检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

68 篇 会议
13 篇 期刊文献

馆藏范围

81 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

76 篇 工学
- 74 篇 计算机科学与技术...
- 13 篇 软件工程
- 5 篇 信息与通信工程
- 4 篇 生物工程
- 2 篇 化学工程与技术
- 2 篇 生物医学工程（可授...
- 1 篇 电气工程
- 1 篇 电子科学与技术（可...
- 1 篇 控制科学与工程
- 1 篇 土木工程
16 篇 理学
- 11 篇 数学
- 8 篇 生物学
- 3 篇 系统科学
- 2 篇 物理学
- 2 篇 化学
- 1 篇 地球物理学
- 1 篇 地质学
- 1 篇 统计学（可授理学、...
6 篇 管理学
- 5 篇 图书情报与档案管...
- 1 篇 管理科学与工程(可...
3 篇 法学
- 3 篇 社会学
2 篇 医学
- 2 篇 基础医学(可授医学...
- 2 篇 临床医学
- 2 篇 药学(可授医学、理...

主题

5 篇 supervised learn...
4 篇 recursive neural...
3 篇 deep learning
3 篇 neural networks
3 篇 face recognition
3 篇 pattern recognit...
3 篇 feature extracti...
2 篇 vision transform...
2 篇 multi-label clas...
2 篇 mixture modeling
2 篇 weighting of dat...
2 篇 semiconductor ma...
2 篇 pattern classifi...
2 篇 human computer i...
2 篇 graph edit dista...
2 篇 feed-forward neu...
2 篇 polynomial appro...
2 篇 class imbalance
2 篇 learning prefere...
2 篇 time series fore...

机构

5 篇 concordia univ d...
4 篇 univ ulm inst ne...
3 篇 univ jaume 1 dep...
2 篇 univ london birk...
2 篇 institute of neu...
2 篇 multimedia univ ...
2 篇 institute for in...
2 篇 helsinki univ te...
2 篇 institute of neu...
2 篇 univ paris 06 la...
2 篇 univ hosp ulm de...
2 篇 ctr pattern regc...
2 篇 concordia univ d...
2 篇 univ toulouse 3 ...
2 篇 univ patras upai...
2 篇 univ ulm dept ne...
2 篇 institute of com...
2 篇 univ catholique ...
2 篇 univ siena dipar...
2 篇 ucl dept comp sc...

作者

12 篇 schwenker friedh...
5 篇 trentin edmondo
5 篇 hernandez-espino...
5 篇 fernandez-redond...
5 篇 torres-sospedra ...
4 篇 riesen kaspar
4 篇 palm guenther
4 篇 kestler hans a.
3 篇 krzyzak adam
3 篇 scherer stefan
2 篇 granger eric
2 篇 piccinini f
2 篇 pudil p
2 篇 frasconi p
2 篇 ziou d
2 篇 simon g
2 篇 inesta jm
2 篇 krzyzak a
2 篇 brown m
2 篇 michel-sendis c

语言

81 篇 英文

检索条件"任意字段=1st IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition"

共 81 条记录，以下是1-10 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Learning Graph Matching with Graph neural networks 11th

Learning Graph Matching with Graph Neural Networks

引用

11th iapr tc3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Dobler, Kalvin Riesen, Kaspar Univ Bern Inst Comp Sci Neubruckstr 10 CH-3012 Bern Switzerland

ISBN: (纸本)9783031716010;9783031716027

Graph matching aims at evaluating the dissimilarity of two graphs by defining a constrained correspondence between their nodes and edges. Error-tolerant graph matching, for instance, introduces the concept of a cost for penalizing structural differences in the matching. A popular method for this approach is graph edit distance, which is based on the cost of the minimal sequence of edit operations to transform a source graph into a target graph. One of the main problems of graph edit distance is the computational complexity, which is exponential in its exact form. In recent years, several approximation methods for graph edit distance have been presented which offer polynomial runtimes. In this paper, we approach the graph edit distance problem in a fundamentally different way. In particular, we propose to learn graph edit distance by means of graph neural networks. In a comprehensive experimental evaluation on six data sets, we verify that our approach not only provides comparable classification performance but also substantially reduces the runtime compared to a prominent algorithm for approximate graph edit distance computation.

关键词： structural pattern recognition Graph Matching Graph Edit Distance Graph Representation Learning

来源：评论

学校读者我要写书评

暂无评论

Comparative study of Deep Learning Models in Melanoma Detection 11th

Comparative Study of Deep Learning Models in Melanoma Detect...

