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

203 篇 会议
10 篇 期刊文献
2 册 图书

馆藏范围

215 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

68 篇 工学
- 56 篇 计算机科学与技术...
- 36 篇 软件工程
- 14 篇 信息与通信工程
- 12 篇 生物医学工程（可授...
- 11 篇 生物工程
- 10 篇 控制科学与工程
- 7 篇 电气工程
- 4 篇 材料科学与工程（可...
- 4 篇 建筑学
- 3 篇 电子科学与技术（可...
- 3 篇 化学工程与技术
- 3 篇 交通运输工程
- 3 篇 安全科学与工程
- 2 篇 光学工程
- 2 篇 仪器科学与技术
32 篇 理学
- 17 篇 数学
- 14 篇 生物学
- 9 篇 统计学（可授理学、...
- 5 篇 化学
- 3 篇 物理学
- 3 篇 系统科学
- 2 篇 大气科学
- 2 篇 地球物理学
14 篇 医学
- 11 篇 基础医学(可授医学...
- 9 篇 临床医学
- 5 篇 公共卫生与预防医...
- 4 篇 药学(可授医学、理...
10 篇 管理学
- 7 篇 图书情报与档案管...
- 4 篇 管理科学与工程(可...
5 篇 法学
- 4 篇 社会学
2 篇 农学
1 篇 经济学
1 篇 军事学

主题

25 篇 machine learning
8 篇 deep learning
8 篇 machine learning...
5 篇 computational mo...
5 篇 asia
5 篇 sentiment analys...
4 篇 training
3 篇 support vector m...
3 篇 classification a...
2 篇 internet of thin...
2 篇 lstm
2 篇 three-dimensiona...
2 篇 computational in...
2 篇 learning algorit...
2 篇 convergence
2 篇 optimization
2 篇 diagnosis
2 篇 lithography
2 篇 security
2 篇 organizations

机构

6 篇 asia pacific uni...
3 篇 asia pacific uni...
3 篇 microsoft res as...
3 篇 asia univ dept c...
3 篇 univ petr & ener...
2 篇 asia pacific uni...
2 篇 school of comput...
2 篇 microsoft resear...
2 篇 beijing engineer...
2 篇 department of co...
2 篇 university of as...
2 篇 computer science...
2 篇 asia pacific ins...
2 篇 intel china rese...
2 篇 emory univ atlan...
2 篇 school of comput...
2 篇 lebanese america...
1 篇 herzzentrum leip...
1 篇 shenzhen univ ge...
1 篇 微软亚洲研究院自...

作者

3 篇 kumar neeraj
2 篇 logeswaran rajas...
2 篇 rana muhammad eh...
2 篇 di martino benia...
2 篇 kumarawadu priya...
2 篇 david leo gertru...
2 篇 hsu ching-hsien
2 篇 hameed vazeerude...
2 篇 esposito antonio
2 篇 mafas raheem
2 篇 juwono filbert h...
2 篇 gupta brij b.
2 篇 nuwara yohanes
1 篇 raihan khan
1 篇 ren shu-hang
1 篇 rizve ahmed
1 篇 rafsanjani marja...
1 篇 jiang si-rui
1 篇 dong xiaoning
1 篇 lukose jacquelin...

语言

214 篇 英文
1 篇 中文

检索条件"任意字段=2022 Asia Conference on Algorithms, Computing and Machine Learning, CACML 2022"

共 215 条记录，以下是11-20 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：

Logit Adjustment for Imbalanced Cognitive Diagnosis

Logit Adjustment for Imbalanced Cognitive Diagnosis

引用

algorithms, computing and machine learning (cacml), asia conference on

作者： Zhengtao Tan Jimin Bao Zhuangzhuang He Dacao Zhang Key Laboratory of Knowledge Engineering With Big Data Hefei University of Technology Hefei China

