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

3,080 篇 会议
141 册 图书
24 篇 期刊文献

馆藏范围

3,244 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,110 篇 工学
- 1,926 篇 计算机科学与技术...
- 1,072 篇 软件工程
- 412 篇 信息与通信工程
- 286 篇 电气工程
- 265 篇 生物工程
- 226 篇 控制科学与工程
- 169 篇 生物医学工程（可授...
- 81 篇 化学工程与技术
- 76 篇 机械工程
- 68 篇 光学工程
- 68 篇 仪器科学与技术
- 64 篇 网络空间安全
- 63 篇 电子科学与技术（可...
- 62 篇 安全科学与工程
- 57 篇 动力工程及工程热...
767 篇 理学
- 435 篇 数学
- 290 篇 生物学
- 149 篇 统计学（可授理学、...
- 137 篇 物理学
- 128 篇 系统科学
- 90 篇 化学
502 篇 管理学
- 304 篇 图书情报与档案管...
- 228 篇 管理科学与工程(可...
- 93 篇 工商管理
222 篇 医学
- 191 篇 临床医学
- 123 篇 基础医学(可授医学...
- 87 篇 药学(可授医学、理...
- 59 篇 公共卫生与预防医...
69 篇 法学
- 67 篇 社会学
52 篇 教育学
- 47 篇 教育学
35 篇 经济学
28 篇 农学
6 篇 艺术学
4 篇 军事学
1 篇 文学

主题

571 篇 data mining
546 篇 machine learning
241 篇 pattern recognit...
183 篇 deep learning
173 篇 feature extracti...
156 篇 artificial intel...
151 篇 support vector m...
114 篇 accuracy
113 篇 machine learning...
111 篇 training
93 篇 data models
82 篇 neural networks
77 篇 data mining and ...
65 篇 classification
62 篇 sentiment analys...
61 篇 classification a...
59 篇 big data
57 篇 predictive model...
50 篇 face recognition
48 篇 prediction algor...

机构

7 篇 maharishi school...
6 篇 guangdong univer...
5 篇 centre of interd...
5 篇 chitkara univers...
5 篇 uttaranchal univ...
5 篇 uttaranchal inst...
5 篇 institute of aut...
4 篇 shanghai jiao to...
4 篇 chitkara univers...
4 篇 school of inform...
4 篇 indian statistic...
4 篇 south china univ...
4 篇 galgotias univer...
4 篇 zhejiang univers...
4 篇 maharishi school...
4 篇 department of cs...
4 篇 pune
4 篇 university of ca...
4 篇 school of comput...
4 篇 department of co...

作者

9 篇 de-shuang huang
7 篇 coenen frans
7 篇 venkatesh svetha
7 篇 petra perner
7 篇 tianrui li
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...
6 篇 jacek m. zurada
6 篇 krasotkina olga
6 篇 lotfi a. zadeh
6 篇 mottl vadim
6 篇 leszek rutkowski
5 篇 vitoantonio bevi...
5 篇 sankar k. pal
4 篇 waiyamai kitsana
4 篇 sozo inoue
4 篇 sergei o. kuznet...
4 篇 duoqian miao
4 篇 shengli xie
4 篇 hepu deng

语言

3,100 篇 英文
135 篇 其他
32 篇 中文
1 篇 西班牙文
1 篇 土耳其文

检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3245 条记录，以下是3021-3030 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：

Detecting Criminal Activities From CCTV by using Object Detection and machine learning Algorithms

Detecting Criminal Activities From CCTV by using Object Dete...

引用

Intelligent Technologies (CONIT), international conference on

作者： Surbhi Singla Raman Chadha CSE AIML University Institute of Engineering Chandigarh University Mohali India CSE University Institute of Engineering Chandigarh University Mohali India

Now, a day’s Crime in every country is increasing day by day. Generally, Every day we listen to the news of different crimes of different categories like rape, assault, Kidnapping ,Robbery ,ATM Theft, Murders etc happening in different states, cities , countries. Almost all the newspapers, TV channels’, social media are filled with the news of Criminal activities happening all around the Whole World. In earlier times there is no method to detect Crime. After That the CCTV cameras were used to detect Crimes. But Watching these Videos manually by humans for detecting crimes is a very time Consuming process especially in today’s world of Artificial Intelligence and machine learning .So this crime detection in CCTV surveillance becomes an important area of research in the field of machine learning. So, there is a very urgent need of the intelligent system which will detect the crimes from the real time CCTV Feed and classify them and provides an alert system to the nearest police stations and ambulances etc. So, that system will help in reducing the crime rate in any country. This paper reviews all prior research in this area, including approaches for object recognition and finding priority frames, techniques and algorithms like Yolo used to detect crimes , various datasets used and algorithms used to analyze crime data and train the dataset .It covers the various recent trends in researches in this field and analyzing the challenges faced and various research gaps and this paper also discuss how we can overcome these gaps in research so as to develop a better intelligence surveillance system in ml field.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Statistical Feature Extraction and Classification of Complex data Based on Deep learning

