检索结果-内蒙古大学图书馆

11th International Conference on Computing and Pattern Recognition, ICCPR 2022

作者： Liang, Hong Li, Nanying Xue, Jiaqi Long, Yaqian Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450397056

Early detection of melanoma and prompt treatment are key approaches to reducing melanoma-related deaths. In order to improve the ability of early detection of melanoma, this paper introduces a set of hyperspectral images (HSIs) data captured by dermoscopy using hyperspectral technology, and based on this data, proposes a principal component self-attention mechanism (PCSAM) method for the classification of dysplastic nevus and melanoma. The proposed method uses principal component analysis technology to amplify the differences in spectral features of the lesions and extract some new features that are convenient for classification. In addition, under the action of the attention mechanism, the spectral features of melanoma are fully paid attention to, and the contextual spatial information between each HSI block can also be utilized. Finally, a comparison experiment is carried out using RGB images and HSIs. Experimental results demonstrate that the spectral features of melanoma can significantly improve the classification accuracy, and it also shows that the participation of hyperspectral technology can effectively improve the recognition accuracy of dysplastic nevus and melanoma, which reflects the advantages of HSI compared with the traditional image. © 2022 ACM.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Adaptive Local Neighborhood Window Laplacian Eigenmaps 24

Adaptive Local Neighborhood Window Laplacian Eigenmaps

引用

13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Xu, Jiayi Huang, Qirui Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China

ISBN: (纸本)9798400717482

Manifold learning method is a dimensionality reduction method that treats the data in non-Euclidean space as a Euclidean space in a local scope. However, most existing manifold learning methods cannot obtain the true neighborbood relationship of each sample point well. Therefore, we propose a manifold learning method based on adaptive local neighborhood. This method can adaptively select a neighborhood window. It can be approximated as a local Euclidean space based on the sample characteristics. To implement this method, a specific manifold learning method, named Laplacian Eigenmap of Adaptive Local Neighborhood Window (ALNWLE) is proposed. ALNWLE mainly contains two modules, namely the manifold structure learning module (MSLM) and the manifold structure pattern recognition module (MSPR). MSLM constructs an adaptive local neighborhood adjacency matrix to constrain the learning of the manifold structure. MSPR uses the interpolation method to recognize new sample patterns. The adaptive local neighborhood method proposed in this paper is a general method which is applicable to any manifold learning method. © 2024 Copyright held by the owner/author(s).

关键词： Dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

Spectral Context-aware Transformer for Cholangiocarcinoma Hyperspectral Image Segmentation 2022

Spectral Context-aware Transformer for Cholangiocarcinoma Hy...

引用

5th International Conference on Image and Graphics Processing, ICIGP 2022

作者： Li, Nanying Xue, Jiaqi Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450395465

In the medical field, medical images generally do not have corresponding accurate annotations. The reason is that doctors do not have enough time to finely annotate a large number of medical images. Therefore, this paper proposes a spectral context-aware Transformer (SCAT) segmentation method for cholangiocarcinoma hyperspectral image under the condition of a small sample. In the SCAT method, each band is input as each patch into the Transformer model, which mines the spatial geometric structure information, and obtains the relevant features of the region of interest. In addition, class token is introduced to better characterize contextual information. Experimental results on cholangiocarcinoma hyperspectral dataset prove that the proposed method can effectively improve the segmentation effect and provide diagnostic assistance for doctors. © 2022 ACM.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Approach for Prediction of Protein Secondary Structure

引用

computers, Materials & Continua 2022年第8期72卷 3705-3718页

作者： Muhammad Zubair Muhammad Kashif Hanif Eatedal Alabdulkreem Yazeed Ghadi Muhammad Irfan Khan Muhammad Umer Sarwar Ayesha Hanif Department of Computer Science Government College UniversityFaisalabadPakistan Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Software Engineering/Computer Science Al Ain UniversityAbu DhabiUnited Arab Emirates

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary *** this reason,it is important to design methods for accurate protein secondary structure *** of the existing computational techniques for protein structural and functional prediction are based onmachine learning with *** deep learning architectures have already been applied to tackle protein secondary structure prediction *** this study,deep learning based models,i.e.,convolutional neural network and long short-term memory for protein secondary structure prediction were *** input to proposed models is amino acid sequences which were derived from CulledPDB *** tuning with cross validation was employed to attain best parameters for the proposed *** proposed models enables effective processing of amino acids and attain approximately 87.05%and 87.47%Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models,respectively.

