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Editorial – Elsevier smart health special issue: Advancing ICT for health, accessibility, and wellbeing

Smart Health 2025年 36卷

作者： Achilleos, Achilleas Pissaloux, Edwige Papadopoulos, George A. Velazquez, Ramiro Faculty of Engineering Universidad Panamericana Mexico Department of Computer Science University of Cyprus 75 Kallipoleos St P.O Box 20537 Nicosia CY 1678 Cyprus Physics Department University of Rouen France Department of Electrical Engineering Computer Engineering and Informatics Frederick University Cyprus

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A Survey on Anomaly Detection with Few-Shot Learning 8th

A Survey on Anomaly Detection with Few-Shot Learning

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Junyang Wang, Changbo Hong, Yifan Mi, Rui Zhang, Liang-Jie Wu, Yirui Wang, Huan Zhou, Yue College of Computer Science and Software Engineering Shenzhen University Shenzhen China Center of Health Administration and Development Studies Hubei University of Medicine Shiyan China College of Informatics Huazhong Agricultural University Wuhan China Hohai University Nanjing China Department of Obstetrics and Gynecology Tianyou Hospital Affiliated to Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031779534

The primary objective of anomaly detection is to identify abnormal or unusual patterns within a dataset, where the number of normal samples typically exceeds that of abnormal samples. Due to the scarcity of labeled abnormal samples, traditional methods face challenges when dealing with anomaly detection. To overcome these limitations, few-shot learning has emerged as a promising solution. By leveraging a limited number of labeled anomaly samples, few-shot learning enables the construction of models that enhance anomaly detection performance and generalization. This paper provides a comprehensive investigation of anomaly detection, covering its definition, fundamental principles, methods, and challenges. Furthermore, it introduces few-shot learning as a solution and explores its principles, applications, and technical categorization, including meta-learning, transfer learning, generative models, prototypical learning, and siamese networks. The paper explores the utilization of few-shot learning in anomaly detection across diverse data types This paper delves into significance across different domains. Additionally, it addresses the challenges faced by few-shot learning in the field of anomaly detection and proposes future directions for development. This comprehensive analysis aims to provide profound insights and guidance for prospective research and application in anomaly detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Finding Optimal Sensor Placements for Traffic Data Reconstruction Using QR Pivoting and Deep Reinforcement Learning

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Transportation Research Procedia 2025年 82卷 2885-2897页

作者： Hoang Dat Pham Sharath MN Ahmed Ashraf Babak Mehran Manitoba Learning and AI Research Department of Electrical and Computer Engineering University of Manitoba Winnipeg Canada R3T5V6 Urban Mobility and Transportation Informatics Group Department of Civil Engineering University of Manitoba Winnipeg Canada R3T5V6

This paper studies and presents data-driven methods for finding rankings of traffic links in a network for optimal traffic data reconstruction based on measurements taken from a subset of links. The link ranking represents the importance of respective links in terms of reconstructing traffic information from sparsely placed sensors, connected vehicles, or other state-of-the-art methods. We first present a baseline method based matrix factorization of the eigen-vector basis matrix, followed by column pivoting. Moreover, we propose a reinforcement learning framework to improve the ranking method when the traffic data is used for the purpose of routing. This study utilizes dynamic traffic data that is observed and estimated from simulation.

关键词： Traffic Data Data Reconstruction Sensor Placement Matrix Factorization Reinforcement Learning

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Optimizing reinforcement learning for large action spaces via generative models: Battery pattern selection

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Pattern Recognition 2025年 160卷

作者： Hu, Jingwei Li, Xinjie Li, Xiaodong Hou, Zhensong Zhang, Zhihong School of Informatics Xiamen University Xiamen China School of Electrical and electronic engineering North China Electric Power University Beijing China Department of Computer Science The University of Hong Kong Hong Kong China

