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

13,239 篇 会议
191 册 图书
158 篇 期刊文献

馆藏范围

13,587 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

9,037 篇 工学
- 7,539 篇 计算机科学与技术...
- 4,285 篇 软件工程
- 1,624 篇 信息与通信工程
- 1,528 篇 电气工程
- 1,231 篇 控制科学与工程
- 962 篇 生物工程
- 513 篇 机械工程
- 500 篇 生物医学工程（可授...
- 470 篇 电子科学与技术（可...
- 461 篇 动力工程及工程热...
- 326 篇 光学工程
- 308 篇 交通运输工程
- 294 篇 安全科学与工程
- 267 篇 材料科学与工程（可...
- 248 篇 化学工程与技术
3,513 篇 理学
- 2,202 篇 数学
- 1,058 篇 生物学
- 605 篇 物理学
- 582 篇 统计学（可授理学、...
- 472 篇 系统科学
- 241 篇 化学
1,861 篇 管理学
- 1,292 篇 管理科学与工程(可...
- 681 篇 图书情报与档案管...
- 542 篇 工商管理
595 篇 医学
- 497 篇 临床医学
- 406 篇 基础医学(可授医学...
- 223 篇 药学(可授医学、理...
306 篇 教育学
- 296 篇 教育学
251 篇 法学
- 228 篇 社会学
221 篇 经济学
- 219 篇 应用经济学
136 篇 农学
37 篇 军事学
21 篇 文学
6 篇 艺术学

主题

1,321 篇 optimization
1,090 篇 machine learning
1,031 篇 deep learning
506 篇 accuracy
462 篇 reinforcement le...
328 篇 computational mo...
313 篇 machine learning...
299 篇 training
279 篇 support vector m...
279 篇 feature extracti...
260 篇 predictive model...
248 篇 neural networks
241 篇 real-time system...
217 篇 genetic algorith...
207 篇 adversarial mach...
163 篇 particle swarm o...
157 篇 convolutional ne...
152 篇 learning algorit...
142 篇 internet of thin...
131 篇 data mining

机构

46 篇 chitkara centre ...
36 篇 chitkara univers...
30 篇 chitkara univers...
29 篇 the islamic univ...
24 篇 department of co...
18 篇 centre of resear...
18 篇 al-farahidi univ...
17 篇 lloyd institute ...
17 篇 department of co...
15 篇 school of comput...
14 篇 school of comput...
14 篇 gla university d...
13 篇 vishwakarma inst...
12 篇 university of ch...
12 篇 college of manag...
11 篇 chitkara univers...
11 篇 school of comput...
10 篇 columbia univers...
10 篇 univ chinese aca...
10 篇 lovely professio...

作者

13 篇 lima jose
13 篇 pereira ana i.
11 篇 wang wei
9 篇 rakesh kumar
9 篇 wang hui
9 篇 de-shuang huang
9 篇 ye jieping
8 篇 yang tianbao
8 篇 neeraj varshney
8 篇 r. santhana kris...
7 篇 zhang tao
7 篇 zhang lei
7 篇 li li
7 篇 buzdalov maxim
7 篇 wang jian
7 篇 huang heng
7 篇 hu rong
7 篇 yang yang
7 篇 todorov venelin
7 篇 zivkovic miodrag

语言

11,970 篇 英文
1,588 篇 其他
137 篇 中文
1 篇 阿拉伯文
1 篇 西班牙文
1 篇 俄文
1 篇 土耳其文

检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13588 条记录，以下是811-820 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：

A Novel Model for Recognising Handwritten Devanagari Numerals using Machine learning 4

A Novel Model for Recognising Handwritten Devanagari Numeral...

引用

4th international conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Prathima, Ch Arava, Ramprakash Reddy Sevitha, K. Manikanth, G. Vinay, D. Surya, C. Mohan Babu Univ Sch Comp Tirupati Andhra Pradesh India KSRM Coll Engn Dept CSE Kadapa Andhra Pradesh India Sree Vidyanikethan Engn Coll Dept CSSE Tirupati Andhra Pradesh India

ISBN: (纸本)9798350386356;9798350386349

this research study performs a comprehensive comparative analysis aimed at developing effective machine learning models for classifying handwritten Devanagari numerals. the research focuses on evaluating the performance of various models to determine the most accurate classification approach. Initiating with data pre-processing, including feature extraction and normalization, the data is prepared for model training and assessment. A diverse range of machine learning models, from traditional methods like Support Vector Machines (SVM) to advanced techniques such as Random Forests, K-Nearest Neighbors, and Convolutional Neural Networks, are considered for the comparative analysis, ensuring a thorough assessment of classification capabilities. Cross-validation techniques are employed during model training and testing to enhance reliability. Statistical tests are utilized to assess the performance variations among models, enhancing the robustness of the analysis. Visual representations of performance metrics and comparison results offer clear insights. this research study aims to identify the most suitable machine learning model for handwritten Devanagari numeral classification, potentially advancing character recognition systems and linguistic applications.

