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
71 篇 期刊文献
10 册 图书

馆藏范围

4,227 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,936 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
- 451 篇 光学工程
- 398 篇 生物医学工程（可授...
- 349 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...
- 80 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
1,309 篇 理学
- 521 篇 数学
- 511 篇 物理学
- 383 篇 生物学
- 172 篇 统计学（可授理学、...
- 139 篇 化学
487 篇 管理学
- 285 篇 图书情报与档案管...
- 223 篇 管理科学与工程(可...
443 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

282 篇 image reconstruc...
238 篇 image segmentati...
214 篇 feature extracti...
188 篇 deep learning
180 篇 data mining
171 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

3,824 篇 英文
380 篇 其他
35 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 4227 条记录，以下是331-340 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：

image Security is Improved by Super Encryption using RSA and Chaos Algorithms 2

Image Security is Improved by Super Encryption using RSA and...

引用

2nd International conference on Electronics and Renewable Systems, ICEARS 2023

作者： Venkata Rao, Dhulipalla Pavan Kumar Reddy, Sanepalli Anusha, Chidaraboina Bhoomika, Dendukuri Venkateswarlu, Rayalla Velagapudi Ramakrishna Siddhartha Engineering College Ece Department Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350346640

The need for image encryption is increasing as a result of the increased usage of the Internet and other communication tools. It is easy to attack and steal images sent over unsecured open channels. Techniques of encryption are necessary to safeguard the photos from attackers. In the modern period, the area of encryption is growing significantly. Given how dangerous cyber assaults become, image security is of the highest importance. One of the most well-liked methods to hide information is using image encryption. Based on various algorithms, there exist several ways for picture encryption. Some of them include the RSA algorithm, which offers faster encryption s peed and less security by encrypting data with public and private keys. Even though it takes longer, a different algorithm known as the Chaos-based algorithm offers greater picture security. The RSA and Chaos-based algorithms may be combined to produce advanced encryption, which gives high security in a shorter period of time. By using an RSA- generated key with a sufficient key s pace to thwart brute force attacks from succeeding, the encryption performance may increase without compromising the security. © 2023 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

MobileNetV2-Transformer Hybrid Architecture for Effective Video Frame Embeddings Prediction 2

MobileNetV2-Transformer Hybrid Architecture for Effective Vi...

引用

2nd IEEE International conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Zhang, Xiangyue Georgia Institute of Technology School of Computer Science AtlantaGA United States

ISBN: (纸本)9798350368208

In the realm of video processing and analysis, accurate prediction of future frames is crucial in applications like video compression, anomaly detection and augmented reality. This paper introduces a novel approach that leverages the powerful feature extraction capabilities of MobileNetV2 combined with the temporal sequence modeling strengths of Transformer models to predict the embeddings of following video frames. More specifically, the paper first employs the MobileNetV2 architecture to extract high-dimensional embeddings from individual frames, capitalizing on its efficiency and robustness in handling image data. These embeddings serve as a compact representation of the visual content of each frame. Then, a Transformer model is utilized to analyze the sequential nature of those embeddings, aiming to predict the embeddings of following frames. The Transformer's multi-head self-attention mechanism allows for an enhanced understanding of the temporal dynamics of the video, facilitating more accurate predictions compared with traditional methods. The paper later evaluates the method on test video datasets. The results indicate that the model needs further development such as incorporating a greater variety and quantity of experimental data. The method involved in the paper opens new avenues for real-time video analysis applications with the combination of Convolutional Neural Networks (CNNs) and Transformer models. The implications of this research extend to improve the performance of video-based systems across various domains, possibly indicating the potential of integrated architectures in advancing video analytic techniques. © 2024 IEEE.

关键词： Video analysis

来源：评论

学校读者我要写书评

暂无评论

ResNet50 for Accurate Plant Leaf Disease Detection and Classification 2

ResNet50 for Accurate Plant Leaf Disease Detection and Class...