Intrinsic and environmental factors contribute to variability in the performance of cells within a battery pack, affecting the lifespan and safety of battery systems. To solve this problem, active and passive equalization methods are proposed. However, existing passive equalization methods suffer from energy loss and low efficiency among batteries, while existing active equalization methods necessitate complex expert knowledge and control algorithms. We propose an active equalization model that leverages a generative model (GM) to assist in pattern selection for a reinforcement learning (RL) scheme, tailored for Dynamic Reconfigurable Battery (DRB) systems. The proposed model overcomes the pattern selection challenge in large-scale discrete action spaces by employing a Variational Autoencoder (VAE) for dimensionality reduction and latent space mapping, actively balancing DRB systems. Moreover, the use of pattern subgraphs diminishes dependence on expert knowledge, enabling the model to recognize structural information and adjust the system's stability. The experimental setup adheres to the laws of physics and tests the model's functionality on a simulation system. Results show that the proposed Generative Model-based Reinforcement Learning (GMRL) approach effectively addresses decision-making challenges in large-scale spaces. It can learn the structured features of the battery network, thus balancing the energy storage system and maximizing discharge efficiency gains. © 2024

关键词： Generative adversarial networks

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The Influence of Information and Communication Technology on Economic Growth in Europe

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Journal of the Knowledge Economy 2025年 1-29页

作者： Pažun, Brankica Langović, Zlatko Stojanović, Vladica S. Milićević, Ana Langović Božović, Ivan Department of Informatics Mathematics and Statistics Faculty of Engineering Management University Union Nikola Tesla Belgrade Serbia Department of Business Economy Faculty of Hotel Management and Tourism University of Kragujevac Kragujevac Serbia Department of Informatics & Computer Sciences Investigation & Police Studies University of Criminal Belgrade Serbia Faculty of Geography Department of Tourism University of Belgrade Belgrade Serbia Faculty of Economics University of Kosovska Mitrovica Kosovska Mitrovica Serbia

The aim of this study is to estimate thtrae influence of information and communication technology, based on the use of the ICT development index (measured by the number of Internet users, fixed broadband Internet subscribers, and the number of mobile subscriptions per 100 inhabitants), on economic development measured by gross domestic product (GDP) per capita. For this purpose, data were collected from a panel of 43 European countries from 2000 to 2020, and three econometric models were used to investigate the impact of ICT use on GDP per capita growth. First, multiple linear regression (MLR) and least absolute shrinkage and selection operator (LASSO) regression were used to research the impact of ICT use on individual countries. Then, a generalized linear dynamic data (GLD) panel model was taken to provide a general model of the dependence of the observed variables. Accordingly, a positive and significant relationship between real GDP per capita and ICT use in Europe is confirmed. The obtained results also show that the impact of ICT on economic growth is greater in low-income countries, i.e., those belonging to Eastern and Central Europe. It is crucial that governments follow the dynamics of the ICT sector and implement specific policies.

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Polychromatic Coloring of Tuples in Hypergraphs

arXiv

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arXiv 2025年

作者： Biniaz, Ahmad De Carufel, Jean-Lou Maheshwari, Anil Smid, Michiel Smorodinsky, Shakhar Stojaković, Miloš School of Computer Science University of Windsor Canada School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada School of Computer Science Carleton University Ottawa Canada Department of Computer Science Ben-Gurion University of the Negev Be’er Sheva Israel Department of Mathematics and Informatics University of Novi Sad Novi Sad Serbia

