominant features of the initial nonlinear point-source plume dynamics, agnostic to plume type and starting only from video. The resulting model is a pre-cursor to standard models such as the Gaussian plume model and has the potential to enable rapid assessment and evaluation of critical environmental hazards, such as methane leaks, chemical spills, and pollutant dispersal from smokestacks. Copyright © 2025, The Authors. All rights reserved.

关键词： Turbulent flow

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Extended Information Geometry: Large Deviation Theory, Statistical Thermodynamics, and Empirical Counting Frequencies

arXiv

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arXiv 2025年

作者： Muppirala, Viswa Virinchi Qian, Hong Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Department of Applied Mathematics University of Washington SeattleWA98195 United States

Combinatorics, probabilities, and measurements are fundamental to understanding information. This work explores how the application of large deviation theory (LDT) in counting phenomena leads to the emergence of various entropy functions, including Shannon’s entropy, mutual information, and relative and conditional entropies. In terms of these functions, we reveal an inherent geometrical structure through operations, including contractions, lift, change of basis, and projections. Legendre-Fenchel (LF) transform that is central to both LDT and Gibbs’ method of thermodynamics offers a novel energetic description of data. The manifold of empirical mean values of statistical data ad infinitum has a parametrization using LF conjugates w.r.t. an entropy function;this gives rise to the additivity known in statistical thermodynamic energetics. This work extends current information geometry to information projection as defined through conditional expectations in Kolmogorov’s probability theory. Copyright © 2025, The Authors. All rights reserved.

关键词： Entropy

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Integration of aggregated data in causally interpretable meta-analysis by inverse weighting

arXiv

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arXiv 2025年

作者： Vo, Tat Thang Le, Tran Trong Khoi Afach, Sivem Vansteelandt, Stijn Research Group EPIDERME Faculty of Medicine University Paris Est Creteil France Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

Obtaining causally interpretable meta-analysis results is challenging when there are differences in the distribution of effect modifiers between eligible trials. To overcome this, recent work on transportability methods has considered standardizing results of individual studies over the case-mix of a target population, prior to pooling them as in a classical random-effect meta-analysis. One practical challenge, however, is that case-mix standardization often requires individual participant data (IPD) on outcome, treatments and case-mix characteristics to be fully accessible in every eligible study, along with IPD case-mix characteristics for a random sample from the target population. In this paper, we aim to develop novel strategies to integrate aggregated-level data from eligible trials with non-accessible IPD into a causal meta-analysis, by extending moment-based methods frequently used for population-adjusted indirect comparison in health technology assessment. Since valid inference for these moment-based methods by M-estimation theory requires additional aggregated data that are often unavailable in practice, computational methods to address this concern are also developed. We assess the finite-sample performance of the proposed approaches by simulated data, and then apply these on real-world clinical data to investigate the effectiveness of risankizumab versus ustekinumab among patients with moderate to severe psoriasis. © 2025, CC BY.

关键词： Inverse problems

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Fractional order modeling of dengue transmission dynamics in Bangladesh

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Partial Differential Equations in applied mathematics 2025年 14卷

作者： Sikder, Arun Kumar Islam, Md Hamidul Department of Computer Science and Engineering Varendra University Rajshahi 6204 Bangladesh Department of Applied Mathematics University of Rajshahi Rajshahi 6205 Bangladesh

Over the past three decades, the epidemiological characteristics of dengue fever in Bangladesh have raised significant public health concerns, with the disease progressively spreading across the country's subtropical and tropical regions since 2004. This study introduces a fractional-order mathematical model that incorporates both human and mosquito populations using Caputo derivatives to account for memory and hereditary effects in disease transmission. Analytical solutions are approximated using the Laplace–Adomian decomposition method, while the Adams–Bashforth-Moulton predictor–corrector (PECE) scheme is applied for numerical solutions. Model parameters are estimated via the maximum likelihood method, using dengue case data from Bangladesh (June 1, 2022–September 30, 2022). Our findings indicate that the fractional-order model effectively captures a range of transmission scenarios, with lower derivative orders correlating with reduced outbreak severity. Moreover, numerical solutions exhibit higher accuracy compared to analytical approximations, particularly for complex nonlinear systems. Additionally, the results suggest that controlling mosquito populations, reducing transmission rates, and improving treatment measures are critical strategies for dengue mitigation. © 2025 The Authors

关键词： Dengue fever Fractional order modeling LADM Mathematical modeling MLE fitting Monte Carlo method PECE method

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