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检索条件"任意字段=2010 International Conference on Biomedical Engineering and Computer Science, ICBECS 2010"

共 36454 条记录，以下是1-10 订阅

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2010 international conference on biomedical engineering and computer science, icbecs 2010

2010 International Conference on Biomedical Engineering and ...

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2010 international conference on biomedical engineering and computer science, icbecs 2010

ISBN: (纸本)9781424453153

The proceedings contain 233 papers. The topics discussed include: robust exponential stability for time-delays neural networks with discontinuous activation functions;corrosion character extract algorithm based on correlation dimension of fractal;sub-pixel coded structured light stripe boundary location using weighted centroid method;the sure approach to SAR image denoising based on multiscale bandelet contextual model;automatic gait recognition using kernel principal component analysis;optimal design of the TET system for achieving maximum power transfer;a fast greedy sparse approximation for image based on indexed dictionary;traveling waves of a class of simplified background neural networks;discrete-time recurrent neural networks for medical image segmentation based on competitive layer model with LT neurons;and a biologically motivated corner detection method based on the oriented receptive fields of simple cortical cells.

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Classification of Colorectal Cancer Tissues Using Stacking Ensemble Learning 7th

Classification of Colorectal Cancer Tissues Using Stacking E...

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7th international conference on Communication, Device and Networking, ICCDN 2024

作者： Das, Abhrodeep Hazra, Animesh Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri735102 India

ISBN: (纸本)9789819764648

Advancement in digital pathology has enabled deep learning-based computer vision techniques for automated diagnosis and prognosis of diseases. The essentiality of early detection and prognosis of any cancer category lies in the fact that it can speed up the subsequent medical treatment procedures of patients. About 10% of all cancer cases worldwide are related to colorectal cancer (CRC), and it is also the third most common category of cancer (Egeblad et al. in Dev Cell 18:884–901, 2010). So, it is clinically important to classify and make an objective evaluation of colorectal cancer histological images. The classification performance of current methodologies primarily relies on the use of various combinations of texture-based features and classifiers or transfer learning to classify different organisational kinds. As a result of the diversity of tissue types and characteristics present in histological images, classification is still a challenging task. In this study, we have put forth a novel and effective stacking (Wolpert in Neural Netw 5:241–59, 1992) ensemble (Zhang and Yunqian (eds) Ensemble machine learning: methods and applications. Springer science;Business Media, 2012) technique for classification on the histopathological image analysis benchmark dataset Kather-5 K (Kather et al. in Sci Rep 6:27988, 2016). The ensemble consists of two cutting-edge deep learning architectures, ResNet18 (He et al. in Proceedings of the IEEE conference on computer vision and pattern recognition 2016, pp 770–778) and EfficientNetB0 (Tan and Le Efficientnet: rethinking model scaling for convolutional neural networks. international conference on machine learning 2019 May 24. PMLR, pp 6105–6114), acting as weak learners, and an ANN acting as the meta-learner. The proposed approach obtained a remarkable accuracy of 97.20% in CRC classification on the said dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Colorectal cancer Convolutional neural network Ensemble learning Residual network Stacking

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A Compressive Memory-based Retrieval Approach for Event Argument Extraction 31

A Compressive Memory-based Retrieval Approach for Event Argu...

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31st international conference on Computational Linguistics, COLING 2025

作者： Liu, Wanlong Zhang, Enqi Cheng, Shaohuan Zhou, Li Zeng, Dingyi Zhang, Chen Zhang, Malu Chen, Wenyu University of Electronic Science and Technology of China China The Chinese University of Hong Kong Shenzhen China National University of Singapore Singapore

ISBN: (纸本)9798891761964

Recent works have demonstrated the effectiveness of retrieval augmentation in the Event Argument Extraction (EAE) task. However, existing retrieval-based EAE methods have two main limitations: (1) input length constraints and (2) the gap between the retriever and the inference model. These issues limit the diversity and quality of the retrieved information. In this paper, we propose a Compressive Memory-based Retrieval (CMR) mechanism for EAE, which addresses the two limitations mentioned above. Our compressive memory, designed as a dynamic matrix that effectively caches retrieved information and supports continuous updates, overcomes the limitations of the input length. Additionally, after pre-loading all candidate demonstrations into the compressive memory, the model further retrieves and filters relevant information from memory based on the input query, bridging the gap between the retriever and the inference model. Extensive experiments show that our method achieves new state-of-the-art performance on three public datasets (RAMS, WikiEvents, ACE05), significantly outperforming existing retrieval-based EAE methods. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Identifying Dengue Hotspots in Baguio City, Philippines Through Spatiotemporal Analysis 4th

Identifying Dengue Hotspots in Baguio City, Philippines Thr...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Marigmen, Joseph Ludwin D. C. Libatique, Criselda P. Viernes, Jhunas Paul T. Lazaga, Junley L. Punzalan, Jean Milnard S. Tubera-Panes, Donnabel Addawe, Rizavel C. Department of Mathematics and Computer Science College of Science University of the Philippines Baguio Baguio City Philippines Department of Language Literature and the Arts College of Arts and Communication University of the Philippines Baguio Baguio City Philippines Department of Family and Community Medicine School of Medicine Saint Louis University Baguio City Philippines

