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

1,439 篇 会议
43 册 图书
43 篇 期刊文献

馆藏范围

1,525 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,208 篇 工学
- 1,039 篇 计算机科学与技术...
- 602 篇 软件工程
- 283 篇 电气工程
- 251 篇 信息与通信工程
- 208 篇 生物工程
- 162 篇 光学工程
- 145 篇 生物医学工程（可授...
- 131 篇 控制科学与工程
- 61 篇 电子科学与技术（可...
- 53 篇 机械工程
- 41 篇 化学工程与技术
- 32 篇 材料科学与工程（可...
- 29 篇 仪器科学与技术
- 25 篇 安全科学与工程
496 篇 理学
- 233 篇 数学
- 227 篇 生物学
- 184 篇 物理学
- 80 篇 统计学（可授理学、...
- 39 篇 化学
- 34 篇 系统科学
215 篇 医学
- 199 篇 临床医学
- 85 篇 基础医学(可授医学...
- 61 篇 药学(可授医学、理...
- 29 篇 公共卫生与预防医...
208 篇 管理学
- 140 篇 图书情报与档案管...
- 83 篇 管理科学与工程(可...
- 34 篇 工商管理
43 篇 农学
- 43 篇 作物学
30 篇 法学
- 27 篇 社会学
18 篇 教育学
- 18 篇 教育学
13 篇 经济学
3 篇 军事学
1 篇 艺术学

主题

141 篇 machine learning
122 篇 deep learning
116 篇 image processing
108 篇 pattern recognit...
70 篇 feature extracti...
63 篇 convolutional ne...
57 篇 image segmentati...
46 篇 face recognition
45 篇 computer vision
41 篇 artificial intel...
39 篇 support vector m...
36 篇 image recognitio...
34 篇 image classifica...
31 篇 neural networks
25 篇 training
24 篇 object detection
24 篇 accuracy
22 篇 machine learning...
22 篇 object recogniti...
21 篇 image enhancemen...

机构

6 篇 institute of ima...
5 篇 centre for inter...
5 篇 chitkara univers...
5 篇 univ south dakot...
4 篇 indian statistic...
4 篇 university of ca...
4 篇 national researc...
4 篇 boston universit...
3 篇 dept. of compute...
3 篇 shanghai jiao to...
3 篇 peter the great ...
3 篇 natl. lab. of pa...
3 篇 technische unive...
3 篇 graphic era deem...
3 篇 bangalore
3 篇 department of co...
2 篇 university of sc...
2 篇 université laval...
2 篇 department of cs...
2 篇 italian inst tec...

作者

6 篇 santosh k. c.
5 篇 yang jie
4 篇 gennaro claudio
4 篇 ma songde
4 篇 falchi fabrizio
4 篇 stan sclaroff
4 篇 cheng h.d.
4 篇 cosimo distante
3 篇 wendling laurent
3 篇 frontoni emanuel...
3 篇 guimaraes silvio...
3 篇 battiato sebasti...
3 篇 giovanni m. fari...
3 篇 anon
3 篇 santosh k.c.
3 篇 hegadi ravindra ...
3 篇 marco leo
3 篇 ozawa shinji
3 篇 hossain syed akh...
3 篇 maier-hein klaus

语言

1,484 篇 英文
40 篇 中文
34 篇 其他
2 篇 土耳其文

检索条件"任意字段=1st International Conference on Image Processing Machine Learning and Pattern Recognition"

共 1525 条记录，以下是581-590 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：

Fusion of Multi-Level CNN With LBP Features For Facial Emotion recognition

Fusion of Multi-Level CNN With LBP Features For Facial Emoti...

