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
188 册 图书
157 篇 期刊文献

馆藏范围

13,583 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
3,512 篇 理学
- 2,202 篇 数学
- 1,058 篇 生物学
- 604 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

1,319 篇 optimization
1,089 篇 machine learning
1,031 篇 deep learning
501 篇 accuracy
462 篇 reinforcement le...
329 篇 computational mo...
310 篇 machine learning...
290 篇 training
286 篇 feature extracti...
279 篇 support vector m...
255 篇 predictive model...
248 篇 neural networks
239 篇 real-time system...
210 篇 genetic algorith...
204 篇 adversarial mach...
168 篇 convolutional ne...
161 篇 particle swarm o...
154 篇 learning algorit...
143 篇 artificial intel...
142 篇 internet of thin...

机构

46 篇 chitkara centre ...
36 篇 chitkara univers...
30 篇 chitkara univers...
29 篇 the islamic univ...
23 篇 department of co...
19 篇 al-farahidi univ...
18 篇 centre of resear...
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...
11 篇 chitkara univers...
10 篇 columbia univers...
10 篇 univ chinese aca...
10 篇 lovely professio...
10 篇 carnegie mellon ...
9 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
9 篇 varshney neeraj
8 篇 yang tianbao
8 篇 neeraj varshney
8 篇 r. santhana kris...
8 篇 misra sanjay
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

语言

11,966 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 13584 条记录，以下是781-790 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：

Crime Pattern Identification and Prediction Using Machine learning 1

引用

4th international conference on Recent Trends in Machine learning, IoT, Smart Cities, and applications, ICMISC 2023

作者： Jhajharia, Kavita Aggarwal, Gaurav Goyal, Mayank Rastogi, Ishank Manipal University Jaipur Dehmi Kalan Jaipur303007 India Amity University in Tashkent Street Labzak Building-70 Tashkent100028 Uzbekistan University of Southern California 3551 Trousdale Pkwy Los AngelesCA90089 United States

ISBN: (数字)9789819994427

ISBN: (纸本)9789819994410

In today’s world, crime rate is reaching the heights of sky, which calls for the help of technology to predict and hence control the crime before it even occurs, thus finally reducing the crime rate. this research’s main aim is to predict whether the crime is going to occur or not based on certain inputs such as location, date, time, and previous crime records related to those particular attributes. this work uses several machine learning algorithms to detect and classify the crime and hence predicting the crime. In the end after comparing several machine learning algorithms, studying several plots and graphs, the algorithm with the highest accuracy is chosen. the result predicts whether the crime will occur or not. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

FedCOA: An Efficient Cluster optimization for Federated learning 4

FedCOA: An Efficient Cluster Optimization for Federated Lear...

引用

4th international conference on Computer Vision and Data Mining, ICCVDM 2023

作者： Jin, Wenxiang Zhang, Jianfei School of Computer Science and Technology Changchun University of Science and Technology Changchun China

ISBN: (纸本)9781510674448

Federated learning is a novel distributed machine learning framework based on data privacy protection. In practical applications, there are often significant differences in data distribution among different clients in federated learning, which can lead to a decrease in the performance of federated learning models. this phenomenon is caused by the problem of independent and identically distributed data. To address the issue of independent and identically distributed data, this paper proposes a novel federated learning algorithm called FedCOA(Federated Cluster optimization Algorithm). the FedCOA algorithm iteratively partitions the clients into different client clusters based on the similarity of their data distributions. Clients with similar data distributions within a cluster collaborate with each other, thereby enhancing model generalization and reducing overfitting. In the simulation experiments, under the same dataset, the FedCOA algorithm achieves an approximately 10% improvement in accuracy compared to the FedAvg algorithm, and 2% improvement compared to the CFL algorithm. Additionally, this paper conducts comparative experiments under different degrees of Non-Independent and Identically Distributed data distribution. the experimental results demonstrate that FedCOA can enhance model accuracy while effectively addressing issues such as the decrease in model accuracy and the inability of the model to converge caused by Non-Independent and Identically Distributed data distribution. © 2024 SPIE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Incremental learning for Remote Sensing Image Detection Based on YOLOv5 4

Incremental Learning for Remote Sensing Image Detection Base...

