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A Web-Based Interface That Leverages Machine Learning to Assess an Individual’s Vulnerability to Brain Stroke

IEEE Access 2025年 13卷 87950-87964页

作者： Bhandari, Divyansh Agarwal, Arnav Reena Roy, R. Priyatharshini, Rajaram Rivero Cristian, Rodriguez Vellore Institute of Technology School of Electronics Engineering Tamil Nadu Chennai600127 India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai600127 India Cardiff Metropolitan University Centre for Engineering Research in Intelligent Sensors and Systems Cardiff School of Technologies CardiffCF5 2YB United Kingdom Cardiff Metropolitan University Cardiff School of Technologies CardiffCF5 2YB United Kingdom Universitat Politècnica de Catalunya Department of Mining Industrial and ICT Systems Engineering Barcelona08034 Spain

Cerebral stroke is a major global health issue, contributing to high mortality and long-term disability. Early identification of individuals at high risk of stroke can significantly improve preventive care outcomes. We present a web-based stroke risk assessment tool that uniquely combines an accessible user interface with robust machine learning modeling. The proposed platform leverages a novel combination of SMOTE oversampling and logistic regression to address class imbalance in patient health records, improving the detection of stroke risk factors over existing methods. We compare a range of algorithms-including traditional classifiers and deep learning models-and report comprehensive performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) for each. Our best model (logistic regression with SMOTE and standard scaling) achieves 93.2% accuracy with a substantially higher F1-score for the stroke-positive class than other models, indicating improved sensitivity to stroke cases. To bridge the gap between complex predictive models and end-users, we deploy this model in an intuitive web interface (built with Streamlit) that non-technical individuals and healthcare providers can easily use. This interface requires no specialized knowledge, preserves user privacy by avoiding any data storage, and provides clear explanations of results. By offering a practical and transparent tool for stroke risk screening, our work advances health informatics with an emphasis on accessibility, interpretability, and early intervention. Potential applications range from personal health self-assessment to integration in clinical workflows for preventive care, ultimately aiming to improve public health outcomes through early detection and intervention in stroke. © 2013 IEEE.

关键词： Health insurance

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Evaluating Large Language Models in Interaction with Open Government Data 24

Evaluating Large Language Models in Interaction with Open Go...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Ioannis Maslaris Areti Karamanou Evangelos Kalampokis Konstantinos Tarabanis Database & Information Retrieval Laboratory Department of Electrical & Computer Engineering Democritus University of Thrace Xanthi Greece Information Systems Laboratory Department of Business Administration University of Macedonia Thessaloniki Greece Information Technologies Institute Center for Research & Technology HELLAS (CERTH) Thessaloniki Greece and Information Systems Laboratory Department of Business Administration University of Macedonia Thessaloniki Greece

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PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI

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Heliyon 2024年第19期10卷 e38596页

作者： Shamrat, F M Javed Mehedi Idna Idris, Mohd Yamani Zhou, Xujuan Khalid, Majdi Sharmin, Sharmin Sharmin, Zeseya Ahmed, Kawsar Moni, Mohammad Ali Department of Computer System and Technology Universiti Malaya Kuala Lumpur 50603 Malaysia School of Business University of Southern Queensland Toowoomba Australia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Bio-photomatiχ Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh AI & Digital Health Technology Artificial Intelligence & Cyber Future Institute Charles Stuart University Bathurst 2795 NSW Australia AI & Digital Health Technology Rural Health Research Institute Charles Stuart University Orange 2800 NSW Australia

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive Rate (0.016), and the False Negative Rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research. © 2024 The Authors

关键词： Deep learning Explainable AI Image processing Pollen grain classification PollenNet model

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Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning

Vulnerability Detection with Graph Simplification and Enhanc...