关键词： Handwritten character recognition Machine learning Support Vector Machine(SVM) Data augmentation Transfer learning Digital document processing

来源：评论

学校读者我要写书评

暂无评论

Turbo-Charging Deep learning Methods for Partial Differential Equations 23

Turbo-Charging Deep Learning Methods for Partial Differentia...

引用

4th ACM international conference on AI in Finance (ICAIF)

作者： Buet-Golfouse, Francois Utyagulov, Islam Pahwa, Parth Hill, Peter JPMorgan Chase Decis Sci London England UCL London England

ISBN: (纸本)9798400702402

Solving partial differential equations (PDEs) is a frequent necessity in numerous domains, ranging from complex systems simulation to financial derivatives pricing and continuous-time optimisation tasks. the challenging nature of PDEs, especially in high dimensions or cases involving non-linearities, calls for robust, innovative solutions. this paper leverages a deep neural network methodology, utilizing differential operators and boundary conditions in tandem with sampling techniques and minimising distinct loss terms. the role of physics-inspired neural networks in this approach is also highlighted. Our primary proposition is a Bayesian interpretation, where we address the issue as a hierarchical multi-objective optimisation problem augmented with adaptive sampling. We also introduce a concept of 'curriculum learning,' which parallels control variates, thereby facilitating further variance reduction and the re-utilisation of solutions derived from assorted problems. Our methods notably enhance the speed of convergence and diminish approximation errors. the effectiveness of our strategies is demonstrated through illustrative examples, solidifying their value in practical applications.

关键词： Partial Differential Equations Deep learning

来源：评论

学校读者我要写书评

暂无评论

Performance optimization of Convolutional Neural Networks (CNN) in Multilabel Fruit Classification 4

Performance Optimization of Convolutional Neural Networks (C...

引用

4th international conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Boukili, Mohamed Jebbor, Nawfal Moulay Ismail University Of Meknes Electronics Instrumentation And Intelligent Systems Team ER2TI Laboratory Department Of Physics Faculty Of Sciences And Technics Errachidia Morocco

ISBN: (纸本)9798350309508

Embedded systems with computer vision via a deep learning approach are becoming increasingly common in a variety of fields, including agriculture, where they can be adapted and used in supermarkets, the food industry and cold stores. By using deep learning such as Convolutional Neural Networks (CNNs) in these sectors, we can improve product quality, reduce waste, ensure regulatory compliance and optimize processes, resulting in significant economic and environmental benefits. However, like any technology, they are not without their challenges. Assessing and improving performance in these systems requires an approach combining model optimization, the use of more efficient specialized hardware and intelligent management of computing resources. In this context, the final objective of our project is to develop and evaluate the performance of an embedded computer vision system via a deep learning-based approach: Application to the sorting and grading of agricultural products. To achieve the final objective, in this article we will first try to present and define the architecture of a type of CNN model, and we will build a fruit classification model based on visual appearance using the 'Fruits-360 dataset-Kaggel '. the deep learning model will be based on Tensorflow and Keras sequential APIs and will aim to efficiently classify different fruit types and predict fruit type. © 2024 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Person Re-Identification in Criminal Investigations Using Deep learning and Vision-Based Technique 5

Person Re-Identification in Criminal Investigations Using De...

