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

5,905 篇 会议
124 册 图书
82 篇 期刊文献

馆藏范围

6,111 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,643 篇 工学
- 3,001 篇 计算机科学与技术...
- 1,517 篇 软件工程
- 950 篇 信息与通信工程
- 844 篇 电气工程
- 713 篇 生物工程
- 393 篇 生物医学工程（可授...
- 387 篇 光学工程
- 375 篇 控制科学与工程
- 144 篇 电子科学与技术（可...
- 138 篇 机械工程
- 111 篇 网络空间安全
- 104 篇 仪器科学与技术
- 100 篇 安全科学与工程
- 86 篇 化学工程与技术
- 75 篇 动力工程及工程热...
- 69 篇 交通运输工程
1,470 篇 理学
- 754 篇 生物学
- 648 篇 物理学
- 358 篇 数学
- 126 篇 统计学（可授理学、...
- 99 篇 化学
747 篇 医学
- 659 篇 临床医学
- 184 篇 基础医学(可授医学...
- 122 篇 药学(可授医学、理...
- 82 篇 特种医学
- 65 篇 公共卫生与预防医...
514 篇 管理学
- 276 篇 管理科学与工程(可...
- 274 篇 图书情报与档案管...
- 75 篇 工商管理
88 篇 农学
- 85 篇 作物学
49 篇 法学
34 篇 教育学
18 篇 经济学
9 篇 军事学
8 篇 文学
2 篇 艺术学
1 篇 哲学

主题

1,165 篇 convolutional ne...
1,137 篇 deep learning
904 篇 neural networks
658 篇 machine learning
622 篇 training
495 篇 feature extracti...
346 篇 accuracy
275 篇 computational mo...
249 篇 image classifica...
246 篇 image processing
245 篇 image segmentati...
226 篇 computer vision
220 篇 image recognitio...
191 篇 deep neural netw...
181 篇 visualization
171 篇 artificial intel...
170 篇 transfer learnin...
159 篇 computer archite...
158 篇 convolution
148 篇 artificial neura...

机构

22 篇 chitkara univers...
9 篇 karnataka bangal...
9 篇 information syst...
9 篇 university of el...
8 篇 school of comput...
8 篇 carnegie mellon ...
8 篇 school of comput...
8 篇 harbin institute...
7 篇 shanghai jiao to...
7 篇 hefei university...
7 篇 south china norm...
7 篇 tongji universit...
7 篇 tech univ munich...
6 篇 chongqing univer...
6 篇 maharishi school...
6 篇 school of comput...
6 篇 univ chinese aca...
6 篇 king saud univer...
6 篇 department of co...
6 篇 swiss fed inst t...

作者

14 篇 eyas el-qawasmeh
13 篇 de-shuang huang
8 篇 azizah abd manaf
7 篇 andrea vedaldi
7 篇 horst bischof
7 篇 mohana
7 篇 lazaros iliadis
7 篇 roy kaushik
6 篇 haijun zhang
6 篇 tianyong hao
6 篇 jan-michael frah...
6 篇 thomas brox
6 篇 zhou wu
6 篇 gokhan bilgin
6 篇 rueckert daniel
6 篇 liu chang
6 篇 zhao zhang
6 篇 chrisina jayne
6 篇 hu clj
5 篇 li li

语言

5,873 篇 英文
182 篇 其他
47 篇 中文
34 篇 土耳其文
1 篇 俄文

检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

共 6111 条记录，以下是131-140 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：

ENGinnSAND: A Reference Dataset for Monocular Depth Prediction of Line Structures 10th

ENGinnSAND: A Reference Dataset for Monocular Depth Predict...

引用

10th International conference on machine learning, Optimization, and Data Science, LOD 2024

作者： Bottesch, Hubert Angermann, Christoph Laubichler, Christian Kiesling, Constantin Haltmeier, Markus Department of Mathematics Universität Innsbruck Innsbruck6020 Austria LEC GmbH Graz8010 Austria

