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

453 篇 会议
4 册 图书
2 篇 期刊文献

馆藏范围

459 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

313 篇 工学
- 267 篇 计算机科学与技术...
- 208 篇 软件工程
- 92 篇 光学工程
- 87 篇 信息与通信工程
- 72 篇 生物工程
- 47 篇 生物医学工程（可授...
- 40 篇 控制科学与工程
- 34 篇 电气工程
- 27 篇 网络空间安全
- 22 篇 机械工程
- 22 篇 电子科学与技术（可...
- 16 篇 化学工程与技术
- 16 篇 安全科学与工程
- 15 篇 仪器科学与技术
- 14 篇 材料科学与工程（可...
- 9 篇 动力工程及工程热...
201 篇 理学
- 89 篇 物理学
- 80 篇 数学
- 75 篇 生物学
- 34 篇 统计学（可授理学、...
- 22 篇 化学
81 篇 管理学
- 40 篇 图书情报与档案管...
- 38 篇 管理科学与工程(可...
- 16 篇 工商管理
- 9 篇 公共管理
45 篇 医学
- 36 篇 临床医学
- 24 篇 基础医学(可授医学...
- 17 篇 公共卫生与预防医...
- 17 篇 药学(可授医学、理...
16 篇 法学
- 16 篇 社会学
8 篇 农学
6 篇 教育学
4 篇 经济学
2 篇 军事学

主题

76 篇 computer vision
23 篇 deep learning
17 篇 feature extracti...
16 篇 face recognition
13 篇 pattern recognit...
11 篇 convolutional ne...
10 篇 image segmentati...
10 篇 machine learning
10 篇 image recognitio...
9 篇 object detection
8 篇 support vector m...
8 篇 local binary pat...
7 篇 visualization
7 篇 image processing
7 篇 training
6 篇 task analysis
6 篇 image color anal...
6 篇 shape
5 篇 pattern matching
5 篇 data mining

机构

6 篇 lovely professio...
6 篇 uttaranchal univ...
6 篇 uttaranchal inst...
6 篇 lloyd law colleg...
5 篇 chitkara univers...
5 篇 department of co...
5 篇 graphic era deem...
4 篇 lloyd institute ...
3 篇 engineering rese...
3 篇 department of au...
3 篇 zhenjiang vocati...
3 篇 school of comput...
3 篇 sr university sc...
3 篇 beijing internat...
3 篇 engineering rese...
2 篇 guangxi science ...
2 篇 college of mlt a...
2 篇 gla university d...
2 篇 csir-niscpr new ...
2 篇 shangdu hengchan...

作者

4 篇 srivastava rajee...
3 篇 kukreja vinay
3 篇 li mi
3 篇 kimmel ron
3 篇 pande sagar dhan...
3 篇 shrivastava anur...
2 篇 huang tao
2 篇 l. bharathi
2 篇 kansal lavish
2 篇 melanie lourens
2 篇 fu xiang
2 篇 ankita suryavans...
2 篇 dong chen
2 篇 vasoya nimish h.
2 篇 joshi kapil
2 篇 atia ayman
2 篇 ji linfeng
2 篇 deepali
2 篇 lourens melanie
2 篇 mohamed el-shimy

语言

432 篇 英文
26 篇 其他
2 篇 中文

检索条件"任意字段=3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023"

共 459 条记录，以下是11-20 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：

Audio Situation analysis by Multiple Signal Features on a Hybrid Parallel Neural Network 3rd

Audio Situation Analysis by Multiple Signal Features on a Hy...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Kumar, Anup Pande, Sagar Dhanraj Khamparia, Aditya School of Computer Science Engineering VIT-AP University Andhra Pradesh Amaravati India Maharashtra Pune India Uttar Pradesh Lucknow India