引用

5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Monika, E. Kumar, T.Rajesh Saveetha School of Engineering Saveetha Institite of Medical and Techinical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331530013

Person re-identification (Re-ID) is a key difficulty in criminal investigations, as precisely matching individuals across different camera perspectives is critical for locating suspects and solving crimes. this research investigates the use of deep learning algorithms and vision-based techniques to improve person re-identification in surveillance systems. Our solution uses convolutional neural networks (CNNs) for feature extraction and optimization techniques like triplet loss and softmax loss for effective metric learning. these methods serve to refine inter- and intra-class variations, resulting in more accurate matching across a variety of situations such as changes in viewpoint, occlusion, and lighting. Attention mechanisms and data augmentation approaches were used to deal with difficult scenarios, while adaptive learning rate optimization was used to speed up model convergence. To improve performance even more, re-ranking algorithms and temporal consistency restrictions were combined to increase the accuracy of re-identifying persons across many cameras across time. the suggested system was tested on benchmark datasets such as Market-1501 and Duke MTMC- reID, and it outperformed cutting-edge approaches with a mean Average Precision (mAP) of 85.6% and Rank-1 accuracy of 92.3%. the usage of triplet and softmax loss reduced false positives by 15%, while attention methods and data augmentation increased robustness under different settings. Re-ranking and temporal consistency lead to a 4% boost in Rank-1 accuracy in multi-camera setups, which improved performance in congested or complicated scenarios. Adaptive learning rate optimization reduced training time by 25% while maintaining accuracy. Overall, the results show that our deep learning-based Re-ID system is very successful and scalable for real-world applications, providing law enforcement with a powerful tool for tracking and identifying suspects in criminal investigations. © 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

2023 4th international conference on Communication Systems, Computing and IT applications, CSCITA 2023 - Proceedings

2023 4th International Conference on Communication Systems, ...

引用

4th international conference on Communication Systems, Computing and IT applications, CSCITA 2023

ISBN: (纸本)9781665459877

the proceedings contain 43 papers. the topics discussed include: deep learning based Marathi sentence recognition using Devanagari character identification;deep learning based Marathi sentence recognition using Devanagari character identification;efficient detection of small and complex objects for autonomous driving using deep learning;implementation of exploratory data analysis on weather data;deep learning model for simulating self-driving car;summarization of video clips using subtitles;reliability stripe coagulation in two failure tolerant storage arrays;efficient video anomaly detection using residual variational autoencoder;situational portfolio forecasting and allocation with deep-learning approach;and recognition of emotions based on facial expressions using bidirectional long-short-term memory and machine learning techniques.

关键词：

来源：评论

学校读者我要写书评

暂无评论

applications of Metaheuristic algorithms for MPPT under Partial Shaded Condition in PV System 4

Applications of Metaheuristic Algorithms for MPPT under Part...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Vankadara, Sampath Kumar Chatterjee, Shamik Kumar Balachandran, Praveen Lovely Professional University School of Electronics and Electrical Engineering Phagwara India Vardhaman College of Engineering Department of EEE Hyderabad India

ISBN: (纸本)9798350335750

Due to the bypass diode operating across the shaded module, the electrical characteristics of the PV system under partly shaded PSC display numerous maxima. In order to get the most out of the PV system, it needs to be forced to run at global MPP under PSC. In this paper, Metaheuristic MPPT strategies Enhanced Grey Wolf optimization algorithm (EGWO) and Marine Predator Algorithm (MPA) are discussed and comparison has been done. the use of suggested MPPT approaches for global MPP tracking exposed to PSC for dynamically changing shading patterns for 8S (Eight series) PV setups is discussed, and tracking results are shown. © 2023 IEEE.

关键词： Solar panels

来源：评论

学校读者我要写书评

暂无评论

Real-Time Fault Detection and Classification in Smart Grids Using Support Vector Machines 4

Real-Time Fault Detection and Classification in Smart Grids ...

引用

4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Gupta, Sarita Gard, Gopal Krishan Varshney, Neeraj Rao, A.L.N. Srivastava, Amit Sharma, Deepti Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Chitkara University Centre of Research Impact and Outreach Chitkara Business School Punjab India GLA University Department of Computer Engineering and Applications Mathura India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India Uttaranchal University Uttaranchal Institute of Management Department of Management Dehradun248007 India

ISBN: (纸本)9798350307757

this research investigates real-time fault detection and classification in smart grids using five machine learning algorithms: Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN' Decision Trees, Random Forest and Gradient Boosting. Extensive experiments were conducted to compare them performance-wise in terms of accuracy, computational efficiency and robustness. the findings showed that Gradient Boosting attained the highest accuracy of 95.2%, which surpassed other algorithms. Second, the RF was illustrated with competitive accuracy (94.6%) less overfitting providing a reliable option for use. Interpretable Decision Trees were challenged by issues surrounding overfitting. 92.5% accuracy was found in the case of SVMs;however, these turned out to be computationally complex procedures as well. K-NN reported competitive accuracy (89.3%), but was computationally expensive. Comparison to related works from all fields underscored the cross-domain relevance of fault detection in critical systems. the study provides useful observations on the choice of fault detection algorithms to be used in smart grids, which recommend a compromise based on accuracy performance and computational efficiency while also being adapted for specific grid features. © 2024 IEEE.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Mathematical Modeling optimization of Recognition Algorithm Based on Multi Feature Extraction 4

