检索结果-内蒙古大学图书馆

Innovative fusion of VGG16, MobileNet, EfficientNet, AlexNet, and ResNet50 for MRI-based brain tumor identification

Iran Journal of Computer Science 2024年第1期8卷 185-215页

作者： Kia, Marjan Sadeghi, Soroush Safarpour, Homayoun Kamsari, Mohammadreza Jafarzadeh Ghoushchi, Saeid Ranjbarzadeh, Ramin Department of Business Information System Business Analysis (ISBA) Park University Parkville United States School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Software Engineering University of Szeged Szeged Hungary Faculty of Electrical Engineering Malek-Ashtar University of Technology(MUT) Esfahan Iran Faculty of Industrial Engineering Urmia University of Technology Urmia Iran School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland

This study presents a novel approach for brain MRI classification by integrating multiple state-of-the-art deep learning (DL) architectures, including VGG16, EfficientNet, MobileNet, AlexNet, and ResNet50, with an attention mechanism. The primary objective was to accurately detect the presence or absence of brain tumors—a critical challenge in medical diagnosis. The methodology was validated on a comprehensive dataset comprising 3264 brain MRI scans, systematically divided into training, validation, and testing subsets. Preprocessing techniques tailored for MRI data were employed to enhance input quality. Each model was initially developed and evaluated individually to establish a performance baseline before their integration. The proposed ensemble technique capitalized on the complementary strengths of these models to enhance classification accuracy and robustness. The inclusion of an attention mechanism further refined the models by emphasizing critical regions in the MRI scans, thereby boosting predictive performance. In addition, data augmentation techniques were applied to mitigate overfitting and increase dataset resilience. Performance metrics, including precision, accuracy, and recall, demonstrated significant improvements across all evaluations, particularly for the models incorporating the attention mechanism. These findings highlight the potential of combining advanced DL architectures with attention mechanisms as a promising strategy for precise and reliable brain tumor classification in MRI scans. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AlexNet Attention mechanism Brain tumor classification MobileNet ResNet50

来源：评论

学校读者我要写书评

暂无评论

Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

引用

Computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

来源：评论

学校读者我要写书评

暂无评论

Distilling Task Specific Models from Ernie for Chinese Medical Intention Classification 9

Distilling Task Specific Models from Ernie for Chinese Medic...

引用

9th IEEE Smart World Congress, SWC 2023

作者： Wang, Zumin Xu, Chang Chen, Liming Qin, Jing Ji, Changqing School of Information Engineering Dalian University Dalian China School of Computing Ulster University Belfast United Kingdom School of Software Engineering Dalian University Dalian China School of Physical Science and Technology School of Information Engineering Dalian University Dalian China

ISBN: (纸本)9798350319804

The improvement of intention classification of medical query can effectively improve the performance of search engines and Question-Answering systems, and further improve medical services. The Pre-Trained language models has achieved good performance in various NLP tasks, but the slow reasoning speed and high requirements for storing computing resources of the model make it difficult for medical institutions to deploy relevant models. In this paper, we propose a knowledge distillation model based on ERNIE to solve the Chinese medical intention classification task, in order to provide a Small-Scale and more efficient intention classification model. We propose a Two-Stage framework, which is distilled on specific domain knowledge and tasks, and we use data augmentation methods to minimize the loss of accuracy caused by model compression. Specifically, we distill the Embedding layer and Transformer layer of the teacher model in the Domain-Knowledge-Specific-Distillation stage, so that our student model can better capture the general domain and Medical specific domain knowledge of the teacher model. In the Intent-Classification Task-specific Distillation stage, we also distilled the Prediction layer and added Soft-Label and Hard-Label to calculate the Prediction layer loss to ensure that the student model acquires knowledge of specific tasks. Considering the lack of labeled data available for training in the medical field and intention classification task, we use word exchange and whole entity masking methods to augment the labeled data and augment the generalization ability of student models. We conducted experiments on a publicly available CBLUE dataset, and the experimental results showed that our proposed 4-layer student model retained more than 98.6% of the language comprehension capabilities of the original 12-layer teacher model - ERNIE, while reducing the computational resources required and significantly accelerating reasoning speed. © 2023 IEEE.