ISBN: (纸本)9789819743582

The human voice is one of the best possible ways to communicate and help the other person to understand better. According to Albert Mehrabian body language, our verbal communication consists of 38% vocal and 7% word only communication. Though there are 55% other ways of communication we need a prominent feature that is easy to detect and at the same time very important. The scientific community has focused on text-to-audio conversion of audio content like songs, debates, news, and political arguments. This paper proposes a hybrid parallel CNN and Attention (transformer) based architecture to represent the given audio into valuable information and classify the given situation. The model gets trained parallelly for the CNN and Transformer and then the output of the feature tensor is combined and fed into a combined model network passing through dense layers finally classifying the audio. The datasets used for the implementation purpose are EMOVO and Corpus. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Attention Audio CNN Deep learning Hybrid MEL-Spectrogram MFCC Neural network NLP Parallel model Sentiment analysis Situation analysis Transformer

来源：评论

学校读者我要写书评

暂无评论

Tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features 3rd

Tracking of Ship and Plane in Satellite Videos Using a Convo...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Sharma, Devendra Srivastava, Rajeev Computer Science and Engineering Department Indian Institute of Technology BHU Varanasi India

ISBN: (纸本)9789819743582

Object tracking is one of the latest research topic in various applications of computer vison. Satellite videos are provided by the commercial satellite makes it possible to extend into earth observation. Moving objects are occupy the small region of pixel and they are confused with neighboring regions also with objects. Due to the resolution constraints, the nearest similar objects can rarely be distinguished by appearance features. Conventional tracking techniques in satellite videos with hand-crafted features do not give better results. Deep learning-based techniques have proven to be superior in visual tracking benchmarks, but this is not fully explored for the object tracking in the satellite videos. Regression network is used to integrate the regression model with convolutional layers. Both motion feature and appearance feature are taken from pre-trained deep neural networks VGG16, VGG19, and ResNet50. When tracker faces obscure appearance information, motion features of object are used for discrimination and complementary information to improve the performances of model. Experimental analysis on ship and plane represents the ResNet50 feature extractor gives better tracking result. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

TrOCR-Enhanced Language Chains: Bridging Image-Based Text and Single Document Question Answering 3rd

TrOCR-Enhanced Language Chains: Bridging Image-Based Text an...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Althaf, Patan Vinta, Surendra Reddy School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819743582

This research aims to integrate TrOCR, an advanced Optical Character Recognition (OCR) technology, with the Langchain framework for Document question answering on image-based queries. The college has many number of students and they face many problems when it comes to the preparation for exams. There may be many question papers they have to prepare manually by searching the answers in books. Our model uses TrOCR to extract text from input images and the large text data is split using Langchain. Vector indexing is to process the text. This integration helps in information retrieval and question answering. The VectorstoreIndexCreator facilitates text vectorization and indexing, and HuggingFaceEmbeddings for semantic understanding. This research aims for information retrieval, document analysis, and natural language processing, offering a connection between image-based text and advanced language technology where users can have easy handling of the model and provides comprehensive answers for given complex questions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Question answering

来源：评论

学校读者我要写书评

暂无评论

Tumor Detection and analysis from Brain MRI Images Using Deep Learning 3rd

Tumor Detection and Analysis from Brain MRI Images Using Dee...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Singh, Shweta Srivastava, Rajeev Department of Computer Science and Engineering Indian Institute of Technology BHU Varanasi Uttar Pradesh Varanasi India

ISBN: (纸本)9789819743582

Establishment of appropriate diagnosis plan for brain tumors is imperative and influences patient survival rate ultimately. Misdiagnosis of some brain tumors though hinders response to treatment and decreases success rate. Traditional ways of discerning brain tumors based on images taken via an MRI machine can be slow and full of mistakes, particularly when processing huge datasets and various shapes of tumor. It is proposed in this study that through such an innovative machine-learning and image processing hybrid technique, brain tumor detection can be enhanced. More precisely, we undertook bilateral filtering and normalization to improve quality of MR images, so as to decrease human errors in tumors’ identification. Therefore, the ResNet-50 model was employed to my significant patterns of data from the preprocessed dataset for further investigation. We did a comparative evaluation of different algorithms for getting the best classifier. Hence, based on the data, one can say with 91.40% accuracy that SVC outperformed all other models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Exploring Deep Learning and Word Embedding Techniques for Sentiment analysis on Diverse Textual Data 3rd

