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

4,146 篇 会议
73 篇 期刊文献
10 册 图书

馆藏范围

4,229 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,938 篇 工学
- 2,152 篇 计算机科学与技术...
- 1,311 篇 软件工程
- 717 篇 信息与通信工程
- 481 篇 电气工程
- 452 篇 光学工程
- 398 篇 生物医学工程（可授...
- 350 篇 生物工程
- 287 篇 控制科学与工程
- 203 篇 电子科学与技术（可...
- 188 篇 机械工程
- 188 篇 仪器科学与技术
- 128 篇 测绘科学与技术
- 120 篇 化学工程与技术
- 111 篇 材料科学与工程（可...
- 99 篇 土木工程
- 89 篇 建筑学
- 82 篇 航空宇航科学与技...
- 81 篇 环境科学与工程（可...
- 71 篇 安全科学与工程
- 68 篇 网络空间安全
1,312 篇 理学
- 521 篇 数学
- 512 篇 物理学
- 384 篇 生物学
- 173 篇 统计学（可授理学、...
- 139 篇 化学
488 篇 管理学
- 285 篇 图书情报与档案管...
- 223 篇 管理科学与工程(可...
445 篇 医学
- 364 篇 临床医学
- 187 篇 基础医学(可授医学...
- 134 篇 药学(可授医学、理...
71 篇 农学
64 篇 法学
25 篇 经济学
23 篇 教育学
12 篇 艺术学
10 篇 军事学
7 篇 文学
1 篇 哲学

主题

281 篇 image reconstruc...
238 篇 image segmentati...
214 篇 feature extracti...
188 篇 deep learning
180 篇 data mining
170 篇 image processing
133 篇 image classifica...
126 篇 training
118 篇 convolutional ne...
110 篇 accuracy
106 篇 data models
92 篇 image recognitio...
88 篇 image enhancemen...
81 篇 remote sensing
73 篇 image analysis
72 篇 computer vision
68 篇 image color anal...
65 篇 machine learning
63 篇 computational mo...
61 篇 visualization

机构

13 篇 chitkara univers...
12 篇 chitkara univ in...
6 篇 school of comput...
6 篇 chitkara univers...
6 篇 school of data a...
5 篇 vishwakarma inst...
5 篇 graphic era deem...
5 篇 uttaranchal univ...
4 篇 beijing institut...
4 篇 china academy of...
4 篇 chennai
4 篇 wuhan univ sch r...
4 篇 center for earth...
4 篇 univ chinese aca...
4 篇 computer science...
4 篇 geomatics engine...
4 篇 cccc big data te...
4 篇 university of ch...
4 篇 sun yat sen univ...
4 篇 waseda universit...

作者

8 篇 li jonathan
7 篇 wang cheng
7 篇 zhang wei
6 篇 sharma neha
6 篇 li li
6 篇 wang wei
6 篇 wang yan
6 篇 li yan
6 篇 chen yen-wei
5 篇 wang jing
5 篇 gupta rupesh
5 篇 b. solaiman
5 篇 m.s. bouhlel
5 篇 rueckert daniel
5 篇 zhang jun
4 篇 vibhute amol d.
4 篇 zhang lei
4 篇 gill kanwarparta...
4 篇 xie bin
4 篇 chen chen

语言

4,038 篇 英文
168 篇 其他
33 篇 中文
2 篇 法文
1 篇 泰文

检索条件"任意字段=2nd Conference on Image Reconstruction from Incomplete Data"

共 4229 条记录，以下是411-420 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：

Prediction of Crime data using Machine Learning Techniques 2

Prediction of Crime Data using Machine Learning Techniques

引用

2nd International conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Rao, P.Varaprasada Sunkari, Sukesh Raghumandala, Tejeshwararao Koka, Ganesh Rayankula, Deepak Chowdary Gokaraju Rangaraju Institute of Engineering & Technology Department of CSE Hyderabad India