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International Conference on Software engineering (ICSE)

作者： Xin-Cheng Wen Yupan Chen Cuiyun Gao Hongyu Zhang Jie M. Zhang Qing Liao School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory and Guangdong Provincial Key Laboratory Novel Security Intelligence Technologies School of Big Data and Software Engineering Chongqing University China Department of Informatics King's College London UK Peng Cheng Laboratory

Prior studies have demonstrated the effectiveness of Deep Learning (DL) in automated software vulnerability detection. Graph Neural Networks (GNNs) have proven effective in learning the graph representations of source code and are commonly adopted by existing DL-based vulnerability detection methods. However, the existing methods are still limited by the fact that GNNs are essentially difficult to handle the connections between long-distance nodes in a code structure graph. Besides, they do not well exploit the multiple types of edges in a code structure graph (such as edges representing data flow and control flow). Consequently, despite achieving state-of-the-art performance, the existing GNN-based methods tend to fail to capture global information (i.e., long-range dependencies among nodes) of code graphs. To mitigate these issues, in this paper, we propose a novel vulnerability detection framework with grAph siMplification and enhanced graph rePresentation LEarning, named AMPLE. AMPLE mainly contains two parts: 1) graph simplification, which aims at reducing the distances between nodes by shrinking the node sizes of code structure graphs; 2) enhanced graph representation learning, which involves one edge-aware graph convolutional network module for fusing heterogeneous edge information into node representations and one kernel-scaled representation module for well capturing the relations between distant graph nodes. Experiments on three public benchmark datasets show that AMPLE outperforms the state-of-the-art methods by 0.39%-35.32% and 7.64%-199.81% with respect to the accuracy and F1 score metrics, respectively. The results demonstrate the effectiveness of AMPLE in learning global information of code graphs for vulnerability detection.

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Blockchain-based Security Framework for Metaverse: A Decentralized Approach

Blockchain-based Security Framework for Metaverse: A Decentr...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Anand Singh Rajawat S.B. Goyal RamKumar Solanki Maria Simona Raboaca Traian Candin Mihaltan Zoltán Illés Chaman Verma School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Building Services Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary

People can act out fake social exchanges in the Metaverse, which is an online community. Security has become a problem in the Metaverse because there are so many online transactions and digital assets. In this thesis, a Blockchain-based security system for the Metaverse is proposed. This would be a decentralised way to keep the virtual world safe. Our system uses Blockchain's autonomous structure to make sure that all financial transactions in the Metaverse are safe, anonymous, and true. We suggest a new consensus process that combines Proof of Stake (PoS) and Proof of Authority (PoA) to make networks safer and allow them to grow. We also have a user-focused, decentralised identity management system that keeps their information safe. We also suggest a way to process Metaverse transactions safely that is based on smart contracts. Our method makes sure that transactional proof, recording, and validation are done in a safe and reliable way. We also show a reputation system that benefits people who follow the law and punishes people who break the law. Lastly, we test how well our suggested structure works with simulations and real-world tests. The results show that our way works to reduce possible security risks in the Metaverse and create a safe, scalable environment for online business. Overall, our Blockchain-based Security Framework for Metaverse: A Decentralised Approach is a good way to keep the security and privacy of digital activities in Metaverse.

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Cooperative multi-modal localization in connected and autonomous vehicles

arXiv

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arXiv 2021年

作者： Piperigkos, Nikos Lalos, Aris S. Berberidis, Kostas Anagnostopoulos, Christos Computer Engineering & Informatics Department University of Patras Greece Industrial Systems Institute Athena Research Center Patras Greece

Cooperative Localization is expected to play a crucial role in various applications in the field of Connected and Autonomous vehicles (CAVs). Future 5G wireless systems are expected to enable cost-effective Vehicle-to-Everything (V2X) systems, allowing CAVs to share with the other entities of the network the data they collect and measure. Typical measurement models usually deployed for this problem, are absolute position from Global Positioning System (GPS), relative distance and azimuth angle to neighbouring vehicles, extracted from Light Detection and Ranging (LIDAR) or Radio Detection and Ranging (RADAR) sensors. In this paper, we provide a cooperative localization approach that performs multi modal-fusion between the interconnected vehicles, by representing a fleet of connected cars as an undirected graph, encoding each vehicle position relative to its neighbouring vehicles. This method is based on: i) the Laplacian Processing, a Graph Signal Processing tool that allows to capture intrinsic geometry of the undirected graph of vehicles rather than their absolute position on global coordinate system and ii) the temporal coherence due to motion patterns of the moving vehicles. © 2021, CC BY-NC-ND.

关键词： 5G mobile communication systems

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Development of Educational Game for Indonesian History Lesson Focusing on Indonesia Pre-Literacy Relics 'Prehistoria' for Grade X High School Student

Development of Educational Game for Indonesian History Lesso...