A hypergraph H consists of a set V of vertices and a set E of hyperedges that are subsets of V . A t-tuple of H is a subset of t vertices of V . A t-tuple k-coloring of H is a mapping of its t-tuples into k colors. A coloring is called (t, k, f)-polychromatic if each hyperedge of E that has at least f vertices contains tuples of all the k colors. Let fH(t, k) be the minimum f such that H has a (t, k, f)-polychromatic coloring. For a family of hypergraphs H let fH(t, k) be the maximum fH(t, k) over all hypergraphs H in H. Determining fH(t, k) has been an active research direction in recent years. This is challenging even for t = 1. We present several new results in this direction for t ≥ 2. • Let H be the family of hypergraphs H that is obtained by taking any set P of points in 2, setting V := P and E := {d ∩ P : d is a disk in R2}. We prove that fH(2, k) ≤ 3.7k, that is, the pairs of points (2-tuples) can be k-colored such that any disk containing at least 3.7k points has pairs of all colors. We generalize this result to points and balls in higher dimensions. • For the family H of hypergraphs that are defined by grid vertices and axis-parallel rectangles in the plane, we show that fH(2, k) ≤ √ck ln k for some constant c. We then generalize this to higher dimensions, to other shapes, and to tuples of larger size. • For the family H of shrinkable hypergraphs of VC-dimension at most d we prove that fH(d+1, k) ≤ ck for some constant c = c(d). Towards this bound, we obtain a result of independent interest: Every hypergraph with n vertices and with VC-dimension at most d has a (d+1)-tuple T of depth at least n/c , i.e., any hyperedge that contains T also contains nc other vertices. We also present analogous bounds for coloring pairs of points with respect to pseudo-disks in the plane. • For the relationship between t-tuple coloring and vertex coloring in any hypergraph H we establish the inequality 1/e · tk1/t ≤ fH(t, k) ≤ fH(1, tk1/t ). For the special case of k = 2, ref

关键词： Knowledge management

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Z-UPSCALING: Optical Flow Guided Frame Interpolation for Isotropic Reconstruction of 3D EM Volumes

Z-UPSCALING: Optical Flow Guided Frame Interpolation for Iso...

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IEEE International Symposium on Biomedical Imaging

作者： Fisseha A. Ferede Ali Khalighifar Jaison John Krishnan Venkataraman Khaled Khairy Center for Bioimage Informatics St. Jude Children's Research Hospital Memphis TN USA Department of Electrical and Computer Engineering University of Memphis Memphis TN USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

We propose a novel optical flow based approach to enhance the axial resolution of anisotropic 3D EM volumes to achieve isotropic 3D reconstruction. Assuming spatial continuity of 3D biological structures in well aligned EM volumes, we reasoned that optical flow estimation techniques, often applied for temporal resolution enhancement in videos, can be utilized. Pixel-level motion is estimated between neighboring 2D slices along z, using spatial gradient flow estimates to interpolate and generate new 2D slices resulting in isotropic voxels. We leverage recent state-of-the-art learning methods for video frame interpolation and transfer learning techniques, and demonstrate the success of our approach on publicly available ultrastructure EM volumes.

关键词： Training Learning systems Interpolation Three-dimensional displays Transfer learning Superresolution Spatial resolution Optical flow Image reconstruction Videos

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Hybrid Heuristic Optimization of Deep Learning Models for Robust Plant Disease Detection

Hybrid Heuristic Optimization of Deep Learning Models for Ro...

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Emerging Smart Computing and informatics (ESCI), Conference on

作者： K. Ananthajothi Pravin R. Kshirsagar Tan Kuan Tak Kailash Kumar Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Electrical Power Engineering Programme Singapore Institute of Technology Singapore College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Plant diseases directly affect farm output by lowering grain, fruit, and vegetable quality, threatening global food security. Thus, farmers inspect plant leaves with their eyes. Plant leaf monitoring is unreliable and error-prone. A multitude of deep learning algorithms have been developed to detect plant leaf diseases; however, most rely on low-resolution photos utilizing convolutional neural nets (CNNs). This research seeks to create an improved nine-layer the CNN network model for the accurate classification of leaf-related illnesses. A contrast enhancement approach is used to pre-process plant leaf pictures. Following binary thresholding, the pre-processed pictures are separated into leaf images and abnormality segmented using the "Enhanced U-Net (EU-Net)" approach. A Multilevel Feature Fusion Network Convolutional Neural Network (MFFN-CNN) is employed to categorize illnesses of leaves based on segmented images. The "Hybrid Leader Cat Swarm Optimization(HLCSO)" method improves U-Net parameter optimization. The effectiveness of the leaf diagnostic model is demonstrated experimentally employing a variety of factors through the use of numerous baseline approaches.

