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

1,327 篇 会议
11 篇 期刊文献
9 册 图书

馆藏范围

1,347 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

915 篇 工学
- 757 篇 计算机科学与技术...
- 497 篇 软件工程
- 304 篇 信息与通信工程
- 205 篇 电气工程
- 175 篇 生物工程
- 127 篇 生物医学工程（可授...
- 107 篇 光学工程
- 90 篇 控制科学与工程
- 80 篇 电子科学与技术（可...
- 58 篇 仪器科学与技术
- 48 篇 机械工程
- 35 篇 化学工程与技术
- 28 篇 安全科学与工程
- 26 篇 交通运输工程
425 篇 理学
- 189 篇 生物学
- 187 篇 数学
- 151 篇 物理学
- 76 篇 统计学（可授理学、...
- 54 篇 系统科学
- 40 篇 化学
170 篇 管理学
- 106 篇 图书情报与档案管...
- 73 篇 管理科学与工程(可...
- 40 篇 工商管理
146 篇 医学
- 130 篇 临床医学
- 88 篇 基础医学(可授医学...
- 65 篇 药学(可授医学、理...
- 24 篇 公共卫生与预防医...
28 篇 教育学
- 28 篇 教育学
27 篇 法学
- 21 篇 社会学
18 篇 经济学
- 17 篇 应用经济学
16 篇 农学
5 篇 军事学
1 篇 文学
1 篇 历史学

主题

211 篇 machine learning
134 篇 deep learning
78 篇 signal processin...
72 篇 accuracy
71 篇 feature extracti...
68 篇 signal processin...
61 篇 support vector m...
57 篇 machine learning...
46 篇 predictive model...
33 篇 neural networks
32 篇 convolutional ne...
31 篇 random forests
29 篇 computational mo...
29 篇 training
26 篇 natural language...
26 篇 prediction algor...
21 篇 technological in...
20 篇 real-time system...
19 篇 adversarial mach...
18 篇 image processing

机构

16 篇 chitkara centre ...
14 篇 centre of resear...
12 篇 shenyang ligong ...
6 篇 chitkara univers...
6 篇 ai & ds vishwaka...
6 篇 school of inform...
5 篇 department of me...
5 篇 khulna universit...
5 篇 department of co...
4 篇 delhi technologi...
4 篇 heilongjiang uni...
4 篇 department of s&...
4 篇 beijing univ pos...
4 篇 it vishwakarma i...
4 篇 chitkara univers...
4 篇 department of co...
4 篇 sri lanka instit...
4 篇 advanced telecom...
4 篇 shenyang ligong ...
4 篇 graduate school ...

作者

6 篇 mehta shiva
5 篇 rasmita panigrah...
4 篇 neelamadhab padh...
4 篇 wang xin
3 篇 m. vigenesh
3 篇 baydeti nagaraju
3 篇 sinambela marzuk...
3 篇 santosh kumar sh...
3 篇 ganapati panda
3 篇 debendra muduli
3 篇 abujar sheikh
3 篇 a. sivanantham
3 篇 li yajie
3 篇 li aihua
3 篇 zhou feng
3 篇 islam md. sanzid...
2 篇 aira kharvel par...
2 篇 nokshangthemba
2 篇 maheswara rao v ...
2 篇 lee tzong-ru

语言

1,273 篇 英文
65 篇 其他
11 篇 中文

检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

共 1347 条记录，以下是981-990 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：

Comparison of Deep learning, Data Augmentation and Bag of-Visual-Words for Classification of Imbalanced Image Datasets 2nd

Comparison of Deep Learning, Data Augmentation and Bag of-Vi...

引用

2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

作者： Saini, Manisha Susan, Seba Delhi Technological University Delhi India

ISBN: (纸本)9789811391804

Image classification is a supervised machine learning task to classify images into different categories. As most real-world datasets are imbalanced in nature, instances are not equally distributed in all classes which often results in biased classification. So considering this objective, we are dealing with an imbalanced dataset. We have created a small size of an imbalanced dataset consisting of Ant and Plane Images from Caltech-101 dataset. This paper illustrates the comparison between Deep learning, Data Augmentation and the Bag-of-Visual Words (BOVW) for classification of imbalanced image datasets. According to the experimental results, it was found that deep learning results in higher accuracy in comparison to bag-of-visual-words (BOVW) and data augmentation. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Real-Time Two Way Communication System for Speech and Hearing Impaired Using Computer Vision and Deep learning 2

Real-Time Two Way Communication System for Speech and Hearin...

