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

6,797 篇 会议
632 册 图书
126 篇 期刊文献

馆藏范围

7,554 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

3,891 篇 工学
- 3,084 篇 计算机科学与技术...
- 1,402 篇 软件工程
- 793 篇 电气工程
- 676 篇 信息与通信工程
- 501 篇 控制科学与工程
- 298 篇 生物工程
- 281 篇 生物医学工程（可授...
- 265 篇 机械工程
- 189 篇 光学工程
- 162 篇 电子科学与技术（可...
- 144 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
1,220 篇 理学
- 671 篇 数学
- 357 篇 物理学
- 333 篇 生物学
- 197 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
273 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
17 篇 经济学
5 篇 军事学

主题

3,153 篇 pattern recognit...
537 篇 feature extracti...
434 篇 artificial intel...
385 篇 neural networks
295 篇 data mining
282 篇 machine learning
233 篇 face recognition
224 篇 pattern matching
216 篇 support vector m...
203 篇 image segmentati...
195 篇 computer imaging...
194 篇 pattern classifi...
183 篇 computer science
182 篇 local binary pat...
172 篇 computational mo...
152 篇 training
147 篇 computer vision
145 篇 testing
141 篇 humans
139 篇 accuracy

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

7,273 篇 英文
216 篇 其他
81 篇 中文

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

共 7556 条记录，以下是1311-1320 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：

A Novel Approach of Ontology-Based Activity Segmentation and recognition Using pattern Discovery in Multi-resident Homes 7th

A Novel Approach of Ontology-Based Activity Segmentation and...

引用

7th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2018

作者： Nguyen, Duy Nguyen, Luong Nguyen, Son Department of Information Technology TDMU Thủ Dầu MộtBinh Duong Viet Nam School of Information Technology UIT Ho Chi Minh City Viet Nam Department of Computer Technology UIT Ho Chi Minh City Viet Nam

ISBN: (纸本)9789813291850

Activity recognition is one of the preliminary steps in designing and implementing assistive services in smart homes. Such services help identify abnormality or automate events generated while occupants do, as well as intend to do, their desired activities of daily living (ADLs) inside a smart home environment. However, most of the existing systems are applied for single-resident homes. Multiple people living together create additional complexity in modeling numbers of overlapping and concurrent activities. In this paper, we introduce a hybrid mechanism between ontology-based and unsupervised machine learning strategies in creating activity models used for activity recognition in the context of multi-resident homes. On comparing with related data-driven approaches, the proposed technique is technically and practically scalable to real-world scenarios due to fast training time and easy implementation. An average activity recognition rate of 95.83% on CASAS Spring dataset was achieved and the run time per recognition operation was measured as 12.86 ms. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

Progressive Unsupervised Domain Adaptation for Image-based Person Re-Identification

Progressive Unsupervised Domain Adaptation for Image-based P...

引用

international conference on pattern recognition

作者： Mingliang Yang Jing Zhao Da Huang Ji Wang Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781728188089;9781728188096

Unsupervised domain adaptation (UDA) has emerged as an effective paradigm for reducing the huge manual annotation cost for Person Re-Identification (Re-ID). Many of the recent UDA methods for Re-ID are clustering-based and select all the pseudo-label samples in each iteration for the model training. However, there are many wrong labeled samples that will mislead the model optimization under this circumstance. To solve this problem, we propose a Progressive Unsupervised Domain Adaptation (PUDA) framework for image-based Person Re-ID to reduce the negative effect of wrong pseudo-label samples on the model training process. Specifically, we first pretrain a CNN model on a labeled source dataset, then finetune the model on unlabeled target dataset with the following three steps iteratively: 1) estimating pseudo-labels for all the images in the target dataset with the model trained in the last iteration; 2) extending the training set by adding pseudo-label samples with higher label confidence; 3) updating the CNN model with the expanded training set in a supervised manner. During the iteration process, the number of pseudo-label samples added increased progressively. In particular, a Moderate Initial Selections (MIS) strategy for pseudo-label sampling is also proposed to reduce the negative impacts of random noise features in the early iterations and mislabeled samples in the late iterations on the model. The proposed framework with MIS strategy is validated on the Duke-to-Market, Market-to-Duke unsupervised domain adaptation tasks and achieves improvements of 4.2 points (absolute, i.e., 80.0% vs. 75.8%) and 1.7 points (absolute, i.e., 70.7% vs. 69.0%) in mAP correspondingly.

