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

4,153 篇 会议
38 册 图书
7 篇 期刊文献

馆藏范围

4,198 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,774 篇 工学
- 2,496 篇 计算机科学与技术...
- 2,042 篇 软件工程
- 615 篇 生物工程
- 614 篇 信息与通信工程
- 364 篇 生物医学工程（可授...
- 295 篇 控制科学与工程
- 223 篇 电气工程
- 211 篇 光学工程
- 161 篇 安全科学与工程
- 142 篇 机械工程
- 129 篇 电子科学与技术（可...
- 120 篇 交通运输工程
- 119 篇 化学工程与技术
- 97 篇 动力工程及工程热...
1,369 篇 理学
- 674 篇 数学
- 645 篇 生物学
- 331 篇 统计学（可授理学、...
- 308 篇 物理学
- 197 篇 系统科学
- 128 篇 化学
748 篇 管理学
- 421 篇 管理科学与工程(可...
- 414 篇 图书情报与档案管...
- 250 篇 工商管理
343 篇 医学
- 315 篇 临床医学
- 308 篇 基础医学(可授医学...
- 221 篇 药学(可授医学、理...
- 126 篇 公共卫生与预防医...
160 篇 法学
- 139 篇 社会学
157 篇 教育学
- 155 篇 教育学
141 篇 农学
- 140 篇 作物学
89 篇 经济学
9 篇 军事学
6 篇 文学

主题

532 篇 machine learning
248 篇 deep learning
117 篇 support vector m...
99 篇 adversarial mach...
93 篇 accuracy
93 篇 machine learning...
83 篇 forecasting
76 篇 convolutional ne...
70 篇 predictive model...
70 篇 feature extracti...
59 篇 artificial intel...
53 篇 students
51 篇 learning systems
48 篇 decision trees
48 篇 training
47 篇 reinforcement le...
47 篇 computational mo...
47 篇 computer vision
46 篇 internet of thin...
41 篇 neural networks

机构

46 篇 chitkara univers...
15 篇 graphic era hill...
10 篇 college of manag...
9 篇 graphic era deem...
8 篇 computer science...
8 篇 beijing universi...
7 篇 school of comput...
7 篇 school of comput...
7 篇 school of comput...
7 篇 department of in...
6 篇 università di ca...
6 篇 infn sezione di ...
6 篇 north-west unive...
6 篇 computer science...
6 篇 lovely professio...
6 篇 in2p3 lpc campus...
6 篇 kkr and ksr inst...
6 篇 institut univers...
6 篇 national centre ...
6 篇 greater bay area...

作者

18 篇 kukreja vinay
15 篇 mehta shiva
10 篇 william p.
8 篇 sharma vikrant
7 篇 mahamudul hasan
7 篇 shiva mehta
6 篇 chen a.
6 篇 benoit d.m.
6 篇 ferrara g.
6 篇 ardid m.
6 篇 calvo d.
6 篇 gagliardini s.
6 篇 eddymaoui a.
6 篇 kulikovskiy v.
6 篇 labalme m.
6 篇 gal t.
6 篇 bozza c.
6 篇 khatun a.
6 篇 kalekin o.
6 篇 huang f.

语言

2,394 篇 英文
1,803 篇 其他
126 篇 中文

检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4198 条记录，以下是1281-1290 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：

Intelligent Deep learning Framework for Breast Cancer Prediction using Feature Ensemble learning 4

Intelligent Deep Learning Framework for Breast Cancer Predic...

引用

4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： Rao, Chintala Nageswara Chatrapathy, K. Jainul Fathima, A. Sathish, Goski Mukherjee, Saptarshi Reddy, Pundru Chandra Shaker CSE-AI&ML Geethanjali College of Engineering and Technology Hyderabad India School of Computing&Information Technology REVA University Karnataka Bengaluru India Information Technology Francis Xavier Engineering College TamilNadu Tirunelveli India Information Technology St. Martins Engineering College Dhullapally Hyderabad India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology Uttarakhand India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350305258

the mortality and morbidity rates associated with breast cancer are extremely high. In the fight against breast cancer (BC), a correct diagnosis is crucial. the diagnostic process can benefit from the application of machine-learning (ML) and deep-learning (DL) strategies. In this paper, a deep-recurrent-neural-network (DRNN) technique optimized for breast cancer analysis using RNN and the Keras-Tuner optimization technique is proposed. the input layer, five hidden layers, five dropout layers, and the output layer make up the optimized DRNN. It fine-tuned the dropout layer's neurologist count and firing rate for each successive hidden layer. the most crucial data points have been elected utilizing three different feature-selection methods. the optimized DRNN was contrasted to five standard ML strategies: decision-tree (DT), support-vector-machine (SVM), random-forest (RF), Naïve-Bayes (NB), and the K-nearest neighbor algorithm (KNN). the elected features have been applied to both standard ML models and optimized DRNN. the outcomes showed that, compared to the other models, the cross-validation (CV) and testing outcomes were best achieved (99.16%) by the optimized DRNN with the features picked by univariate. © 2023 IEEE.

