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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Martins, Inês Matos, João Gonçalves, Tiago Celi, Leo A. Wong, An-Kwok Ian Cardoso, Jaime S. Faculty of Engineering University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science Porto Portugal Duke University Durham United States Massachusetts Institute of Technology Cambridge United States

ISBN: (纸本)9783031820069

Algorithmic bias in healthcare mirrors existing data biases. However, the factors driving unfairness are not always known. Medical devices capture significant amounts of data but are prone to errors;for instance, pulse oximeters overestimate the arterial oxygen saturation of darker-skinned individuals, leading to worse outcomes. The impact of this bias in machine learning (ML) models remains unclear. This study addresses the technical challenges of quantifying the impact of medical device bias in downstream ML. Our experiments compare a "perfect world", without pulse oximetry bias, using SaO2 (blood-gas), to the "actual world", with biased measurements, using SpO2 (pulse oximetry). Under this counterfactual design, two models are trained with identical data, features, and settings, except for the method of measuring oxygen saturation: models using SaO2 are a "control" and models using SpO2 a "treatment". The blood-gas oximetry linked dataset was a suitable test-bed, containing 163,396 nearly-simultaneous SpO2 - SaO2 paired measurements, aligned with a wide array of clinical features and outcomes. We studied three classification tasks: in-hospital mortality, respiratory SOFA score in the next 24 h, and SOFA score increase by two points. Models using SaO2 instead of SpO2 generally showed better performance. Patients with overestimation of O2 by pulse oximetry of ≥ 3% had significant decreases in mortality prediction recall, from 0.63 to 0.59, P © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Oximeters

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Pharmacy Management System using Artificial Intelligence 2

Pharmacy Management System using Artificial Intelligence

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

作者： Nishane, Om Ajankar, Vinod Datta Meghe Institute of Higher Education and Research Faculty of Engineering & Technology Department of Computer Science & Engineering Maharastra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering & Technology Maharastra Wardha India

ISBN: (纸本)9798331505745

Pharmacy Management System (PMS) is an advanced software solution designed to increase the efficiency, accuracy and safety of pharmaceutical operations. System automates important functions such as inventory management, prescription processing, billing and compliance tracking, thus enables spontaneous pharmacy operations and enhances patient care. With integration of upcoming technologies such as Artificial Intelligence (AI), Cloud Computing, and Barcode scanning, PMS reduces errors, eliminates stockouts, and increases drug safety. The system also includes real-time analytics and reporting, which enables decision making and adaptation to resource use. The upcoming technologies such as IoT-based intelligent inventory management and integration give the PMS in the event of changing the pharmacy management by ensuring enhancement of accessibility, streamlining operations and compliance. The project emphasizes the importance of digital change in modern healthcare, which has increased service distribution and improves the patient's satisfaction. © 2025 IEEE.

关键词： Risk perception

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Path Overlap Detection and Property Graph Construction from TCX Data for Advanced Analysis

Path Overlap Detection and Property Graph Construction from ...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Tilen Hliš Iztok Fister Faculty of Electrical Engineering and Computer Science University of Maribor iD

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This paper presents a novel approach for processing cycling data from TCX files using a custom-built Julia library, TCX2Graph.j1. The method includes constructing property graphs from GPS track points and detecting overlapping path segments. These detected overlapping points are enriched with track point metrics, enabling detailed analysis of cycling sessions. The proposed approach processes large datasets efficiently, pro-viding insights into overlapping paths and athlete performance.

关键词： Measurement Heart rate Wind Predictive models Libraries Robustness Numerical models Machine intelligence Global Positioning System Sports

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A Novel Audio-Visual Multimodal Semi-Supervised Model Based on Graph Neural Networks for Depression Detection

A Novel Audio-Visual Multimodal Semi-Supervised Model Based ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yaqin Li Chenjian Sun Yihong Dong Faculty of Electrical Engineering and Computer Science Ningbo University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

