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

27,372 篇 会议
3,821 篇 期刊文献
1 篇 资讯

馆藏范围

31,194 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

7,401 篇 工学
- 4,761 篇 电气工程
- 3,940 篇 计算机科学与技术...
- 2,284 篇 信息与通信工程
- 1,377 篇 控制科学与工程
- 700 篇 软件工程
- 529 篇 仪器科学与技术
- 460 篇 交通运输工程
- 410 篇 机械工程
- 209 篇 电子科学与技术（可...
- 176 篇 生物医学工程（可授...
- 150 篇 航空宇航科学与技...
- 133 篇 土木工程
- 109 篇 测绘科学与技术
- 84 篇 石油与天然气工程
- 60 篇 生物工程
- 59 篇 网络空间安全
- 54 篇 动力工程及工程热...
- 43 篇 材料科学与工程（可...
752 篇 理学
- 217 篇 系统科学
- 206 篇 物理学
- 136 篇 数学
- 134 篇 生物学
- 76 篇 地球物理学
- 57 篇 统计学（可授理学、...
535 篇 管理学
- 513 篇 管理科学与工程(可...
- 32 篇 图书情报与档案管...
334 篇 医学
- 251 篇 临床医学
- 69 篇 基础医学(可授医学...
60 篇 农学
- 45 篇 作物学
49 篇 文学
- 48 篇 新闻传播学
19 篇 教育学
11 篇 经济学
10 篇 艺术学
7 篇 法学
2 篇 哲学
2 篇 军事学
1 篇 历史学

主题

31,194 篇 prediction algor...
11,106 篇 predictive model...
4,930 篇 accuracy
4,221 篇 machine learning...
3,997 篇 training
3,041 篇 data models
2,747 篇 heuristic algori...
2,730 篇 feature extracti...
2,704 篇 classification a...
2,534 篇 computational mo...
2,297 篇 support vector m...
2,291 篇 optimization
2,193 篇 machine learning
1,868 篇 algorithm design...
1,703 篇 neural networks
1,681 篇 real-time system...
1,512 篇 deep learning
1,339 篇 clustering algor...
1,166 篇 data mining
1,082 篇 analytical model...

机构

73 篇 chitkara univers...
33 篇 lovely professio...
31 篇 tsinghua univers...
30 篇 school of automa...
29 篇 state key labora...
26 篇 department of co...
25 篇 faculty of infor...
23 篇 univ chinese aca...
22 篇 chitkara centre ...
21 篇 university of sc...
21 篇 centre of resear...
19 篇 shanghai jiao to...
19 篇 school of comput...
18 篇 hong kong polyte...
18 篇 college of infor...
17 篇 china electric p...
17 篇 school of comput...
17 篇 school of comput...
16 篇 computer science...
16 篇 nanyang technol ...

作者

35 篇 lei wang
28 篇 yu wang
27 篇 wei liu
25 篇 jun wang
25 篇 wei li
24 篇 yang yang
22 篇 jian wang
22 篇 jun zhang
21 篇 xi chen
20 篇 li li
18 篇 ralph kennel
17 篇 ye yuan
17 篇 ashwini kodipall...
17 篇 jie li
16 篇 chao wang
16 篇 xiang li
15 篇 pengyu liu
15 篇 liu yang
15 篇 li zhang
14 篇 isman kurniawan

语言

31,068 篇 英文
61 篇 其他
47 篇 中文
1 篇 西班牙文

检索条件"主题词=prediction algorithms"

共 31194 条记录，以下是621-630 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：

Heart Disease prediction using Hybrid ML algorithms

Heart Disease Prediction using Hybrid ML Algorithms

引用

Sustainable Computing and Data Communication Systems (ICSCDS), International Conference on

作者： Suhitha Katari Thanguturu Likith Muthineni Phani Sai Sree Venubabu Rachapudi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

Nowadays, digitalization in the healthcare organizations places great emphasis on technological advances in clinical healthcare providers. Traditional methods for measuring and evaluating outcomes for patients in forecasting and diagnosing chronic diseases are being substituted by techniques that capture the most significant insights from medical information by combining predictive modeling with a highly valuable application of machine learning. Heart disease is nowadays among the worst disorders in the world. Because the death rate from heart disease remained largely significant, more intensive efforts in preventive are required, such as enhancing the accuracy of a heart disease prediction system. Additionally, an early diagnosis supports in the appropriate diagnosis of the condition as well as the management of its symptoms. By creating forecasting analytics, Machine Learning (ML) techniques can be used to anticipate chronic diseases including kidneys and cardiac disorders. Hence in this approach, coronary heart disease prediction and classification using Hybrid Machine Learning algorithms is presented. In this approach the combination of Decision Tree (DT) and Ada Boosting algorithms is used as a hybrid ML algorithm to predict the CHD. This method’s performance is determined by the amount of accuracy, True Positive Rate (TPR)and precision.

关键词： Heart Training Machine learning algorithms Medical services prediction algorithms Boosting Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

An Integrated Approach of MCDM Methods and Machine Learning algorithms for Employees' Churn prediction

An Integrated Approach of MCDM Methods and Machine Learning ...