ISBN: (纸本)9781665491990

Crime has a devastating effect on society and can take many forms, ranging from violent acts such as murder and assault to less serious but still damaging offenses like burglary and vandalism. Crime records are records of various criminal activities in a given area. These records typically include information such as the date, time, and location of reported crime, the nature of the crime, and the name of the suspect. However, there are many problems associated with keeping accurate and up-to-date records of criminal activity. The most common issue is a lack of uniformity in the way different states and municipalities track and record criminal activities. Additionally, many states and municipalities lack the resources to properly maintain accurate records, resulting in incomplete or inaccurate information. Finally, maintaining crime records is time consuming and expensive, leading to a backlog in the processing of reported crimes. This can complicate investigations and create a backlog of cases that need to be addressed. Therefore, this study proposes a model with an objective to make criminal investigation more effective. The two main facets of this research are crime analysis and prediction. Based on these aspects the datasets which contain all the data is pre-processed using different pre-processing techniques. Further, the dataset is processed using machine learning algorithms like Support Vector Machine, Random forest classifier, Decision tree, and K-Means. In conclusion, crime data analysis and prediction can provide valuable insights into criminal activity and aid in the development of effective crime prevention strategies. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Deep-learning Residual Network Based image Analysis for An Efficient Two-Stage Recognition of Neurological Disorders 2

Deep-learning Residual Network Based Image Analysis for An E...

引用

2nd International conference for Innovation in Technology, INOCON 2023

作者： Battula, Naga Datha Saikiran Kambhampaty, Harish Rohan Vijayalata, Y. Ashlin Deepa, R.N. Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Hyderabad India KG Reddy College of Engineering & Technology Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350320923

An increase in research and awareness of neurological disorders has caused several medical practitioners to consider taking the assistance of AI/ML and reduce the recovery time for patients. MRI-based detection of neurological disorders has become popular in the field of bio-medical analytics. In this paper, a novel two-stage approach was proposed, which split the task of disease identification and severity identification from MRI data. The proposed two-stage approach benefits from improved efficiency due to automated simultaneous testing of multiple diseases. This reduces the time to diagnosis or diagnostic delay. The proposed two-stage approach was able to yield an accuracy of up to 94.29%, which was significantly better than the simpler one-stage approach while also being flexible to future upgrades. © 2023 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Comparison of Pansharpening Algorithms: Developing a data-Driven Evaluation Framework 2

Comparison of Pansharpening Algorithms: Developing a Data-Dr...

引用

2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Liu, Yu Tong, Qingwei Zhao, Zhipeng Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9798331515669

Convolutional neural network (CNN)-based methods are increasingly crucial in image processing and computer vision. In remote sensing image pansharpening, CNNs have shown excellent fusion performance, attracting significant research interest. However, challenges remain, such as the lack of detailed pansharpening dataset simulations, standardized training-testing datasets, and simple, unified coding frameworks. Additionally, there is insufficient evaluation of recent deep learning pansharpening methods and evaluation of algorithms from the perspective of different subsequent applications, limiting comprehensive evaluation. This study constructs a comprehensive evaluation system for datasets, fusion algorithms, and evaluation methods. By statistically analyzing results, improve the accuracy and richness of algorithm evaluations, enabling more effective and objective method comparisons. Used WorldView-2 satellite imagery for visual assessments, supervised and unsupervised quantitative evaluations on 100 images, and land cover classification accuracy experiments. Traditional methods like BDSD and PRACS showed strong fusion results and land cover classification accuracy, deep learning methods generally outperformed traditional ones. This research provides detailed images and data resources for pansharpening, offering valuable references for future studies in this field. © 2024 IEEE.

关键词： deep learning Pansharpening quality assessment (key words) remote

来源：评论

学校读者我要写书评

暂无评论

Classification of Satellite images and Predicting Field Areas after Fine- Tuning Using Sequential CNN Model 2

Classification of Satellite Images and Predicting Field Area...

引用

2nd IEEE International conference on Futuristic Technologies, INCOFT 2023

作者： Gill, Kanwarpartap Singh Anand, Vatsala Chauhan, Rahul Garg, Ashish Gupta, Rupesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Hill University Computer Science and Engineering Punjab India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350308846

The categorization of satellite images is a discipline within the domains of remote sensing and computer vision. It is the process of assigning pixels or areas within satellite imagery to predetermined classifications or categories related to land cover or land use. The aforementioned procedure is of utmost importance in the extraction of meaningful information from international Earth observation data for the research development. The categorization of satellite images is dependent upon the use of data obtained from Earth-observing satellites. The satellites in question are designed to acquire electromagnetic radiation, including various wavelengths such as visible, infrared, and microwave, which is either reflected or emitted by the Earth's surface. Multispectral or hyperspectral sensors provide the acquisition of pictures including several spectral bands, hence enabling the retrieval of intricate data pertaining to the Earth's surface. The present research employs a suggested Sequential Convolutional Neural Network (CNN) model to classify the Satellite image. The graphics processing unit (GPU) is used for the purpose of doing data pre-processing. The Deep Learning model is then visualised via a study of the Loss and Accuracy curves. The suggested model is used to forecast the geographical coverage in four distinct categories, namely cloudy, desert, green area, and water respectively which is essential for ecosystem management. © 2023 IEEE.