关键词： Breast cancer Carcinoma Deep learning KNN machine learning RNN SVM

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of machine learning and Deep learning Models for Weather Image Classification 4

A Comparative Study of Machine Learning and Deep Learning Mo...

引用

2023 4th international conference on computer Engineering and Intelligent Control, ICCEIC 2023

作者： Liu, Dun Shi, Wen Jiang, Linfeng Sun, Jiazhi Xin, Changyi Beihang University School of Astronautics Beijing102206 China Fose University of Nottingham Ningbo China Ningbo315100 China Chongqing Nankai Secondary School Chongqing400030 China University of California Davis College of Letter and Science Davis95616 United States The University of Manchester Department of Computer Science ManchesterM139PL United Kingdom

ISBN: (纸本)9798350308877

the study of identifying and categorizing weather images is closely connected to our daily routines and the economy. It affects decisions such as whether to bring an umbrella for the day or the agricultural earnings of an entire country for the year. However, the complexities within the domain of weather classification are manifold, owing to the subtle nature of weatherrelated image features and the multitude of factors contributing to image variability. In this context, we explore the efficacy of three distinct deep learning models: Convolutional Neural Networks (CNNs), Visual Geometry Group (VGG) networks, and Vision Transformer (ViT) models, to address the challenges posed by ambiguous attributes of weather images. the above models exhibit notable differences in their architectural nuances. CNNs excel at capturing local features through convolutional layers. VGG networks are adept at learning intricate feature hierarchies via their deep architecture. ViT, leveraging self-attention mechanisms, excels in capturing global context from images. these models collectively tackle the issue of indistinct weather features by extracting and learning relevant patterns at different scales and levels of abstraction. Our experimentation employs a diverse weather dataset encompassing ten categories, including sunny, cloudy, rainy, snowy, and others, each comprising 500 images. the results are promising, with all three models achieving classification accuracies exceeding 90%. this study underscores the potential of CNNs, VGG networks, and ViT models in effectively categorizing weather images despite the inherent challenges, contributing to improved weather understanding and forecasting, and ultimately benefiting various sectors of society. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

the application of Simplified Strassen Algorithm to Snow Simulation with Material Point Method 4

The Application of Simplified Strassen Algorithm to Snow Sim...

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Wang, Mingrui St. Olaf College NorthfieldMN55057 United States

ISBN: (纸本)9798400709449

As a beautiful yet complicated material, snow and its simulations are significantly challenging in the computer graphics community. Since the introduction of the material point method, abbreviated as MPM, the performance of snow simulations has been augmented in many aspects, including accuracy, stability, and efficiency. Nowadays, researchers work on developing simpler mathematical models or incorporating better hardware design to accelerate the snow simulations, but the overall running speed remains a problem. Instead of improving on the simulation methods, this paper presents a novel approach to optimize the efficiency of snow simulations by applying the simplified Strassen algorithm, an abridgment of a famous fast matrix multiplication algorithm, to snow simulations implemented with MPM. Rather than straightforwardly showing the triumph of the simplified Strassen algorithm over the standard matrix multiplication algorithm in runtime, the results present the evidence that leads to the inference that for simulations with a certain level of disorder, the simplified Strassen algorithm will surpass the standard matrix multiplication algorithm as the simulations progress. © 2023 ACM.

关键词： Snow

来源：评论

学校读者我要写书评

暂无评论

11th international conference on Intelligent Computing and applications, ICRTC 2023

11th International Conference on Intelligent Computing and A...

引用

11th international conference on Intelligent Computing and applications, ICRTC 2023