关键词： Intent Classification Knowledge Distillation Medical NLP Pre-Trained Language Models

来源：评论

学校读者我要写书评

暂无评论

Quantization and Deployment Study of Classification Models for Embedded Platforms 28

Quantization and Deployment Study of Classification Models f...

引用

28th International Conference on Automation and computing, ICAC 2023

作者： Huang, Zihan Jin, Jin Zhang, Chaolong Xu, Zhijie Xu, Yuanping Kong, Chao Wen, Qin Tang, Dan School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing & Engineering University of Hudderseld Queensgate Hudderseld United Kingdom College of Artificial Intelligence Neijiang Normal University

ISBN: (纸本)9798350335859

Deep learning models find extensive applications across various domains. However, their large number of parameters, high storage requirements, and computational overhead pose challenges for deploying these models on resource-constrained embedded devices. This study focuses on addressing this issue by exploring techniques to optimize and deploy lightweight models on embedded devices. The approach involves optimization and adjustment of the model, followed by model conversion, quantization, and quantization calibration, aimed at reducing model size and improving inference speed. Notably, improvements are made to the quantization calibration algorithm to mitigate accuracy loss caused by model quantization. The experimental results demonstrate that light quantization significantly reduces model size, facilitating storage on embedded devices. Although there is a slight reduction in accuracy, the inference speed is substantially improved, enabling real-time human face recognition in video scenarios. © 2023 IEEE.

关键词： Calibration

来源：评论

学校读者我要写书评

暂无评论

A survey for light field super-resolution

引用

High-Confidence computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

来源：评论

学校读者我要写书评

暂无评论

Schrödinger Mechanisms: Optimal Differential Privacy Mechanisms for Small Sensitivity

Schrödinger Mechanisms: Optimal Differential Privacy Mechan...

引用

2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Alghamdi, Wael Asoodeh, Shahab Calmon, Flavio P. Felipe Gomez, Juan Kosut, Oliver Sankar, Lalitha Harvard University School of Engineering and Applied Sciences United States McMaster University Department of Computing and Software Canada Arizona State University School of Electrical Computer and Energy Engineering United States

ISBN: (纸本)9781665475549

We consider the problem of designing optimal differential privacy mechanisms with a favorable privacy-utility tradeoff in the limit of a large number n of compositions (i.e., sequential queries). Here, utility is measured by the average distance between the mechanism's input and output, evaluated by a cost function c. We show that if n is sufficiently large and the sensitivities of all queries are small, then the optimal additive noise mechanism has probability density function fully characterized by the ground-state eigenfunction of the Schrödinger operator with potential c. This leads to a family of optimal mechanisms, dubbed the Schrödinger mechanisms, depending on the choice of the cost function. Instantiating this result, we demonstrate that for c(x) = x2 the Gaussian mechanism is optimal, and for c(x) = |x|, the optimal mechanism is obtained by the Airy function, thereby leading to the Airy mechanism. © 2023 IEEE.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

An examination of critical factors influencing consumers' behavior in live streaming e-commerce 24

An examination of critical factors influencing consumers' be...

