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

6,793 篇 会议
630 册 图书
130 篇 期刊文献

馆藏范围

7,552 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

3,899 篇 工学
- 3,090 篇 计算机科学与技术...
- 1,403 篇 软件工程
- 794 篇 电气工程
- 679 篇 信息与通信工程
- 503 篇 控制科学与工程
- 299 篇 生物工程
- 281 篇 生物医学工程（可授...
- 265 篇 机械工程
- 191 篇 光学工程
- 163 篇 电子科学与技术（可...
- 145 篇 仪器科学与技术
- 140 篇 化学工程与技术
- 59 篇 安全科学与工程
- 45 篇 材料科学与工程（可...
- 36 篇 交通运输工程
1,222 篇 理学
- 673 篇 数学
- 356 篇 物理学
- 334 篇 生物学
- 198 篇 统计学（可授理学、...
- 147 篇 化学
- 105 篇 系统科学
553 篇 管理学
- 314 篇 管理科学与工程(可...
- 255 篇 图书情报与档案管...
- 59 篇 工商管理
274 篇 医学
- 230 篇 临床医学
- 109 篇 基础医学(可授医学...
- 75 篇 药学(可授医学、理...
66 篇 法学
- 62 篇 社会学
51 篇 艺术学
- 49 篇 设计学（可授艺术学...
42 篇 农学
- 39 篇 作物学
21 篇 教育学
19 篇 文学
18 篇 经济学
5 篇 军事学

主题

3,150 篇 pattern recognit...
535 篇 feature extracti...
429 篇 artificial intel...
385 篇 neural networks
295 篇 data mining
278 篇 machine learning
234 篇 face recognition
224 篇 pattern matching
215 篇 support vector m...
203 篇 image segmentati...
194 篇 pattern classifi...
193 篇 computer imaging...
183 篇 computer science
182 篇 local binary pat...
168 篇 computational mo...
159 篇 computer vision
147 篇 humans
146 篇 testing
142 篇 accuracy
141 篇 training

机构

40 篇 school of comput...
38 篇 national laborat...
25 篇 institute of ima...
24 篇 center for resea...
24 篇 computer vision ...
22 篇 institute of ima...
22 篇 university of ch...
21 篇 department of co...
20 篇 indian statistic...
19 篇 national laborat...
19 篇 institute of com...
16 篇 tongji universit...
16 篇 chinese academy ...
15 篇 institute for pa...
14 篇 national center ...
14 篇 chinese acad sci...
14 篇 częstochowa univ...
13 篇 agh university o...
13 篇 school of electr...
12 篇 indian stat inst...

作者

35 篇 yang jie
31 篇 de-shuang huang
25 篇 tieniu tan
23 篇 umapada pal
19 篇 ryszard tadeusie...
19 篇 liang wang
18 篇 ajith abraham
17 篇 marcin korytkows...
17 篇 lu hanqing
17 篇 jacek m. zurada
17 篇 leszek rutkowski
16 篇 jie yang
16 篇 zhenan sun
16 篇 shamsuddin siti ...
16 篇 pal umapada
15 篇 wang da-han
15 篇 chew lim tan
15 篇 rafał scherer
14 篇 xu changsheng
14 篇 lotfi a. zadeh

语言

7,403 篇 英文
84 篇 其他
77 篇 中文

检索条件"任意字段=International Conference on Soft Computing and Pattern Recognition"

共 7554 条记录，以下是151-160 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：

Texture Analysis on Digital Microscopic Leather Images For Species Identification 9

Texture Analysis on Digital Microscopic Leather Images For S...

引用

9th international conference on soft computing and Machine Intelligence (ISCMI)

作者： Varghese, Anjli Jawahar, Malathy Prince, A. Amalin Gandomi, Amir H. BITS Pilani KK Birla Goa Campus Sancoale 403726 Goa India Cent Leather Res Inst Chennai 600020 Tamil Nadu India Univ Technol Sydney 123 Broadway Sydney NSW 2007 Australia

ISBN: (纸本)9798350320886

This paper describes the relevance of texture analysis on leather images. The aim is to improve the prediction accuracy by quantifying the morphological and statistical behavior of the leather images. Hence, the present work proposed to combine the multi-resolution discrete wavelet transform (DWT) and local binary pattern (LBP) texture operators. The hybrid texture features (DWT + LBP) offer better species-specific feature discrimination. This work adopts a multi-layer perceptron (MLP) model to evaluate the discriminatory behavior of the texture features. The proposed work extract, analyze and learn the species' distinct texture features of the novel digital microscopic leather image data. The experimental results noted a significant improvement in species prediction with 99.58% accuracy. Therefore, texture analysis elevates the ability to interpret the leather images per species. It is thus a necessary key to learn the permissible leather species' behavior so as to prevent the trade of non-permissible leather and its products.