引用

2nd international conference on Smart Systems and Inventive Technology, ICSSIT 2019

作者： Bohra, Tanuj Sompura, Shaunak Parekh, Krish Raut, Purva Dwarkadas J. Sanghvi College of Engineering Information Technology Mumbai India

ISBN: (纸本)9781728121192

Sign Language is the most expressive form of communication for speech and hearing impaired people to communicate with normal person but a normal person cannot understand sign language. So in order to break this barrier of communication there needs to be a system that can enable conversion of sign language to voice or text and voice or text to sign language and do it in real time. The systems that currently exist are not real time, do not facilitate two-way communication, require static surrounding conditions or have low recognition accuracy. There exist systems that have good accuracy but require external hardware like gloves [3] which increases the cost. Our contribution to solving this problem consists of a Sign Language Communication System. It is a real-time communications system built using the advancements in Image processing, Deep learning and Computer Vision that provides real-time sign language to text and text to sign language conversion. The project is software-based which can be installed on any computer with good specifications. It is also a two-way communication system allowing not just speech and hearing impaired to communicate with normal people but also other way around. The primary goal of our system is to enable hearing and speech impaired people to communicate with people that are not disabled in real time by interpreting alphabets, numbers and words in the Indian sign language. The system is able to predict 17600 test images in 14 seconds with an average prediction time of 0.000805 seconds with an accuracy of 99%. © 2019 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

2nd International Conference on Recent Trends in Image Proce...

引用

2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

ISBN: (纸本)9789811391835

The proceedings contain 173 papers. The special focus in this conference is on Recent Trends in Image processing and Pattern Recognition. The topics include: Pathological Brain Tumour Detection Using Ridgelet Transform and SVM;color Transfer Method for Efficient Enhancement of Color Images and Its Application to Peripheral Blood Smear Analysis;Medical Image Encryption with Integrity Using DNA and Chaotic Map;A Systematic Approach for Constructing 3D MRI Brain Image over 2D Images;classification of Rheumatoid Arthritis Based on Image processing Technique;DRAODM: Diabetic Retinopathy Analysis Through Optimized Deep learning with Multi Support Vector machine for Classification;Skewness and Kurtosis of Apparent Diffusion Coefficient in Human Brain Lesions to Distinguish Benign and Malignant Using MRI;segmentation of Kidney Stones in Medical Ultrasound Images;osteoarthritis Stages Classification to Human Joint Imagery Using Texture Analysis: A Comparative Study on Ten Texture Descriptors;comparison with Evaluation of Intra Ocular Pressure Using Different Segmentation Techniques for Glaucoma Diagnosis;recurrent Neural Network Based Classification of Fetal Heart Rate Using Cardiotocograph;automatic Diagnosis of Myocardial Infarction with Left Bundle Branch Block;Exudates Detection from Digital Fundus Images Using GLCM Features with Decision Tree Classifier;WT and PDE Approach for Forest Species Recognition in Macroscopic Images;diabetes Detection Using Principal Component Analysis and Neural Networks;Microaneurysm Detection in Diabetic Retinopathy Using Genetic Algorithm and SVM Classification Techniques;compressive Sensing for Three-Dimensional Brain Magnetic Resonance Imaging;segmentation of Lungs from Chest X Rays Using Firefly Optimized Fuzzy C-Means and Level Set Algorithm;image Enhancement Using Filters on Alzheimer’s Disease.

关键词：

来源：评论

学校读者我要写书评

暂无评论

2019 2nd IEEE international conference on Information Communication and signal processing (ICICSP 2019)

2019 2nd IEEE International Conference on Information Commun...

引用

IEEE international conference on Information Communication and signal processing (ICICSP)

Presents the title page of the proceedings record.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms for Cardiac Arrhythmia Detection

Convolutional-Recurrent Neural Networks on Low-Power Wearabl...