ISBN: (数字)9798331543143

ISBN: (纸本)9798331543150

Cognitive diagnosis is a critical task in intelligent education, aimed at inferring students’ mastery of knowledge concepts based on their response logs. Although existing cognitive diagnosis models achieve excellent performance, they underestimate the difficulty of easy exercises and overestimate the difficulty of hard exercises. We attribute this to the class imbalance in the response logs of easy and hard exercises. Moreover, the convergence speed varies from exercise to exercise during model training, which further challenges generalization. To address these problems, we propose an exercise’s correct rate-based logit adjustment approach for a wide range of cognitive diagnosis models. Specifically, we enforce logit adjustment in the loss during training to overcome the class imbalance in response logs. Then, we apply group distributionally robust optimization for generalization. Finally, extensive experiments demonstrate the effectiveness of our model, especially on easy and hard exercises.

关键词： Training machine learning algorithms Accuracy Computational modeling asia machine learning Optimization Convergence

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of machine learning Classification algorithms in the Case of Heart Failure Prediction 18

Performance Analysis of Machine Learning Classification Algo...

引用

18th IEEE International Wireless Communications and Mobile computing conference (IWCMC)

作者： De Silva, Chameera Kumarawadu, Priyantha Asia Pacific Inst Informat Technol APIIT Sch Comp Colombo Sri Lanka

ISBN: (纸本)9781665467490

machine learning is one of the most rapidly developing technologies, and it is currently being utilized in every kind of application. The Healthcare industry has been benefiting exponentially from this emerging technology. Among the number of advantages heart failure prediction is one of them. Almost 17.9 million people die from heart disease every year. This research predicts heart failure with seven machine learning classification algorithms by the factors or features of health conditions such as age, resting blood pressure, chest pain type, cholesterol, fasting blood sugar, resting electro diagram, maximum heart rate, etc. Then, it is accompanied by a comparative performance analysis of these algorithms. The research shows that Naive-bias, Random Forest, and Support Vector machines are outperformed to predict heart failure. The accuracy of these algorithms is almost 85-86%.

关键词： machine learning Heart Diseases Prediction Classification algorithms Supervised learning

来源：评论

学校读者我要写书评

暂无评论

Application of machine learning in Enterprise Financial Risk Assessment: A Study About China's A-Share Listed Manufacturing Companies 5th

Application of Machine Learning in Enterprise Financial Risk...

引用

5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Yul, Kexin Yul, Zengyi Ma, Shuomin Xu, Pan Zhejiang Univ Technol Hangzhou 310023 Peoples R China SpringAI LLC Bellevue WA 98007 USA

ISBN: (纸本)9789819770038;9789819770045

Based on the financial data of domestic A-share listed firms from 2000 to 2022, this paper aims to explore the effectiveness of machine learning in identifying the financial risk of Chinese A-share listed manufacturing firms. To this end, a variety of machine learning models, including logistic regression, decision tree, random forest, XGBoost, SVM, and LSTM, are used to assess the financial risk of enterprises, and the key attributes of enterprise financial risk are extracted through the interpretability exploration and importance coefficient measurement. From this paper, the following conclusions are drawn: (i) XGBoost model performs the best on all attributes, showing its strong ability in dealing with complex financial datasets, and LSTM, which adds time-series factors, performs poorly, which is speculated that it may be related to the incompatibility of the characteristics of the financial data, the reliance on the time-series features, and the need for financial fine features. (ii) Audit opinion, Net Profit and ROA are key influencing factors.

关键词： Financial risk machine learning XGBoost LSTM

来源：评论

学校读者我要写书评

暂无评论

A Survey on Deep learning-Based Medical Image Registration 5th

A Survey on Deep Learning-Based Medical Image Registration

引用

5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Xu, Ronghao Liu, Chongxin Liu, Shuaitong Huang, Weijie Zhang, Menghua Univ Jinan Sch Elect Engn Jinan 250000 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

In recent years, various methods have been proposed to address the fundamental task of medical image registration in medical image analysis. This paper systematically reviews the research progress in medical image registration using deep learning techniques. These methods utilize deep learning algorithms to achieve precise alignment of medical image data from different modalities, time points, or sources, thereby providing dependable help for clinical identification and care. This paper seeks to provide researchers and practitioners with the most current technological advances and potential applications in the realm of medical image registration.