关键词： Convolutional neural network machine learning protein secondary structure deep learning long short-term memory protein secondary structure prediction

来源：评论

学校读者我要写书评

暂无评论

LigNet: Lightweight Hand Tracking for Edge Intelligence 15

LigNet: Lightweight Hand Tracking for Edge Intelligence

引用

2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Huang, Yijing Luo, Chengwen Feng, Xingyu Yang, Zhongru Zhang, Jin Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

In recent years, as the rise of edge intelligence hand tracking applications have emerged in various IoT systems and applications, such as human-computer interaction, sign language translation, and motion rehabilitation, etc. Due to the hardware and energy limitation of embedded IoT devices, hand tracking is difficult to achieve real-time continuous operation. To achieve this goal, in this paper we propose a set of optimization methods such as space-based error loss and Gaussian process constraints to design a lightweight hand tracking model with low storage overhead and high recognition efficiency. We design and implement the system, and the effectiveness of our proposed methods is verified in the evaluation comparing with the state-of-the-art. © 2022 IEEE.

关键词： Human computer interaction

来源：评论

学校读者我要写书评

暂无评论

Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5026-5039页

作者： Li, Mengke Cheung, Yiu-Ming Lu, Yang Hu, Zhikai Lan, Weichao Huang, Hui Shenzhen518132 China Hong Kong Baptist University Department of Computer Science 999077 Hong Kong Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen361005 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tail classes correctly. In the literature, several existing methods have addressed this problem by reducing classifier bias, provided that the features obtained with long-tailed data are representative enough. However, we find that training directly on long-tailed data leads to uneven embedding space. That is, the embedding space of head classes severely compresses that of tail classes, which is not conducive to subsequent classifier learning. This article therefore studies the problem of long-tailed visual recognition from the perspective of feature level. We introduce feature augmentation to balance the embedding distribution. The features of different classes are perturbed with varying amplitudes in Gaussian form. Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead. Subsequently, the distorted embedding spaces of all classes can be calibrated. In such balanced-distributed embedding spaces, the biased classifier can be eliminated by simply retraining the classifier with class-balanced sampling data. Extensive experiments conducted on benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art ones. © 2020 IEEE.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Integrating Real-Time and Non-Real-Time Collaborative Programming: Workflow, Techniques, and Prototypes

引用

Proceedings of the ACM on Human-computer Interaction 2023年第GROUP期7卷 1-19页

作者： Ma, Yifan Qi, Batu Xu, Wenhua Wang, Mingjie Du, Bowen Fan, Hongfei School of Software Engineering Tongji University Shanghai China College of Design and Innovation Tongji University Shanghai China Department of Computer Science University of Warwick Coventry United Kingdom

Real-time collaborative programming enables a group of programmers to edit shared source code at the same time, which significantly complements the traditional non-real-time collaborative programming supported by version control systems. However, one critical issue with this emerging technique is the lack of integration with non-real-time collaboration. Specifically, contributions from multiple programmers in a real-time collaboration session cannot be distinguished and accurately recorded in the version control system. In this study, we propose a scheme that integrates real-time and non-real-time collaborative programming with a novel workflow, and contribute enabling techniques to realize such integration. As a proof-of-concept, we have successfully implemented two prototype systems named CoEclipse and CoIDEA, which allow programmers to closely collaborate in a real-time fashion while preserving the work's compatibility with traditional non-real-time collaboration. User evaluation and performance experiments have confirmed the feasibility of the approach and techniques, demonstrated the good system performance, and presented the satisfactory usability of the prototypes. © 2023 ACM.