关键词： Artificial Intelligence Classification CNN Computer Vision Confusion matrix Deep Learning image Processing Model Training Remote Sensing Satellite image Sequential Model

来源：评论

学校读者我要写书评

暂无评论

image Classification of Nespresso Capsules by the Use of Convolutional Neural Networks Through Deep Learning 2

Image Classification of Nespresso Capsules by the Use of Con...

引用

2nd International conference on Computer, Communication and Control, IC4 2024

作者： Agarwal, Muskan Gill, Kanwarpartap Singh Kumar, Mukesh Rawat, Ruchira Chythanya, Kanegonda Ravi Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350387933

Accurately identifying product variations is critical for industrial operations due to the rising demand for automated quality control and effective inventory management. Convolutional Neural Networks (CNNs) offer the best option;they are famous for their exceptional performance on image recognition and classification jobs. This research explores the use of cutting-edge deep learning methods to the problem of Nespresso capsule image categorization using CNNs. In order to carry out the study, a dataset is assembled and produced using several photographs of Nespresso capsules. Care is taken to ensure that the images encompass a range of lighting conditions, perspectives, and backdrops. The EfficientNet design is notable for its exceptional balancing act between processing economy and accuracy, boasting a depiction accuracy of 94%. To improve generalizability and accelerate convergence, one may use transfer learning to start the model with pre-trained weights on a large dataset. Several criteria are employed to evaluate the model's efficacy during training, such as loss, training and validation accuracy, and overall performance. Careful consideration is given to the computation of epochs in order to avoid overfitting and yet acquire complicated patterns from the training data. We also examine the model's confusion matrix and classification report in detail after running it on a test dataset to determine its strengths and shortcomings. The goal of this project is to create a system that can correctly categorise Nespresso capsules in order to enhance inventory management and manufacturing quality control. This study's results have wider implications for convolutional neural networks (CNNs) in picture classification tasks across several industries, demonstrating the effectiveness of deep learning methodologies in real-world contexts. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Privacy-preserving Hybrid Cryptosystem Based on Chaos Theory and ECC Algorithm 2

Privacy-preserving Hybrid Cryptosystem Based on Chaos Theory...

引用

IEEE 2nd International conference on Computer Communication and Artificial Intelligence (CCAI)

作者： Liu, Yu Tong, Haopeng Si, Nong Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9781665496636

data encryption is a practical approach to protect biomedical image information security. Towards the massive amount of data generated in the medical field, chaotic model cryptosystems, with high sensitivity to initial conditions and overall stability and randomness of the system, are inevitably becoming an appropriate platform for communication protection. This paper proposes a privacy-preserving Hybrid encryption scheme based on Chaos theory and ECC Algorithm (HCEA) for the secure delivery of medical data. Specifically, we employ the logistic map with randomness characteristics to protect published data privacy against the complex network environment and other non-subscribers. The proposed cryptosystem can essentially achieve the secrecy effect of a "one-time pad." To achieve double encryption of textual information and keys, we propose an improved image steganography technique with secondary scrambling encryption of carrier images by the MLNCML system, enhancing encryption efficiency and security. Different from existing standard LSB image encryption methods, the HCEA can encrypt initial parameters of logistic and MLNCML with the asymmetric encryption ECC algorithm while guaranteeing the security of key distribution in complex network environments. The security proof and performance evaluation show that the proposed HCEA scheme is secure in medical data transmission and effective in practice.

关键词： logistic map MLNCML model image encryption privacy preservation asymmetric encryption

来源：评论

学校读者我要写书评

暂无评论

Poster: Machine Learning Based False Position Detection Using data-to-image Transformation 2

Poster: Machine Learning Based False Position Detection Usin...

引用

2nd IEEE International conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Chen, Xian Jiang, Yili Huan, Jiaqi Gyawali, Sohan University of Mississippi Department of Computer and Information Science United States University of Central Missouri Department of Computer Science and Cybersecurity United States East Carolina University Department of Technology Systems United States

ISBN: (纸本)9798350307733

Vehicular Ad-hoc Network (VANET) connects vehicles to improve road safety and service efficiency. However, VANET communication is vulnerable to various attacks. In particular, malicious vehicles can freely send false messages to other vehicles in the network, leading to serious consequences, such as traffic congestion and accidents. Therefore, to ensure road safety, it is critical to detect such false messages promptly and accurately. In this paper, we propose a novel machine learning (ML)-based method for misbehavior detection. In our approach, we first convert the received message data into images, then feed the images into a well-trained CNN model. The images that contain false messages can be accurately identified. As one single image is converted from several messages, our approach allows to detect false messages with less time than the state-of-the-art solutions, where detection is conducted directly over messages. Thus, the time efficiency of detection is significantly improved. © 2024 IEEE.