关键词： Image registration Deep learning CNN Supervised learning

来源：评论

学校读者我要写书评

暂无评论

Human Trafficking Through Data Sharing and Analytics

Human Trafficking Through Data Sharing and Analytics

引用

2022 IEEE International conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Xian, Lam Ying Logeswaran, Rajasvaran Asia Pacific University of Technology & Innovation Kuala Lumpur Malaysia

ISBN: (纸本)9781665483162

Human trafficking remains a defining issue in the 21st century. Analytics in human trafficking is explored to examine the existing methods used to combat the problem. A systematic approach of reviewing the related works is adopted, investigating each element within the definition of human trafficking, analytics techniques adopted by previous studies and the analysis of the related works. The analytics techniques were categorized into machine learning and statistical analysis. Significant findings in this paper include: (1) Most of the previous studies explored sexual exploitation using machine learning and statistical analysis;(2) In machine learning, classification algorithms were widely used;(3) In statistical analysis, data sampling methods were commonly applied;and (4) No machine learning method was found to detect forced labor, which remains an area that needs to be further explored. © 2022 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

12th International Advanced computing conference, IACC 2022

12th International Advanced Computing Conference, IACC 2022

引用

12th International Advanced computing conference, IACC 2022

ISBN: (纸本)9783031356407

The proceedings contain 78 papers. The special focus in this conference is on Advanced computing. The topics include: ANN Model to Predict Religiosity Based on Social Online Behaviors and Personality Traits;concrete Crack Detection Using Deep Convolutional Generative Adversarial Network;chlorophyll Prediction System with machine learning algorithms in Lake Titicaca (Peruvian Sector);the Perceived Impact of Correlative Relationship between Depression, Anxiety, and Stress among University Students;machine learning Approach for Predicting the Maladies of Humans;Forecasting Futuristic COVID-19 Trend Using machine learning Models;machine learning Techniques in Cardiovascular Disease Prediction;comparative Analysis of Various machine learning algorithms for Detection of Malware and Benign;XACML: Explainable Arrhythmia Classification Model Using machine learning;intelligent Analytical Randomization of Clinical Trials;underwater Image Denoising and 3D Modelling of Poompuhar Site;optimizing Beat Management System Using Soft Biometrics;towards Better Gait Predictions: Sensor-Based Detection of Flexion and Extension of Human Lower Limb Joints During Walking;a Comparative Study of Stroke Prediction algorithms Using machine learning;intelligent Crop Recommendation System Using machine learning algorithms;plant Disease Detection Using Multispectral Imaging;Whale Optimization Based Approach to Compress and Fasten CNN for Crop Disease and Species Identification;Few-Shot learning for Plant Disease Classification Using ILP;deep learning-Based Multiclass Classification of Cotton Leaf Images Using ResNet and Transfer learning;identification of Potato Leaf Diseases Using Hybrid Convolution Neural Network with Support Vector machine;a Substantial Deep learning Approach for Classification of Local and Coastal Fish;formulating Mechanisms for Reduction in Greenhouse Gas Emissions at Ghatkesar;intelligent & Smart Navigation System for Visually Impaired Friends.

关键词：

来源：评论

学校读者我要写书评

暂无评论

12th International Advanced computing conference, IACC 2022

12th International Advanced Computing Conference, IACC 2022

引用

12th International Advanced computing conference, IACC 2022

ISBN: (纸本)9783031356438

关键词：

来源：评论

学校读者我要写书评

暂无评论

Foreign Currency Exchange Market Prediction using machine learning Techniques

Foreign Currency Exchange Market Prediction using Machine Le...