There is a significant correlation between depression, verbal behavior, and facial expressions. By analyzing patients’ audio and facial visuals, depression assessments can be conducted. However, existing work is predominantly based on single modalities. Additionally, acquiring a sufficient amount of labeled data in clinical settings is challenging and costly. To leverage multimodal audio-visual data while addressing the issue of lacking trainable labeled data, we propose an audiovisual multimodal semi-supervised depression detection model based on Graph Neural Networks (AVS-GNN). This model first extracts dual-modality temporal information from audio features and facial visual features of patients and obtains modality-specific high-level embedding representations through graph representation learning. Subsequently, it utilizes graph-based contrastive unsupervised learning to capture consistency information between pairs of unlabeled samples across different modalities and to facilitate cross-modal interactions. We specifically designed a hybrid weighted pseudo-labeling strategy to assign high-confidence pseudo-labels to unlabeled data and further retraining the model. Experiments on two depression datasets show that this model outperforms baseline methods across all evaluation metrics.

关键词： Representation learning Visualization Signal processing Semisupervised learning Depression Feature extraction Graph neural networks Data models Speech processing Unsupervised learning

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Role of Machine Learning in Climate Change Prediction and Mitigation 2

Role of Machine Learning in Climate Change Prediction and Mi...

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

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. Machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action. © 2025 IEEE.

关键词： Global warming

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The Role of IoT and Machine Learning in Automating Space Docking: Challenges, Advancements, and Future Prospects 2

The Role of IoT and Machine Learning in Automating Space Doc...

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

ISBN: (纸本)9798331505745

Space docking is an important aspect of modern-day space exploration, enabling crewed missions, shipment transport, and spacecraft refuelling. Traditionally, manual docking has trusted astronaut precision and floor manipulation coordination, which has grown to be inefficient and blunders-susceptible in deep-space missions because of conversation delays. Integrating the Internet of Things (IoT) and Machine Learning (ML) is reworking area docking by introducing self-reliant, actual-time selection-making structures. IoT-enabled sensors continuously monitor spacecraft role, pace, and orientation, while ML algorithms optimize docking manoeuvres, beautify predictive upkeep, and make certain collision avoidance. Reinforcement learning similarly allows adaptive management strategies, making self-sufficient docking more efficient and dependable. This paper explores the improvements in IoT and ML for automating space docking, reading real-global programs along with SpaceX Dragon, NASA's AR&D systems, and China's Tianzhou cargo deliver. By addressing key demanding situations-precision, pace, and safety-those technologies pave the way for sustainable space exploration, including future Mars missions and autonomous area stations. The observation highlights how AI-driven docking may be integral for deep-space operations, making sure safer and more green missions are made. © 2025 IEEE.

关键词： Reinforcement learning

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Publication performance and trends in virtual reality research in education fields: a bibliometric analysis

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Multimedia Tools and Applications 2025年第10期84卷 6999-7026页

作者： Elaish, Monther M. Yadegaridehkordi, Elaheh Ho, Yuh-Shan Computer Science Department Information Technology Faculty University of Benghazi Benghazi Libya College of Information and Communications Technology School of Engineering and Technology CQUniversity Sydney Australia Trend Research Centre Asia University No. 500 Lioufeng Road Taichung41354 Taiwan

Virtual Reality (VR) technology has the potential to enhance education by providing immersive and engaging learning experiences that can improve teaching and learning outcomes. While there is a growing interest in utilizing VR in education settings, further research is needed to understand its pedagogical effectiveness and address associated considerations and challenges. This bibliometric study comprehensively analyzes 1,157 relevant articles from the Social science Citation Index (SSCI) and the science Citation Index Expanded (SCI-EPANDED) to gain insights into the current state of VR integration in education. The analysis revealed variation in VR adoption and research output across countries and institutions, underscoring the importance of collaboration and knowledge-sharing in the field. Key research areas and trends, such as the use of VR for skill development and training, were identified. Additionally, the study highlighted the need for more research on the pedagogical effectiveness of VR. The findings carry practical implications for guiding future research shaping policy decisions and advocating for a concerted effort to harness VR’s capabilities in education. This study serves as a practical roadmap, promoting the implementation and improvement of VR technology and fostering equitable and inclusive educational practices. © The Author(s) 2024.