引用

5th International Conference on Computer Science and Management Technology, ICCSMT 2024

作者： Chen, Jian Sun, Wenying Zhao, Wenjin School of Software Engineering Chengdu University of Information Technology Sichuan Chengdu China Cox School of Business Southern Methodist University Dallas United States School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Sichuan Chengdu China

ISBN: (纸本)9798400709999

The rapid development of live streaming e-commerce calls for a deeper understanding of the factors influencing consumer *** paper uses web crawling to gather data, combining Latent Dirichlet Allocation (LDA) and word frequency analysis for feature *** regression is applied to validate critical factors influencing consumer behavior in live streaming *** data covers March to September 2023, including 361.68 hours of live streaming, 3, 093, 402 words of anchor speeches, and 4, 095, 309 records of consumer *** critical factors under six dimensions from three perspectives were identified and the combination of three perspectives has a role in influencing consumer *** of critical factors influenced consumer behavior, with the most significant impacts of the anchor's positivity and promotion, the product's delivery service, and the number of bullet screens in the *** paper highlights the combined influence of anchor, product, and scene perspectives on consumer behavior, providing valuable insights for optimization. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Web crawler

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Analysis of Climate Change Impacts on Public Health: A Systematic Review and Future Directions

Data-Driven Analysis of Climate Change Impacts on Public Hea...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Yarmohammadtoosky, Sahar Xie, Ying Kennesaw State University School of Data Science and Analytics MariettaGA United States Kennesaw State University College of Computing and Software Engineering MariettaGA United States

ISBN: (纸本)9798350362480

Climate change profoundly impacts human health, ecosystems, and food and water security. This review provides a comprehensive analysis of climate change's effects on human health using data science methods, including statistical and machine learning models, to assess current knowledge. It identifies critical gaps in data collection and methodological limitations across different regions and climates. Key recommendations include improving data resolution and duration to enhance model accuracy, as well as fostering collaboration between data scientists, public health experts, and climate specialists. The review underscores the need for innovative, data-driven models to better predict and address the health impacts of climate change. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Optimal Multidimensional Differentially Private Mechanisms in the Large-Composition Regime

Optimal Multidimensional Differentially Private Mechanisms i...

引用

2023 IEEE International Symposium on Information Theory, ISIT 2023

ISBN: (纸本)9781665475549

We construct vector differentially-private (DP) mechanisms that are asymptotically optimal in the limit of the number of compositions growing without bound. First, we derive via the central limit theorem a reduction from DP to a KL-divergence minimization problem. Second, we formulate the general theory of spherically-symmetric DP mechanisms in the large-composition regime. Specifically, we show that additive, continuous, spherically-symmetric DP mechanisms are optimal if one considers a spherically-symmetric cost (e.g., bounded noise variance) and an l2 sensitivity metric. We then formulate a finite-dimensional problem that produces noise distributions that can get arbitrarily close to optimal among monotone mechanisms. Finally, we demonstrate numerically that our proposed mechanism achieves better DP parameters than the vector Gaussian mechanism for the same variance constraint. © 2023 IEEE.

关键词： Information theory

来源：评论

学校读者我要写书评

暂无评论

Explainable AI: A Way to Achieve Trustworthy AI 10

Explainable AI: A Way to Achieve Trustworthy AI

引用

10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024

作者： Li, Yanlu Xiao, Yufeng Gong, Yinyan Zhang, Ronglin Huo, Yingzi Wu, Yanbing Hunan Key Laboratory for Service Computing Novel Software Technology Xiangtan411201 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350389524

AI is black-box and non-explainable, in other words, due to the complexity of the decision-making process of AI, people are unable to know why and how AI makes the decision. For these reasons, people will question and worry about AI's decision-making. Against this background, researchers have proposed the concept of Explainable Artificial Intelligence (XAI). The aim of this paper is to help readers be able to better understand XAI through extensive surveys. In this paper, we discuss the concept of XAI, the motivation of the research, the current status of the research, and the difficulties in the research process. At the same time, we also discuss the specific methods to realize AI explainability by dividing AI explainability methods into two ways: transparent design and ex post facto interpretation. In addition, we select the three fields of healthcare, finance, and autonomous driving, discuss the application of XAI in these three fields, and select some representative results as examples through surveys. Finally, we summarize this article and present our outlook on the future development of XAI. © 2024 IEEE.

关键词： Machine 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：