引用

IEEE international conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Antonino Faraone Ricard Delgado-Gonzalo ETH Zürich Switzerland CSEM Neuchâtel Switzerland

ISBN: (数字)9781728149226

ISBN: (纸本)9781728149233

Low-power sensing technologies, such as wearables, have emerged in the healthcare domain since they enable continuous and non-invasive monitoring of physiological signals. In order to endow such devices with clinical value, classical signal processing has encountered numerous challenges. However, data-driven methods, such as machine learning, offer attractive accuracies at the expense of being resource and memory demanding. In this paper, we focus on the inference of neural networks running in microcontrollers and low-power processors which wearable sensors and devices are generally equipped with. In particular, we adapted an existing convolutional-recurrent neural network, designed to detect and classify cardiac arrhythmias from a single-lead electrocardiogram, to the low-power embedded System-on-Chip nRF52 from Nordic Semiconductor with an ARM's CortexM4 processing core. We show our implementation in fixed-point precision, using the CMSIS-NN libraries, yields a drop of F 1 score from 0.8 to 0.784, from the original implementation, with a memory footprint of 195.6 KB, and a throughput of 33.98 MOps/s.

关键词： Hardware Quantization (signal) Convolution Artificial neural networks Training Sensitivity

来源：评论

学校读者我要写书评

暂无评论

A machine learning Approach to Employability Evaluation Using Handwriting Analysis 2nd

A Machine Learning Approach to Employability Evaluation Usin...

引用

2nd international conference on Advanced Informatics for Computing Research (ICAICR)

作者： Joshi, Prachi Ghaskadbi, Pranav Tendulkar, Sarvesh MIT Coll Engn Pune 411038 Maharashtra India

ISBN: (纸本)9789811331404;9789811331398

Writing is a process which is rooted deep within the creative processes of the mind. While writing, the mind and the hand synchronize to act as a smoothly functioning unit, thus enabling the pen to act as an extension of the person's innermost self. Emotional factors often dictate the writing strokes and mannerisms. The science of graphology can be applied to discern a person's behavior and inner psychological makeup from their handwriting. Certain features such as the page margins, handwriting size etc. are often reflective of mood changes and characterize the writer's state of mind at the moment of writing. An automated process for extracting these features and mapping them to the various personality traits can definitely prove to be a boon for many applications like recruitment process or even psychological analysis. In this paper, we propose feature extraction methods implemented using image processing techniques to select features to be used further for this trait identification. Once the features have been selected, existing classifiers have been put to work to determine the employability evaluation of a candidate from an HR perspective.

关键词： Graphology Handwriting Feature extraction Template matching Image processing machine learning

来源：评论

学校读者我要写书评

暂无评论

Gamma Source Location learning from Synthetic Multi-pinhole Collimator Data 2nd

Gamma Source Location Learning from Synthetic Multi-pinhole ...

引用

2nd international Workshop on machine learning for Medical Image Reconstruction (MLMIR) as part of the 22nd Medical Image Computing and Computer Assisted Intervention (MICCAI) conference

作者： von Niederhausern, Peter A. Seppi, Carlo Pezold, Simon Nicolas, Guillaume Gkoumas, Spyridon Haerle, Stephan K. Cattin, Philippe C. Univ Basel Dept Biomed Engn Allschwil Switzerland Univ Hosp Basel Radiol & Nucl Med Clin Basel Switzerland DECTRIS Ltd Baden Switzerland Hirslanden Clin Ctr Head & Neck Surg Oncol & Reconstruct Surg Luzern Switzerland

ISBN: (纸本)9783030338435;9783030338428

Sentinel lymph node biopsy (SNB) is a surgical method to stage certain cancer types in a minimally invasive manner. However, the current sensing methods for SNB are limited in accuracy, as they are based on acoustic feedback radiation probes to detect tracer enriched sentinel lymph nodes. We present a deep neural network approach to learn the latent spatial activity distributions from a simulated gamma source on 2D activity images. Data processing can then be applied for multi-pinhole collimator optimization, lymph node visualization or surgical navigation to further support SNB. Using simulations of photon multi-pinhole collimator interaction, we generate labeled synthetic 2D activity images to train convolutional neural networks (CNN). These CNNs are then evaluated on synthetic as well as on real experimental data from a radioactive point-like source, collected by our own stationary small form factor multi-pinhole collimator. We achieve good results on synthetic data for the xy-component ensemble learners with a localization class accuracy of 0.97, while depth estimation achieves a localization class accuracy of 0.55. Accuracy on real experimental data is limited due to the small sample set and its variability, compared to the simulation.