关键词： Virtual reality

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Integrated Microfluidic Rapid Response and Highly Sensitive Electrochemical Aptasensor for Simultaneous Detection of Aflatoxin B1 and Deoxynivalenol

Integrated Microfluidic Rapid Response and Highly Sensitive ...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Jinlei Wu Qinghui Jin Ping Yang Wanlei Gao The Faculty of Electrical Engineering and Computer Science Ningbo University China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

A novel electrochemical aptasensor was designed for the simultaneous detection of aflatoxin B1 (AFB1) and deoxynivalenol (DON) using dual-working microelectrodes and PDMS-based microfluidic channels. The system provides high sensitivity, with detection limits of 7.74 pg/mL for AFB1 and 0.172 ng/mL for DON across broad ranges (0.03 − 300 ng/mL for AFB1, 0.3 − 3000 ng/mL for DON). Results can be obtained within 20 minutes, and the platform exhibits excellent reproducibility, selectivity, and stability. Validation with corn extract highlights its potential for on-site food safety monitoring.

关键词： Nanoparticles Micromechanical devices Electric potential Microelectrodes Sensitivity Food safety Reproducibility of results Sensors Microfluidics Monitoring

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Enhancing Solar Photovoltaic Efficiency:A Computational Fluid Dynamics Analysis

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Energy engineering 2025年第1期122卷 153-166页

作者： Rahool Rai Fareed Hussain Mangi Kashif Ahmed Sudhakar Kumaramsay Faculty of Mechanical and Automotive Engineering Universiti Malaysia Pahang Al-Sultan AbdullahPekan26600PahangMalaysia Faculty of Engineering Science&Technology Hamdard UniversityKarachi74600Pakistan Electrical Engineering Department SUKKUR IBA UniversitySukkur65200Pakistan Berlin School of Business and Innovation BSBI Faculty of Computer Science and InformaticsBerlin12043Germany Automotive Engineering Centre University Malaysia Pahang Al Sultan AbdullahPekan26600PahangMalaysia Centre for Research in Advanced Fluid&Processes(Fluid Centre) Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)Paya BesarKuantan26300PahangMalaysia

The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable *** energy,among the various renewable sources,is particularly appealing due to its abundant ***,the efficiency of commercial solar photovoltaic(PV)modules is hindered by several factors,notably their conversion efficiency,which averages around 19%.This efficiency can further decline to 10%–16%due to temperature increases during peak sunlight *** study investigates the cooling of PV modules by applying water to their front surface through Computational fluid dynamics(CFD).The study aimed to determine the optimal conditions for cooling the PV module by analyzing the interplay between water film thickness,Reynolds number,and their effects on temperature reduction and heat *** CFD analysis revealed that the most effective cooling condition occurred with a 5 mm thick water film and a Reynolds number of *** specific parameters were found to maximize the heat transfer and temperature reduction *** finding is crucial for the development of practical and efficient cooling systems for PV modules,potentially leading to improved performance and longevity of solar *** cooling fluids or advanced cooling techniques that might offer even better efficiency or practical benefits.

关键词： PV module efficiency water film thickness reynolds number CFD analysis PV/T renewable energy

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Towards a Data-Centric Framework for Modelling, Managing and Mining BPM Processes over Pandemic Events

Towards a Data-Centric Framework for Modelling, Managing and...

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Workshops of the EDBT/ICDT 2025 Joint Conference, EDBT/ICDT-WS 2025

作者： Cuzzocrea, Alfredo Belmerabet, Islam Combi, Carlo Franconi, Enrico Terenziani, Paolo iDEA Lab University of Calabria Rende Italy Department of Computer Science University of Paris City Paris France Department of Computer Science University of Verona Verona Italy Faculty of Engineering Free University of Bozen-Bolzano Bolzano Italy DISIT Department University of Piemonte Orientale Alessandria Italy

The COVID-19 pandemic exposed significant shortcomings in the ability of modern healthcare information systems to manage and mitigate pandemics, especially when faced with unforeseen events. This paper addresses these shortcomings by introducing PROTECTION, a cutting-edge data-centric framework aimed at enhancing pandemic control and prevention, specifically focusing on BPM processes. PROTECTION offers a comprehensive approach to handling pandemic-related complexities. We provide an overview of PROTECTION's structure and main functions. Copyright © 2025 for this paper by its authors.

关键词： COVID-19

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