ISBN: (纸本)9783031824838

High-resolution depth images form the basis for various imaging applications in science, technology, and industry. However, capturing them with specialized equipment is not always feasible. Instead, in monocular depth prediction, a depth image is reconstructed from a conventional reflectance image that is more readily obtainable. Recently, machine learning methods have emerged as a particularly effective way of achieving this task. However, the success of these methods heavily depends on the available training data. Again, it often takes a lot of effort to obtain suitable data. In this paper, we present a method to easily generate surrogate surface images. For that purpose, we introduce ENGinnSAND, a database comprising 5000 pairs of reflection images and corresponding depth images of line-like surface structures. Additionally, we develop a hybrid Radon transform network, where the characteristic features in the images are analyzed in Radon space, and evaluate it against other established network architectures. Both the database and the proposed network architecture can be useful in any application where the underlying dataset contains line-like structures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： benchmark dataset depth prediction engine monitoring line structures neural networks Radon transform surface analysis

来源：评论

学校读者我要写书评

暂无评论

Integrating the dynamic optimization strategies of Adam and AdaGrad

Integrating the dynamic optimization strategies of Adam and ...

引用

International conference on Intelligent Control and Information processing (ICICIP)

作者： Zhixuan Huang Xiaonan Luo Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates.

关键词： Training Heuristic algorithms Optimization methods machine translation Logic Artificial intelligence Robots Recommender systems Convergence Intelligent control

来源：评论

学校读者我要写书评

暂无评论

A Review on DeepDream neural networks

A Review on DeepDream Neural Networks

引用

Computational Intelligence Approaches and applications (ICCIAA), International conference on

作者： Salam Al-E'mari Yousef Sanjalawe Fuad Fataftah Information Security Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (UJ) Amman Jordan Mutlimedia systems and Virtual Reality School of Computer science Universiti Sains Malaysia (USM) Penang Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper provides a comprehensive review of DeepDream neural networks or the Inceptionism technique, a type of deep learning model that has gained popularity due to its ability to generate visually captivating and surrealistic images. The review begins with an introduction to deep learning and its importance in image recognition and generation tasks. It then delves into the details of the DeepDream algorithm, including its architecture, training process, and mathematical foundations. The paper also discusses various applications of DeepDream, such as artistic image generation, style transfer, and image enhancement. Additionally, the review highlights some of the challenges and limitations of DeepDream networks, including issues with overfitting and computational resources. Finally, the paper concludes with a discussion of future directions for research in this area, including the exploration of more advanced architectures and the integration of DeepDream networks with other machine-learning techniques. Overall, this review serves as a valuable resource for researchers and practitioners interested in understanding the capabilities and limitations of DeepDream neural networks.

关键词： Deep learning Training Visualization Art Reviews Shape Computational modeling Computer architecture Real-time systems Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral images for Crop Prediction using machine and Deep learning Models

Hyperspectral Images for Crop Prediction using Machine and D...

引用

Electronics and Renewable Systems (ICEARS), International conference on

作者： R. Mahalakshmi K. Kumutha M. Sakthivanitha R. Bagavathi Lakshmi Mohamed Sirajudeen Sankar Padmanabhan Department of Computer Applications-PG Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Computer Applications-PG Vels Institute of Science Technology Advanced Studies Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Information Technology Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India PG and Research Department of Computer Science Nilgiri College of Arts and Science The Nilgiris Tami Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. machine learning (ML) and Deep learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional neural networks (CNN) and Recurrent neural networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data.

关键词： Support vector machines Radio frequency Deep learning Recurrent neural networks Accuracy Crops Spatial databases Convolutional neural networks Reliability Hyperspectral imaging

来源：评论

学校读者我要写书评

暂无评论

Application of machine learning Algorithms in Artificial Intelligence image Recognition Technology

Application of Machine Learning Algorithms in Artificial Int...

引用

Intelligent Systems and Computational networks (ICISCN), International conference on

作者： Qi Ouyang Siyang Dai Kexin Xie Zeyu Sun Yao Wang Sustainable Development of Architecture and Landscape Polytechnic of Milan Milan Italy Faculty of Engineering and Information Technology The University of Melbourne Victoria Australia College of Business and Public Management Wenzhou-Kean University Wenzhou China School of Environment and Science Griffith University Brisbane Australia Nanjing Stomatological Hospital Affiliated Hospital of Medical School Institute of Stomatology Nanjing University Nanjing China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In smart city applications such as medical image analysis, autonomous driving, and security monitoring, image recognition faces challenges like complex backgrounds, low-quality images, and diverse targets, affecting accuracy and robustness. This study explores machine learning algorithms to enhance image recognition performance by addressing noise interference, intricate backgrounds, and feature extraction difficulties. It combines Convolutional neural networks (CNN) with transfer learning, starting with data preprocessing to reduce noise, using pre-trained CNN models to extract high-level features, and fine-tuning with a ResNet transfer learning strategy for specific tasks. Additionally, ensemble learning methods are employed to further improve model robustness and accuracy. Experimental results show that the ensemble model maintains around 85% accuracy even with high background complexity, and transfer learning achieves 90% accuracy when the sample size reaches 1000. These findings demonstrate that transfer and ensemble learning effectively enhance image recognition accuracy and resilience in complex environments.