Statistical Feature Extraction and Classification of Complex...

引用

Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Dongmei Wu Chengdu College of University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

This research is focused on elucidating the extraction of statistical features and the classification methodologies for intricate data leveraging deep learning techniques. The objective is to address the challenge of proficiently handling and precisely categorizing diverse forms of complex data in practical scenarios. Within this study, a composite model has been devised, merging the functionalities of Convolutional Neural Networks (CNN) with Recurrent Neural Networks (RNN), and augmenting the framework with an Attention Mechanism (AM) to bolster the capability of the model in extracting features and classifying intricate datasets. Through extensive experimentation, this paper validates the efficacy and versatility of the proposed model across diverse data types, including images, texts, and audio. The experimental findings demonstrate that, in contrast to traditional machine learning methods, the deep learning model proposed in this study excels at capturing abstract data features, thereby enhancing classification accuracy and generalization capability. The research presented herein offers novel insights and methodologies for the domain of statistical feature extraction and classification of complex data, thus playing a pivotal role in advancing the practical applications of deep learning technology.

关键词： Deep learning Representation learning Recurrent neural networks Accuracy Merging Feature extraction data models data mining Convolutional neural networks Autonomous robots

来源：评论

学校读者我要写书评

暂无评论

Use of Natural Language Processing and Deep learning towards Guiding Healthy Cholesterol Free Life

Use of Natural Language Processing and Deep Learning towards...

引用

Advancements in Computing (ICAC), 2019 international conference on

作者： Dilith Sasanka H. K. N Malshani Uchitha I. Wickramaratne Yashmitha Kavindi Muditha Tissera Buddhima Attanayaka Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781665408639

High blood cholesterol is a key risk factor for cardiovascular diseases such as coronary heart disease and stroke. This has become a severe health problem, because it causes a considerable amount of deaths annually. The major risk factors that affect a person’s cholesterol level include unawareness of cholesterol risk, unhealthy dietary habits, lack of proper exercises, and high stress conditions. In this research, novel approaches are introduced to provide an automated and personalized guidance to maintain healthy cholesterol level and raise the awareness of each risk factors mentioned above. This research associates with four novel approaches. Natural Language Processing (NLP) based Cholesterol risk analyzer, Fuzzy based Food management with Meal predictor, machine learning based Physical exercise planner and Stress controller. Altogether with results, this research will provide a complete and facts-proven solution to reduce and guide people towards a cholesterol-free healthy lifestyle.

关键词： Optical filters Schedules Natural language processing Frequency measurement Software reliability data mining Stress

来源：评论

学校读者我要写书评

暂无评论

machine learning Approach for Discovering Successful Books Based on the Achievement of Award-Winning

Machine Learning Approach for Discovering Successful Books B...

引用

Advanced Research in Computing (ICARC), international conference on

作者： S. M. S. T. Wijekoon R. A. H. M. Rupasingha Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Economics and Statistics Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

Many people are very interested in reading books. Some of them like to read books for various reasons like as a hobby or to learn something new. At present, with modern technology, people have become accustomed to reading books online in addition to physically reading books. The most important factor in identifying which books are the most successful is the award-winning. The goal of this research is to forecast how the most successful books such as award/winning books are identified based on 10 distinct attributes including publishing date, average ratings, author, genre, language, publisher, number of pages, ratings, title, and reviews. A data set was gathered for this purpose through the online community platform Kaggle. The dataset provides information about books gathered from the website 'Goodread' for the period between 2000 and 2021. Firstly, the data set has been pre-processed. For that, duplicate data, unnecessary special characters, and missing values have been removed. To rank the preprocessed data, the Waikato Environment for Knowledge Analysis (WEKA) data mining tool was utilized, and results are improved by hyperparameter tuning. The predictive model is generated using six machine learning approaches at the classification level: Random Forest, Support Vector machine (SVM), Decision Trees, Multilayer Perceptron (MLP), Logistic Regression, and Naive Bayes. A high accuracy of 86.32% was gained with the lowest error rates in addition to high precision, recall, and f-measure values using a Naive Bayes algorithm. Based on the results, the Naive Bayes algorithm makes a reliable prediction of how to find the most successful books using the above features.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Improved Sensor pattern Noise Estimation Method Based on Edge Guided Weighted Averaging 3rd

An Improved Sensor Pattern Noise Estimation Method Based on ...