Exploring Deep Learning and Word Embedding Techniques for Se...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Lavanya, B.N. Rathnam, K. V. Anitha Kiran, K. Appaji, Abhishek Shenoy, P. Deepa Venugopal, K.R. Department of Computer Science and Engineering University of Visvesvaraya College of Engineering BangaloreUniversity Bengaluru India Department of Medical Electronics BMS College of Engineering Bangalore India

ISBN: (纸本)9789819743582

The rapid expansion of data poses a significant challenge for analyzing sentiments. The importance of user-generated reviews highlights the need to carefully curate and evaluate text data to extract opinions. This research delves into the world of learning for sentiment analysis exploring models such as Bi-LSTM, CNN, and GRU along with various word embeddings like N-grams, Keras embedding, BERT, Roberta, CT-Bert, and Elmo. It effectively categorizes sentiments and detects sarcasm in Twitter and IMDB data making it a vital tool for applying learning to real-world sentiment analysis tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of Polycystic Ovary Syndrome Detection Using Deep Learning Algorithms 3rd

Comparative Analysis of Polycystic Ovary Syndrome Detection ...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Neha Kumar, A. Ranjith Pande, Sagar Dhanraj School of Computer Science Engineering Lovely Professional University Punjab Phagwara India Pune India

ISBN: (纸本)9789819743582

People of reproductive age, primarily women, often experience the endocrine condition known as polycystic ovary syndrome (PCOS). This complex illness is characterized by various clinical symptoms, including irregular menstrual periods, hyperandrogenism, and polycystic ovarian morphology. PCOS is associated with a range of metabolic and cardiovascular issues that significantly impact the overall health and well-being of affected individuals. Early diagnosis and treatment are crucial to mitigate these risks and enhance the quality of life for PCOS patients. This article explores the classification of images using both Deep CNN and Deep CNN with SVM layer. The extensive dataset comprises 7,805 samples, divided into training (5,464 samples), testing (780 samples), and validation (1,562 samples) sets. These samples are categorized for the binary classification which gives out the result as Consists of PCOS and does not consist of PCOS, and the results are remarkable, showcasing the potential of this deep learning system for early PCOS identification, achieving an accuracy of 99.89% and a loss of 0.0101. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Recommendation of Music Based on Facial Recognition and Emotion analysis 3rd

Recommendation of Music Based on Facial Recognition and Emot...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Chintha, Sai Ganesh Bharathi, V.C. Mayaluri, Anusha Bylapudi, Lahari Iqbal, M. Mohammed School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravathi India Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai603110 India

ISBN: (纸本)9789819743582

Emotions pose a challenge when it comes to precise definition and interpretation. As a remedy, music emerges as a suggested art form for its potential in aiding mood regulation. Our proposed approach exhibits the potential to enhance system accuracy. This is achieved through a reduction in computation time for result generation, leading to improved overall efficiency and lowered operational costs of the devised system. To evaluate the effectiveness of our approach, we employed the FER2013 dataset, utilizing an integrated camera to capture facial expressions. The process involves extracting features from facial photographs to discern various emotions, encompassing happiness, anger, sadness, surprise, and neutrality. Leveraging a Convolutional Neural Network (CNN), we implemented a mechanism for detecting facial expressions, attaining an impressive detection accuracy rate of approximately 95.14 percent. The implementation employs a range of Python packages including WebRTC, Keras, and MediaPipe to facilitate the capture and analysis of users’ facial emotional cues. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Multiclass Classification of Insect Bites Using Deep Learning Techniques 3rd

Multiclass Classification of Insect Bites Using Deep Learnin...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Sushma, K.V.N.D. Pande, Sagar Dhanraj School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Pune India

