检索结果-内蒙古大学图书馆

P2TC: A Lightweight Pyramid Pooling Transformer-CNN Network for Accurate 3D Whole Heart Segmentation

IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4279-4289页

作者： Hengfei Cui Yifan Wang Fan Zheng Yan Li Yanning Zhang Yong Xia National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China Chongqing Innovation Center Northwestern Polytechnical University Chongqing China

Cardiovascular disease is a leading global cause of death, requiring accurate heart segmentation for diagnosis and surgical planning. Deep learning methods have been demonstrated to achieve superior performances in cardiac structures segmentation. However, there are still limitations in 3D whole heart segmentation, such as inadequate spatial context modeling, difficulty in capturing long-distance dependencies, high computational complexity, and limited representation of local high-level semantic information. To tackle the above problems, we propose a lightweight Pyramid Pooling Transformer-CNN (P2TC) network for accurate 3D whole heart segmentation. The proposed architecture comprises a dual encoder-decoder structure with a 3D pyramid pooling Transformer for multi-scale information fusion and a lightweight large-kernel Convolutional Neural Network (CNN) for local feature extraction. The decoder has two branches for precise segmentation and contextual residual handling. The first branch is used to generate segmentation masks for pixel-level classification based on the features extracted by the encoder to achieve accurate segmentation of cardiac structures. The second branch highlights contextual residuals across slices, enabling the network to better handle variations and boundaries. Extensive experimental results on the Multi-Modality Whole Heart Segmentation (MM-WHS) 2017 challenge dataset demonstrate that P2TC outperforms the most advanced methods, achieving the Dice scores of 92.6% and 88.1% in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) modalities respectively, which surpasses the baseline model by 1.5% and 1.7%, and achieves state-of-the-art segmentation results.

关键词： Transformers Image segmentation Three-dimensional displays Heart Accuracy Feature extraction Convolutional neural networks Context modeling Computational modeling Solid modeling

来源：评论

学校读者我要写书评

暂无评论

A Study and Recognition of Alzheimer's Disease Using Machine Learning 8

A Study and Recognition of Alzheimer's Disease Using Machine...

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Basha, S.K. Masthan Ashalatha, G. Vara Prasad, Srisailapu D Al-Hameed, Mazin R. Zearah, Sajad Ali Saikumar, K. Vignan's Institute of Management and Technology for Women ECE Department Hyderabad India VNR Vignana Jyothi Institute of Engineering and Technology Department of Computer Science-CS-DS&AI&DS Telangana Hyderabad India GITAM Deemed to Be University Computer Science and Engineering School of Technology Telangana Hyderabad India National University of Science and Technology College of Engineering Technology Department of Medical Device Industry Engineering Dhi Qar Iraq Al-Ayen University Scientific Research Center Thi-Qar Iraq Koneru Lakshmaiah Education Foundation Andhra Pradesh India

ISBN: (纸本)9798350334012

Nowadays, Alzheimer's disease is one of the most severe threats to people of all ages and socioeconomic backgrounds. Their everyday activities may measure this disorder, and how they speak about it shows how concerned they are about it. The way we live today is different from how it was in the past because of our working environment and the lack of opportunities to interact with people. WHO estimates that about 10 billion individuals perish each year due to Alzheimer's disease since not been appropriately diagnosed and treated. An advanced Machine Learning-based recognition system design performs in this research work. The proposed model has been designed based on the human being EEG dataset(testing) and the Kaggle Alzheimer dataset (training). The developed intelligent system will be updated with data collection and pre-processing through the Random Forest machine model to locate our health condition and recognize daily routine changes. Only by developing a machine-based approach will we be able to discover Alzheimer's patients who previously had no idea that they had the illness and are now unable to reverse the progression of their condition. The designed application has attained a sensitivity of 93.01%, an accuracy of 91.32%, a recall of 97.28%, and a detection rate of 98.91%. The implemented application is beneficial to investigators and surgeons for easy treatment. The proposed RFO Machine learning design competes with present technology and outperforms methodology. © 2023 IEEE.

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Systematic Biases in LLM Simulations of Debates

Systematic Biases in LLM Simulations of Debates

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Taubenfeld, Amir Dover, Yaniv Reichart, Roi Goldstein, Ariel The Hebrew University of Jerusalem School of Computer Science and Engineering Israel Google Research United States The Hebrew University Business School Jerusalem Israel Federmann Center for the Study of Rationality Hebrew University Jerusalem Israel Faculty of Data and Decision Sciences Technion Israel Department of Cognitive and Brain Sciences Hebrew University Jerusalem Israel