引用

2nd International conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Rani, Ritu Gupta, Sheifali Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India

ISBN: (纸本)9798331540661;9798331540678

ResNet50 deep learning network in this research report obviously finds plant diseases. Divided into "Healthy," "Powdery," and "Rust," the data compiles 1530 images overall and generates testing, training, and sets. Every image is modified and scaled using techniques of validation data augmentation aimed to increase variance. ResNet50 models gain from pre-trained weights to be multi-class classification focused. Changes in learning rate and early training phase stopping help to prevent overfits. To ensure good performance, the model is assessed with accuracy, precision, recall, F1-score, and confusion matrix. Plotting of training and validation accuracy and loss curves helps one to track learning development. After that, the model is ready for use with an easy interface;field testing is carried out to confirm its performance on fresh data. Consistent updates and active learning strategies help to guarantee ongoing development. This approach seeks to generate an accurate, scalable, efficient system for plant disease classification thereby improving agricultural output and sustainability.

关键词： Plant Disease Classification ResNet50 Deep Learning image Recognition Agricultural Technology Disease Detection Convolutional Neural Networks (CNN) ect

来源：评论

学校读者我要写书评

暂无评论

Automated Intracranial Hemorrhage Detection Using Deep Learning in Medical image Analysis 2

Automated Intracranial Hemorrhage Detection Using Deep Learn...

引用

2nd IEEE International conference on data Science and Network Security, ICDSNS 2024

作者： Kar, Naresh Kumar Jana, S. Rahman, Abdur Ashokrao, Patil Rahul Indhumathi, G. Mangai, R. Alarmelu Gitam Deemed to Be University Department of Cse Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India School of Business & Technology Emporia State University Science in Information Technology Department of Accounting and Technology United States Kthm College Department of Computer Science Nashik India Rajalakshmi Engineering College Department of Ece Chennai India Dr. Sns Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore India

ISBN: (纸本)9798350373110

The research work introduces a novel approach to automatically detect intracranial hemorrhage (ICH) using advanced deep learning algorithms. The method being examined efficiently extracts and localizes information from computed tomography (CT) brain data by utilizing attention processes that are connected to convolutional neural networks (CNNs). The results achieved through training and evaluating the model using a dataset of CT images was highly impressive. The model showed accuracy value of 99.76% and has an AUC-score of 0.9976. Through the incorporation of attention strategies into the CNN framework, the interpretability of the model can be enhanced, allowing for the identification of the most relevant regions for ICH detection. The findings of the paper highlight the potential of AI-driven medical diagnostics to enhance the speed and accuracy of neuroimaging analysis, leading to better patient outcomes. Significant advancements have been achieved in the automated diagnosis of cerebral hemorrhage through the utilization of deep learning algorithms with the proposed method. © 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

Applying Deep Neural Networks and NLP Techniques for Sentiment Analysis in Social Media data 2

Applying Deep Neural Networks and NLP Techniques for Sentime...

引用

2nd International conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Brinda, B.M. Rajan, C. Geetha, K. Nathiya, N. Raguraman, P.J. Srinivasan, K. Paavai College of Engineering Computer Science and Engineering Namakkal India Information Technology KSRCT College of Technology Namakkal India Excel Engineering College Computer Science and Engineering Namakkal India Paavai College of Engineering Artificial Intelligence and Data Science Namakkal India

ISBN: (纸本)9798350349221

This work offers a comprehensive investigation of sentiment analysis in social media communication through the integration of deep learning techniques with a natural language processing (NLP) methodology. The goal of the project is to create a matching model that can be used in real-world social processes. This model will allow for the precise identification of pertinent content that is dynamically changing and the realtime selection of key phrases based on available data. The paper builds a message sentiment analysis model and an image message multimodal sentiment analysis model, exploring unimodal and multimodal sentiment analysis algorithms in social networks. The study shows how well it works to extract complex emotions from social media writing by fusing advanced deep neural networks - like transformers or recurrent neural networks - with natural language processing techniques. Furthermore, the study presents noteworthy results, such as 96.1% order accuracy for brief texts in deep learning models that have been optimized, sentiment filtering for positive, neutral, and negative comments in social network data that has been successful, and an assessment of sophisticated semantic similarity models that offers a thorough comprehension of their performance in classification tasks. This work provides insightful information that may be used to improve sentiment analysis models and their usefulness in tasks involving semantic similarity and social network data interpretation. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Distance based Similarity Search with Distinct Encrypted image Storage on Cloud 2

Distance based Similarity Search with Distinct Encrypted Ima...

