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International Conference on Information Networking

作者： Hyeonjin Kim Hyukjoon Lee Jinheung Kong Dept. of Computer Engineering Kwangwoon University Seoul Korea Dept. of Embedded Software Enngeeing Kwangwoon University Seoul Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

The reconstruction of 3D face shapes and expressions from 2D images remains unconquered due to the lack of detailed modeling of human facial movement based on the correlation between the different parts of faces. We propose to use facial action units (AUs), which are detailed taxonomy of the human facial movements based on observation of activation of muscles or muscle groups, in the 3D facial expression reconstruction to allow for detailed modeling of various facial expression types. We present a novel 3D face reconstruction framework called Action Unit feature-based Graph Attention Network Encoder (AUGANE) that can generate a 3D face model that is responsive to AU activation given a single monocular 2D image to capture expressions. AUGANE leverages AU-specific features as well as facial global features to enable accurate 3D facial expression reconstruction with Graph Attention Network Encoder (GANE). We also introduce a novel loss function which is to force learning toward the minimal discrepancy in AU activation between the input and rendered reconstruction. The experimental results demonstrate the superior performance of the proposed framework over state-of-the-art methods.

关键词： Solid modeling Gold Three-dimensional displays Shape Taxonomy Force Muscles Transformers Image reconstruction Faces

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Explainable Multiclass Blood Cell Classification: Combining Custom CNNs with SHAP

Explainable Multiclass Blood Cell Classification: Combining ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md. Ismiel Hossen Abir Shantanu Dey Anik Hasibul Islam Peyal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Birulia Savar Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Blood cell classification is a crucial task in medical diagnostics. This work can identify different blood cell types that play essential roles in the immune system and overall body function. This research introduces a custom Convolutional Neural Network (CNN) model with SHAP for explainable multiclass classification of blood cell images. We classify eight types of blood cells—basophil, eosinophil, erythroblast, immunoglobulin (Ig), lymphocyte, monocyte, neutrophil, and platelet. An ablation study was conducted to optimize the CNN architecture. We compared its performance with eleven transfer learning models, including VGG16, ResNet101, and EfficientNet variants. Our model achieved 92.60% accuracy while maintaining lower complexity. The use of SHAP provides transparency by highlighting crucial features in the model’s decision-making process.

关键词： Training Accuracy Microprocessors Transfer learning Lymphocytes computer architecture Convolutional neural networks Medical diagnosis Blood Testing

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Language Independent Named Entity Recognition via Orthogonal Transformation of Word Vectors

arXiv

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arXiv 2025年

作者： Rakha, Omar E. Abbas, Hazem M. Dept. Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo11571 Egypt

Word embeddings have been a key building block for NLP in which models relied heavily on word embeddings in many different tasks. In this paper, a model is proposed based on using Bidirectional LSTM/CRF with word embeddings to perform named entity recognition for any language. This is done by training a model on a source language (English) and transforming word embeddings from the target language into word embeddings of the source language by using an orthogonal linear transformation matrix. Evaluation of the model shows that by training a model on an English dataset the model was capable of detecting named entities in an Arabic dataset without neither training or fine tuning the model on an Arabic language dataset. © 2025, CC BY.

关键词： Embeddings

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Rule-Augmented Neural Networks for Improved Answer Validation in Question Answering systems

Rule-Augmented Neural Networks for Improved Answer Validatio...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Eman Alduweib Waseem Alromema Nisreen Otoum dept. of Computer Science University of Petra Amman Jordan dept. of Computer Science and Information Taibah University Medinah Saudi Arabia dept. of Software Engineering University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This work presents a hybrid method for answer validation in question answering systems, combining neural network methods with explicit logical rules. The Rule-Augmented Neural Network (RANN) framework integrates the expressiveness of neural networks and precision of rule-based systems for improving answer validation accuracy. Using a sample of the Natural Questions dataset, we demonstrate how we can achieve more reliable and interpretable answer validation by complementing traditional neural architectures with domain-specific rules. The experimental results validate the RANN framework. The experimental results emphasize that the hybrid approach outperforms both neural-based models and rule-based reasoning by achieving the best accuracy, precision, recall, and F1 score.

关键词： Accuracy Computational modeling Neural networks computer architecture Question answering (information retrieval) Cognition Reliability Artificial intelligence Computational intelligence

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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

Bidirectional Multi-Step Domain Generalization for Visible-I...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Mahdi Alehdaghi Pourya Shamsolmoali Rafael M. O. Cruz Eric Granger Dept. of Systems Engineering ETS LIVIA ILLS Montreal Canada Dept. of Computer Science University of York UK

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by learning and aligning body part features extracted from both I and V modalities. In particular, our method aims to minimize the cross-modal gap in two steps. First, BMDG aligns modalities in the feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. Based on these prototypes, multiple bridging steps enhance the feature representation. Experiments 1 1 Our code is available at: ***/BMDG conducted on V-I ReID datasets indicate that our BMDG approach can outperform state-of-the-art part-based and intermediate generation methods, and can be integrated into other part-based methods to enhance their V-I ReID performance.