关键词： Sentinel lymph node biopsy Radioguided surgery Inverse problem machine learning Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Local, Mid-Level and Convolutional Features Fusion Using Multiple Kernel learning for Image Classification

Local, Mid-Level and Convolutional Features Fusion Using Mul...

引用

IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Yao Lu Hui Zhang Bojun Xie Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China

Feature representation and feature fusion are important factors in image classification problem. In this paper, the local features, mid-level features and convolutional features are combined using the multiple kernel learning method. Experimental results show that the local features, mid-level features and convolutional features can be fused effectively to improve the classification performance about 4%-6% on several popular benchmarks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Short-Term Solar PV Forecasting Based on Recurrent Neural Network and Clustering 2

Short-Term Solar PV Forecasting Based on Recurrent Neural Ne...

引用

2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Sodsong, Nattawat Yu, Kun-Ming Ouyang, Wen Chuang, Ken H. Chung Hua Univ Dept Comp Sci & Informat Engn Hsinchu Taiwan Natl Yang Ming Univ Inst Biomed Informat Taipei Taiwan

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

With the large-scale deployment of solar photovoltaic (PV) installation, managing the efficiency of the generation system has become essential. One of the main challenges facing solar PV power output lies in the difficulty in managing solar irradiance fluctuation. Generally speaking, the power output is heavily influenced by solar irradiance and sky conditions which are consistently changing. Thus, the ability to accurately forecast the solar PV power is critical for optimizing the generation system and ensuring the quality of service. In this paper, we propose a solar PV forecasting model using Recurrent Neural Network (RNN) in a Cascade model combined with Hierarchical Clustering for improving the overall prediction accuracy of solar PV forecast. The proposed model, upon comparing with other learning algorithms, namely, Feed-forward Artificial Neural Network (FFNN), GRU, Support Vector Regression (SVR) and K Nearest Neighbors (KNN) using the cluster data from K-Means Clustering and Hierarchical Clustering, had the lowest average NRMSE of 8.88% using Hierarchical clustered data. According to the results, Hierarchical Clustering suits better for solar PV forecast than K-means clustering.

关键词： Solar PV Artificial Neural Network Deep learning Hierarchical Clustering Recurrent Neural Network

来源：评论

学校读者我要写书评

暂无评论

Acceleration of Server-side Image processing by Client-side Pre-processing in Web Application Environment 42

Acceleration of Server-side Image Processing by Client-side ...

引用

42nd international conference on Telecommunications and signal processing (TSP)

作者： Juranek, Lubos Stastny, Jiri Skorpil, Vladislav Junek, Lukas Mendel Univ Brno Dept Informat Brno Czech Republic Brno Univ Technol Inst Automat & Comp Sci Brno Czech Republic Brno Univ Technol Dept Telecommun Brno Czech Republic

ISBN: (纸本)9781728118642

The goal of this paper is to verify whether the use of client-side ***, a WebGL-accelerated JavaScript library for machine learning, can accelerate the processing of common photos by computer vision cloud services, such as detection and recognition of specific features like age, sex, expression or specific people in the image. This acceleration is based on pre-processing the input image, namely detecting human faces, which greatly changes the amount of input data that need to be uploaded to the cloud service and thus the amount of uploaded data compared to the original photograph. The upload speed of Internet connection often is, in the case of computer vision cloud services, the bottleneck of the whole system. That's why decreasing the amount of uploaded data in time shorter than the difference between the total of upload and cloud service processing time of the original and the pre-processed image leads to acceleration.

关键词： cloud face detection image preprocessing TensorFlow WebGL

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135页 << < 95 96 97 98 99 100 101 102 103 104 > >>

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：