ISBN: (纸本)9789819743582

Many people suffer due to insect bites every year and it may even be life-threatening sometimes. Insect bites and stings come in different shapes and sizes and itchy red colors, and it might be a difficult task to accurately identify which bite is caused by which insect. On different skin, it may be in different colors or textures. This task may be even challenging for researchers and doctors. analysis of insect bites is crucial for treatment and disease prevention. Deep learning (DL) algorithms have demonstrated promising gains in a variety of image identification applications in recent years. To enable automatic identification and classification of insect species, this study explores the use of DL algorithms for multi-class classification of insect bites. The main purpose of the paper is to propose a model that classifies insect bites on human skin using deep learning algorithms aiming to classify the insect bites more accurately. This paper first tried to classify whether the given image is a skin or any type of object and received an accuracy of 95% using pre-trained inception-v3 and for insect bite classification a proposed VINMOBCONCAT (hybrid model) received an accuracy of 93%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

An Ensemble Technique for Classification of Oral Cancer by Using Histopathological Imaging 3rd

An Ensemble Technique for Classification of Oral Cancer by...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Saikia, Trishna Dhamaniya, Ashutosh Gupta, Puneet Singh, Koushlendra Kumar Department of Computer Science and Engineering IIT Indore Indore India Machine Vision and Intelligence Lab NIT Jamshedpur Jamshedpur India

ISBN: (纸本)9789819743582

Oral cancer primarily affects the oral chamber within the head and neck area, and underscores the critical need for effective classification to initiate timely treatment. Deep learning (DL)-based computer-aided diagnostic (CAD) systems have demonstrated notable success in various applications, offering accurate and prompt diagnosis of oral squamous cell carcinomas (OSCC). One of the challenges in biomedical image classification is the acquisition of a sufficiently large training dataset. Transfer learning presents an efficient solution by extracting general features from natural image datasets and adapting them to new image datasets. In this study, we focus on classifying OSCC histopathology images to develop a productive DL-based CAD solution. To this end, we employ an average weighted ensemble technique, harnessing the strengths of deep learning-based models. To address the limitation of a small dataset, we fine-tune pre-trained deep CNN models, specifically VGG-16, VGG-19, MobileNet-V2, and Inception-V3, within our proposed method. Additionally, we conduct a comprehensive comparative analysis of these models, considering classification accuracy, precision, recall, and F-score as metrics. Our experimental findings reveal that VGG-19 consistently delivers substantially superior performance compared to the other fine-tuned deep CNN models. However, our proposed weighted ensemble technique outperforms all these deep CNN models, particularly when employing the RMSProp optimizer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ensemble Histopathological imaging Inception-V3 MobileNet-V2 OSCC Transfer learning VGG-16 VGG-19

来源：评论

学校读者我要写书评

暂无评论

Advanced Attendance Management System with Independent Yet Unified Face Recognition Pipeline 3rd

Advanced Attendance Management System with Independent Yet U...

引用

3rd international conference on Machine vision and Augmented Intelligence, MAI 2023

作者： Shantilal, Sankhala Bhavik Singh, Amaresh Chandra Gupta, Nidhi Research and Development Isnartech PVT. LTD. Saharanpur India Department of Computer Applications National Institute of Technology Kurukshetra Kurukshetra India

ISBN: (纸本)9789819743582

An attendance management system allows us to track the attendance of students, teachers, employees, and other staff. It assists us in saving time and effort. Digital attendance systems have been used by some schools and companies in the past, but their accuracy and chances of locating an unidentified face aren’t all that significant. This attendance system is particularly effective for preventing errors while doing manual data entry and it comprises three main stages named Face Detection, Recognition, and Verification. Behavior analysis is combined with the attendance system. Although face detection and recognition have several applications in security, biometrics, and medicine, their best implementation in companies or schools is still in the early stages of research. The proposed approach is the most efficient alternative after comparing its accuracy and computational cost to those of preceding algorithms. This paper describes how an attendance management system integrates various facial recognition algorithms and object recognition techniques. The proposed model’s experimental results, which integrate detection, two-stage recognition, and analysis, are likely to be effective in modern schools and businesses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Attendance management system Behavior analysis Face detection Face recognition Face recognition pipeline FaceNet model

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46页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：