ISBN: (纸本)9783031812439

This study analyzes the dengue incidence and potential hotspots in the 129 administrative units or barangays of Baguio City from 2010 to 2022 by investigating the yearly empirical spatial means, empirical temporal means, and incidence rates per morbidity week. The spatial mean was computed using the daily average incidence rates per 1,000 individuals in each barangay. The temporal mean was calculated using the daily average of the cumulative cases of all barangays. As a result, the barangays in the center of Baguio City were detected as dengue hotspots with the highest temporal means of 0.2558 and 0.1705 during September 2022 and August 2016, respectively. These barangays were mapped utilizing the Geographic Information System. Identification of these hotspots can assist the local government in designing control strategies to minimize the spread of disease. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information use

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Classifying Land Cover Using Hyperspectral Image and LiDAR Data 13

Classifying Land Cover Using Hyperspectral Image and LiDAR D...

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13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Zhang, Nian Thompson, Lara A. Department of Electrical and Computer Engineering University of the District of Columbia WashingtonDC20008 United States

ISBN: (纸本)9798331516147

This paper proposes an advanced approach to land cover classification by integrating hyperspectral and LiDAR data to leverage their complementary strengths. Hyperspectral sensors capture detailed reflectance across numerous spectral bands, providing unique spectral signatures that differentiate various land cover types. On the other hand, LiDAR technology offers high-resolution elevation data, enabling accurate characterization of surface structures. This paper details the process of converting unorganized LiDAR point clouds into organized formats for effective analysis, followed by a robust data fusion technique that combines spectral and spatial information. Utilizing a land cover classification methodology on the MUUFL Gulfport dataset, collected in November 2010, we evaluate the effectiveness of our approach by comparing classification accuracies of multiple machine learning algorithms, including kernel classification model, support vector machines, and others. Our results demonstrate significant improvements in classification accuracy when hyperspectral and LiDAR data are fused, highlighting the efficacy of this integrated methodology for complex land cover analysis. © 2025 IEEE.

关键词： Support vector machines

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Small, Medium, and Large Language Models for Text-to-SQL 43rd

Small, Medium, and Large Language Models for Text-to-SQL

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43rd international conference on Conceptual Modeling

作者： Oliveira, Aiko Nascimento, Eduardo Pinheiro, Joao Avila, Caio Viktor S. Coelho, Gustavo Feijo, Lucas Izquierdo, Yenier Garcia, Grettel Paes Leme, Luiz Andre P. Lemos, Melissa Casanova, Marco A. Pontificia Univ Catolica Rio de Janeiro Tecgraf Inst BR-22451900 Rio de Janeiro RJ Brazil Pontificia Univ Catolica Rio de Janeiro Dept Informat BR-22451900 Rio de Janeiro RJ Brazil Univ Fed Fluminense Inst Comp BR-24210310 Niteroi RJ Brazil Univ Fed Ceara UFC BR-60440900 Fortaleza Ceara Brazil

ISBN: (纸本)9783031758713;9783031758720

This paper investigates how the model size affects the ability of a Generative AI Language Model, or briefly a GLM, to support the text-to-SQL task for databases with large schemas typical of real-world applications. The paper first introduces a text-to-SQL framework that combines a prompt strategy and a Retrieval-Augmented Generation (RAG) technique, leaving as flexibilization points the GLM and the database. Then, it describes a benchmark based on an open-source database featuring a schema much larger than the schemas of most of the databases in familiar text-to-SQL benchmarks. The paper proceeds with experiments to assess the performance of the text-to-SQL framework instantiated with the benchmark database and GLMs of different sizes. The paper concludes with recommendations to help select which GLM size is appropriate for a text-to-SQL scenario, characterized by the difficulty of the expected NL questions and the data privacy requirements, among other characteristics.

关键词： text-to-SQL Generative AI Language Model Retrieval-Augmented Generation Prompt engineering Real-World Databases

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Textile-Based Incognito RFID for Patient Tracking

Textile-Based Incognito RFID for Patient Tracking

引用

2025 United States National Committee of URSI National Radio science Meeting, USNC-URSI NRSM 2025

作者： Nunnally, Amber Topsakal, Erdem Department of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA United States

ISBN: (纸本)9781946815200

The advancement of wireless technologies enabled the use of radio frequency identification (RFID) tags at an increasing rate in the past decade. In retail, RFIDs are placed in tags on merchandise for analysis of supply chain, inventory, and loss prevention (K. Finkenzeller, RFID Handbook: Fundamentals and Applications in Contactless Smart Cards, Radio Frequency Identification and Near-Field Communication, 3rd ed. Hoboken, NJ: Wiley, 2010.). However, in healthcare applications, RFIDs are not only used for item tracking but can also be utilized for patient tracking. When treating patients with memory impairment, RFID placement must be done in a noninvasive and unobtrusive way so as to not frighten the patient with the applied technology (R. Nayak, A. Singh, R. Padhye and L. Wang, "RFID in textile and clothing manufacturing: technology and challenges", Fashion and Textiles, vol. 2, no. 9, June 2015.), (J. Lundquist et al., "Textile-Based Inkjet-Printed RFIDs: Exploring wearable antennas in the real world [Bioelectromagnetics]", in IEEE Antennas and Propagation Magazine, vol. 66, no. 1, pp. 50-62, Feb. 2024.). © 2025 USNC-URSI.