关键词： Training Adaptation models Annotations Manuals Feature extraction pattern recognition Reliability

来源：评论

学校读者我要写书评

暂无评论

Tensorized Feature Spaces for Feature Explosion

Tensorized Feature Spaces for Feature Explosion

引用

international conference on pattern recognition

作者： Ravdeep S. Pasricha Pravallika Devineni Evangelos E. Papalexakis Ramakrishnan Kannan University of California Riverside Riverside CA USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA

In this paper11This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-000R22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the ar... 详细信息

In this paper¹¹This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-000R22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://***/downloads/doe-public-access-plan) This research used resources of the Oak Ridge Leadership computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-000R22725., we present a novel framework that uses tensor factorization to generate richer feature spaces for pixel classification in hyperspectral images. In particular, we assess the performance of different tensor rank decomposition methods as compared to the traditional kernel-based approaches for the hyperspectral image classification problem. We propose Orion, which takes as input a hyperspectral image tensor and a rank and outputs an enhanced feature space from the factor matrices of the decomposed tensor. Our method is a feature explosion technique that inherently maps low dimensional input space in R^K to high dimensional space in R^R, where R ≫ K, say in the order of 1000x, like a kernel. We show how the proposed method exploits the multi-linear structure of hyperspectral three dimensional tensor. We demonstrate the effectiveness of our meth

关键词： Training Support vector machines Tensors Government Supervised learning Explosions pattern recognition

来源：评论

学校读者我要写书评

暂无评论

The efficiency of soft sensors modelling in advanced control systems in oil refinery through the application of hybrid intelligent data mining techniques 2

The efficiency of soft sensors modelling in advanced control...

引用

2nd Joint international conference on Emerging computing Technology and Sports, JICETS 2019

作者： Al-Jlibawi, Ali Othman, Mohammed Lutfi Bin Al-Huseiny, Muayed S. Aris, Ishak Bin Bahari, Samsul Dept. of Electrical and Electronic Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia Faculty of Engineering University of Wasit Kut Iraq

Quality variables cannot be automatically measured to all nor be measured at a high cost, infrequently, nor with high delays, such as laboratory analysis and online analyser. Therefore, data-driven soft sensors are inferential models which use online available sensors, such as temperature, pressure, and flow rate among others, to predict the quality variables. soft sensors which are built using historical data of the processes are normally developed from the supervisory control and data acquisition (SCADA) systems connected with PLC or DCS (distribution control systems) as the daily reports on the oil refinery processes. These systems are then obtained from laboratory observation/measurements. Notably, the main issue in the development of the soft sensor is the treatment of missing data, outlier detection, selection of input variables, model training, validation, and soft sensor maintenance to adopt the heavy-duty oil refineries to improve the products of the crude oil and increase yield. In this article, the improvement in the virtual sensor based on hybrid soft computing methods (FLS and NN), which are combined into ANFIS, will be employed to construct the soft sensor model. Moreover, RST will be used to reduce the fuzzy rules and discretisation method to optimise and deal with the large continuous data. It was found from the implementation of rough set theory and discretisation methods that these two methods solved the complexity and nonlinearity of the soft sensor model. This model was employed for the refining process measurements data of the oil refinery from two different crude oil sources, in which the database of the measurements and processes was combined to improve the quality of data and discover the knowledge stored in the data pattern. It was indicated from this study result that the ANFIS model is able to manage the complex data to predict two important parameters of light naphtha (API and RVP) compared to the simple regression model. Additionally, co

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

1st International Conference on Computational Intelligence i...