引用

Iranian conference on Electrical Engineering (ICEE)

作者： Ehsan Bahmanabady Maryam Imani Hassan Ghassemian Image processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

A facial emotion recognition framework is proposed in this work. The convolutional neural network (CNN) has high ability in extraction of hierarchical spatial features from low level texture characteristics to high level contextual features. A simple CNN model with three layers is suggested in this paper where the features extracted in all layers containing multi-level features are activated and used for classification. In addition, the local binary pattern (LBP) descriptor is used to extract discriminative features from the spatial structure of the input image. Therefore, four feature sources are provided by multi levels of CNN and the LBP descriptor. Each feature source is used for facial emotion recognition by applying to the support vector machine (SVM) classier. Finally, the majority voting rule is used for decision fusion to provide the final emotional label of each given face image. The proposed method with 84% overall accuracy, 83% weighted F1-score and 81% kappa coefficient provides the best performance compared to LBP, multi-level CNN and two-dimensional principal component analysis (2DPCA) methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Survey on Various Deep learning Models for Automatic image Captioning 1

A Survey on Various Deep Learning Models for Automatic Image...

引用

1st international conference on Mechatronics and Artificial Intelligence, ICMAI 2021

作者： Gaurav Mathur, Pratistha Department of Information Technology Manipal University Jaipur Rajasthan303007 India

Automatic image captioning means the generation of a caption for an image by a machine. image captioning is performed by recognizing objects, attributes and interconnection between them. This task involves computer vision for image understanding, natural language processing for syntax and semantics purpose and machine learning for caption generation. Preferably CNN is used to understand features of an image and RNN is used for sentence generation. Earlier, machine learning approach was used for this purpose. Input data is used to extract the features in traditional machine learning. Extracting features like handcrafted from large dataset is not so easy and feasible. Later on, Various deep learning-based approaches were proposed. In deep learning, retrieval based and template-based methods were proposed but faced some issues like missing important objects and fixed length caption respectively. Then end to end learning approach based on deep learning network came into existence and image captioning task became more efficient. The objective of this paper is to study and compare various end to end learning-based framework for image captioning using standard evaluation metric and to understand how can these frameworks be used for various research applications. Along with the comparison, futuristic challenges have also been discussed. © Published under licence by IOP Publishing Ltd.

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

A machine learning Approaches with Special Emphasis for Identification of Plant Leaf Diseases in Crop Yield Prediction 1st

A Machine Learning Approaches with Special Emphasis for Iden...

引用

1st international conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Babu, Ch Ravindra Jyothi, E.V.N. Prassana, K. Naga Jayanth, T. Siva Sandani, Sk. Chand, S. Veera Venkata Gopi Shwetha, R.J. Pace Institute of Technology and Sciences AP Ongole India IV B. Tech CSIT Pace Institute of Technology and Sciences AP Ongole India Global Payex Technologies Pvt. Ltd. Mumbai India

ISBN: (纸本)9783031756047

Productivity in farming is very essential to the Indian economic system. When a plant gets any disease it can significantly reduce production, cost money and reduce the quality and quantity of farm produce. To prevent agriculture from losing quantity and quality, it is essential to recognise diseases of plants. Plant disease identification is receiving more and more attention these days since large crops area is being monitored. Monitoring plant health and identifying diseases is critical for sustainable agriculture. This requires a lot of work, knowledge of plant diseases, and a lot of processing time. Utilizing machine learning algorithms, To predict increased agricultural yields, crop yield projections are made. One of the difficult problems in the agricultural industry is this. Given the growing significance of agricultural yield prediction, with a concentrate on agricultural yield prediction, it is important to apply machine learning techniques to crop yield predictions. Techniques like decision tree, support vector machine, Bayesian Network, and random forest, among others, are used in machine learning support in the automatic identification of plant disease based on the plant’s visual symptoms. This research presents an analysis of several machine learning approaches for plant disease prediction. The outcomes demonstrate that this technique conducted illness diagnosis with success and enhanced diffusion pattern analysis and disease progression level detection accuracy. Thus machine learning approaches with special emphasis for identification of plant leaf diseases in crop yield prediction results show that plant diseases can be accurately classified. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

12th international Workshop on Biological Knowledge Discovery from Data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered Data, ProTime2021 and 1st international Workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on Database and Expert Systems Applications, DEXA 2021

12th International Workshop on Biological Knowledge Discover...