引用

2022 IEEE International conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Tak, Adriel Li Chi Logeswaran, Rajasvaran Asia Pacific University of Technology and Innovation Technology Park Malaysia

ISBN: (纸本)9781665483162

The importance of predicting foreign currency exchange rate is evident in both academic and business sectors, despite financial time series data being known to be chaotic, noisy and dynamic. Even though this problem has been vastly explored and studied since 1971, to the best of our knowledge, there is yet to be an effective systematic or theoretical framework for approaching the problem. Prior traditional approaches in predicting the future currency rate relied on technical and fundamental analysis, but over recent decades, prolific machine learning models capable of recognizing complex patterns, have been dominating the field with promising accuracy. Furthermore, researchers have demonstrated that hybridizing appropriate machine algorithms can improve the predictive ability, although their sustainability is still questionable. Of late, behavioral economist supposed that finance market is driven by sentiment of the market participants, thus, text mining has further enhanced the potential of machine learning techniques, whereby unstructured data for example news, forums and blogs can be used as features. These evaluations terminate by the place of insight recommendations for outlook research. © 2022 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

An Analysis on Balance Model of Exploration and Exploitation Under Decoupled-learning Pattern for Large-Scale Particle Swarm Optimizers 5th

An Analysis on Balance Model of Exploration and Exploitation...

引用

5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Ni, Wenke Guo, Weian Li, Dongyang Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China Tongji Univ Sino German Coll Appl Sci Shanghai Peoples R China

ISBN: (纸本)9789819770007;9789819770014

To effectively tackle large-scale optimization problems, numerous efforts have been made by various swarm intelligence methodologies to strike a balance between two critical functions: exploration and exploitation. However, in the majority of existing particle swarm optimizers, these two functions often remain indistinguishable throughout the optimization process. To mitigate the mutual interference between exploration and exploitation, designers of optimizers have attempted to design these functions separately. Nonetheless, there has been limited research into analyzing the roles and the relationship between exploration and exploitation. Building upon our previous work, we have redesigned several state-of-the-art large-scale particle swarm optimizers using a decoupled learning pattern. Through comprehensive experimental analysis on the synergistic effect of exploitation and exploration, we delve into the optimization performance and introduce a mathematical model to illustrate their interrelationship. Our investigation and analysis reveal that most existing algorithms exhibit sensitivity in the parameter settings for exploration and exploitation, an aspect that has not been adequately addressed before. Based on the proposed mathematical analysis of the parameters, we provide clear balance model for algorithm designers and users on how to balance these two factors when solving large-scale optimization problems.

关键词： Large scale optimization problems Exploration and exploitation Decoupled learning pattern Balance model

来源：评论

学校读者我要写书评

暂无评论

A Cohesive machine learning-Based Model for Energy Consumption Prediction in Smart Homes

A Cohesive Machine Learning-Based Model for Energy Consumpti...

引用

2022 International conference on Electrical and computing Technologies and Applications, ICECTA 2022

作者： Toshpulotova, Sarvinoz Fayaz, Muhamamd University of Central Asia Department of Computer Science Naryn Kyrgyzstan

ISBN: (数字)9781665456005

ISBN: (纸本)9781665456005

Energy is the most important and costly resource and its plays a vital role in our daily lives. With the passage of time technologies are advancing, hence, the demand for energy uses is also increasing. In this work, a model has been proposed for energy consumption prediction in the smart home. The proposed model consists of four modules, namely data acquisition, data pre-processing, prediction, performance evaluation, and application. The pre-processing module has four sub-modules namely, output rectification, data cleaning, data transformation, and data reduction. The processed data is then fed to the prediction module, and different machine learning algorithms have been applied to the pre-processed data to predict energy consumption in smart homes. Next, the performance of these algorithms has been evaluated in the performance evaluation stage, in this stage different performance metrics have been considered, such as mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) in order to measure the performance of machine learning algorithms on the given data. The results indicate that the random forest algorithm performance is better as compared to other counterpart algorithms on the given data. The trained random forest algorithm is then used in the web-based interface in order to make able a layman to use the system for energy consumption prediction. © 2022 IEEE.

关键词： Energy utilization

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22页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：