引用

2nd International conference on Electronics and Renewable Systems, ICEARS 2023

作者： Mani, V. Akash, K. Mohamed Sulaiman, S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India

ISBN: (纸本)9798350346640

Secure picture recovery just on cloud has become increasingly popular in recent years as a result of the introduction of cloud computing. Both businesses and people frequently use the internet to manage and store their sensitive information, including such old photos and personal health records, due to its high level of convenience and financial savings. The typical method would be to encrypt the information prior to it being exported to using cloud to guarantee the secrecy of the information. The retrieval of information from encrypted data, for instance, may be impossible due to conventional encryption. The difficult issue in the cipher - text scenario is how to accomplish an effective retrieval while maintaining client anonymity. This study suggests a cutting-edge image retrieval method that achieves excellent efficiency and confidentiality over encrypted cloud data. An indexing tree is frequently used in the information extraction strategy to increase search performance. The security of private cloud data, including outsourced photos, the search tree, and query requests, is also a major concern. First, use an algorithm for extracting features from integrated picture features, which are made up of fundamental aspects like colour and shape. In particular, proposed technique can reach logarithmic search time thanks to the use of a balancing index tree. Second, the picture and query feature are encrypted using the secure inner product. Additionally, put in place a method to detect duplicate image material. Finally, extract the data owner details based on uploaded user-submitted image attributes that have been linked with distance measurements. © 2023 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Enhancing Insect image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction 2

Enhancing Insect Image Recognition with Sand Cat Swarm Optim...

引用

2nd IEEE International conference on Advances in Information Technology, ICAIT 2024

作者： Yogaraja, C.A. Priyanka, C. Priyadharshini, S.P. Babu, J. Jagan Rajagopal, S. Poorani, S. Ramco Institute of Technology Rajapalayam India School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore Campus India St. Joseph's College of Engineering OMR Chennai119 India Department of ECE R.M.D Engineering College Kavaraipettai India Department of IT National Engineering College Kovilppati India Kongu Engineering College Perundurai Erode India

ISBN: (纸本)9798350383867

Insect image recognition (IIR) is a specified field in machine learning (ML) and computer vision that efforts to automatically recognise and detection of insect species utilizing visual data attained from images. Leveraging deep learning (DL) techniques, convolutional neural network (CNN), and image processing enables exact and effective classification of a huge range of insect species. IIR has many applications that range from biodiversity observation and pest control in farming in order to entomological study and disease vector identification. The unique procedure of insect detection, it permits experts, entomologists, and ecologists to attain valuable insights, make knowledgeable decisions, and update challenges that contain insect detection which contribute to more real conservation efforts, ecological research, and agriculture management. This work contains a framework of Insect image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction (IIR-SCSODFR) model. The developed IIR-SCSODFR method incorporates a complete procedure that starts with Gaussian filtering-based image pre-processing, improving image quality and decreasing noise in order to offer a clear basis for precise analysis. Deep feature extraction is carried out to capture intricate visual designs essential to numerous insect species, using advanced models for inclusive insect characterization. For exact insect detection, Long Short-Term Memory (LSTM) systems are employed, proficient in demonstrating time-based needs in image sequences. Besides, model leverages SCSO technique for parameter tuning, adjusting model's performance to adjust unique features of a dataset. The proposed IIR-SCSODFR model signifies an important leap forward in IIR, and provides a forceful and precise solution for automated identification of various insect species. © 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

Towards Efficient Encrypted Medical image Retrieval from Heterogeneous Multi Cloud Environment 2

Towards Efficient Encrypted Medical Image Retrieval from Het...