ISBN: (纸本)9789819717231

the proceedings contain 70 papers. the special focus in this conference is on Intelligent Computing and applications. the topics include: RNN learning for Dynamic Selection of Channel Access Scheme in FANETs;a Crime Knowledge Discovery Scheme Based on Entity Recognition, Relation Extraction, and Development of Criminal Profiling Using Modus Operandi;optimized Biometric Key Management System for Enhanced Security;human Posture Identification and Recognition Using Deep learning Techniques;twitter Sentiment Analysis Using Different machine learning Techniques;exploring Empirical Mode Decomposition for Music Genre Classification Using Deep learning;a Comparative Analysis of Fog Computing’s Problems, Challenges and Future Directions;explainable Artificial Intelligence Insight: An Orderly Survey;deep learning-Based Sign Language Translator;Analysis of Arrhythmia from Electrocardiogram (ECG) Data Using ML Framework;brain Tumor Detection Using Quantum Neural Network;augmented Reality-Based application for Indian Monuments;fog Obscurity Mitigation;a Load Balancing Using Multi-population Grasshopper Optimization Approach for Workflow Tasks in Clouds;parental Control for Techie Child Using Keylogger;modified Box Filter Design and Noise Analysis on Two-Dimensional Images;Change Detection in Remote Sensing SAR Image Using a Ratio-Based Operator;automatic Question Generation: A Comparative Analysis of Rule-Based and Neural Network-Based Models;mango Leaf Images Quality Improvement Techniques Using Subjective Approach of Image Enhancement;real-Time 3D Texture and Motion Analysis for Face Anti-spoofing Using Deep learning and computer Vision;Hybrid Sentiment Polarity Prediction Scheme in Social Networks using Attention Mechanism and Improved CNN;survival Analysis of Heart Failure Patients with Advanced machine learning Models;a Differential Privacy Perturbation with Random Forest Classifier in Medical Database;smart application for Early Detection of Rice Plant Disease Using Incept

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-Label Feature Selection Based on Improved Fisher Score with Label Correlation 13

Multi-Label Feature Selection Based on Improved Fisher Score...

引用

13th international conference on Information Technology in Medicine and Education, ITME 2023

作者： Mao, Yu Cheng, Yuxuan Shi, Chunyu Lin, Yaojin School of Computer Science Minnan Normal University Zhangzhou China Minnan Normal University Lab of Data Science and Intelligence Application Zhangzhou China

ISBN: (纸本)9798350319156

Feature selection for multi-label classification has received extensive attention in the fields of machine learning and data mining. However, some feature selection methods fail to incorporate label correlations, resulting in the degradation of learning performance. In this paper, we dedicate to fuse the Fisher score model and neighborhood mutual information model for multi-label learning. the neighborhood mutual information is initially constructed to identify the coefficients between labels, by measuring the granularity of each instance. After that, an improved Fisher score model based on label coefficients is formed to select features with multiple crucial labels. Extensive experiments demonstrate the effectiveness and stability of the proposed method. © 2023 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Association Rules Based Feature Extraction for Deep learning Classification 4th

Association Rules Based Feature Extraction for Deep Learning...

引用

4th international conference on Soft Computing and its Engineering applications (icSoftComp)

作者： Kharsa, Ruba Al Aghbari, Zaher Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates

ISBN: (纸本)9783031276088;9783031276095

the number of extracted features from medical data, such as computer-aided diagnosis, has been known to be too large and affects the performance of the used classifiers. Moreover, the large number of input features affect the accuracy of the classifiers, such as the traditional machine learning classifier. therefore, in this paper, we proposed the use of association rules to select features from medical data, which result in dimensionality reduction of the input feature space. the selected features become the input to a deep neural network, particularly ResNet, which is known for its high accuracy of classification results. the conducted experiments prove that the use of association rules to select the most representative features and the use of deep neural networks as a classifier outperformed other traditional machine learning models in terms of accuracy of classification.

关键词： Association rules mining Feature selection Deep learning classification Resnet Neural networks Classification

来源：评论

学校读者我要写书评

暂无评论

Refined Meta-learning Approach Leveraging Residual Networks for Enhanced Performance 4

Refined Meta-Learning Approach Leveraging Residual Networks ...

引用

4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Wang, Yihan Wu, Bo Guo, Hongchen School of Cyberspace and Technology Beijing Institute of Technology Beijing China Information Technology Center Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350308082

Meta-learning, as a new way of small-sample learning, corrects the network parameters of the overall model for classification problems by treating tasks as inputs, thus obtaining a more rapid iteration effect and more generalizable network parameters. For current image classification problems, after model training, learning for new tasks often requires retraining with a large amount of new and past data, requiring the entire model parameters to be updated. Using the meta-learning method, the overall model parameters can be updated through sub-tasks, so as to quickly update the parameters according to new tasks, reducing the required training time and data. We propose an improved model based on the MAML algorithm, which has the characteristic of continuously updating the parameters through sub-tasks to obtain better initial training parameters, and focuses on obtaining higher accuracy on new tasks while ensuring training speed. this balances the accuracy and iteration speed of the model, and experimental results show that meta-learning can achieve a good balance between convergence speed and accuracy on new samples. © 2023 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Admission Prediction in Undergraduate applications: an Interpretable Deep learning Approach 5

Admission Prediction in Undergraduate Applications: an Inter...