引用

12th international Workshop on Biological Knowledge Discovery from Data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered Data, ProTime2021 and 1st international Workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on Database and Expert Systems Applications, DEXA 2021

ISBN: (纸本)9783030871000

The proceedings contain 23 papers. The special focus in this conference is on Database and Expert Systems Applications. The topics include: Semantic Influence Score: Tracing Beautiful Minds Through Knowledge Diffusion and Derivative Works;robust and Efficient Bio-Inspired Data-Sampling Prototype for Time-Series Analysis;membership-Mappings for Data Representation learning: Measure Theoretic Conceptualization;membership-Mappings for Data Representation learning: A Bregman Divergence Based Conditionally Deep Autoencoder;data Catalogs: A Systematic Literature Review and Guidelines to Implementation;task-Specific Automation in Deep learning Processes;approximate Fault Tolerance for Edge stream processing;deep learning Rule for Efficient Changepoint Detection in the Presence of Non-Linear Trends;time Series pattern Discovery by Deep learning and Graph Mining;a Conceptual Model for Mitigation of Root Causes of Uncertainty in Cyber-Physical Systems;integrating Gene Ontology Based Grouping and Ranking into the machine learning Algorithm for Gene Expression Data Analysis;SVM-RCE-R-OPT: Optimization of Scoring Function for SVM-RCE-R;short-Term Renewable Energy Forecasting in Greece Using Prophet Decomposition and Tree-Based Ensembles;a Comparative study of Deep learning Approaches for Day-Ahead Load Forecasting of an Electric Car Fleet;Security-Based Safety Hazard Analysis Using FMEA: A DAM Case study;Privacy Preserving machine learning for Malicious URL Detection;remote Attestation of Bare-Metal Microprocessor Software: A Formally Verified Security Monitor;Provenance and Privacy in ProSA: A Guided Interview on Privacy-Aware Provenance;placeholder Constraint Evaluation in Simulation Graphs;Walk Extraction strategies for Node Embeddings with RDF2Vec in Knowledge Graphs;bridging Semantic Web and machine learning: First Results of a Systematic Mapping study.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Graph-Based M-tortuosity Estimation 1st

Graph-Based M-tortuosity Estimation

引用

1st IAPR international Joint conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Hammoumi, Adam Moreaud, Maxime Jolimaitre, Elsa Chevalier, Thibaud Novikov, Alexey Klotz, Michaela IFP Energies Nouvelles Rond Point Echangeur Solaize BP3 F-69360 Solaize France PSL Res Univ CMM MINES ParisTech F-77305 Fontainebleau France IFP Energies Nouvelles 1&4 Ave Bois Preeau F-92852 Rueil Malmaison France St Gobain Res Provence LSFC Lab Synth & Fonctionnalisat Ceram UMR 3080 CNRSST GOBAIN CREE 550 Ave Alphonse Jauffret Cavaillon France

ISBN: (纸本)9783030766566;9783030766573

The sinuosity of a porous microstructure may be quantified by geometric tortuosity characterization, namely the ratio of geodesic and euclidean distances. The assessment of geometric tortuosity, among other descriptors, is of importance for rigorous characterization of complex materials. This paper proposes a new way of calculation, based on a graph structure, of the topological descriptor M-tortuosity introduced in [3]. The original M-tortuosity descriptor is based on a geodesic distance computation algorithm. A pore network partition [7] method is used to extract pores and construct a graph from the void of a porous microstructure. Through this scheme, pores are the nodes, distances between pores are the arcs between nodes and the goal boils down to the determination of the shortest paths between nodes. Solving this on a graph requires a tree search formulation of the problem. Our results have shown a drastic time complexity decrease while preserving good agreement with the original results. The added value of our method consists in its simplicity of implementation and its reduced execution time.

关键词： Geometric tortuosity Pore network partition Shortest path search

来源：评论

学校读者我要写书评

暂无评论

Penguin Search Optimization with Deep learning Based Cybersecurity Malware Spectrogram image Classification 1st

Penguin Search Optimization with Deep Learning Based Cyberse...