引用

4th international conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2024

作者： Xue, Jiefeng Qiu, Yijuan Jiang, Ping Zhang, Gang National Laboratory on Adaptive Optics Sichuan Chengdu610209 China Institute of Optics and Electronics Chinese Academy of Sciences Sichuan Chengdu610209 China University of Chinese Academy of Sciences Sichuan Beijing101408 China

ISBN: (纸本)9798350378542

Deep learning techniques have been widely applied to remote sensing image detection tasks. this paper proposes an improved method based on YOLOv5, named CI-YOLOv5 (Class Increment YOLOv5), which aims to achieve incremental learning for remote sensing images. CI-YOLOv5 incorporates the Spatial Pyramid Pooling-Fast (SPPF) and the Scale-agnostic Channel Attention Module (SimAM) to enhance its ability to extract aircraft-related features. During the incremental learning phase, the model is adjusted using a knowledge distillation-based approach and loss function optimization to learn the features of new category samples. this approach effectively addresses the issue of catastrophic forgetting, ensuring that the model maintains accurate retention of previously acquired knowledge when exposed to new data. © 2024 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Intelligent steady-state operation dispatching strategy for power grid based on deep reinforcement learning 4

Intelligent steady-state operation dispatching strategy for ...

引用

4th international conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2024

作者： Shi, Xincong Guo, Chuangxin Wang, Weiru Zhao, Jin Zhejiang University College of Electrical Engineering Zhejiang Province Hangzhou City China State Grid Shanxi Electric Power Research Institute Shanxi Province Taiyuan City China

ISBN: (纸本)9798350356472

With the construction of new-type power systems, the proportion of renewable energy access continues to increase, and the uncertainty of the power grid increases. the current power grid dispatch plan based on physical models has slow and time-consuming optimization decision-making calculations. To this end, this paper proposes an intelligent decision-making method for power grid forward-looking dispatch based on deep reinforcement learning. the offline training module utilizes grid data and historical scenarios to evolve dispatch strategies, while the online module implements these strategies based on real-time data, ensuring compliance with operational and safety standards. the adoption of an Actor-Critic framework, particularly the Deep Deterministic Policy Gradient algorithm, demonstrates significant improvements in computational efficiency and decision-making speed compared to traditional optimization methods. the case study verifies the effectiveness of the proposed algorithm and technology based on the IEEE 30-bus test system. © 2024 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Predictive Modeling of Students' Stress Levels Using Machine learning Algorithm 4

Predictive Modeling of Students' Stress Levels Using Machine...

引用

4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Ali, Hassan Amin, M. Hamza Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9798350353266

the increasing prevalence of stress among university students has raised concern about its impact on academic performance and overall well-being. this conference paper explores the applications of machine learning algorithms to predict and analyze the student's stress level. A comprehensive dataset encompassing various stress-related factors, including academic workload, social interactions, and lifestyle, was collected from Kaggle which consists of 1100 instances and 19 attributes. Our methodology places a premium on human connection, integration of qualitative data to refine the accuracy of stress predictions. By seamlessly merging the capability of machine learning with an empathetic understanding of the human experience, our approach strives to pave the way for a more holistic and personalized educational ecosystem. the insights derived from this study aspire to guide educators, administrators, and policy makers in crafting nurturing environments that empower students to excel both academically and emotionally. Embarking on a journey to unravel the myriad factors influencing students' stress, we consider academic pressures, social dynamics, and personal experiences. Harnessing machine learning algorithms such as decision trees, neural networks, we navigate expansive student populations. Acknowledging the nuanced nature of stress, our study integrates a compassionate approach with cutting-edge technology. the study employs state-of-the-art machine learning techniques using various classification algorithms, to build predictive models for assessing stress levels out of which random forest gives 97.45% accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