关键词： image recognition Accuracy machine learning algorithms Transfer learning Noise Interference Feature extraction Robustness Ensemble learning Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Advanced Detection of Diseases in Tomato Plant Leaf Using Deep learning 3rd

Advanced Detection of Diseases in Tomato Plant Leaf Using ...

引用

3rd International conference on Intelligent Systems and applications, ICISA 2024

作者： Madhan Kumar, G.S. Chandrakanth, S.N. Akash Patil, H.D. Sanjay Kumar, M.R. Vamshi Ram, K. Maharaja Institute of Technology Thandavapura NH 766 Nanjangud Taluk Mysuru571302 India

ISBN: (纸本)9789819798384

India, the world’s second-largest tomato producer, plays an essential role in feeding millions and providing substantial income for farmers. However, the backbone of Indian agriculture, the tomato, faces a significant challenge in early disease detection. Traditional disease detection methods, relying on expertise, prove time-consuming and prone to human error. The presence of undetected diseases poses a continual threat to the health and productivity of tomato plants, resulting in reduced yields and profits. To surpass these constraints and expedite the detection process, this paper employs a deep learning model built using a Convolutional neural Network (CNN). The model follows a workflow involving image acquisition, pre-processing, extraction of features, and CNN training to identify diseases in tomato leaf plants. The proposed model exhibits an average score of detecting all tomato diseases categories with 98.27% of accuracy, 98.27% of precision, 97.81% of recall, and 98.06% of F1 score surpassing traditional approaches like SVM, KNN, and backpropagation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Parameter-Efficient image Classification with Convolutional Spiking neural Network via Fast Sigmoid Surrogate Gradient Descent

Parameter-Efficient Image Classification with Convolutional ...

引用

International conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Joy Datta Fabliha Afaf Sarwar Rawhatur Rabbi Puja Saha Md. Golam Rabiul Alam Jia Uddin Aniqua Nusrat Zereen Department of Computer Science & Engineering School of Data & Sciences Brac University Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon South Korea

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Spiking neural networks (SNNs) offer several advantages over traditional Artificial neural networks (ANNs), especially in terms of biologically inspired event-driven mechanisms and energy efficiency. This paper aims to demonstrate parameter efficiency of Convolutional Spiking neural networks (CSNNs) by training extremely small CSNNs and comparing those with conventional Convolutional neural networks (CNNs) of same parameter count and depth, as well as against state-of-the-art SNN implementations. Through experiments conducted on multiple small-scale CSNN models, we observed that CSNNs are able to achieve exceptional parameter efficiency and performance with the help of a fast sigmoid surrogate gradient descent, proving an astonishing balance between accuracy and compactness. The findings fulfill the promise that CSNNs hold as a feasible solution for resource-constrained and low-power applications at the same time, as it demonstrates the biological plausibility of learning in such complex machine learning tasks.

关键词： Training image recognition Biological system modeling Neurons Spiking neural networks machine learning Computer architecture Energy efficiency Convolutional neural networks image classification

来源：评论

学校读者我要写书评

暂无评论

CD-SEM Anomaly Detection Through machine learning and Generative AI

CD-SEM Anomaly Detection Through Machine Learning and Genera...

