检索结果-内蒙古大学图书馆

Classifying Multi-Lingual Reviews Sentiment Analysis in Arabic and English Languages Using the Stochastic Gradient Descent Model

引用

computers, Materials & Continua 2025年第4期83卷 1275-1290页

作者： Yasser Alharbi Sarwar Shah Khan College of Computer Science and Engineering University of HailHail55436Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan

Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic *** study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie *** allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language *** adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better *** distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for *** proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and *** SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both *** indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English *** study helps deepen the understanding of sentiments across various linguistic *** many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.

关键词： Sentiment analysis stochastic gradient descent reviews English IMDb dataset Arabic dataset

来源：评论

学校读者我要写书评

暂无评论

A Modified Bi-Directional Evolutionary Structural Optimization Procedure with Variable Evolutionary Volume Ratio Applied to Multi-Objective Topology Optimization Problem

引用

computer Modeling in engineering & sciences 2023年第4期135卷 511-526页

作者： Xudong Jiang Jiaqi Ma Xiaoyan Teng School of Mechanical and Power Engineering Harbin University of Science and TechnologyHarbin150080China College of Mechanical and Electrical Engineering Harbin Engineering UniversityHarbin150001China

Natural frequency and dynamic stiffness under transient loading are two key performances for structural design related to automotive,aviation and construction *** article aims to tackle the multi-objective topological optimization problem considering dynamic stiffness and natural frequency using modified version of bi-directional evolutionary structural optimization(BESO).The conventional BESO is provided with constant evolutionary volume ratio(EVR),whereas low EVR greatly retards the optimization process and high EVR improperly removes the efficient *** address the issue,the modified BESO with variable EVR is *** compromise the natural frequency and the dynamic stiffness,a weighting scheme of sensitivity numbers is employed to form the Pareto solution *** numerical examples demonstrate that the optimal solutions obtained from the modified BESO method have good agreement with those from the classic BESO *** importantly,the dynamic removal strategy with the variable EVR sharply springs up the optimization ***,it is concluded that the modified BESO method with variable EVR can solve structural design problems using multi-objective optimization.

关键词： Bi-directional evolutionary structural optimization variable evolutionary volume ratio multi-objective optimization weighted sum topology optimization

来源：评论

学校读者我要写书评

暂无评论

Space debris environment engineering model 2019:Algorithms improvement and comparison with ORDEM 3.1 and MASTER-8

引用

Chinese Journal of Aeronautics 2024年第5期37卷 392-409页

作者： Yuyan LIU Runqiang CHI Baojun PANG HU Diqi Wuxiong CAO Dongfang WANG School of Astronautics Harbin Institute of TechnologyHarbin 150001China College of Safety Science&Engineering Civil Aviation University of ChinaTianjin 300300China

As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment engineering Model(SDEEM)has been developed to provide support for risk assessment of *** contrast with SDEEM2015,SDEEM2019,the latest version,extends the orbital range from the Low Earth Orbit(LEO)to Geosynchronous Orbit(GEO)for the years *** this paper,improved modeling algorithms used by SDEEM2019 in propagating simulation,spatial density distribution,and spacecraft flux evaluation are *** debris fluxes of SDEEM2019 are compared with those of three typical models,i.e.,SDEEM2015,Orbital Debris engineering Model 3.1(ORDEM 3.1),and Meteoroid and Space Debris Terrestrial Environment Reference(MASTER-8),in terms of two assessment *** orbital cases,including the Geostationary Transfer Orbit(GTO),Sun-Synchronous Orbit(SSO)and International Space Station(ISS)orbit,are selected for the spacecraft assessment mode,and the LEO region is selected for the spatial density assessment *** analysis indicates that compared with previous algorithms,the variable step-size orbital propagating algorithm based on semi-major axis control is more precise,the spatial density algorithm based on the second zonal harmonic of the non-spherical Earth gravity(J_(2))is more applicable,and the result of the position-centered spacecraft flux algorithm is more *** comparison shows that SDEEM2019 and MASTER-8 have consistent trends due to similar modeling processes,while the differences between SDEEM2019 and ORDEM 3.1 are mainly caused by different modeling approaches for uncatalogued debris.