ISBN: (纸本)9798891761643

The emergence of Large Language Models (LLMs), has opened exciting possibilities for constructing computational simulations designed to replicate human behavior accurately. Current research suggests that LLM-based agents become increasingly human-like in their performance, sparking interest in using these ai agents as substitutes for human participants in behavioral studies. However, LLMs are complex statistical learners without straightforward deductive rules, making them prone to unexpected behaviors. Hence, it is crucial to study and pinpoint the key behavioral distinctions between humans and LLM-based agents. In this study, we highlight the limitations of LLMs in simulating human interactions, particularly focusing on LLMs' ability to simulate political debates on topics that are important aspects of people's day-to-day lives and decision-making processes. Our findings indicate a tendency for LLM agents to conform to the model's inherent social biases despite being directed to debate from certain political perspectives. This tendency results in behavioral patterns that seem to deviate from well-established social dynamics among humans. We reinforce these observations using an automatic self-fine-tuning method, which enables us to manipulate the biases within the LLM and demonstrate that agents subsequently align with the altered biases. These results underscore the need for further research to develop methods that help agents overcome these biases, a critical step toward creating more realistic simulations. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

SINDy-CRN: Sparse Identification of Chemical Reaction Networks from data

SINDy-CRN: Sparse Identification of Chemical Reaction Networ...

引用

IEEE Conference on Decision and Control

作者： Nirav Bhatt Bayu Jayawardhana Santiago Sánchez-Escalonilla Plaza Department of Biotechnology Research Center for Data Science and AI (DSAI) Indian Institute of Technology Madras India Faculty of Science and Engineering Engineering and Technology Institute Groningen University of Groningen Groningen The Netherlands

This work considers an important problem of identifying the dynamics of chemical reaction networks from time-series data. We propose an approach to identify complex chemical reaction networks (CRN) from concentration data using the concept of sparse model identification. Particularly, we demonstrate challenges associated with the application of the sparse identification of nonlinear dynamics (SINDy) and its variants to data obtained from CRNs. We develop a SINDy-CRN algorithm based on the properties of CRNs for identifying governing equations of a CRN. The proposed algorithm is illustrated using a numerical simulation example.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evolving Multi-Access Edge Computing (MEC) for Diverse Ubiquitous Resources Utilization: A Survey

引用

Telecommunication Systems 2025年第2期88卷 1-41页

作者： Khattak, Muhammad Ilyas Yuan, Hui Khan, Ajmal Ahmad, Ayaz Ullah, Inam Ahmed, Manzoor School of Control Science and Engineering Shandong University Shandong Province Jinan China Communication and Information Research Center Sultan Qaboos University Muscat Oman Department of Electrical Engineering College of Engineering King Faisal University Al-Ahsa Saudi Arabia Jinan China School of Computer and Information Science and AI Industrial Technology Research Hubei Engineering University Xiaogan China

The surge in smart device usage and the demand for high-throughput, low-latency mobile networks have accelerated the adoption of distributed smart applications and Collaborative Resource Management (CRM) techniques such as Multi-Access Edge Computing (MEC). With its ability to handle large data volumes, computational demands, scalability, and high energy consumption, MEC represents the next generation of Mobile Cloud Computing (MCC), addressing key challenges in emerging smart applications. MEC supports real-time, high-throughput, and energy-efficient techniques like task offloading and ultra-reliable low-latency communication (URLLC). However, as these techniques become more widespread, their diversity increases, leading to complexities that make them harder to understand and validate in different applications. This includes their use in areas like assistance for unmanned vehicles (UVs), autonomous driving, unmanned aerial vehicles (UAVs), high altitude platforms (HAPs), underwater unmanned vehicles (UUVs), satellite air-ground communication (SAGC), and other emerging systems that rely on Collaborative Resource Management (CRM). Although a number of review articles pertaining to MEC systems exist, we analyzed techniques beyond task offloading that explore MEC system applicability in diverse domains, including UUVs, vehicular edge computing, Re-configurable Intelligent Surfaces (RIS), Massive MIMO, autonomous driving, HAPs, and SAGC. From the perspective of these optimization techniques, this article highlights their role in shaping MEC’s evolution, focusing on parameters such as real-time response, energy efficiency, and other critical factors in task offloading processes. We also explored the role of Standard Development Organizations (SDOs), such as ETSI and IEEE, in advancing MEC systems. This included an analysis of offloading techniques based on their principles, features, and trade-offs, using key performance indicators (KPIs) for fair comparison, while ident

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series 41

An Empirical Examination of Balancing Strategy for Counterfa...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Huang, Qiang Meng, Chuizheng Cao, Defu Huang, Biwei Chang, Yi Liu, Yan School of Artificial Intelligence Jilin University Jilin Changchun China International Center of Future Science Jilin University Jilin Changchun China Department of Computer Science University of Southern California California Los Angeles United States Halicioğlu Data Science Institute University of California San Diego San DiegoCA United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Jilin Changchun China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings. Copyright 2024 by the author(s)

关键词： Time series analysis

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor Detection Using ADARN Optimizer

Brain Tumor Detection Using ADARN Optimizer

引用

Disruptive Technologies (ICDT), International Conference on

作者： Rajiv Kumar Avinash Dwivedi Shivani Joshi Vikas Chandra Tripathi Department of Applied Computational Science and Engineering NVIDIA AI Learning & Research Center G L Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science & Engineering Galgotias University Greater Noida Department of Computer Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida India