引用

1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

作者： Jeyalakshmi, J. Santhiya, M. Jegatha, R. Department of CSE Amrita Vishwa Vidhyapeetham Coimbatore India Department of CSE Rajalakshmi Engineering College Mevalurkuppam India Department of IT Sri Sai Ram Institute of Technology Chennai India

ISBN: (纸本)9783031730641

Background: Cybercriminals typically employ malware to achieve their objectives, which include botnets, ransomware, etc. Encryption, packaging, and polymorphism make it difficult to identify malware files, especially when they are generated in large quantities every day. The new technique of image-based detection focuses on detecting malware by transforming it into an image and classifying the image as benign or malicious. The raw binary of malware is converted into a grayscale image, and neural networks are used to classify it as benign or malicious, or to classify the image to its respective malware family. Proposed Method: This system proposal introduces a new Penguin Search Optimisation with Deep learning-based image Classification (PSODL-IC) for Malware Spectrogram. The presented PSODL-IC technique uses spectrogram images in conjunction with the DL model to classify malware files and differentiate them from benign files. In the presented PSODL-IC technique, the noise elimination stage employs Gaussian filtration (GF). In addition, the presented PSODL-IC method employs the Xception feature extractor in conjunction with a PSO-based hyperparameter optimizer. In this investigation, the auto encoder (AE) is utilised for classification purposes. Outcome and Discussion: Extensive experimental analysis has demonstrated that the PSODL-IC technique yields superior results to other deep learning models, with 97.21 percent accuracy. The obtained results demonstrate the advantages of the PSODL-IC method over other models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Spectrographs

来源：评论

学校读者我要写书评

暂无评论

Melanoma Skin Lesions Classification using Deep Convolutional Neural Network with Transfer learning 1

Melanoma Skin Lesions Classification using Deep Convolutiona...

引用

1st international conference on Artificial Intelligence and Data Analytics, CAIDA 2021

作者： Islam, Md. Khairul Ali, Md Shahin Ali, Md Mosahak Haque, Mst. Farija Das, Abhilash Arjan Hossain, Md. Maruf Duranta, D S Rahman, Md Afifur Islamic University Department of Biomedical Engineering Kushtia7003 Bangladesh

ISBN: (纸本)9780738131771

Skin cancer is basically the unnatural growth of skin tissues and it can be fatal. Lately, it has evolved into one of the most perilous types of other cancers in the human body. Premature detection can help to endure the patient. Detection of skin cancer is quite difficult. At present in medical image diagnosis, the performance of computer vision is quite conducive. Together with the progress in technology and impetuous increment in computer provision, different types of machine learning techniques and deep learning models have arisen for the analysis of medical images particularly skin lesion images. In this study, we propose a deep learning model with some image pre-processing steps that help to categorize skin lesions with a better classification rate than other existing models. Normalization, data reduction, and data augmentation are used in pre-processing steps to classify benign and malignant cancer lesions from the HAM10000 dataset. From the experimental result, the proposed model gained an accuracy of 96.10% in training and 90.93% during testing. This model reduces the execution time and performs well-handled. © 2021 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Cluster Analysis: Unsupervised Classification for Identifying Benign and Malignant Tumors on Whole Slide image of Prostate Cancer 5

Cluster Analysis: Unsupervised Classification for Identifyin...

引用

5th IEEE international conference on image processing Applications and Systems (IPAS)

作者： Bhattacharjee, Subrata Hwang, Yeong-Byn Sumon, Rashadul Islam Rahman, Hafizur Hyeon, Dong-Woo Moon, Damin Carole, Kouayep Sonia Kim, Hee-Cheol Choi, Heung-Kook Inje Univ Dept Comp Engn Gimhae South Korea Inje Univ Dept Digital Antiaging Healthcare Gimhae South Korea JLK Artificial Intelligence R&D Ctr Seoul South Korea