引用

3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Zhang, Wen-Na Liu, Yun-Xia Zhou, Jin Yang, Yang Law, Ngai-Fong School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China School of Information Science and Engineering Shandong University Qingdao266237 China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030624590

Sensor pattern Noise (SPN) has proven to be an effective fingerprint for source camera identification. However, its estimation accuracy is still greatly affected by image contents. In this work, considering the confidence difference in varying image regions, an image edge guided weighted averaging scheme for robust SPN estimation is proposed. Firstly, the edge and non-edge regions are estimated by a Laplacian operator-based detector, based on which different weights are assigned to. Then, the improved SPN estimation is obtained by weighted averaging of image residuals. Finally, an edge guided weighted normalized cross-correlation measurement is proposed as similarity metric in source camera identification (SCI) applications. The effectiveness of the proposed method is verified by SCI experiments conducted on six models from the Dresden data set. Comparison results on different denoising algorithms and varying patch sizes reveal that performance improvement is more prominent for small image patches, which is demanding in real forensic applications. © 2020, Springer Nature Switzerland AG.

关键词： Mathematical operators

来源：评论

学校读者我要写书评

暂无评论

Heart disease Classification Utilising machine learning Techniques

Heart disease Classification Utilising Machine Learning Tech...

引用

Advancement in Technology (ICONAT), international conference for

作者： Shiv Ashish Dhondiyal Himanshu Verma Rohit Kumar Vikash Sawan Ishita Uniyal Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Haridwar University Roorkee Department of Computer Engineering & Applications GLA University Mathura Department of Electrical Engineering Graphic Era Hill University

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world’s biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various data mining and machine learning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994.

关键词： Heart Support vector machines Accuracy machine learning algorithms Simulation Supervised learning Prediction algorithms Classification algorithms Diseases Testing

来源：评论

学校读者我要写书评

暂无评论

Terrain Classification through Satellite Images using Deep learning

Terrain Classification through Satellite Images using Deep L...

引用

Advancement in Technology (ICONAT), international conference for

作者： Virendrasinh Khuman Shubh Arora Siddhant Rajeev Kumar Gupta Samir Patel Computer Science & Engineering Pandit Deendayal Energy University Raisan Gandhinagar Gujarat

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

With the exponential growth of remote sensing data, particularly multispectral imagery, the integration of deep learning techniques such as Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) has become relevant for efficient classification tasks. Satellite imagery is procured in the manner of multispectral imagery, which is a collection of a few image layers enlist of the same scene, each of them acquired at a particular wavelength band. Challenges in multispectral images arise owing to the large volume of image data and the data variety due to high dimensionality and number of channels. Land Cover and Terrain classification is the process of identifying, mapping and delineating the surface of the earth into different units. It remains vital for its applications in a variety of fields. It enables environmental monitoring allowing for adaptation to ecosystem variations. Fields like Urban Planning, Disaster Management and Infrastructure development find essential advantages in their planning phases owing to such classification. Deep CNNs, or Convolutional Neural Networks, are advanced machine learning models renowned for their ability to extract intricate patterns from visual data, crucial in tasks like image recognition and classification. The reason behind the application of deep CNNs arises from their unparalleled computing efficiency. Through our work we try to explore the utilization of above mentioned deep learning and machine learning techniques in the classification of land cover, focusing on both multispectral imagery and provide an overview of the existing literature, examining the methodologies employed and the datasets utilized and the processing and handling of these large datasets. Additionally, we introduce various data sources and datasets commonly utilized in these studies, shedding light on the advancements, challenges, and future directions in the field of remote sensing-based land use and land cover classification.

关键词： Deep learning Urban planning Land surface Feature extraction data models Satellite images Convolutional neural networks data mining Surface treatment Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Location-Free CSI-Based Lightweight Deeping learning Model for Human Activity recognition

Location-Free CSI-Based Lightweight Deeping Learning Model f...