An Efficient Deep learning Technique for Emotion Detection Using EEG Signal 4

An Efficient Deep Learning Technique for Emotion Detection U...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Vishwakarma, Aishwarya Sakalle, Vikas Lnct University Cse Department Bhopal India

ISBN: (纸本)9798350387490

EEG based emotion detection has been widely focused and it has applications in health care, HCI, and affective computing. In this paper, a new approach for detecting the emotion from the EEG signal is proposed by using Long Short-Term Memory (LSTM) networks. the proposed method is suitable for EEG data given temporal differences since it plays an important role when measuring emotional states. the first step is noise and artifacts reduction, the second step is extraction of features from time and frequency domain. All these constitute as a data input into an LSTM network, used for learning and outlining temporal characteristics inherent in the EEG sequences. and the performance of our approach is assessed on a standard EEG dataset with emotional annotations. the proposed LSTM-based model outperforms conventional ML methods and other deep learning models in terms of accuracy and their resistance to distortions. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Reducing Bias in Deep learning optimization: the RSGDM Approach 4

Reducing Bias in Deep Learning Optimization: The RSGDM Appro...

引用

4th international conference on Computer Science and Blockchain, CCSB 2024

作者： Qin, Honglin Zheng, Hongye Wang, Bingxing Wu, Zhizhong Liu, Bingyao Yang, Yuanfang Stevens Institute of Technology Hoboken United States Chinese University of Hong Kong Hong Kong Hong Kong Illinois Institute of Technology Chicago United States University of California Berkeley Berkeley United States University of California Irvine Irvine United States Southern Methodist University Dallas United States

ISBN: (纸本)9798350356502

Currently, widely used first-order deep learning optimizers include non-adaptive learning rate optimizers and adaptive learning rate optimizers. the former is represented by SGDM (Stochastic Gradient Descent with Momentum), while the latter is represented by Adam. Both of these methods use exponential moving averages to estimate the overall gradient. However, estimating the overall gradient using exponential moving averages is biased and has a lag. this paper proposes an RSGDM algorithm based on differential correction. Our contributions are mainly threefold: 1) Analyze the bias and lag brought by the exponential moving average in the SGDM algorithm. 2) Use the differential estimation term to correct the bias and lag in the SGDM algorithm, proposing the RSGDM algorithm. 3) Experiments on the CIFAR datasets have proven that our RSGDM algorithm is superior to the SGDM algorithm in terms of convergence accuracy. © 2024 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Short-Term Wind Power Forecasting Based on VMD-QPSO-LSTM 4

Short-Term Wind Power Forecasting Based on VMD-QPSO-LSTM

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Xin, Peiqi Wang, Hongtao Wang, Haobo Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China

ISBN: (纸本)9798350359589

the unpredictable and highly variable nature of wind power generation demands advanced predictive models to enhance the operational stability of power systems under non-stationary wind conditions. this study presents an innovative approach for short-term wind power prediction, ingeniously integrating Variational Mode Decomposition (VMD), Quantum Particle Swarm optimization (QPSO), and Long Short-Term Memory (LSTM) network into a cohesive forecasting methodology. Initially, VMD is applied to deconstruct wind power data into distinct sub-modes, thus reducing data complexity and facilitating more accurate analysis. the LSTM model is then fine-tuned using QPSO to refine its hyperparameters, thereby optimizing forecast performance. Each sub-mode is forecasted through a specialized LSTM model, and the collective forecasts are compounded to form the final prediction. Comparative analyses with conventional LSTM, PSO-enhanced LSTM, and QPSO-LSTM models reveal that our proposed VMD-QPSO-LSTM approach yields significantly higher accuracy in forecasting, highlighting its potential to efficiently navigate the uncertainties associated with wind power generation. © 2024 IEEE.