关键词： SDEEM2019 Space debris propagating algorithm Spatial density algorithm ORDEM 3.1 MASTER-8

来源：评论

学校读者我要写书评

暂无评论

Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

引用

computers, Materials & Continua 2024年第7期80卷 1543-1561页

作者： Bing Li Liangyu Wang Xia Liu Hongbin Fan Bo Wang Shoudi Tong School of Automation Harbin University of Science and TechnologyHarbin150080China Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration Harbin150080China School of Computer Engineering Technology Guangdong Institute of Science and TechnologyZhuhai519090China

Nuclearmagnetic resonance imaging of breasts often presents complex *** tumors exhibit varying sizes,uneven intensity,and indistinct *** characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor ***,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention ***,the breast region of interest is extracted to isolate the breast area from surrounding tissues and ***,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor *** incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion ***,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel ***,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional *** was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the *** results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.

关键词： Dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI) breast tumor segmentation multi-scale dilated convolution boundary attention the hybrid loss function with boundary weight

来源：评论

学校读者我要写书评

暂无评论

DeepGAN: Utilizing generative adversarial networks for improved deep learning

引用

International Journal of Knowledge-Based and Intelligent engineering Systems 2024年第4期28卷 732-748页

作者： V, Edward Naveen A, Jenefa T.M, Thiyagu A, Lincy Taurshia, Antony Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Department of Computer Science and Engineering National Engineering College India

In the realm of deep learning, Generative Adversarial Networks (GANs) have emerged as a topic of significant interest for their potential to enhance model performance and enable effective data augmentation. This paper addresses the existing challenges in synthesizing high-quality data and harnessing the capabilities of GANs for improved deep learning outcomes. Unlike traditional approaches that heavily rely on manually engineered data augmentation techniques, our work introduces a novel framework that leverages DeepGANs to autonomously generate diverse and high-fidelity data. Our experiments encompass a diverse spectrum of datasets, including images, text, and time series data. In the context of image classification tasks, we conduct experiments on the widely recognized CIFAR-10 dataset, which consists of 50,000 image samples. Our results demonstrate the remarkable efficacy of DeepGANs in enhancing model performance across various data domains. Notably, in image classification using the CIFAR-10 dataset, our innovative approach achieves an impressive accuracy of 97.2%. This represents a substantial advancement beyond conventional CNN models, underscoring the profound impact of DeepGANs in the realm of deep learning. In summary, this research sheds light on DeepGANs as a fundamental component in the pursuit of enhanced deep learning performance. Our framework not only overcomes existing limitations but also heralds a new era of data augmentation, with generative adversarial networks leading the way. The attainment of an accuracy rate of 97.2% on CIFAR-10 serves as a compelling testament to the transformative potential of DeepGANs, solidifying their pivotal role in the future of deep learning. This promises the development of more robust, adaptive, and accurate models across a myriad of applications, marking a significant contribution to the field. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Outlier Detection Model Based on Autoencoder and Data Augmentation for High-Dimensional Sparse Data 9th

Outlier Detection Model Based on Autoencoder and Data Augmen...

引用

9th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Zhang, Haitao Ma, Wenhai Han, Qilong Ma, Zhiqiang College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9789819959679

This paper aims to address the problems of data imbalance, parameter adjustment complexity, and low accuracy in high-dimensional data anomaly detection. To address these issues, an autoencoder and data augmentation-based anomaly detection model for high-dimensional sparse data is proposed (SEAOD). First, the model solves the problem of imbalanced data by using the weighted SMOTE algorithm and ENN algorithm to fill in the minority class samples and generate a new dataset. Then, an attention mechanism is employed to calculate the feature similarity and determine the structure of the neural network so that the model can learn the data features. Finally, the data are dimensionally reduced based on the autoencoder, and the sparse high-dimensional data are mapped to a low-dimensional space for anomaly detection, overcoming the impact of the curse of dimensionality on detection algorithms. The experimental results show that on 15 public datasets, this model outperforms other comparison algorithms. Furthermore, it was validated on industrial air quality datasets and achieved the expected results with practicality. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Nonspherical photonic scatterers and design criterion for efficient passive daytime radiative cooling

引用

science China(Technological sciences) 2025年第2期68卷 1-14页

作者： Xinping ZHANG Tairong KANG Fuqiang WANG Xiang LI Hongliang YI School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China College of New Energy Harbin Institute of Technology at WeihaiWeihai 264209China Construction Eco-Environmental Protection Technology Co. Ltd.Suzhou 215123China

Passive daytime radiative cooling(PDRC)exhibits efficient cooling performance to reduce global fossil energy consumption and has attracted enormous *** efficient PDRC technology for achieving daytime subambient cooling is supposed to exhibit ultra-high solar reflectivity and infrared emissivity within atmospheric windows,among which ultra-high solar reflectivity is the principal *** most PDRC technologies,spherical particles are selected as scatterers to achieve high solar reflectivity because the Mie scattering effect of spherical scatterers can provide a high scattering ***,the weak backward scattering of spherical scatterers remains a *** this study,several nonspherical scatterers with strong backward scattering are introduced and researched *** numerical analysis results indicate that pyramidal scatterers provide the strongest backward scattering and square/circular scatterers exhibit the highest scattering ***,by considering scattering efficiency and asymmetry factor comprehensively,a dimensionless evaluation parameter is proposed,which can be employed as a quantitative design criterion to achieve the optimal-object-oriented designing of superior *** work can provide an innovative strategy for designing efficient passive daytime radiative cooling materials.