引用

5th international conference on Transdisciplinary AI, TransAI 2023

作者： Priyadarshini, Amisha Martinez-Neda, Barbara Gago-Masague, Sergio University of California Irvine Department of Computer Science IrvineCA United States

ISBN: (纸本)9798350358018

this article addresses the challenge of validating the admission committee's decisions for undergraduate admissions. In recent years, the traditional review process has struggled to handle the overwhelmingly large amount of applicants' data. Moreover, this traditional assessment often leads to human bias, which might result in discrimination among applicants. Although classical machine learning-based approaches exist that aim to verify the quantitative assessment made by the application reviewers, these methods lack scalability and suffer from performance issues when a large volume of data is in place. In this context, we propose deep learning-based classifiers, namely Feed-Forward and Input Convex neural networks, which overcome the challenges faced by the existing methods. Furthermore, we give additional insights into our model by incorporating an interpretability module, namely LIME. Our training and test datasets comprise applicants' data with a wide range of variables and information. Our models achieve higher accuracy compared to the best-performing traditional machine learning-based approach by a considerable margin of 3.03 %. Additionally, we show the sensitivity of different features and their relative impacts on the overall admission decision using the LIME technique. © 2023 IEEE.

关键词： Lime

来源：评论

学校读者我要写书评

暂无评论

On Necessity of Conscious learning: From Robots to Humans 4th

On Necessity of Conscious Learning: From Robots to Humans

引用

4th international Symposium on Automation, Mechanical and Design Engineering, SAMDE 2023

作者： Weng, Juyang Brain-Mind Institute OkemosMI48864 United States GENISAMA OkemosMI48864 United States

ISBN: (纸本)9783031626630

Background: this is a position paper as a companion paper for the keynote talk at the same conference SAMDE 2024. this paper is about the necessity of understanding conscious learning for humans and machines. Conscious learning is the main subject of the keynote talk. Problem: the necessity of conscious learning for Natural General Intelligence (NGI) and Artificial General Intelligence (AGI). Approach: Since Conscious learning has been published as cited in the paper, this paper does not explain Conscious learning per se and its algorithm, but instead proposes and proves a theorem about the necessity of conscious learning for humans and machines. the paper further explains the importance of this necessity by discussing the potential impact in three application domains—human governance, science & technology, and international relations. Contribution: the new position is that understanding conscious learning is necessary for humans and machines in the three domains. this work casts major problems in the three domains on the scientific basis—consciousness, beyond a subjective, eclectic, or political basis. Detailed evidence about the events discussed here is referred to Weng et al. v. NSF, ACM, AAAS, SNG, Alphabet, the United States District Court for the Western District of Michigan (Civil Action No. 1:22-cv-998) and the United States Court of Appeal for the 6th Circuit (Civil Action No, 23-1567). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Intelligent robots

来源：评论

学校读者我要写书评

暂无评论

A printed formula recognition method based on the 'Encoder-Decoder' deep learning model 4

A printed formula recognition method based on the 'Encoder-D...

引用

2023 4th international conference on computer Engineering and Intelligent Control, ICCEIC 2023

作者： Chen, Yilin Shi, Zhan Institute of Computer Engineering Nanjing Institute of Technology Nanjing China Shanghai Normal University The College of Information Mechanical and Electrical Engineering Shanghai China

ISBN: (纸本)9798350308877

With the popularization of computers and the rapid development of internet technology, electronic books and literature have become important information carriers. therefore, digitization and informatization of scientific and technological books and literature containing a large number of mathematical formulas has become an important task. Although OCR (Optical Character Recognition) technology has high efficiency and accuracy in text recognition processing, it provides a convenient method for digitization and informatization of scientific and technological books, literature, etc. However, due to the complex two-dimensional structure of mathematical formulas and the diverse logical relationships and varying sizes between characters, OCR technology is not ideal for recognizing mathematical formulas. this paper proposes a deep learning model of 'encoder-decoder' to solve the recognition problem of printed mathematical formulas. Utilize the powerful expression and sequence modeling capabilities of Vision Transformer and Transformer architecture to achieve accurate recognition of printed mathematical formulas. through experimental verification, the model has achieved the expected recognition results on the publicly available printed mathematical formula datasets, with high recognition accuracy, fast running speed, and automatic generation of editable formats. this indicates that the model has potential in this field and provides new directions and choices for further improvement and application. © 2023 IEEE.

关键词： Optical character recognition

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420页 << < 125 126 127 128 129 130 131 132 133 134 > >>

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：