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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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High-Lift Prediction Workshop 5: Hybrid RANS/LES Technology Focus Group Summary

High-Lift Prediction Workshop 5: Hybrid RANS/LES Technology ...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Ashton, Neil Batten, Paul Holst, Kevin Skaperdas, Vangelis Amazon Web Services 60 Holborn Viaduct LondonEC1A 2FD United Kingdom Metacomp Technologies Inc 31365 Oak Crest Drive #250 Westlake VillageCA91361 United States Electrical Engineering and Computer Science University of Tennessee Knoxville United States BETA-CAE Systems Thessaloniki57500 Greece

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This study summarizes the work of the Hybrid RANS/LES (HRLES) Technology Focus Group (TFG) for the 5th High-Lift Prediction Workshop (HLPW-5). Results indicate that, as shown in HLPW-4, HRLES methodologies continue to consistently outperform SA-based RANS models in predicting maximum lift coefficients (CL,ax) and flow-field correlations for arange of high-lift aircraft geometry variations - from clean wing to those with flap, slat and engine nacelles. However, challenges persist in simulating low angle-of-attack configurations with deployed flaps, as well as the transitional flow in some of the lower-Reynold-number cases studied. This work also highlights the importance of mesh resolution and low-dissipation numerical schemes in achieving consistent results across different HRLES implementations. While HRLES methods demonstrates promise for industrial applications, the work of theHLPW-5 HRLES TFG underscores the need for further research to enhance the maturity and widespread applicability of these methods. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Angle of attack

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Predictive model for daily risk alerts in sepsis patients in the ICU:visualization and clinical analysis of risk indicators

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Precision Clinical Medicine 2025年第1期8卷 28-39页

作者： Hao Yang Jiaxi Li Chi Zhang Alejandro Pazos Sierra Bairong Shen Information Center West China Hospital of Sichuan UniversityChengdu 610041China Department of Computer Science and Information Technologies Research Center for Information and Communications TechnologiesUniversity of A CoruñaBiomedical Research Institute of a CoruñaA Coruña 15071Spain Department of Clinical Laboratory Medicine Jinniu Maternity and Child Health Hospital of ChengduChengdu 610031China Joint Laboratory of Artificial Intelligence for Critical Care Medicine Department of Critical Care Medicine and Institutes for Systems GeneticsFrontiers Science Center for Disease-related Molecular NetworkWest China HospitalSichuan UniversityChengdu 610041China

This study introduces a novel Transformer-based time-series framework designed to revolutionize risk stratification in Intensive Care Units(ICUs)by predicting patient outcomes with high temporal *** sequential data from the eICU database,our two-stage architecture dynamically captures evolving health trajectories throughout a patient’s ICU stay,enabling real-time identification of high-risk individuals and actionable insights for personalized *** model demonstrated exceptional predictive power,achieving a progressive AUC increase from 0.87(±0.021)on admission day to 0.92(±0.009)by day 5,reflecting its capacity to assimilate longitudinal physiological *** external validation across geographically diverse cohorts—including an 81.8%accuracy on Chinese sepsis data(AUC=0.73)and 76.56%accuracy on MIMIC-IV-3.1(AUC=0.84)—confirmed robust ***,SHAP-derived temporal heatmaps unveiled mortality-associated feature dynamics over time,bridging the gap between model predictions and clinically interpretable *** findings establish a new paradigm for ICU prognostics,where data-driven temporal modeling synergizes with clinician expertise to optimize triage,reduce diagnostic latency,and ultimately improve survival outcomes in critical care.

关键词： sepsis Transformer time-series visualization predicting mortality

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ENHANCING DISINFORMATION DETECTION WITH EXPLAINABLE AI AND NAMED ENTITY REPLACEMENT

arXiv

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arXiv 2025年

作者： González Silot, Santiago Montoro-Montarroso, Andrés Cámara, Eugenio Martínez Gómez-Romero, Juan Universidad de Jaén Spain Department of Technologies and Information Systems Universidad de Castilla-La Mancha Spain Department of Computer Science and Artificial Intelligence Universidad de Granada Spain

The automatic detection of disinformation presents a significant challenge in the field of natural language processing. This task addresses a multifaceted societal and communication issue, which needs approaches that extend beyond the identification of general linguistic patterns through data-driven algorithms. In this research work, we hypothesise that text classification methods are not able to capture the nuances of disinformation and they often ground their decision in superfluous features. Hence, we apply a post-hoc explainability method (SHAP, SHapley Additive exPlanations) to identify spurious elements with high impact on the classification models. Our findings show that non-informative elements (e.g., URLs and emoticons) should be removed and named entities (e.g., Rwanda) should be pseudo-anonymized before training to avoid models’ bias and increase their generalization capabilities. We evaluate this methodology with internal dataset and external dataset before and after applying extended data preprocessing and named entity replacement. The results show that our proposal enhances on average the performance of a disinformation classification method with external test data in 65.78% without a significant decrease of the internal test performance. © 2025, CC BY.