引用

International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Shefayatuj Johara Chowdhury Mainul Islam Mahi Sadiqul Alam Saimon Aynur Nahar Urme Rashidul Hasan Nabil Department of CSE International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Department of EEE International Islamic University Chittagong Chittagong Bangladesh

Employee churn is a notable nuisance for organizations to maintain a cost-effective position and brand strategy. This research has proposed an integrated approach to overcome this issue systematically. All employees in this study were divided into three categories using a combination of machine learning algorithms and Multi-Criteria Decision Making (MCDM) techniques. The techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Analytical Hierarchy Process (AHP) have been consolidated. The dataset's feature importance was retrieved, and AHP was used to determine the criteria that caused the most turnover. TOPSIS was given the derived criterion weights from AHP to rank every personnel according to their propensity to depart the organization. To estimate staff turnover, seven machine learning algorithms were applied. After comparing the results, the Random Forest algorithm produces the best accuracy for assessing employee churn.

关键词： Industries Machine learning algorithms Signal processing algorithms Companies Signal processing prediction algorithms Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Analysis of Diabetic prediction Using Machine Learning algorithms on BRFSS Dataset

Analysis of Diabetic Prediction Using Machine Learning Algor...

引用

International Conference on Trends in Electronics and Informatics (ICEI)

作者： Lakshmi H.N. A. Srinivasa Reddy Kritika Naidu Department of ET CVR College of Engineering Hyderabad India Department of CSIT CVR College of Engineering Hyderabad India

Due to the detrimental effects it has on everyone's health, diabetes is a chronic condition that still poses a serious threat to the global population. It is a metabolic disorder that increases blood sugar levels and increasing the risk of heart disease, kidney failure, stroke, issues with the nerves and heart, among other issues. Over the years, several scholars have sought to create reliable diabetes prediction models. Due to a lack of adequate data sets and prediction techniques, this discipline still faces many unsolved research issues, which forces researchers to apply big data analytics and ML-based methodology. The paper investigates healthcare prediction analytics and addresses the issues using four different machine learning methods. This study has utilized the Early detection and Binary 012 databases. Based on these datasets, the precision, recall, and accuracy of KNNs and Random Forest methods are calculated. The study's findings may be valuable to health professionals, stakeholders, students, and researchers engaged in diabetes prediction research and development because SVM performs better than KNN and Logistic Regression.

关键词： Support vector machines Heart Machine learning algorithms Sociology Predictive models prediction algorithms Diabetes

来源：评论

学校读者我要写书评

暂无评论

Different algorithms for Lung Cancer Detection and prediction

Different Algorithms for Lung Cancer Detection and Predictio...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： N. Sudhir Reddy V Khanaa Bharath Institute of Higher Education and Research (BIHER) Chennai Tamil Nadu Department of Information Technology Bharath Institute of Higher Education and Research (BIHER) Chennai Tamil Nadu

In many cases we see the lung cancer play main role for human death. The premature detection of cancer is hampered by the overlapping of malignant cells. To create more effective methods for its prevention, identifying genetic and environmental causes is essential and time is a crucial aspect in identifying anomalies in target photos. The decision tree algorithm is utilized in this study to predict lung cancer, and the key patterns are examined along with their related weights and scores. The Histogram Equalization is make use of for tasks like feature extraction and image preprocessing and a neural network use different classifier that used to determine if the patient is normal or abnormal. Early detection of lung cancer will limit many treatment options, improve survival rates and lower the likelihood of invasive surgery. Therefore, a simple, affordable, and time-saving technology will yield encouraging results for lung cancer screening and prediction.

关键词： Neural networks Lung cancer Surgery prediction algorithms Classification algorithms Internet Decision trees

来源：评论

学校读者我要写书评

暂无评论

Improving Accuracy of Heart Disease prediction through Machine Learning algorithms

Improving Accuracy of Heart Disease Prediction through Machi...

引用

Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： Mohammed Ali Shaik Radhandi Sreeja Safa Zainab Panthangi Shiva Sowmya Thipparthy Akshay Sudireddy Sindhu School of Computer Science &Artificial Intelligence SR University Warangal Telangana State India

One of the main causes of death in the world is heart disease. Early heart disease detection and treatment can lower mortality rates and enhance quality of life which is the major challenge. “Machine learning” algorithms can accurately predict the likelihood of getting the heart disease by using data like: “age, gender, lifestyle factors, medical history, and laboratory testing”. Building a ML model for “heart disease prediction” which is merely relies on the various relevant factors is the primary goal of this paper. For this research project, we used 4 different datasets which comprises of distinct factors that are relevant to heart disease. The model building is made through ML algorithms: “Random forest, K-nearest neighbour, logistic regression, and decision tree”. The study demonstrates that, when compared to other ML techniques, logistic regression and KNN provide better prediction accuracy in a shorter amount of time.