ISBN: (纸本)9781665462198

Recently, many fields have widely used cluster analysis: psychology, biology, statistics, pattern recognition, information retrieval, machine learning, and data mining. Diagnosis of histopathological images of prostate cancer is one of the routine tasks for pathologists and it is challenging for pathologists to analyze the formation of glands and tumors based on the Gleason grading system. In this study, unsupervised classification has been performed for differentiating malignant (cancerous) from benign (non-cancerous) tumors. Therefore, the unsupervised-based computer-aided diagnosis (CAD) technique would be of great benefit in easing the workloads of pathologists. This technique is used to find meaningful clustering objects (i.e., individuals, entities, patterns, or cases) and identify useful patterns. Radiomic-based features were extracted for cluster analysis using the gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and gray-level size zone matrix (GLSZM) techniques. Multi-clustering techniques used for the unsupervised classification are K-means clustering, K-medoids clustering, Agglomerative Hierarchical (AH) clustering, Gaussian mixture model (GMM) clustering, and Spectral clustering. The quality of the clustering algorithms was determined using Purity, Silhouettes, Adjusted Rand, Fowlkes Mallows, and Calinski Harabasz (CH) scores. However, the best-performing algorithm (i.e., K-means) has been applied to predict and annotate the cancerous regions in the whole slide image (WSI) to compare with the pathologist annotation.

关键词： unsupervised classification prostate cancer radiomic texture feature histopathological

来源：评论

学校读者我要写书评

暂无评论

Using Deep learning for Object Distance Prediction in Digital Holography

Using Deep Learning for Object Distance Prediction in Digita...

引用

international conference on Computer, Control and Robotics (ICCCR)

作者： Couturier, Raphael Salomon, Michel Abou Zeid, Elie Abou Jaoude, Chady Univ Bourgogne Franche Comte UBFC CNRS FEMTO ST Inst Belfort France Antonine Univ Ticket Lab Hadat Baabda Lebanon

ISBN: (纸本)9781728190358

Deep learning (DL) has marked the beginning of a new era in computer science, particularly in machine learning (ML). Nowadays, there are many fields where DL is applied such as speech recognition, automatic navigation systems, image processing, etc [1]. In this paper, a Convolutional Neural Network (CNN), more precisely a CNN built on top of DenseNet169, is proven to be helpful in predicting object distance in computer-generated holographic images. The problem is addressed as a classification problem where 101 classes of images were generated, each class corresponding to a different distance value from the object at a micrometer scale. Experiments show that the proposed network is efficient in this context, being able to classify with a 100% accuracy level if trained properly.

关键词： deep learning convolutional neural networks holographic images digital holography

来源：评论

学校读者我要写书评

暂无评论

Global Prediction of COVID-19 Cases and Deaths using machine learning 6

Global Prediction of COVID-19 Cases and Deaths using Machine...

引用

6th international conference on image Information processing, ICIIP 2021

作者： Bhardwaj, Sumit Bhardwaj, Harshit Bhardwaj, Jyoti Gupta, Punit Amity University Noida India Govt. PG Degree College Himachal Pradesh India Manipal University Jaipur Department of Computer and Communication Engineering Jaipur India

ISBN: (纸本)9781665433617

Coronavirus Disease or COVID-19 pandemic has taken over the world by storm. It has horrifying effect on the health of the people. Continuously rising number of COVID-19 cases has and still creating huge stress on the governing bodies of all countries, and they are finding it hard to find solution for the situation. This project's goal is to explore machine learning and develop a COVID-19 model that can predict number of cases with high accuracy. The proposed study employs SVR and PR models to forecast the number of recovered cases, confirmed cases, deaths, and daily case count. The data is collected from the 1st of March to the 30th of April 2020. The confirmed number of cases as of April 30th were 35043, with 1147 total deaths and 8889 recovered patients. The model was created in Python 3.8.5. We will look at various machine learning prediction algorithms and compare them. In conclusion, supervised learning algorithms proved to be better than unsupervised learning algorithms. These prediction models can help us to brace for another COVID-19 wave and to ensure the availability of the required resources. © 2021 IEEE.

关键词： COVID-19

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153页 << < 55 56 57 58 59 60 61 62 63 64 > >>

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：