引用

IEEE/SEMI conference and Workshop on Advanced Semiconductor Manufacturing

作者： Susan Sun Xiaoyan Zhuo Phong Nguyen Todd Smith Jinhua Ding Troy Stansell Amit Srivastava Yousef Hanif Data Science Micron Technology Inc. Manassas VA USA Lithography Process Eng Micron Technology Inc. Manassas VA USA Data Science Micron Technology Inc. Singapore Product Quality Assurance Micron Technology Inc. Manassas VA USA

ISBN: (数字)9798331531850

ISBN: (纸本)9798331531867

In a complex semiconductor manufacturing environment, critical dimension scanning electron microscope (CD-SEM) images are captured at metrology step to monitor structural measurements and detect anomaly to meet the stringent process control requirements. This paper focuses on advanced CD-SEM image processing and anomaly detection using machine learning and generative AI models, which include computer vision (CV) image processing, Residual neural Network (ResNet) deep learning and Generative Adversarial Network (GAN) model for various use cases. The applications of these models during in-line monitoring are crucial to identify potential process issues for yield and quality improvement.

关键词： Deep learning Semiconductor device measurement Generative AI image processing Computational modeling Semiconductor device manufacture Generative adversarial networks Servers Anomaly detection Monitoring

来源：评论

学校读者我要写书评

暂无评论

HPDNET: Hyper Prior Dependent Demosaic neural Network

HPDNET: Hyper Prior Dependent Demosaic Neural Network

引用

International image processing, applications and Systems conference (IPAS)

作者： Youngil Seo Sungho Jun Dongpan Lim Seongwook Song System LSI Division Samsung Electronics Yongin Republic of Korea

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Recent advancements in deep neural networks have shown remarkable improvements in image quality during the demosaicking process, surpassing conventional algorithms. However, these deep neural network techniques are often characterized by heavy computational requirements, rendering them unsuitable for deployment on resource-constrained platforms. This presents a critical challenge in the field of image demosaicking: while deep learning approaches excel in enhancing image quality, their computational intensity poses a significant hindrance to their wider adoption. Consequently, there is a pressing need for methodologies that can strike an optimal balance between achieving superior image quality and maintaining computational efficiency. In this work, we propose a new deep framework, hyper-prior dependent demosaic neural network, HPDNet that utilizes the significant concepts of the conventional algorithm and the characteristic of image sensor data. We designed the network that exploits three concepts, those are pixel gradient prior attention, phase separation, and multi-level sparse and dense feature extraction. We designed the deep neural network that extracts the optimal gradient prior and the multi-level extracted features are fused and attended by gradient prior. It can fully utilize spatially variant information. Also to make the network deployed in the mobile platform, we devised self-pruned image convolution that adopts image filter characteristic and reduce computations. Experiments show that proposed network outperforms SOTA demosaic networks both in terms of image quality and computation.

关键词： image quality Deep learning image sensors Artificial neural networks Pressing Feature extraction Rendering (computer graphics) Information filters Computational efficiency image restoration

来源：评论

学校读者我要写书评

暂无评论

Line Search Stochastic Gradient Algorithm with A-priori Rule for Monitoring the Control of the Variance 4th

Line Search Stochastic Gradient Algorithm with A-priori Rul...

引用

4th International conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Franchini, Giorgia Porta, Federica Ruggiero, Valeria Trombini, Ilaria Zanni, Luca Dipartimento di Scienze Fisiche Informatiche e Matematiche Università di Modena e Reggio Emilia Via Campi 213A Modena41125 Italy Dipartimento di Matematica e Informatica Università di Ferrara Via Machiavelli 30 Ferrara44121 Italy Dipartimento di Scienze Matematiche Fisiche e Informatiche Università di Parma Parco Area delle Scienze 7/A Parma43124 Italy Institute of Informatics and Telematics National Research Council Pisa56124 Italy

ISBN: (纸本)9783031812408

The aim of this paper is to analyze a different practical implementation of the LIne Search based stochastic gradient Algorithm (LISA) recently proposed by Franchini et al. The LISA scheme belongs to the class of stochastic gradient methods and it practically relies on a line search strategy to select the learning rate and a dynamic technique to increase the mini batch size of the stochastic directions. Despite promising performance of LISA in solving optimization problems from machine learning applications, its mini batch increasing strategy involves checking for a condition which may be computationally expensive and memory demanding, especially in the presence of both deep neural networks and very large-scale dataset. In this work we investigate an a-priori procedure to select the size of the current mini batch which allows to dampen the computational execution time of the LISA method and to control the requests of memory resources, especially when dealing with hardware accelerators. A numerical experience on training both statistical models for binary classification and deep neural networks for multi-class image classification confirms the effectiveness of the proposal. © 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

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500页 << < 10 11 12 13 14 15 16 17 18 19 > >>

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：