引用

1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

ISBN: (纸本)9789811390418

The proceedings contain 88 papers. The special focus in this conference is on Computational Intelligence in pattern recognition. The topics include: Classification of Speech and Song Using Co-occurrence-Based Approach;Intelligent Analysis for Personality Detection on Various Indicators by Clinical Reliable Psychological TTH and Stress Surveys;automatic Multilingual System from Speech;an Improved Data Hiding Scheme Through Image Interpolation;generalization of Multi-bit Encoding Function Based Data Hiding Scheme;a Machine Learning Approach to Comment Toxicity Classification;a Minutia Detection Approach from Direct Gray-Scale Fingerprint Image Using Hit-or-Miss Transformation;face Identification via Strategic Combination of Local Features;a Study on Reversible Image Watermarking Using Xilinx System Generator;a Novel Enhancement and Segmentation of Color Retinal Image Based on Fuzzy Measure and Fuzzy Integral;SVM and MLP Based Segmentation and recognition of Text from Scene Images Through an Effective Binarization Scheme;user Identification and Authentication Through Voice Samples;template Matching for Kinship Verification in the Wild;Attack Prevention Scheme for Privacy Preservation (APSP) Using K Anonymity in Location Based Services for IoT;Statistical Analysis of the UNSW-NB15 Dataset for Intrusion Detection;Singer Identification Using MFCC and CRP Features with Support Vector Machines;object Proposals Based on Variance Measure;deep Learning Approach in Predicting Personal Traits Based on the Way User Type on Touchscreen;Robust Non-blind Video Watermarking Using DWT and QR Decomposition;detection and Classification of Earthquake Images from Online Social Media;On the Cryptanalysis of S-DES Using Binary Cuckoo Search Algorithm;numerical Integration Based Contrast Enhancement Using Simpson’s Method;self Driving Car.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Changes in marketing brought by AI

Changes in marketing brought by AI

引用

software Engineering, Artificial Intelligence, Networking and Parallel/Distributed computing (SNPD-Winter), ACIS international Winter conference on

作者： Jin-Hee Lee Korea Soongsil Cyber University Seoul Korea

After the fourth industrial revolution, a business that utilizes Artificial Intelligence (AI) is expanding centered around IT industries and it is expected that the quality of AI services will improve. This study aims to examine changes in marketing through the advance and development of AI and to establish and apply marketing strategies to respond to future market changes. Based on existing data, the development of AI technology was examined and looked into changes in marketing and counterstrategies through cases overseas and South Korea. Artificial Intelligence technology has a close impact on our lives, changing our lives, and thus changing consumer patterns, perceptions, and consumer culture. In the future, innovative changes in AI technologies will require government policies, the vision of the corporation, and it is necessary to establish longer- term success strategies. Collaboration between companies and industries is also important.

关键词： Industries Government policies Collaboration Companies pattern recognition Artificial intelligence software engineering

来源：评论

学校读者我要写书评

暂无评论

Complex pattern recognition using Feedback Algebraic Activation Function

Complex Pattern Recognition using Feedback Algebraic Activat...

引用

Communication, computing and Industry 4.0 (C2I4), international conference on

作者： P. Dhivya A. Bazilabanu Bannari Amman Institute of TechnologyErode Tamilnadu India Bannari Amman Institute of Technology Erode Tamilnadu India

ISBN: (纸本)9781665420143

Various researches on the Wisconsin breast cancer dataset in the prediction of benign and malignant were proposed in this work. The various classification models were used in the literature survey in which logistic regression give the better accuracy of 95.41%. But in machine learning model, the linear relationship between the features may lead to over fitting in that model. The neural network is a fully connected layer to find and understand the nonlinear combinations of the features in the dataset. To reduce the over fitting and improves the accuracy of the model, the Neural Network with Feedback Algebraic Activation (FAA) function is introduced and applied to the hidden layer in the network . In this, the error will be added as a feedback to the network and uses the keras callbacks and keras tuner for selecting the optimal parameters and produces the better results with the accuracy of 95.91% compared with all other activation functions in the neural network.

关键词： Tuners Error analysis Neural networks Fitting FAA Machine learning Propagation losses

来源：评论

学校读者我要写书评

暂无评论

Traffic Prediction for Intelligent Transportation System using Machine Learning 3

Traffic Prediction for Intelligent Transportation System usi...

引用

3rd international conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things, ICETCE 2020

作者： Meena, Gaurav Sharma, Deepanjali Mahrishi, Mehul Central University of Rajasthan Department of Computer Science Ajmer Rajasthan India Swami Keshvanand Institute of Technology Department of Information Technology Jaipur Rajasthan India