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UAV -Based Object Tracking Using Siamese Networks and Kalman Filtering

UAV -Based Object Tracking Using Siamese Networks and Kalman...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramanan, S.V. Rajasekar, M. Shanmugapriya, G. Sujitha, R. Akshya, J. Choudhry, Mani Deepak P Pg Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Vsb college of Engineering Department of Computer Science and Engineering Technical Campus Coimbatore India Dr.N G.P Institute of Technology Department of Computer Science and Business Systems Coimbatore India Christ The King Engineering College Department of Information Technology Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

The ability to match features and keep track of objects in changing dynamic environments is still an important problem, particularly due to varying noise levels, diverse datasets, and high dimensional feature spaces. Euclidean Distance and Cosine Similarity based matching are existing methods that commonly do not generalize well, particularly in the case of cross domain scenarios, where noise and motion dynamics are powerful factors to limit the accuracy of tracking. However, Deep Metric Learning approaches can extract robust features at the cost of computational efficiency and are unsuitable for real-time applications. This research proposes a Siamese Network framework to solve these problems by using robust feature matching and multi-object tracking. Based on that, the proposed framework uses a dual-branch network architecture in which feature embeddings extracted from paired inputs are mapped through the same shared latent space. Furthermore, the adaptive learning rate leads to efficient convergence during the training. Experimental results show that the Siamese Network not only achieves better accuracy than the existing methods and the more such as Deep Metric Learning but also performs significantly more efficient computation. With an accuracy of 90.2 % at a precision of 88.5%, the framework's inference time is much faster than that of Deep Metric Learning at 2.0ms. Furthermore, the proposed method performs well on the generalization over datasets with a 15% improvement of cross-domain matching accuracy on Euclidean Distance. By striking this balance between accuracy, efficiency and adaptability, it shows its suitability for dynamic and noisy environments for real-time tracking. The proposed method fills existing gaps and achieves a new state of the art for feature matching and tracking performance. © 2025 IEEE.

关键词： Object tracking

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A lightweight attention-driven YOLOv5m model for improved brain tumor detection

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computers in Biology and Medicine 2025年 188卷 109893-109893页

作者： Muksimova, Shakhnoza Umirzakova, Sabina Mardieva, Sevara Iskhakova, Nargiza Sultanov, Murodjon Cho, Young Im Department of Computer Engineering Gachon University Sujeong-gu Seongnam-si461-701 Korea Republic of Department of Information Systems and Technologies of the Tashkent State University of Economic Tashkent100066 Uzbekistan Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Tashkent100200 Uzbekistan

Brain tumors are regarded as one of the most lethal, devastating, and aggressive diseases, significantly reducing the life expectancy of affected individuals. For this reason, in pursuit of advancing brain tumor diagnostics, this study introduces a significant enhancement to the YOLOv5m model by integrating an Enhanced Spatial Attention (ESA) layer, tailored specifically for the analysis of magnetic resonance imaging (MRI) brain scans. Traditional brain tumor detection methods, heavily reliant on expert interpretation of MRI, are fraught with challenges such as high variability and the risk of human error. Our innovative approach leverages the ESA layer to acutely focus on salient features, significantly improving the method ability to differentiate between common classes of brain tumors—meningioma, pituitary, and glioma tumors. By processing spatial features with enhanced precision, the model minimizes false positives and maximizes detection reliability. Validated against a comprehensive dataset of 3064 T1-weighted contrast-enhanced MRI images from 233 patients, our modified YOLOv5m architecture demonstrates superior performance metrics compared to the standard model, highlighting its potential as a robust tool in clinical applications for automated and precise brain tumor diagnosis. © 2025 The Authors

关键词： Dynamic contrast enhanced MRI

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Clustering based Medical Image Segmentation: A Study on MRI Scans of Brain Tumors 24

Clustering based Medical Image Segmentation: A Study on MRI ...

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8th International Conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Mimenbayeva, Aigul B. Aruova, Aliya A. Bekmagambetova, Gulmira K. Niyazova, Rozamgul S. Turebayeva, Rakhila D. Naizagarayeva, Akgul A. Tursumbayeva, Ainur F. Departament of Computational and Data Science Astana IT University Astana Kazakhstan Department of Computer Science Agrotechnical Research University Named after S. Seifullin Astana Kazakhstan L.N. Gumilyov Eurasian National University Department of Artificial Intelligence Technologies Astana Kazakhstan Department of Information Systems Kazakh Agrotechnical Research University Named after S. Seifullin Astana Kazakhstan

ISBN: (纸本)9798400718014

This study investigates the application of Hierarchical clustering for image segmentation, with a focus on its efficacy in analyzing medical images, particularly MRI scans of brain tumors. Image segmentation plays a pivotal role in computer vision, facilitating various applications across industries. Leveraging a systematic approach, we conduct a comprehensive review of recent literature on machine learning algorithms for image segmentation. Subsequently, utilizing a dataset comprising MRI images with and without tumors, we preprocess and analyze the data using the Histogram of Oriented Gradients (HOG) technique to extract pertinent features. These features serve as input for the Hierarchical clustering algorithm to partition the images into distinct regions of interest. For each row of vectors, the Jensen-Shenton distance was calculated. The resulting symmetric matrices are distances among the corresponding vectors, quantifying dissimilarity in cluster analysis. Our findings underscore the effectiveness of Hierarchical clustering in clustering medical images, with potential implications for advancing computational analysis in healthcare and related domains. © 2024 Copyright held by the owner/author(s).