引用

2nd IEEE International conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Shanavaz, Mohammad Manikanta, G. Charan Sree Gnanaprasoona, M. Kishore, K. Sai Karthikeyan, R. Jabbar, M.A. Hyderabad India

ISBN: (纸本)9798350383287

The use of encryption for medical images offers several benefits. Firstly, it enhances the confidentiality and privacy of patient data, preventing unauthorized individuals or entities from accessing sensitive medical information. Secondly, encrypted medical images may be sent securely via unreliable networks, like the Internet, without running the danger of data eavesdropping or tampering. Traditional methods of storing and retrieving medical images often lack efficient encryption and privacy-preserving mechanisms. This project delves into enhancing the security and accessibility of medical image storage across diverse cloud environments. Through the implementation of encryption methods, pixel scrambling techniques, and integration with AWS S3, the research aimed to fortify the confidentiality of medical images while ensuring rapid retrieval. These findings collectively illuminate the security, and operational efficiency of the implemented encryption, scrambling techniques, AWS integration, and offer a foundation for advancing secure medical image retrieval in multi-cloud settings. © 2024 IEEE.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Can Machines and Humans Use Negation When Describing images? 2nd

Can Machines and Humans Use Negation When Describing Images?

引用

2nd International conference on Human and Artificial Rationalities (HAR)

作者： Sato, Yuri Mineshima, Koji Ochanomizu Univ Tokyo Japan Keio Univ Tokyo Japan

ISBN: (纸本)9783031552441;9783031552458

Can negation be depicted? It has been claimed in various areas, including philosophy, cognitive science, and AI, that depicting negation through visual expressions such images and pictures is challenging. Recent empirical findings have shown that humans can indeed understand certain images as expressing negation, whereas this ability is not exhibited by machine learning models trained on image data. To elucidate the computational ability underlying the understanding of negation in images, this study first focuses on the image captioning task, specifically the performance of models pre-trained on large linguistic and image datasets for generating text from images. Our experiment demonstrates that a state-of-the-art model achieves some success in generating consistent captions from images, particularly in photographs rather than illustrations. However, when it comes to generating captions containing negation from images, the model is not as proficient as humans. To further investigate the performance of machine learning models in a more controlled setting, we conducted an additional analysis using a Visual Question Answering (VQA) task. This task enables us to specify where in the image the model should focus its attention when answering a question. As a result of this setting, the model's performance was improved. These results will shed light on the disparities in the attentional focus between humans and machine learning models.

关键词： negation grounding image captioning visual question answering machine learning cognitive science

来源：评论

学校读者我要写书评

暂无评论

Pothole Detection Using an Accelerometer and image Processing 2

Pothole Detection Using an Accelerometer and Image Processin...

引用

2nd International conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Sable, Sunil Shivane, Bhushan Shivakumar, Vaishnavi Todkar, Ashlesha Kadam, Shivraj Singh, Prakriti Tonape, Shreya Vishwakarma Institute of Technology Department of Multidisciplinary Engineering Pune India

ISBN: (纸本)9798350344882

This paper presents a pothole detection system that uses an accelerometer and GPS. The system detects potholes by measuring the magnitude of acceleration and triggering an alert when the acceleration exceeds a predefined threshold. The GPS module is utilized to determine and provide the accurate location of the pothole., which is then sent to a remote server. To provide the date, time, longitude, and latitude data from a Google Sheet to an AppSheet app this system provides a pothole detection AppSheet app. These Appsheets can be accessed by authorized users to view the location of the potholes detected by the system. The system was tested on a moving vehicle and demonstrated promising results in detecting potholes accurately. © 2023 IEEE.

关键词： Accelerometers

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 << < 30 31 32 33 34 35 36 37 38 39 > >>

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：