ISBN: (纸本)9781728116839

This paper aims to develop a tool for predicting accurate and timely traffic flow Information. Traffic Environment involves everything that can affect the traffic flowing on the road, whether it's traffic signals, accidents, rallies, even repairing of roads that can cause a jam. If we have prior information which is very near approximate about all the above and many more daily life situations which can affect traffic then, a driver or rider can make an informed decision. Also, it helps in the future of autonomous vehicles. In the current decades, traffic data have been generating exponentially, and we have moved towards the big data concepts for transportation. Available prediction methods for traffic flow use some traffic prediction models and are still unsatisfactory to handle real-world applications. This fact inspired us to work on the traffic flow forecast problem build on the traffic data and *** is cumbersome to forecast the traffic flow accurately because the data available for the transportation system is insanely huge. In this work, we planned to use machine learning, genetic, soft computing, and deep learning algorithms to analyse the big-data for the transportation system with much-reduced complexity. Also, Image Processing algorithms are involved in traffic sign recognition, which eventually helps for the right training of autonomous vehicles. © 2020 IEEE.

关键词： soft computing

来源：评论

学校读者我要写书评

暂无评论

19th international conference on Artificial Intelligence and soft computing, ICAISC 2020

19th International Conference on Artificial Intelligence and...

引用

19th international conference on Artificial Intelligence and soft computing, ICAISC 2020

ISBN: (纸本)9783030614003

The proceedings contain 110 papers. The special focus in this conference is on Artificial Intelligence and soft computing. The topics include: Challenging Human Supremacy: Evaluating Monte Carlo Tree Search and Deep Learning for the Trick Taking Card Game Jass;how Motivated Are You? A Mental Network Model for Dynamic Goal Driven Emotion Regulation;multi-agent Architecture for Internet of Medical Things;automatic Visual Quality Assessment of Biscuits Using Machine Learning;classifying Image Series with a Reoccurring Concept Drift Using a Markov Chain Predictor as a Feedback;explainable Cluster-Based Rules Generation for Image Retrieval and Classification;SURF Algorithm with Convolutional Neural Network as Face recognition Technique;preface;a New Approach to Detection of Abrupt Changes in Black-and-White Images;grouping Handwritten Letter Strokes Using a Fuzzy Decision Tree;a Density-Based Prototype Selection Approach;flexTrustRank: A New Approach to Link Spam Combating;a Comparative Analysis of Similarity Measures in Memory-Based Collaborative Filtering;constructing Interpretable Decision Trees Using Parallel Coordinates;a Framework for e-Recruitment Recommender Systems;the Influence of Feature Selection on Job Clustering for an E-recruitment Recommender System;n-ary Isolation Forest: An Experimental Comparative Analysis;in-The-Limit Clustering Axioms;hybrid Features for Twitter Sentiment Analysis;active Region-Based Full-Disc Solar Image Hashing;computer Based Stylometric Analysis of Texts in Ukrainian Language;newsminer: Enriched Multidimensional Corpus for Text-Based Applications;detecting Causalities in Production Environments Using Time Lag Identification with Cross-Correlation in Production State Time Series;Identification of Delays in AMUSE Algorithm for Blind Signal Separation of Financial Data;pre-training Polish Transformer-Based Language Models at Scale.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Proceedings - IEEE Congress on Cybermatics: 2020 IEEE international conferences on Internet of Things, iThings 2020, IEEE Green computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020

Proceedings - IEEE Congress on Cybermatics: 2020 IEEE Intern...

引用

2020 IEEE Congress on Cybermatics: 13th IEEE international conferences on Internet of Things, iThings 2020, 16th IEEE international conference on Green computing and Communications, GreenCom 2020, 13th IEEE international conference on Cyber, Physical and Social computing, CPSCom 2020 and 6th IEEE international conference on Smart Data, SmartData 2020

ISBN: (纸本)9781728176475

The proceedings contain 135 papers. The topics discussed include: real-time operating systems for cyber-physical systems: current status and future research;a semi-supervised dynamic ensemble algorithm for IoT anomaly detection;an efficient hybrid approach for brain tumor detection in MR images using Hadoop-MapReduce;drug-drug interaction extraction using pre-training model of enhanced entity information;breast cancer image classification based on CNN classifier;an assessment of the usability of machine learning based tools for the security operations center;machine learning recognition of gait identity via shoe embedded accelerometer;leveraging walking inertial pattern for terrain classification;multicast traffic throughput maximization through dynamic modulation and coding scheme assignment in wireless sensor networks;and X-DOG: an intelligent x-ray-based dangerous goods detection and automatic alarm system.

关键词：

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 << < 128 129 130 131 132 133 134 135 136 137 > >>

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：