关键词： Discrete wavelet transform (DWT) local binary pattern (LBP) leather image multi-layer perceptron (MLP) species prediction texture analysis

来源：评论

学校读者我要写书评

暂无评论

A Framework for Recommending Products to Solve Cold Starts Problem Using FP Growth Algorithm 15th

A Framework for Recommending Products to Solve Cold Starts P...

引用

15th international conference on soft computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Dani, Virendra Bhati, Nisha Nagar, Sneha Mandloi, Ayesha Matkar, Mayank Department of Computer Science and Engineering Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India Department of Computer Science and Engineering Medicaps University Indore India Department of Electronics and Communication Engineering Indore Institute of Science & Technology Indore India Department of Computer Science and Engineering SKITM-Technical Campus Indore India

ISBN: (纸本)9783031889912

Different product characteristics and consumer expectations must be analyzed when making a product or service recommendation based on use. However, if all types of knowledge are inaccessible, this is known as the clod start problem in a recommendation system. To explain and address the issue of cold start, this paper suggests a structure for user experience-based product recommendation. As a result, this paper introduces a four-phase recommendation scheme. In the first step, site usage data is preprocessed and used to make product recommendations. The frequent pattern-based recommendation is performed in the next step. The decision is made in the third process based on the user's current search (user click streams) and related user habits seen in network access logs. In addition, the final recommendation is based on filtering the results obtained in the third step, as well as the product or service's cost, brand, and social review. The results show that the prediction is accurate and precise. Furthermore, compared result of proposed system to traditional system using Apriori Algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Apriori cold start problem Data Mining frequent pattern k-NN Prediction Recommendation System Web Log

来源：评论

学校读者我要写书评

暂无评论

Advancements in Alzheimer's Disease Diagnosis: The MRI-CNN Synergy for Early Detection 3rd

Advancements in Alzheimer's Disease Diagnosis: The MRI-CNN S...

引用

3rd international conference on Advanced Network Technologies and Intelligent computing (ANTIC)

作者： Ahmad, Siftain Gnyani, Kasula Rajhans, Abhishek Sinha, Bam Bahadur Indian Inst Informat Technol Ranchi Ranchi 834010 Jharkhand India

ISBN: (纸本)9783031640698;9783031640704

Alzheimer's disease is a neurodegenerative condition with profound implications for both physical and mental well-being. Its clinical manifestations encompass a range of high-risk symptoms, including impaired self-care abilities, emotional and behavioral disturbances, cognitive deficits, memory impairment, declining motor skills and coordination, and reduced appetite. If left unaddressed, Alzheimer's disease can lead to dire outcomes, including fatality. Timely diagnosis is paramount to mitigating the irreversible neurological damage associated with the condition. Raising awareness among at-risk individuals can empower them to proactively adopt preventive measures. Recent research has demonstrated the potential of computational approaches in Alzheimer's diagnosis. Early detection plays a pivotal role in enhancing treatment efficacy and pharmaceutical interventions. Our study is focused on the early identification of Alzheimer's disease, with a specific emphasis on the utilization of MRI as a diagnostic input. Employing a 17-layer Convolutional Neural Network (CNN), renowned for its pattern recognition capabilities, we have achieved an impressive accuracy of 99.8%.

关键词： Convolutional Neural Network pattern recognition Decision Support System Deep Learning Classification

来源：评论

学校读者我要写书评

暂无评论

Web based Real Time Bowel Cancer Stage Suggestion using CNN 2

Web based Real Time Bowel Cancer Stage Suggestion using CNN

引用

2nd international conference on Sustainable computing and Smart Systems (ICSCSS)

作者： Kandasamy, V Aravind, M. Ganesh, Siva P. Raman, Thulasi G. Panimalar Engn Coll Dept Informat Technol Chennai Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

Cancer that develops in the colon or rectum of the large intestine is called bowel cancer (CRC), although it goes by other names as well. The early detection of bowel cancer relies largely on the microscopic inspection of entire tissues samples. Due to its inherent subjectivity, the conventional visual examination is both laborious and wildly inaccurate. Using a deep learning method based on Convolutional Neural Networks (CNN) to distinguish between tumor tissue and healthy tissues, we provide a web-based flask application that may predict the stage of bowel cancer. 4550 image data of seven different tissue types in total are collected and analyzed using pandas and sklearn. A CNN model is trained after preprocessing and cleaning of data, the accuracy of the model is calculated and analyzed using Confusion Matrix. The model is deployed to a production environment using Flask as a Web Application. Though the information may not replace the advice of a doctor, the Application may serve as an intermediate between traditional and automated analysis of tissue for fast prediction of the stage of the cancer by Doctors.