引用

11th iapr tc3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Haghshenas, Farnaz Krzyzak, Adam Osowski, stanislaw Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada Warsaw Univ Technol Fac Elect Engn Pl Politech 1 PL-00661 Warsaw Poland

ISBN: (纸本)9783031716010;9783031716027

The increasing number of skin cancers underscores the critical importance of early detection and accurate classification to improve treatment outcomes. Melanoma, a malignant skin cancer, has the highest mortality rate among all skin cancer types. Early detection of melanoma significantly enhances the chances of effective treatment and survival rates. This research evaluates advanced deep learning techniques in medical imaging, specifically Vision Transformers (ViT) and Convolutional neural networks (CNNs), for detecting melanoma. In this study, we used an annotated dataset of melanoma dermoscopic images. In addition, we employed the k-fold cross-validation technique to evaluate the reliability of our models. Our experimental results indicate that pre-trained Vision Transformers achieved an exceptional diagnostic accuracy of 97.97%, outperforming other models, specifically the pre-trained CNNs models.

关键词： Medical image analysis Deep learning Vision transformer Melanoma detection Skin cancer detection

来源：评论

学校读者我要写书评

暂无评论

Deep Multi-label Classification of Personality with Handwriting Analysis 11th

Deep Multi-label Classification of Personality with Handwrit...

引用

11th iapr tc3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Shamsabad, Marzieh Adeli Suen, Ching Yee Concordia Univ Ctr Pattern Recognit & Machine Intelligence CENPA Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9783031716010;9783031716027

Handwriting analysis has traditionally been used to infer personality traits from the stylistic features of writing. With advances in machine learning, the accuracy and applicability of these analyses have significantly improved. This paper presents a new multi-label classification approach to classify personality traits, such as Extraversion and Conscientiousness, into Low, Average, and High categories. This approach uses Binary Cross-Entropy with Logits Loss and Focal Loss to handle multi-label classification and class imbalance. Image segmentation techniques are also employed to enhance the handling of limited handwriting samples. The paper evaluates the complexity and performance of ResNet-50 and ResNet-101 architectures in recognizing complex handwriting patterns using three optimizers: SGD with momentum, Adam, and AdaBelief. The results demonstrate the efficacy of our proposed method, improving overall accuracy from 67.09% to 90.16% for ResNet-50 and from 69.43% to 90.07% for ResNet-101, with an overall AUC of 0.96. These improvements emphasize the model's capability for practical automated handwriting analysis.

关键词： Handwriting Multi-label classification Class Imbalance Binary Cross-Entropy with Logits Loss Focal Loss

来源：评论

学校读者我要写书评

暂无评论

Palmprint Classification via Filter Faces and Feature Extraction 11th

Palmprint Classification via Filter Faces and Feature Extrac...

引用

11th iapr tc3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Chen, Guang Yi Krzyzak, Adam Valev, Ventzeslav Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada Bulgarian Acad Sci Inst Math & Informat Sofia 1113 Bulgaria

ISBN: (纸本)9783031716010;9783031716027

Palmprint classification is a popular method for today's biometrics, and it can be combined with iris or fingerprint to identify a person's identification. In this paper, we propose a novel method for palmprint classification by using filter faces and feature extraction. We extract such features as histogram of oriented features (HOG) and local binary patterns (LBP). We select to use both nearest neighbor (NN) classifier and collaborative representation classifier (CRC) for palmprint classification. Experiments demonstrate that filter faces always improve the classification accuracy for palmprint classification no matter what features are used and how much noise is present in palmprint images. Based on our experiments, the HOG features are very robust to noise whereas the LBP features are very sensitive to noise. As a result, it is preferable to use the HOG features for noisy palmprint images and the LBP features for clean palmprint images to improve classification accuracy.

关键词： Histogram of oriented features (HOG) Local binary patterns (LBP) Filter faces (FF) Collaborative representation classifier (CRC) Nearest neighbor classifier (NN)

来源：评论

学校读者我要写书评

暂无评论

Vision Transformer Features-Based Leukemia Classification 11th

Vision Transformer Features-Based Leukemia Classification

引用

11th iapr tc3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Ben-Suliman, Karima Krzyzak, Adam Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9783031716010;9783031716027

Acute Lymphoblastic Leukemia (ALL) is a disease that is caused by the uncontrollable growth of immature and malignant White Blood Cells (WBCs) which are called lymphoblasts. It occurs when the bone marrow contains 20% or more lymphoblasts. Therefore, Leukemia is diagnosed by counting White Blood Cells (WBCs) in the microscopic smears of bone marrow and blood. There are several attempts for effective leukemia classification with the help of computer aided-based methods by analyzing microscopic smear images. However, how to further improve the classification accuracy is still challenging. Therefore, in this paper, we propose an end-to-end framework to classify ALL. First, we train a more robust specific pre-trained Vision Transformer (ViT) trained on the same type of data, ALL-IDB1. Then, we examine the extracted features of Deep ViT across layers and utilize these features as dense descriptors to make predictions. Based on the observation in this paper, we find and empirically demonstrate that such features, when extracted from the ViT model, consistently improve the classification performance. Moreover, we observe that the pre-trained ViT which is fine-tuned on specific dataset is more effective than the general images dataset. We show that our proposed method achieves an average accuracy of 98.75% which is a competitive result with recent state-of-the-art by a large margin.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Explaining Network Decision Provides Insights on the Causal Interaction Between Brain Regions in a Motor Imagery Task 11th

Explaining Network Decision Provides Insights on the Causal ...