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2020 International Conference on computer engineering, Network, and Intelligent Multimedia, CENIM 2020

作者： Nugroho, Supeno Mardi Susiki Rahman, Muhammad Daffa Abiyyu Hariadi, Mochamad Arief, Muhammad Faculty of Intelligent Electrical and Informatics Technology Sepuluh Nopember Institute of Technology Computer Engineering Department Surabaya Indonesia Tokyo University of Technology Media Science Program Graduate School of Bionics Computer and Media Sciences Tokyo Japan

ISBN: (纸本)9781728182834

The development of game technology has opened up new possibilities in learning methods through educational games. However, in Indonesia, the application of this concept is lacking. One lesson that can benefit from educational games is Indonesian History in class X, with a focus on history in the pre-literacy period. This study aims to create a puzzle-Adventure type of digital game that can help educate students about the pre-literacy period, developed with Unity, and uses WebGL as a target device while referring to Indonesia's revised 2013 national curriculum. The result of this research is the creation of a digital game that can be played on average of 254.36 seconds, with potential for educational use. A survey result shows that many of the ancient objects and some of the ages are well-recognized. However, the survey also shows many of the gameplay aspects, such as UI, gameplay features, and adventure elements, need improvements. © 2020 IEEE.

关键词： Surveys

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Optimal Sizing of Security Constrained Unit Commitment Problem Integrated with Renewable Energy Sources and PEVs

Optimal Sizing of Security Constrained Unit Commitment Probl...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Pravin G. Dhawale Vikram Kumar S.K. Bath Chaman Verma Maria Simona Raboaca Constantin Filote Deepak Kumar School of Electrical and Electronics Engineering Lovely Professional University Phagwara University of Calgary Calgary Canada Department of Electrical Engineering Maharaja Ranjit Singh Punjab Technical University Bhatinda India Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Universitatii Street Suceava Romania Apex Institute of Technology Chandigarh Univerity Mohali Punjab India

Optimal sizing of SCUCP with solar energy and PEVs is crucial for electric power system operation and planning. This paper presents a Chaotic Arithmetic Optimization Algorithm to minimize cost while optimizing the sizing of solar energy and PEV systems. The proposed framework integrates probabilistic forecasting models for solar power generation and realistic EV charging profiles based on travel patterns and infrastructure characteristics. It aims to find optimal sizes for Solar power plants and EV charging infrastructure, considering system demand, cost, emissions, and reliability. The Chaotic Arithmetic Optimization Algorithm efficiently solves the optimization problem by accounting for the complexities of SCUCP. The research evaluates the proposed approach using benchmark test systems of varying dimensions and power systems. The results enable informed decision-making by identifying optimal capacities for solar power plants and charging infrastructure, leading to improved system reliability, reduced emissions, and minimized costs.

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Current and future directions in network biology

arXiv

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arXiv 2023年

作者： Zitnik, Marinka Li, Michelle M. Wells, Aydin Glass, Kimberly Gysi, Deisy Morselli Krishnan, Arjun Murali, T.M. Radivojac, Predrag Roy, Sushmita Baudot, Anaïs Bozdag, Serdar Chen, Danny Z. Cowen, Lenore Devkota, Kapil Gitter, Anthony Gosline, Sara Gu, Pengfei Guzzi, Pietro H. Huang, Heng Jiang, Meng Kesimoglu, Ziynet Nesibe Koyuturk, Mehmet Ma, Jian Pico, Alexander R. Pržulj, Nataša Przytycka, Teresa M. Raphael, Benjamin J. Ritz, Anna Sharan, Roded Shen, Yang Singh, Mona Slonim, Donna K. Tong, Hanghang Yang, Xinan Holly Yoon, Byung-Jun Yu, Haiyuan Milenković, Tijana Department of Biomedical Informatics Harvard University United States Department of Computer Science and Engineering University of Notre Dame United States Lucy Family Institute for Data and Society University of Notre Dame United States Eck Institute for Global Health University of Notre Dame United States Channing Division of Network Medicine Brigham and Women’s Hospital Harvard Medical School United States Department of Statistics Federal University of Paraná Brazil Department of Physics Northeastern University United States Department of Biomedical Informatics University of Colorado United States Department of Computer Science Virginia Tech United States Khoury College of Computer Sciences Northeastern University United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison United States Wisconsin Institute for Discovery United States Aix Marseille Université INSERM MMG Marseille France Department of Computer Science and Engineering University of North Texas United States Department of Mathematics University of North Texas United States Department of Computer Science Tufts University United States Morgridge Institute for Research United States Pacific Northwest National Laboratory United States Department of Medical and Surgical Sciences University Magna Graecia of Catanzaro Italy Department of Computer Science University of Maryland College Park United States National Center of Biotechnology Information National Library of Medicine National Institutes of Health Department of Computer and Data Sciences Case Western Reserve University United States Computational Biology Department School of Computer Science Carnegie Mellon University United States Institute of Data Science and Biotechnology Gladstone Institutes Department of Computer Science University College London United Kingdom ICREA Catalan Institution for Research and Advanced Studies Spain Department of Computer Science Princeton University United States Departmen