关键词： radiative cooling nonspherical scatterers solar reflectivity backward scattering design criterion

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Retrieval of High-Resolution Fingerprints Based on Pore Feature

Hierarchical Retrieval of High-Resolution Fingerprints Based...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jing Xu, Yuanrong Dong, Suyu Wang, Wei Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Harbin Institute of Technology School of Computer Science and Technology Weihai China Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350386226

Faced with an escalating number of fingerprint images, most existing retrieval approachs suffer from a common problem: diminishing computational efficiency. This paper presents a hierarchical retrieval system tailored for high-resolution fingerprint images that utilizes abundant pore features and robust recognizability to improve retrieval performance. The framework comprises two core components. Firstly, a CNN-based feature extraction network is established, incorporating an attention mechanism to capture pore features in fingerprint images comprehensively. Subsequently, a hierarchical fingerprint retrieval approach is introduced, involving connection graph construction and a hierarchy of jump table structures for efficient retrieval of query pores. Empirical experiments conducted on high-resolution fingerprint image datasets underscore the system's effectiveness. Compared with other advanced pore-based fingerprint retrieval methods, the proposed method exhibits a notable rise in the hit rate with reduced penetration rates, significantly reducing the retrieval time. © 2024 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

A Weak Supervision-based Robust Pretraining Method for Medical Visual Question Answering

A Weak Supervision-based Robust Pretraining Method for Medic...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： He, Shuning Pan, Haiwei Zhang, Kejia Gong, Cheng Li, Zhe Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350337488

Medical images are complex, and the annotation of medical images requires high expertise. It would be time-consuming and costly to annotate directly for experts. As a result, one of the primary challenges currently faced by medical visual question answering (VQA) is the lack of large-scale annotated data. To address this issue, a Weak Supervision-based Robust Pretraining (WSRP) method for medical VQA is proposed. Specifically, our method builds upon a contrastive language-image pretraining framework by introducing adversarial training. However, the contrastive language-image pretraining framework, by treating each image-text pair as a separate category, may lead to class collision problems, thereby affecting the quality of image representation. Therefore, weakly supervised contrastive learning is introduced to generate weak labels, enabling the model to learn fine-grained feature representations. The proposed weak supervision-based robust pretraining method for medical VQA is empirically evaluated and experimental results on public datasets demonstrate its superior performance. © 2023 IEEE.

关键词： Adversarial Training Contrastive Learning Medical Visual Question Answering Robust Pretraining Weak Supervision

来源：评论

学校读者我要写书评

暂无评论

An Underwater Sonar Small Sample Image Transformation for Feature Domain Optimization 6

An Underwater Sonar Small Sample Image Transformation for Fe...

引用

6th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2023

作者： Chen, Weijing Wang, Xingmei Li, Menghao Wang, Kunhua Meng, Jiaxiang Li, Xiaoqing College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9798350399059

Underwater Image transformation uses deep learning to generate images with the target domain and the source domain. It can quickly and accurately solve transformation problems such as image blurring for underwater images. To solve the problem of the poor transformation effect caused by the limited number of sonar images, we propose an underwater small sample image transformation (SSIT) for the feature domain with representation enhancement. We use Canny and histogram equalization to extract the details of images. Then, a channel attention mechanism with sub-pixel convolution is applied to improve the feature extraction ability and enhance image representation. We adopt a noise addition strategy and an image reconstruction strategy to scale the loss of cycle consistency. The weakened cyclic semantic consistency is applied to improve the quality of transformed images. Due to the different image datasets having different data characteristics, the underwater sonar small sample dataset is selected for image transformation with Generative Adversarial Networks (GAN). We choose FUNIT as the baseline, and do the ablation experiment to demonstrate the validity of the model. Comparing with different situations, the experimental results show that the image representation enhancement constructed in this paper can effectively enhance the details of images. © 2023 IEEE.

关键词： Image enhancement

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：