引用

11th iapr tc3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Borra, Davide Ravanelli, Mirco Univ Bologna Dept Elect Elect & Informat Engn Guglielmo Marcon Cesena Campus Cesena Italy Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada Mila Quebec AI Inst Montreal PQ Canada

ISBN: (纸本)9783031716010;9783031716027

neural decoding widely exploits machine learning for classifying electroencephalographic (EEG) signals for brain-computer interface applications. Recent advancements in neural decoding regards the use of brain functional connectivity estimates as input features and the adoption of convolutional neural networks (CNNs) to realize decoders. Moreover, explainable artificial intelligence (XAI) approaches based on CNNs are growing interest in the neuroscience community, for validating the knowledge learned by networks and for using the decoder not only to classify the EEG but also to analyze it in a data-driven way, without a priori assumptions. However, the adoption of connectivity estimates for neural decoding is still in its infancy, as adopts non-directed connectivity measures, limits the analysis of few interactions/frequency ranges, and exploits classic machine learning approaches without exploring CNNs. Moreover, XAI approaches have never been applied to analyze EEG-based functional connectivity. To overcome these limitations, we design and apply a CNN for processing directed connectivity measures estimated via spectral Granger causality. The CNN automatically learns features in the frequency and spatial domains, and it is coupled with an explanation technique (DeepLIFT) for highlighting the most relevant connectivity inflow and outflow associated to each decoded brain state. Our approach is applied to motor imagery decoding, and achieves state-of-the-art performance compared to existing networks. DeepLIFT relevance representations match the directional interactions known occurring when imagining movements, validating the features related to the brain network, as learned by the CNN.

关键词： Electroencephalogram Functional connectivity Convolutional neural networks Explainable AI Motor imagery

来源：评论

学校读者我要写书评

暂无评论

Automatic Interpretation of ¹⁸F- Fluorocholine PET/CT Findings in Patients with Primary Hyperparathyroidism: A Novel Dataset with Benchmarks 11th

Automatic Interpretation of <SUP>18</SUP>F- Fluorocholine PE...

引用

11th iapr tc3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Sharma, Natasha Balogova, Sona Noskovicova, Lucia Montravers, Francoise Talbot, Jean-Noel Trentin, Edmondo Univ Siena Dipartimento Ingn Informaz & Sci Matemat Siena Italy Comenius Univ Dept Nucl Med Bratislava Slovakia St Elisabeth Oncol Inst Bratislava Slovakia Bory Hosp AS Bratislava Slovakia Hop Tenon Serv Med Nucl Paris France Inst Natl Sci & Tech Nucl INSTN Saclay France

ISBN: (纸本)9783031716010;9783031716027

Primary hyperparathyroidism (pHPT) is a prevalent endocrine disorder characterized by excessive production of parathyroid hormone due to hyperactive parathyroid glands. This paper aims to enhance the diagnostic accuracy of PET/CT using F-18-fluorocholine (FCH) by employing radiomic image analysis to differentiate hyperfunctioning parathyroid glands (HPTG) from thyroid gland (TG) normal or adenomatous tissue. To this aim, the paper contributes a novel, real-life dataset (made publicly available online for download) along with several benchmarks. First, we collected and labeled FCH PET/CT images from 92 patients with pHPT, extracting 56 s-order and higher-order radiomic features using the software LIFEx. These features were analyzed in 98 HPTG and 91 TG findings, comparing clinical characteristics via nonparametric Wilcoxon rank sum tests. Then, several variants of pattern recognition models (k-nearest neighbor and random forest) and deep neural networks were applied to the task of discriminating between HPTG and TG over the dataset in order to fix baseline results for the Community to challenge. Moreover, two ensemble methods are proposed that combine the aforementioned classifiers, achieving an area under the curve of up to 92.30%. In conclusion, the present integration of radiomic features and machine learning provides a promising approach to the task, setting a benchmark for future research in the field.