The use of MRI scans to identify and categorise brain tumors is the main focus of this study. The goal is to develop a precise and reliable method for early identification and accurate categorisation of brain tumours, including pituitary neoplasms, meningiomas, and gliomas. Deep learning methods were utilized to analyze MRI image databases, resulting in a 96% identification accuracy for brain tumors and a 98% categorization accuracy for the three types. This research demonstrates the potential of deep learning techniques for accurate brain tumor identification and categorization. The early identification and precise categorization of brain tumors can assist medical professionals in making informed decisions about the best treatment options for patients, leading to better outcomes and survival rates. The article provides an extensive review of current methods for identifying and classifying brain tumors using MRI data. It emphasizes the critical importance of this field of study in improving patient outcomes and reducing unnecessary treatments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MuTCELM: An optimal multi-TextCNN-based ensemble learning for text classification

引用

Heliyon 2024年第19期10卷 e38515页

作者： Agbesi, Victor Kwaku Chen, Wenyu Yussif, Sophyani Banaamwini Ukwuoma, Chiagoziem C. Gu, Yeong Hyeon Al-antari, Mugahed A. School of Computer Science and Engineering University of Electronic Science and Technology of China No. 2006 Xiyuan Ave West Hi-Tech Zone Sichuan Chengdu China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology No. 610059 Sichuan Chengdu China Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul 05006 South Korea

Feature extraction plays a critical role in text classification, as it converts textual data into numerical representations suitable for machine learning models. A key challenge lies in effectively capturing both semantic and contextual information from text at various levels of granularity while avoiding overfitting. Prior methods have often demonstrated suboptimal performance, largely due to the limitations of the feature extraction techniques employed. To address these challenges, this study introduces Multi-TextCNN, an advanced feature extractor designed to capture essential textual information across multiple levels of granularity. Multi-TextCNN is integrated into a proposed classification model named MuTCELM, which aims to enhance text classification performance. The proposed MuTCELM leverages five distinct sub-classifiers, each designed to capture different linguistic features from the text data. These sub-classifiers are integrated into an ensemble framework, enhancing the overall model performance by combining their complementary strengths. Empirical results indicate that MuTCELM achieves average improvements across all datasets in accuracy, precision, recall, and F1-macro scores by 0.2584, 0.2546, 0.2668, and 0.2612, respectively, demonstrating significant performance gains over baseline models. These findings underscore the effectiveness of Multi-TextCNN in improving model performance relative to other ensemble methods. Further analysis reveals that the non-overlapping confidence intervals between MuTCELM and baseline models indicate statistically significant differences, suggesting that the observed performance improvements of MuTCELM are not attributable to random chance but are indeed statistically meaningful. This evidence indicates the robustness and superiority of MuTCELM across various languages and text classification tasks. © 2024 The Author(s)

关键词： Deep learning Ensemble learning Feature extraction Multi-TextCNN Text classification

来源：评论

学校读者我要写书评

暂无评论

Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

引用

IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning for Pediatric Glucose Forecasting 4

Transfer Learning for Pediatric Glucose Forecasting

引用

4th Machine Learning for Health Symposium, ML4H 2024

作者： Ryser, Alain Feng, Chuhao Scheithauer, Tobias Pfister, Marc Burckhardt, Marie-Anne Bachmann, Sara Marx, Alexander Vogt, Julia E. Department of Computer Science ETH Zurich Zürich Switzerland Department of Statistics Research Center Trustworthy Data Science and Security of the University Alliance Ruhr TU Dortmund University Dortmund Germany Department of Biosystems Science and Engineering ETH Zurich Basel Switzerland Pediatric Pharmacology and Pharmacometrics University Children's Hospital Basel Basel Switzerland Department of Clinical Research University of Basel Basel Switzerland Pediatric Endocrinology and Diabetology University Children's Hospital Basel Basel Switzerland SIB Swiss Institute of Bioinformatics Ecublens Switzerland

Effective blood glucose forecasting is crucial for detecting events such as hypo- or hyperglycemia in people with diabetes, yet remains challenging in domains with only small, heterogeneous datasets, such as in the pediatric field. We present GluTFT, a novel transfer learning approach that allows leveraging models pretrained on publicly available adult diabetes datasets for pediatric glucose forecasting. We systematically evaluate multiple transfer learning strategies, including zero-shot prediction and fine-tuning across the entire dataset as well as specific subgroups of participants. Our extensive experiments reveal that GluTFT excels on the pretraining datasets and significantly outperforms baseline methods when fine-tuned. To validate the clinical relevance of our approach, we evaluate Parkes Error Grids, demonstrating the quality of GluTFT's blood glucose forecasts and its potential for enhancing clinical decision-making for pediatric diabetes. © 2024 A. Ryser, C. Feng, T. Scheithauer, M. Pfister, M.-A. Burckhardt, S. Bachmann, A. Marx & J.E. Vogt.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：