关键词： Deep learning Plant diseases Image segmentation Computational modeling Neural networks Convolutional neural networks Particle swarm optimization informatics Optimization Monitoring

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Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models

arXiv

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arXiv 2025年

作者： Lai, Yuxiang Zhong, Jike Li, Ming Zhao, Shitian Yang, Xiaofeng Department of Computer Science and Informatics Emory University United States Department of Computer Science University of Southern California United States Department of Computer Science University of Tokyo Japan Department of Computer Science Johns Hopkins University United States Department of Biomedical Engineering Georgia Institute of Technology Emory University United States Department of Radiation Oncology Winship Cancer Institute Emory University United States

Vision-language models (VLMs) have made significant progress in reasoning within natural scenes, yet their potential in medical imaging remains largely underexplored. Medical reasoning tasks, which require robust image analysis and the generation of well-justified answers, present unique challenges due to the inherent complexity of medical images. Transparency and trustworthiness are critical not only for gaining clinicians’ confidence but also for meeting stringent regulatory requirements. To address these challenges, we propose Med-R1, a novel framework that investigates whether reinforcement learning (RL) can enhance the generalizability and trustworthiness of VLMs in medical reasoning. Building on the recently introduced DeepSeek strategy, we adopt Group Relative Policy Optimization (GRPO) for RL, which encourages models to explore reasoning paths guided by reward signals. Unlike supervised fine-tuning (SFT), which often overfits to training data and struggles with generalization, RL enables models to develop more robust and diverse reasoning capabilities. We comprehensively evaluate Med-R1 across eight distinct medical imaging modalities: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Dermoscopy, Fundus Photography, Optical Coherence Tomography (OCT), Microscopy Images, and X-ray Imaging. Compared to the base model, Qwen2-VL-2B, Med-R1 achieves a 29.94% improvement in average accuracy across these modalities and even outperforms Qwen2-VL-72B—a model with 36 times more parameters. To assess model’s generalization abilities, we further test on five different question types: modality recognition, anatomy identification, disease diagnosis, lesion grading, and biological attribute analysis. Med-R1 also demonstrates superior cross-task generalization, outperforming Qwen2-VL-2B by 32.06% and Qwen2-VL-72B in question-type generalization accuracy. These results highlight that RL not only enhances medical reasoning capabilities but also enables p

关键词： Visual languages

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Development and Evaluation of an Interactive Web Interface for BPMN Elements Identification from Natural Language Text

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Procedia computer Science 2025年 256卷 431-438页

作者： Vitor Camargo de Moura Guilherme Rego Rockembach Marcelo Soares Pimenta Isabela Gasparini Encarna Sosa-Sánchez Lucineia Heloisa Thom Institute of Informatics – Federal University of Rio Grande do Sul (UFRGS) Porto Alegre 91501-970 Brazil Departament of Computer Science University of Santa Catarina State Joinville 89219-710 Brazil Department of Computer Systems and Telematics Engineering – University of Extremadura Cáceres 10003 Spain

Process discovery represents a high percentage of the time demanded along the business process management lifecycle. Texts in natural language, or process descriptions, can be explored as a critical information source. Extracting information automatically from these documents is challenging due to their ambiguity and complexity. While various (semi-)automated solutions are proposed in the literature, functional code access is rare, and tool deployment is almost nonexistent. This research introduces an accessible web interface that allows users to upload process descriptions and interact with the text, automatically identifying the main process elements from the Business Process Model and Notation. Interactions include highlighting elements associated with specific participants. An evaluation experiment with ten potential real-world users was conducted to assess the tool’s benefits. The experiment compared performance in answering questions related to process descriptions across three scenarios: using plain text, using the proposed tool, and using ChatGPT. Results indicated an advantage in using the tool, particularly regarding time consumption. Data collected on ChatGPT usage also provided insights into how users leverage this technology for problem-solving.

关键词： BPMN process description natural language Web interface development evaluation

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