关键词： Primary hyperparathyroidism F-18-fluorocholine PET/CT deep neural network ensemble method stacking

来源：评论

学校读者我要写书评

暂无评论

Graph Augmentation for neural networks Using Matching-Graphs 10th

Graph Augmentation for Neural Networks Using Matching-Graphs

引用

10th iapr tc3 workshop on artificial neural networks for pattern recognition (ANNPR)

作者： Fuchs, Mathias Riesen, Kaspar Univ Bern Inst Comp Sci CH-3012 Bern Switzerland Univ Appl Sci Northwestern Inst Informat Syst CH-4600 Olten Switzerland

ISBN: (纸本)9783031206498;9783031206504

Both data access and data collection have become increasingly easy over the past decade, leading to rapid developments in many areas of intelligent information processing. In some cases, however, the amount of data is still not sufficiently large (e.g. in some machine learning applications). Data augmentation is a widely used mechanism to increase the available data in such cases. Current augmentation methods are mostly developed for statistical data and only a small part of these methods is directly applicable to graphs. In a recent research project, a novel encoding of pairwise graph matchings is introduced. The basic idea of this encoding, termed matching-graph, is to formalize the stable cores of pairs of patterns by means of graphs. In the present paper, we propose to use these matching-graphs to augment training sets of graphs in order to stabilize the training process of state-of-the-art graph neural networks. In an experimental evaluation on five graph data sets, we show that this novel augmentation technique is able to significantly improve the classification accuracy of three different neural network models.

关键词： Graph matching Matching-graphs Graph edit distance Graph augmentation GNN

来源：评论

学校读者我要写书评

暂无评论

Utilization of Vision Transformer for Classification and Ranking of Video Distortions 10th

Utilization of Vision Transformer for Classification and Ran...

引用

10th iapr tc3 workshop on artificial neural networks for pattern recognition (ANNPR)

作者： AlDahoul, Nouar Karim, Hezerul Abdul Tan, Myles Joshua Toledo Multimedia Univ Fac Engn Cyberjaya Malaysia Univ St La Salle Dept Nat Sci Bacolod Philippines Univ St La Salle Dept Chem Engn Bacolod Philippines Univ St La Salle Dept Elect Engn Bacolod Philippines

ISBN: (纸本)9783031206498;9783031206504

The perceptual quality of video surveillance footage has impacts on several tasks involved in the surveillance process, such as the detection of anomalous objects. The videos captured by a camera are prone to various distortions such as noise, smoke, haze, low or uneven illumination, blur, rain, and compression, which affect visual quality. Automatic identification of distortions is important when enhancing video quality. Video quality assessment involves two stages: (1) classification of distortions affecting the video frames and (2) ranking of these distortions. A novel video dataset was utilized for training, validating, and testing. Working with this dataset was challenging because it included nine categories of distortions and four levels of severity. The greatest challenge was the availability of multiple types of distortions in the same video. The work presented in this paper addresses the problem of multi-label distortion classification and ranking. A vision transformer was used for feature learning. The experiment showed that the proposed solution performed well in terms of F1 score of single distortion (77.9%) and F1 score of single and multiple distortions (69.9%). Moreover, the average accuracy of level classification was 62% with an average F1 score of 61%.

关键词： Distortion classification and ranking Multi-label classification Video quality assessment Vision transformer

来源：评论

学校读者我要写书评

暂无评论

White Blood Cell Classification of Porcine Blood Smear Images 10th

White Blood Cell Classification of Porcine Blood Smear Image...

引用

10th iapr tc3 workshop on artificial neural networks for pattern recognition (ANNPR)

作者： Novia, Jemima Loise U. Alipo-On, Jacqueline Rose T. Escobar, Francesca Isabelle F. Tan, Myles Joshua T. Karim, Hezerul Abdul AlDahoul, Nouar Univ St La Salle Nat Sci Bacolod Philippines Univ St La Salle Chem Engn Bacolod Philippines Multimedia Univ Fac Engn Cyberjaya Malaysia

ISBN: (纸本)9783031206498;9783031206504

Differentiating white blood cells has been a fundamental part of medical diagnosis as it allows the assessment of the state of health of various organ systems in an animal. However, the examination of blood smears is time-consuming and is dependent on the level of the health professional's expertise. With this, automated computer-based systems have been developed to reduce the time taken for examination and to reduce human error. In this work, an image processing technique was explored to investigate the classification of white blood cells. Through this technique, color and shape features were gathered from segmented nuclei and cytoplasms. Various deep learning algorithms where transfer learning methods were also employed for comparison. Experimental results showed that handcrafted features via image processing are better than features extracted from pre-trained CNNs, achieving an accuracy of 91% when using a non-linear SVM classifier. However overall, deep neural networks were superior in WBC classification as the fine-tuned DenseNet-169 model was found to have the highest accuracy of 93% against all used methods.

关键词： Classification CNN Deep learning Image processing Transfer learning White blood cells

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共9页 << < 1 2 3 4 5 6 7 8 9 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：