引用

Computing, Communication, Perception and Quantum Technology (CCPQT), international conference on

作者： Xin Jiang Bin Li Yirui Du Daosen Zhai Ruonan Zhang School of Computer Science and Engineering Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798331528386

ISBN: (纸本)9798331528393

In recent years, activity recognition based on channel state information(Csi) has a wide range of application scenarios in the field of human-computer interaction(HCI) due to its advantages such as no need to carry special equipment, no privacy disclosure, and no light intensity. Many approaches based on traditional machine learning and deep learning have encountered two challenges. One is that the problem of activity recognition limited to fixed locations and complex backgrounds remains unsolved. The second is that some studies require complex CSI processing, which increases the network parameters, significantly lengthens the recognition time and raises the deployment cost. To this end, this study develops a Wi-Fi-based location-independent lightweight recognition model. We propose a lightweight CSI processing strategy that is able to effectively extract the main relevant features while compressing the model size. We combine 3D convolution with spatio-temporal information selection gates to extract activity-related information from CSI data, and employ knowledge distillation techniques to migrate the features learned from this model to a simple model. Extensive experimental results show that our system outperforms other deep learning models in terms of efficiency, with recognition accuracy up to 98.6%.

关键词： Deep learning Solid modeling Accuracy Three-dimensional displays Computational modeling Logic gates Feature extraction Real-time systems data models Human activity recognition

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence and Computational Intelligence 2012

引用

丛书名： Lecture Notes in Computer Science

2012年

作者： Jingsheng Lei Fu Lee Wang Hepu Deng Duoqian Miao

ISBN: (数字)9783642334788

ISBN: (纸本)9783642334771

This volume proceedings contains revised selected papers from the 4th international conference on Artificial Intelligence and Computational Intelligence, AICI 2012, held in Chengdu, China, in October 2012. The total of 163 high-quality papers presented were carefully reviewed and selected from 724 submissions. The papers are organized into topical sections on applications of artificial intelligence, applications of computational intelligence, data mining and knowledge discovery, evolution strategy, expert and decision support systems, fuzzy computation, information security, intelligent control, intelligent image processing, intelligent information fusion, intelligent signal processing, machine learning, neural computation, neural networks, particle swarm optimization, and pattern recognition.

关键词：

来源：评论

学校读者我要写书评

暂无评论

machine learning Based pattern recognition with HOG Features for Detection of Islanding

Machine Learning Based Pattern Recognition with HOG Features...

引用

international conference on Electrical Power and Energy Systems (ICEPES)

作者： Kumaresh Pal Subhadip Goswami Anshuman Bhuyan Santanu Koley Bidisha Bera Sarmistha Basu Department of Electrical Engineering National Institute of Technology Jamshedpur India Department of Electrical Engineering National Institute of Technology Arunachal Pradesh India Department of Electrical Engineering Gandhi Institute for Education & Technology Bhubaneswar India Department of Computer Science Engineering Haldia Institute of Technology Haldia India Department of Computer Science and Technology Global Institute of Science and Technology Haldia India Department of Physics Calcutta Institute of Engineering and Management Kolkata India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

Islanding is a significant difficulty that arises from the high integration of distributed generation sources. Islanding could potentially damage the clients and their equipment. According to the IEEE 1547 DG interconnection requirements, islanding must be detected within two seconds, and the DG must be shut down. This research introduces a new method for identifying islanding using image pattern recognition utilising a machine learning classification algorithm. The suggested technique utilises the Random Forest (RF) classification algorithm. Images are utilised to extract the histogram of oriented gradient (HOG) features in order to detect both non-islanding and islanding occurrences. The spectrogram images are formed from the time-series signal collected from the point of common coupling. The images are utilised to extract features from histogram of oriented gradient, which are subsequently used as feature vector inputs for training and testing the RF classifiers. The spectrogram image is derived using the rate of change of the voltage parameter. The efficacy of the RF classifier is evaluated using 5, 10, 20, and 25-fold cross-validations. The classification findings indicates that islanding detection utilising a Random Forest classifier and Histogram of Oriented Gradients (HOG) feature for image pattern recognition obtained outstanding performance with 98.75% accuracy and a detection time of 192 milliseconds.

关键词： Histograms Accuracy Voltage Feature extraction Vectors pattern recognition Classification algorithms

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325页 << < 299 300 301 302 303 304 305 306 307 308 > >>

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：