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These challenges stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology and highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on the future directions of network biology. Additionally, we offer insights into scientific communities, educational initiatives, and the importance of fostering diversity within the field. This paper establishes a roadmap for an immediate and long-term vision for network biology. © 2023, CC BY.

关键词： Machine learning

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Jagged/Notch proteins promote endothelial-mesenchymal transition-mediated pulmonary arterial hypertension via upregulation of the expression of GATAs

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Journal of Cellular and Molecular Medicine 2023年第8期27卷 1110-1130页

作者： Lin, Kun-Chen Yeh, Jui-Ning Shao, Pei-Lin Chiang, John Y. Sung, Pei-Hsun Huang, Chi-Ruei Chen, Yi-Ling Yip, Hon-Kan Guo, Jun Department of Anesthesiology Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Kaohsiung Taiwan Department of Cardiology The First Affiliated Hospital of Jinan University Guangzhou China Department of Nursing Asia University Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Division of Cardiology Department of Internal Medicine Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Kaohsiung Taiwan Center for Shockwave Medicine and Tissue Engineering Kaohsiung Chang Gung Memorial Hospital Kaohsiung Taiwan Institute for Translational Research in Biomedicine Kaohsiung Chang Gung Memorial Hospital Kaohsiung Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Division of Cardiology Department of Internal Medicine Xiamen Chang Gung Hospital Xiamen China

This study tested the hypothesis that Jagged2/Notches promoted the endothelial-mesenchymal transition (endMT)-mediated pulmonary arterial hypertension (PAH) (i.e. induction by monocrotaline [MCT]/63 mg/kg/subcutaneous injection) through increasing the expression of GATA-binding factors which were inhibited by propylthiouracil (PTU) (i.e. 0.1% in water for daily drinking since Day 5 after PAH induction) in rodent. As compared with the control (i.e. HUVECs), the protein expressions of GATAs (3/4/6) and endMT markers (Snail/Zeb1/N-cadherin/vimentin/fibronectin/α-SMA/p-Smad2) were significantly reduced, whereas the endothelial-phenotype markers (CD31/E-cadherin) were significantly increased in silenced JAG2 gene or in silenced GATA3 gene of HUVECs (all p < 0.001). As compared with the control, the protein expressions of intercellular signallings (GATAs [3/4/6], Jagged1/2, notch1/2 and Snail/Zeb1/N-cadherin/vimentin/fibronectin/α-SMA/p-Smad2) were significantly upregulated in TGF-ß/monocrotaline-treated HUVECs that were significantly reversed by PTU treatment (all p < 0.001). By Day 42, the results of animal study demonstrated that the right-ventricular systolic-blood-pressure (RVSBP), RV weight (RVW) and lung injury/fibrotic scores were significantly increased in MCT group than sham-control (SC) that were reversed in MCT + PTU groups, whereas arterial oxygen saturation (%) and vasorelaxation/nitric oxide production of PA exhibited an opposite pattern of RVW among the groups (all p < 0.0001). The protein expressions of hypertrophic (ß-MHC)/pressure-overload (BNP)/oxidative-stress (NOX-1/NOX-2) biomarkers in RV and the protein expressions of intercellular signalling (GATAs3/4/6, Jagged1/2, notch1/2) and endMT markers (Snail/Zeb1/N-cadherin/vimentin/fibronectin/TGF-ß/α-SMA/p-Smad2) in lung parenchyma displayed an identical pattern of RVW among the groups (all p < 0.0001). Jagged-Notch-GATAs signalling, endMT markers and RVSBP that were increased in PAH were suppressed by P

关键词： endothelial-mesenchymal transition biomarkers intercellular signalling propylthiouracil pulmonary artery hypertension vascular wall proliferation

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