关键词： Control systems

来源：评论

学校读者我要写书评

暂无评论

Local-Global-Aware Convolutional Transformer for Hyperspectral Image Classification 23

Local-Global-Aware Convolutional Transformer for Hyperspectr...

引用

23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Yifan Min, Zhichao Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781665494571

In recent years, more and more end-to-end deep learning methods have been applied to remote sensing image classification. Convolutional neural networks (CNN) is the most representative one. However, the convolution kernel that makes CNN successful also limits its ability of global perception due to its fixed size. Transformer, as an emerging model, has achieved great success in both computer vision and natural language processing (NLP). Without any convolution module, Transformer is almost entirely composed of Attention mechanism that brings it context-aware ability and makes computational parallelization easier. Therefore, this paper proposes a two-branch convolutional Transformer network, which combines the local feature extraction capabilities of CNN and context-aware capabilities of Transformer to get a better feature representation while modeling dual-path long-range dependence. In addition, we flexibly combine 1D convolutional with linear projection to realize feature embedding while fusing convolutional features. The comparative experiments results on the public dataset confirm the effectiveness of our method, and the ablation experiment proves the necessity of our module and network design. © 2021 IEEE.

关键词： Adaptation models Convolution Smart cities Computational modeling Transformers Feature extraction Natural language processing

来源：评论

学校读者我要写书评

暂无评论

Real-Time CNN-Based Driver Distraction&Drowsiness Detection System

引用

Intelligent Automation & Soft Computing 2023年第8期37卷 2153-2174页

作者： Abdulwahab Ali Almazroi Mohammed A.Alqarni Nida Aslam Rizwan Ali Shah University of Jeddah College of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia University of Jeddah College of Computer Science and EngineeringDepartment of Software EngineeringJeddahSaudi Arabia Department of Computer Science National College of Business Administration&EconomicsBahawalpur Campus63100Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjab64200Pakistan

Nowadays days,the chief grounds of automobile accidents are driver fatigue and *** the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them,reducing *** paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor *** the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network(CNN)any changes by focusing on the eyes and mouth zone,precision is *** of the tasks that must be performed in the transit system is seat belt detection to lessen accidents caused by sudden stops or high-speed collisions with other cars.A method is put forth to use convolution neural networks to determine whether the motorist is wearing a seat belt when a driver is sleepy,preoccupied,or not wearing their seat belt,this system alerts them with an alarm,and if they don’t wake up by a predetermined time of 3 s threshold,an automatic message is sent to law enforcement *** suggested CNN-based model exhibits greater accuracy with 97%.It can be utilized to develop a system that detects driver attention or sleeps in real-time.

关键词： Deep learning convolutional neural network Tensorflow drowsiness and yawn detection seat belt detection object detection

来源：评论

学校读者我要写书评

暂无评论

A Neural Network Framework for Classifying Educational Content Aligned with Learning Outcomes 16

A Neural Network Framework for Classifying Educational Conte...

引用

16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Alkhalil, Adel Department of Software Engineering College of Computer Science and Engineering University of ha'Il Ha'il81481 Saudi Arabia

Rapid growth of digital educational content necessitates efficient and accurate methods for organizing and mapping resources to ensure well-alignment with targeted learning outcomes, academic standards, and competency frameworks. Traditional text classification approaches, including rule-based and classical machine learning techniques, often fail to address the semantic diversity and scalability demands of modern educational systems. This study investigates the application of neural networks for text classification to automate the mapping of educational content into predefined categories. Leveraging state-of-the-art architectures such as Long Short-Term Memory (LSTM) networks, and transformers like BERT, we present an architecture of a systematic Classification of educational materials. We discuss the implications of this work for adaptive learning environments, emphasizing the potential of neural networks to enhance the efficiency and scalability of content mapping. This study contributes to the growing body of research in artificial intelligence for education and sets the stage for further exploration into multilingual and domain-specific content classification methods. © 2025 Elsevier B.V.. All rights reserved.

关键词： Mapping

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：