关键词： Deep learning Bowel Cancer Flask pattern recognition Convolutional Neural Networks (CNN) Data analytics Web Application Pandas Sklearn Python

来源：评论

学校读者我要写书评

暂无评论

Head Point Positioning and Spatial-Channel Self-Attention Network for Multi-Object Tracking 26

Head Point Positioning and Spatial-Channel Self-Attention Ne...

引用

26th international conference on pattern recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Zhang, Xin Gao, Song Yang, Yuanzhe Chu, Chengxiang Zhou, Zhong Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Multi-Object Tracking (MOT) aims to generate trajectories for multiple objects in the surveillance scene. This is a challenging task because the pedestrians in tracking video often gather together and occlude each other. Consequently, the two main problems in the popular tracking-by-detection framework are how to alleviate unreliable detection and extract robust object appearance features. In this paper, we propose a new tracking method that is composed of two novel types of modules - an object detection strategy based on pedestrian head point positioning and a Spatial-Channel Self-Attention feature extraction network (SCSAN). Specifically, the proposed detection strategy generates more accurate tracking object bounding boxes with soft-Head-NMS, which combines the advantages of object detection and head point positioning. The head point location information is used as a guidance to screen unreliable detection. The SCSAN utilizes the Spatial-Channel Self-Attention mechanism to lead and determine the optimal attention value for each area and channel. Extensive experiments are carried out to demonstrate the proposed tracker achieves competitive results and is state-of-the-art in half metrics.

关键词： Measurement Head Surveillance Object detection Feature extraction Trajectory pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Texture and Orientation-based Feature Extraction for Robust Facial Expression recognition 21

Texture and Orientation-based Feature Extraction for Robust ...

引用

21st IEEE/ACIS international conference on software Engineering Research, Management and Applications, SERA 2023

作者： Khan, Sajid Ali Bin Abdulrahman Alawairdhi, Mohammed Al-Akhras, Mousa Foundation University Islamabad Department of Software Engineering Islamabad Pakistan Saudi Electronic University College of Computing and Informatics Saudi Arabia The University of Jordan Amman King Abdullah II School for Information Technology Computer Information Systems Department Jordan

ISBN: (纸本)9798350345889

Facial expressions are the most effective way to characterize people's motives, emotions, and feelings. Several new methods are proposed each year;however, the accuracy of facial expression recognition still needs to be improved especially in uncontrolled conditions. In this paper, we propose a hybrid facial expression model that considers both texture and orientation features to classify expressions. Two types of descriptors namely Local binary pattern and Weber local descriptor are used to preserve the local intensity information and orientation of edges. In the next step, computing the Histograms of oriented gradients (HOG) features from the Local binary pattern and Weber local descriptor images to capture micro-expressions. Then, the AdaBoost feature selection algorithm is utilized to choose the best features from the combined HOG features. The results of the experiments demonstrate that the method proposed in this study performs better than existing methods. © 2023 IEEE.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

A Riemannian Residual Learning Mechanism for SPD Network

A Riemannian Residual Learning Mechanism for SPD Network

引用

international Joint conference on Neural Networks (IJCNN)

作者： Cai, Zhenyu Wang, Rui Xu, Tianyang Wu, Xiaojun Kittler, Josef Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Jiangsu Peoples R China Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi Jiangsu Peoples R China Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

ISBN: (纸本)9798350359329;9798350359312

The generalization of Euclidean network para-digm to the Riemannian manifolds has attracted much attention for offering useful geometric representations in processing manifoldvalued data in recent years. However, the information degradation during data compression mapping hinders Riemannian networks from going deeper, and there are very few solutions specifically designed for this problem. Given the remarkable success of deep Residual learning in Euclidean networks, a novel Riemannian residual learning mechanism (RRLM) is proposed in the context of Symmetric Positive Definite (SPD) manifolds, enabling the characterization of deep spatiotemporal features while preserving the manifold properties. Based on RRLM, a stack of SPD manifold-constrained residual-like blocks is designed on the tail of the original SPDNet(backbone) for the sake of conducting deep Riemannian residual learning. For simplicity, we refer to the network architecture introduced above as Riemannian residual SPD network (ResSPDNet). The experimental results achieved on three types of visual classification tasks, i.e., facial emotion recognition, drone recognition, and action recognition, demonstrate that our method can achieve improved accuracy with a deepened network structure.

关键词： Riemannian manifold SPD matrix Information degradation Riemannian residual learning Visual classification.

来源：评论

学校读者我要写书评

暂无评论

Parallelized Partial Discharge Feature Fusion and pattern recognition Based on MaxCompute 5

Parallelized Partial Discharge Feature Fusion and Pattern Re...