关键词： Magnetic resonance imaging

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Designing and Developing a Fixed-Wing Tail-sitter Tethered VTOL UAV with Custom Autopilot: A MIMO $H_{\infty}$ Robust Control Approach

Designing and Developing a Fixed-Wing Tail-sitter Tethered V...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Ahmad Safaee Antonio Paulo Moreira A. Pedro Aguiar Faculty of Engineering SYSTEC-ARISE Research Center for Systems and Technologies University of Porto Porto Portugal Department of Electrical and Computer Engineering INESC-TEC and DEEC/FEUP Faculty of Engineering University of Porto Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

This article presents the development of a tethered fixed-wing tail-sitter VTOL (Vertical Take-Off and Landing) Unmanned Aerial Vehicle system. The design focuses on improving energy efficiency by utilizing the wings to harness wind power, similar to a kite, while maintaining VTOL functionality. A distinguishing feature is the purpose-built autopilot system, with custom hardware and software components specifically engineered for this application. The study presents the system identification process for obtaining five MIMO (Multiple-Input Multiple-Output) transfer functions that characterize the dynamics between roll-yaw commands and responses, including the tether angle feedback. To address the inherent coupling effects and uncertainties in the system, robust mixed sensitivity (H∞) MIMO controllers are developed. The controllers were validated through both simulations and experimental flights, demonstrating effective performance in handling cross-coupling effects and maintaining stability under various operating conditions. According to flight test findings, the system can precisely manage the tether angle while adjusting for ground effect disturbances. This allows for accurate tethered navigation, a stable attitude, and the maintenance of an adequate yaw heading.

关键词： Couplings Uncertainty Sensitivity Navigation Wind power generation Autonomous aerial vehicles Software Hardware Autopilot System identification

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High-Power LED Lamp Driver Modelling

High-Power LED Lamp Driver Modelling

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Igor V. Chukhraev Marina A. Sevastyanova Vladimir E. Drach Julia V. Torkunova Computer Science and Control Department Bauman Moscow State Technical University Moscow Russian Federation Department of Innovative Engineering and Digital Technologies Sochi State University Sochi Russian Federation Department of Information Technologies and Intelligent Systems Kazan State Power Engineering University Kazan Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The growing demand for energy-saving lamps highlights critical limitations in traditional LED secondary optics, which reduce energy efficiency and complicate heat dissipation — a key factor in LED lamp longevity. This study proposes an optimized circuit design for a 20-watt LED driver that addresses these issues. The proposed driver ensures effective LED control while improving thermal management and eliminating drawbacks of conventional designs. Key principles of modern LED lamp design and current challenges are identified. The electronic schematic of the high-power LED driver was simulated using LTSpice software in order to analyze and optimize the circuit's performance, resulting in a driver solution that enhances efficiency and reliability.

关键词： Thermal management of electronics Reactive power Power engineering LED lamps Power supplies Lighting Energy efficiency Software Software reliability Standards

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Impact of PHEV operating conditions and demand side management on the economic operation of a community microgrid system

Impact of PHEV operating conditions and demand side manageme...

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Power Engineering Conference (SAUPEC), Southern African Universities

作者： Bishwajit Dey Nande Fose Senthil Krishnamurthy Mukovhe Ratshitanga Prathaban Moodley Department of Electrical Electronics and Computer Engineering Centre for Intelligent Systems and Emerging Technologies Cape Peninsula University of Technology Bellville South Africa South African National Energy Development Institute (SANEDI) Sandton South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

By classifying loads into shiftable and non-shiftable groups and rearranging a distribution system's load demand model, demand-side management (DSM) reduces operating expenses. This is possible by shifting variable loads when utility costs per unit are lower. This work adopts a bi-level optimization technique to minimize the operational costs of a low-voltage microgrid (MG) system that incorporates plug-in hybrid electric vehicles (PHEVs), renewable energy sources, and fossil fuel generators while operating in grid-connected mode. According to the DSM participation level, the load model is reconfigured at the first optimization stage. Then, the restructured load demand models are considered, and recommendations for distributed generator scheduling are developed to reduce the production costs of the second-level microgrid system. The study's optimization technique used the popular differential evolution (DE) method applied to several power system optimization issues. When DSM, various grid participation and pricing schemes, and other factors were considered, the cost of producing active power was reduced. To ascertain their effect on the MG system's generation cost, scenarios involving different PHEV state of charge (SoC) levels and a taxable rate during V2G were also investigated.

关键词： Vehicle-to-grid Renewable energy sources Low voltage Costs Demand side management Microgrids Pricing Charging stations Generators Load modeling

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