关键词： Vehicular ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Review of Transform Domain Medical image Watermarking Techniques 2

Review of Transform Domain Medical Image Watermarking Techni...

引用

2nd International conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Ali, Kouadri Ali, Benziane Abdelhalim, Rabehi Ziane Achour University of Djelfa Telecommunications and Smart Systems Laboratory Faculty of Sciences and Technologies Djelfa Algeria

ISBN: (纸本)9798350314243

Nowadays, to enhance patient care and streamline the management of medical information, medical images obtained in hospitals or imaging centers can be readily shared among multiple healthcare professionals. However, this sharing often takes place over insecure networks, exposing medical images to various security threats where attackers aim to manipulate, steal, or destroy sensitive information, any variation to the medical record might result in an incorrect diagnosis and hence harm the patient's life. This highlights the increased need for security measures to ensure confidentiality, availability, and reliability, which encompass integrity and authenticity. Watermarking techniques have emerged as a popular and effective means of protecting patient data from these attacks and problems. Watermarking techniques exhibit a range of categorizations, encompassing factors such as their classification based on robustness, imperceptibility, capacity, and the transform domain. Transform domain watermarking techniques have garnered significant interest due to their ability to offer a high level of robustness against attacks, all while ensuring the imperceptibility of the watermark. In this review, we explore a variety of transform domain watermarking techniques specifically designed for medical images. Additionally, we provide a comparative analysis of different kinds of transform domains commonly employed in medical image watermarking. © 2023 IEEE.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor Detection using modified VGG-19 and Inception ResnetV2 models 2

Brain Tumor Detection using modified VGG-19 and Inception Re...

引用

2nd IEEE International conference on Industrial Electronics: Developments and Applications, ICIDeA 2023

作者： Prasad, Ch. Rajendra Hussain, Shayaan Srinivas, B. Samala, Srinivas Janapati, Ravichander Yalabaka, Srikanth Sr University Dept.of Ece Warangal India Sr Engineering College Dept.of Ece Warangal India Kits Warangal Department of Cse Warangal India

ISBN: (纸本)9798350381979

A brain tumor is characterized as an aggregation of abnormal cells within the brain. These tumors can be classified into two categories: malignant and benign. Malignant is cancerous whereas benign is not. Both tumors are very hazardous as they grow rapidly and attack different parts of the cerebrum. Even after extensive research, the cause of the brain tumor is unknown. In this paper, a VGG-19 and an Inception-Resnet V2 model are presented for detecting brain tumor by employing images of MRI scans. The dataset is gathered from Kaggle and preprocessed using Keras image data Generator. The VGG-19 model provided an accuracy of 99.71% and the Inception-Resnet V2 provided an accuracy of 99.28%. The proposed models performed well to achieve the task. © 2023 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Accuracy improvement of SAR image classification method using Greedy Hierarchical Learning in comparison with Support Vector Machine 2

Accuracy improvement of SAR image classification method usin...

引用

2nd IEEE International conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Krishna Prasad, Ch. Vaikunta Amudha, V. Soundharyaa Shri Harini, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Tamilnadu Chennai602105 India

ISBN: (纸本)9798350315905

We contrast the classification of SAR images using a novel greedy hierarchical learning method with a supported vector machine. 20 dataset samples in total were taken from the Kaggle learning platform. The dataset is divided into training and testing sets. In a total of 20 datasets, 10 were used for testing and 10 for training. The classification and comparison of the recognition accuracy for support vector machine and the novel greedy hierarchical learning are done using MATLAB and synthetic aperture radar images. By fixing alpha (0.05) and power (80%), the sample size is calculated using the G power. Results: Support vector machine and novel greedy hierarchical learning both achieved classification accuracy of 80.41% and 93.08% from the MATLAB simulation, respectively. The significance found using the SPSS analysis is 0.009 (p 0.05). Conclusion: The accuracy of the novel greedy hierarchical learning outperforms the support vector machine significantly for the given data set of synthetic aperture radar image classification. © 2023 IEEE.

关键词： Synthetic aperture radar

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423页 << < 38 39 40 41 42 43 44 45 46 47 > >>

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：