Mathematical Modeling Optimization of Recognition Algorithm ...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Sun, Xiaokun Dalian University of Technology City Institute Dalian116600 China

ISBN: (纸本)9798350335750

the research on recognition algorithm of multi feature extraction is to find the best method to extract features from images. Researchers used different algorithms and experiments to find the best algorithm. the algorithm adopts the idea of classification and discrimination in the matching and discrimination stage. While inputting the Chinese character image, the input structure characteristics of the Chinese character image are recorded. On this basis, the stroke characteristics of the input unknown Chinese characters are obtained. From the stroke characteristics, the unknown Chinese characters can be roughly classified, which can narrow the range of Chinese characters in the discrimination. Some of these algorithms include: SIFT (scale invariant feature transformation) - this algorithm is used in many applications, such as face detection, image retrieval and target tracking. It can be applied to any type of image, including video sequences. Sift uses a set of parameters based on scale invariance, so it can process any type of image or video sequence without distortion. © 2023 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Addressing the Permutation Flowshop Scheduling with Grey Wolf Optimizer 10th

Addressing the Permutation Flowshop Scheduling with Grey Wol...

引用

10th international conference on Machine learning, optimization, and Data Science, LOD 2024

作者： Tsiftsoglou, Maria Marinaki, Magdalene Marinakis, Yannis School of Production Engineering and Management Technical University of Crete Chania73100 Greece

ISBN: (纸本)9783031824807

this study investigates the efficacy of the Grey Wolf Optimizer (GWO), a newer Swarm Intelligence (SI) algorithm, in addressing the Permutation Flowshop Scheduling Problem (PFSP). A hybrid approach integrating GWO with Variable Neighborhood Search (VNS) and Path Relinking strategies is proposed to enhance scheduling optimization in industrial settings. Emphasizing the importance of initial population quality in SI algorithms, a randomized NEH (Nawaz-Enscore-Ham) method is advocated for generating high-quality initial points for GWO. through a comprehensive comparative study involving prominent SI algorithms such as Particle Swarm optimization (PSO), Firefly Algorithm (FA), Bat Algorithm (BA), and Artificial Bee Colony (ABC), GWO’s performance is evaluated using statistical analyses including Friedmans Test, Friedmans Aligned Test, and Quade Test. the findings reveal GWO’s satisfactory results, positioning it as a promising contender for PFSP optimization. the study underscores the significance of hybridization techniques and statistical analyses in driving improvements in operational efficiency and performance in industrial scheduling applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

Diversified Ensemble learning: Integrating Bagging and Teaching-learning-Based optimization with a Pairwise Dissimilarity Measure 1

引用

4th international conference on Advanced Engineering optimization through Intelligent Techniques

作者： Arukonda, Srinivas Cheruku, Ramalingaswamy VIT AP Univ Amaravathi Andhra Pradesh India Natl Inst Technol Warangal Telangana India

ISBN: (数字)9789819746545

ISBN: (纸本)9789819796618;9789819746545;9789819746538

Ensemble learning has emerged as a powerful technique for improving classification accuracy by combining multiple base models. this study presents an innovative approach to enhance ensemble learning through diversification. the proposed method integrates bagging, a resampling technique, with teaching-learning-based optimization (TLBO), and incorporates a pairwise dissimilarity measure to promote diversity within the ensemble. the TLBO algorithm optimizes the composition of the ensemble by iteratively selecting optimal bags of instances from the training data. the diversity measure quantifies the dissimilarity between bags, ensuring that the ensemble consists of diverse and complementary models. Our proposed model experimented on four benchmarked disease datasets and experimental results demonstrate that the proposed approach achieves superior performance compared to traditional ensemble methods. the ensemble models generated through this approach exhibit improved performance. the proposed model is statistically evaluated using the statistically paired T-test, and the results show our proposed model differs from base models.

关键词： Ensemble learning Bagging Teaching-learning-based optimization (TLBO) Diversity measure Classification Base models Instance selection optimization Pairwise dissimilarity Performance improvement

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 << < 78 79 80 81 82 83 84 85 86 87 > >>

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：