关键词： deep learning long short-term memory network variational mode decomposition wind power forecasting

来源：评论

学校读者我要写书评

暂无评论

Interactive Hyperparameter optimization in Multi-Objective Problems via Preference learning 38

Interactive Hyperparameter Optimization in Multi-Objective P...

引用

38th AAAI conference on Artificial Intelligence (AAAI) / 36th conference on Innovative applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Giovanelli, Joseph Tornede, Alexander Tornede, Tanja Lindauer, Marius Univ Bologna Alma Mater Studiorum Bologna Italy Leibniz Univ Hannover Inst Artificial Intelligence Res Ctr Hannover Germany

ISBN: (纸本)1577358872

Hyperparameter optimization (HPO) is important to leverage the full potential of machine learning (ML). In practice, users are often interested in multi-objective (MO) problems, i.e., optimizing potentially conflicting objectives, like accuracy and energy consumption. To tackle this, the vast majority of MO-ML algorithms return a Pareto front of non-dominated machine learning models to the user. Optimizing the hyperparameters of such algorithms is non-trivial as evaluating a hyperparameter configuration entails evaluating the quality of the resulting Pareto front. In literature, there are known indicators that assess the quality of a Pareto front (e.g., hypervolume, R2) by quantifying different properties (e.g., volume, proximity to a reference point). However, choosing the indicator that leads to the desired Pareto front might be a hard task for a user. In this paper, we propose a human-centered interactive HPO approach tailored towards multi-objective ML leveraging preference learning to extract desiderata from users that guide the optimization. Instead of relying on the user guessing the most suitable indicator for their needs, our approach automatically learns an appropriate indicator. Concretely, we leverage pairwise comparisons of distinct Pareto fronts to learn such an appropriate quality indicator. then, we optimize the hyperparameters of the underlying MO-ML algorithm towards this learned indicator using a state-of-the-art HPO approach. In an experimental study targeting the environmental impact of ML, we demonstrate that our approach leads to substantially better Pareto fronts compared to optimizing based on a wrong indicator pre-selected by the user, and performs comparable in the case of an advanced user knowing which indicator to pick.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

the Application of Imaging Methods and Machine learning in the Agroindustry Sector at Production Activity 4th

The Application of Imaging Methods and Machine Learning in t...

引用

4th international conference on Advanced Research in Technologies Information Innovation and Sustainability-ARTIIS-Annual

作者： Vardasca, Ricardo Pratas, Antonio Tereso, Marco Bento, Fernando ISLA Santarem Santarem Portugal Univ Porto LAETA INEGI Porto Portugal Univ Evora VISTA Lab ALGORITMI Evora Portugal ISCTE IUL ISTAR Lisbon Portugal

ISBN: (纸本)9783031832093;9783031832109

the agricultural production sector is of great interest to national economies, their GDP and the population's food chain. During digitalization and modernization, imaging techniques and machine learning are important driving factors. In this research, a narrative critical literature review was conducted on the most used imaging modalities and their use with machine learning methods, which are examined along with their applications in the agricultural industry in production activities, as well as their limitations and existing challenges. this research is intended to support a further development of a technological framework for intelligent precision agroindustry production. It was found that the most used imaging methods are hyperspectral and multispectral imaging, infrared thermal imaging, magnetic resonance imaging, X-ray imaging, scanning electron microscopy and ultraviolet imaging. the majority of employments of machine learning along with imaging was using supervised learning algorithms, there were a few applications using unsupervised and reinforcement learning algorithms. From the results and analysis, it can be concluded that the use of imaging techniques enables an increase in quality and profit maximization of agro-industrial products and their characterization data, which can be better analyzed with the help of machine learning methods and lead to a more sustainable and innovative agro-industrial productive sector.

关键词： Agroindustry Imaging Machine learning Production activity

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 << < 75 76 77 78 79 80 81 82 83 84 > >>

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：