关键词： Training computer vision Accuracy Codes Prototypes Feature extraction Identification of persons

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Impact of Artificial Intelligence on Decision-Making in Information systems

Impact of Artificial Intelligence on Decision-Making in Info...

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Advanced Research in Computing (ICARC), International Conference on

作者： RWMR Dharani Rajasinghe Dept. of Software Engineering & Information Systems Faculty of Computing NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues.

关键词： Industries Surveys Ethics Adaptation models Technological innovation Explainable AI Law Decision making Data models Information systems

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

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IEEE Access 2025年 13卷 87950-87964页

作者： Bhandari, Divyansh Agarwal, Arnav Reena Roy, R. Priyatharshini, Rajaram Cristian, Rodriguez Rivero Vellore Institute of Technology School of Electronics Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Cardiff Metropolitan University Cardiff School of Technologies Centre for Engineering Research in Intelligent Sensors and SystemsComputer Science Wales United Kingdom Cardiff Metropolitan University Academic Associate Cardiff School of Technologies Cardiff United Kingdom Universitat Politècnica de Catalunya Industrial and ICT Systems Engineering Dept. of Mining 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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Design and Control of a High-Bandwidth Dual Active Bridge DC-DC Converter

Design and Control of a High-Bandwidth Dual Active Bridge DC...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Alper Uzum Syed Imam Hasan Yilmaz Sozer Kenneth A. Loparo Dept. of Electrical and Computer Engineering The University of Akron Akron OH USA Dept. of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland OH USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

For the stable operation of sensitive systems, achieving high-bandwidth control in power converters is critical, as the current and voltage ripple directly reflects the quality of the synthesized waveform. The selection of an appropriate DC-link capacitor, which significantly influences the system’s bandwidth, plays a pivotal role in ensuring performance. This paper explores the intricate relationship between input and output DC-link capacitance and its effect on the system bandwidth in Dual Active Bridge (DAB) converters, a topic that remains relatively unexplored despite numerous research on high-bandwidth control for other power converters. To address the challenges posed by the bulky DC-link capacitor typically required to manage ripple current, the proposed approach integrates a harmonic trap filter to effectively mitigate these limitations. Using state-space modeling and state-variable feedback control, the design and control strategy is validated on a 5 kW DAB converter, demonstrating its ability to enhance system bandwidth without compromising ripple performance.

关键词： Electric potential Capacitors Bridge circuits Bandwidth Harmonic analysis Power electronics Feedback control Voltage control Power harmonic filters System analysis and design

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ML-Inspired Multiple Relay Selection in UAV-Enabled Untrusted Networks

ML-Inspired Multiple Relay Selection in UAV-Enabled Untruste...

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IEEE Conference on Wireless Communications and Networking

作者： Lefteris Tsipi Emmanouel T. Michailidis Konstantinos Maliatsos Demosthenes Vouyioukas Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece Dept. of Informatics and Computer Engineering University of West Attica Egaleo Greece Dept. of Digital Systems University of Piraeus Piraeus Greece

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, multiple relay selection (MRS) schemes for untrusted unmanned aerial vehicle (UAV)-enabled networks are proposed. In this context, various machine learning (ML) models are employed to improve secrecy performance by optimally selecting multiple aerial relays from the available ones, all of which are regarded as untrusted. Notably, these ML models can cope with the quickly changing and random positioning of the aerial relays, effectively decoupling the intricate coupling relationship between the secrecy rate, channel coefficients, and inter-node distances. The results indicate that the ML-based MRS schemes obtain sufficient accuracy and better decoupling than a respective exhaustive searching (ES) approach, while also maintaining a lower computational complexity.

关键词： Couplings Accuracy Predictive models Autonomous aerial vehicles Security Ensemble learning Relays Computational complexity Context modeling

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Forest Fire Prevention and Detection System

Forest Fire Prevention and Detection System

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Power Electronics, computer Applications (ICPECA), IEEE International Conference on

作者： Lasitha Jayakody Chamathka Edirisinghe Tharush Senasinghe Shaminda Dissanayake Narmada Gamage Dinithi Pandithage Jaliya L. Wijayaraja Dept. of Computer Systems Engineering Sri Lanka Institute of Information Malabe Sri Lanka

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires.

关键词： Temperature sensors Technological innovation Wildfires Prevention and mitigation Forestry Sensor phenomena and characterization Sensor systems Real-time systems Gas detectors Resilience

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