引用

5th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2024

作者： Zhong, Zhaoyang Zheng, Xiang Yang, Wenjiao School of Automation and Electrical Engineering Dalian Jiaotong University Dalian China CRRC Changchun Railway Vehicles Co. Ltd. Changchun China

ISBN: (纸本)9798350355253

With the rapid growth of the power grid scale and the increasing complexity of grid structures, the scale of partial discharge data in electrical equipment has gradually increased from the TB level to the PB level. The extraction of partial discharge characteristics is a crucial step in pattern recognition to accurately represent the features of partial discharge and improve the accuracy of identification results. In this paper, a parallel partial discharge feature fusion and pattern recognition method based on MaxCompute is proposed. Addressing the problem of information loss and the need to improve identification accuracy caused by single signal-level features or image-level features, this paper uses the ResNet50 residual neural network to extract PRPD spectrum features and fuses them with statistical features through Principal Component Analysis and Weighted Load Evaluation (PCA-WLE). The fused features are input into a Support Vector Machine (SVM) for classification and identification. Furthermore, t-Distributed Stochastic Neighbor Embedding (t-SNE) is used to compare and visualize feature distributions. The algorithm's computational speed is compared in three different computing environments. The results demonstrate that compared to single feature extraction methods, the proposed feature extraction method exhibits better clustering discernibility. When compared with common algorithms, the proposed method achieves over 95% accuracy in the identification of four types of partial discharge. In the MaxCompute environment, the algorithm maintains fast and stable computational performance, confirming the reliability and superiority of the proposed method. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Big Data and Reinforcement Learning, Emg Clustering 6

Big Data and Reinforcement Learning, Emg Clustering

引用

6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Naaz, Rohaila Bajpai, Yashi Kumar Verma, Pushpendra Balaji, N.V. Ramesh, P.N. Aher, Vijaya N. Teerthankar Mahaveer University Uttar Pradesh Moradabad India Invertis University Department of Computer Science & Engineering Uttar Pradesh Bareilly India School of Computer Science and Applications IIMT University Uttar Pradesh Meerut India Karpagam Academy of Higher Education Department of Computer Science Tamilnadu Coimbatore India Karpagam Institute of Technology Department of Computer Science Engineering Tamilnadu Coimbatore India Vishwakarma Institute of Information Technology E & TC Maharashtra Pune India

ISBN: (纸本)9798350304480

The need of creating improved that are better equipped to manage 'big data'has significantly grown due to the growing volume of data in electromyographic signal study. As a result, increasingly sophisticated created. This essay starts with a succinct overview of the key driving forces behind the expansion of big data age, then moves on to discuss the recent development of current shared data sets. After that, we cover current research and advancements in pattern recognition techniques that can be used for big data analytics. These new signal analysis approaches may be classified into two primary categories engineering utilizing a potential large specific focus on 'deep learning'. Finally, suggestions for further pattern research are presented and explored. © 2023 IEEE.

关键词： Big data Classification accuracy Convolutional neural networks (CNN) Deep learning Electromyographic signal study EMG clustering EMG pattern recognition Feature engineering High-density surface EMG pattern recognition techniques Recurrent neural network (RNN) Reinforcement learning Surface EMG

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Face recognition Algorithms: Evaluating LBPH and FaceNet with Haar Cascade 14

Comparative Analysis of Face Recognition Algorithms: Evaluat...

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Irfan, Ayaan Mohammed Deshmukh, Shantanu Atul Goel, Harsh Dharamdatt Vernekar, Gayatri Manoj Thomas, Sneha Suzanna Patil, Rajesh A. Veermata Jijabai Technological Institute Electronics and Telecommunications Mumbai India Veermata Jijabai Technological Institute Dept. of Electrical Engineering Mumbai India

ISBN: (纸本)9798350335095

Facial recognition is a widely utilized technology in numerous industries, such as security, retail, delivery, and criminal detection. However, it encounters significant challenges when dealing with low-resolution images and videos. To achieve accurate facial recognition, researchers have explored various techniques, including LBPH, Face++, and ReKognition. Despite advancements, there remains considerable room for improving recognition accuracy. LBPH, a feature-based method, demonstrates remarkable proficiency in recognizing faces from diverse viewpoints. In contrast, deep learning approaches like FaceNet and Haar Cascade have emerged as prominent techniques. Haar Cascade specifically focuses on face recognition. This study evaluates the performance of LBPH face recognition system in comparison with a combined approach employing FaceNet and Haar Cascade using real-time video data. © 2023 IEEE.

关键词： Local binary pattern

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500页 << < 12 13 14 15 16 17 18 19 20 21 > >>

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：