关键词： Wearable antennas

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A Deep Learning Approach for Automatic Detection of Learner Engagement in Educational Context

A Deep Learning Approach for Automatic Detection of Learner ...

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17th international conference on computer Supported Education, CSEDU 2025

作者： Ayari, Asma Chaabouni, Mariem Ghezala, Henda Ben Univ. Manouba ENSI RIADI LR99ES26 Campus Manouba 2010 Tunisia Esprit School of Engineering Tunis Tunisia

ISBN: (纸本)9789897587467

The rise of online learning modalities, including fully online, hybrid, hy-flex, blended, synchronous, and asynchronous formats, has transformed educational landscapes. However, assessing learners' engagement in these environments, where direct teacher-student interaction is limited, poses a significant challenge for educators. In this context, Artificial Intelligence (AI) emerges as a transformative force within education, leveraging advanced algorithms and data analysis to personalize learning experiences and enhance teaching methodologies. The detection of engagement in educational settings is critical for evaluating the effectiveness of instructional strategies and fostering student participation. This study presents an approach to assess the learner’s engagement through detecting the facial emotions and classifying the level of engagement. This approach proposes a deep learning model specifically designed for automatic engagement detection in educational environments, employing a Convolutional Neural Network (CNN) approach. We introduce an optimized CNN model tailored for recognizing learner engagement through facial expressions in distance learning contexts. By integrating the foundational elements of traditional CNN architectures with the widely acclaimed VGG-16 model, our approach harnesses their strengths to achieve exceptional performance. Rigorous training, testing, and evaluation on an augmented dataset demonstrate the efficacy of our model, which significantly surpasses existing methodologies in engagement recognition tasks. Notably, our approach achieves an accuracy of 94.10% with a loss rate of 10.39%, underscoring its potential to enhance the assessment of learner engagement in online education. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Teaching

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Mild Cognitive Impairment Prediction Using Facial and Speech Data 22nd

Mild Cognitive Impairment Prediction Using Facial and Speech...

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22nd international conference on Advances in Mobile Computing and Multimedia Intelligence, MoMM 2024

作者： Lee, Chien-Cheng Huang, Wei-Chieh Chuang, Yi-Fang Department of Electrical Engineering Yuan Ze University Taoyuan Taiwan Institute of Public Health College of Medicine National Yang Ming Chiao Tung University Taipei112 Taiwan Department of Psychiatry Far Eastern Memorial Hospital New Taipei City220 Taiwan

ISBN: (纸本)9783031780486

Mild cognitive impairment (MCI) represents a transitional stage between the cognitive decline associated with normal aging and more severe conditions such as dementia. Early diagnosis of MCI is crucial for effective healthcare intervention. However, current detection methods are often costly and time-consuming. This study introduces a multimodal fusion network (MFN) designed to predict MCI more efficiently. The proposed network utilizes dual-stream ResNets to process both facial and speech features. These features, extracted from the convolutional and subsampling layers of the ResNets, are subsequently fused in a fully connected layer to generate the final prediction. The dataset comprises a total of 52 participant videos, with an equal distribution: 26 videos from participants with normal cognitive function and 26 videos from participants diagnosed with MCI. Experimental results demonstrate the effectiveness of this approach, with an F1 score of 0.89 across test participants. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Facial and Speech Data MCI Prediction Multimodal Fusion Network ResNet

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Forecasting Tax Revenue with Machine Learning and Granger Causality

Forecasting Tax Revenue with Machine Learning and Granger Ca...

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2025 international conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Prasad, Rajesh Segun, Ajisola Ayotola Ajay Kumar Garg Engineering College Department of Computer Science & Engineering Ghaziabad India African University of Science and Technology Department of Computer Science Abuja Nigeria

ISBN: (纸本)9798331527495

This paper examines various tax elements that give Nigeria's government a collection basket. This work examines how taxes provide an adequate revenue mechanism and their implications for fiscal policy and macroeconomic stability. To do this, FIRS (Federal Inland Revenue Service) information on various tax components was collected monthly from 2010 to 2021. Our final model that was the best predictor of revenue was chosen after weighing models: multivariate extended short-term memory networks (MLSTM), seasonal autoregressive integrated moving average (SARIMA), multivariate linear regression (MLR) and a hybrid model (MLSTM & XGBoost). Each model was rated on predicted value and accuracy. This multiple independence of prediction factor was considered in the study. Granger Causal relationship between the Dependent and Independent was conducted for hypothesis testing. With increasing numbers of independent variables, the hybrid, MLSTM, and MLR models were better than one another. The hybrid model achieved an R2 value of 99.7%. © 2025 IEEE.

关键词： Multiple linear regression

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