关键词： Heart Training Machine learning algorithms Soft sensors Buildings prediction algorithms Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Vehicle Efficiency prediction using Machine Learning algorithms

Vehicle Efficiency Prediction using Machine Learning Algorit...

引用

Smart Data Intelligence (ICSMDI), International Conference on

作者： Parvathi R Ashish Choudhary Pulak Jain Om Kajave School Of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai Tamil Nadu India

The performance analysis and efficiency of a vehicle play a prominent role and a very necessary step to do in today's scenario. There are various instances when the user feels reluctant to discard the vehicle. In such cases where the user is ignorant of the fact to discard the car, the concerned authorities must come forward to check whether the user is using the car beyond the limit. Therefore, there is an increasing need to save the environment and nature to live a sustainable life. The performance analysis of the car is based on the engine type, number of engine cylinders, fuel type, etc. This study predicts the mpg value by using machine learning models like Random Forest (RF), K-Nearest Neighbors (KNN), XG-Boost, Ridge Regression, Lasso Regression, etc. and based on that it is compared with the optimum value of mpg and hence one can reach to a decision to discard the vehicle.

关键词： Training Machine learning algorithms Predictive models prediction algorithms Cleaning Performance analysis Automobiles

来源：评论

学校读者我要写书评

暂无评论

Customer Churn prediction Based on Interpretable Machine Learning algorithms in Telecom Industry

Customer Churn Prediction Based on Interpretable Machine Lea...

引用

Computer Simulation and Modeling, Information Security (CSMIS), International Conference on

作者： Liwen Ou College of Arts and Science New York University New York United States of America

ISBN: (数字)9798350343724

ISBN: (纸本)9798350343731

Research regarding the interpretability and feature importance of customer churn prediction is rare compared to study on its performance. Nevertheless, knowing what type of customer information are the most significant for predicting churn is critical for firms to layout cost-efficient investment strategies. In this work, feature importance for churn prediction in telecom industry is investigated using a dataset with nearly 40 customer features, and 3 interpretable, tree-based machine learning models. In particular, the data is trained in random forest, decision tree and extra tree classifier algorithms, and uses their built-in feature importance functions to interpret the significance of each feature. The feature importance scores from all three models are compared to analyze which features are the most significant overall. As the result, all models yield optimal prediction accuracy, with decision tree model's slightly lower than the others. The resulting top features from all three models have large overlaps, indicating all models are suitable for the purpose of this study. Random forest and extra tree classifier, as the more complicated models, produce the most satisfactory results, which demonstrate that features related to customer's tenure, consumption in the firm and referral amount are the most important to make accurate churn prediction. This analysis result is reasonable since all these features imply customers' personal preference towards the company.

关键词： Industries Machine learning algorithms Layout Companies Predictive models prediction algorithms Telecommunications

来源：评论

学校读者我要写书评

暂无评论

Diabetes prediction in Teenagers using Machine Learning algorithms

Diabetes Prediction in Teenagers using Machine Learning Algo...

引用

International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Jayavrinda Vrindavanam Raye Haarika Sindhu MG Kilari Sumanth Kumar Department of Computer Science & Engineering (AI & ML) School of Engineering Dayananda Sagar University Bengaluru India

Diabetes Mellitus (DM) is a hazardous condition that can lead to worldwide health problems owing to an increase in blood glucose levels, age, lack of movement, high blood pressure, poor nutrition, and other factors. The aim of this study is to develop a system that can predict diabetes in individuals aged 10 to 30 by merging the results of different machine-learning algorithms. Machine learning is used for increasing performance and forecasting accuracy. Some of the methods used to detect diabetes early include Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbours (KNN), and XGBoost. In comparison to the other methods, the random forest and XGBoost Classifiers algorithm outperformed all other ML algorithms, with a maximum accuracy of 86

关键词： Support vector machines Radio frequency Machine learning algorithms Computational modeling Forestry prediction algorithms Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Automatic prediction of MOSFETs threshold voltage by machine learning algorithms

Automatic prediction of MOSFETs threshold voltage by machine...

引用

IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Seoyeon Choi Dong Geun Park Min Jung Kim Seain Bang Jungchun Kim Seunghee Jin Ki Seok Huh Donghyun Kim Sanghyeok Kim Inkyu Yoon Jerome Mitard Cheol E. Han Jae Woo Lee Department of Electronics and Information Engineering Korea University Sejong-ro Sejong Republic of Korea imec Leuven Belgium

Automatic V th extraction with ML is proposed. Various V th extraction methods have been suggested for different types of FETs. It takes lots of time and effort to find an appropriate extraction method for each device, and V th can be differently estimated depending on the insight of researcher. These problems are solved through an objective evaluation method using ML. The final model is represented by kNN regression with normalized both V G and I D .

关键词： Performance evaluation Semiconductor device modeling Training Machine learning algorithms Materials reliability Predictive models prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

A Comparison of Supervised Learning algorithms to prediction Heart Disease

A Comparison of Supervised Learning Algorithms to Prediction...

引用

Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), International Conference on

作者： Kuchlpudi Prasanth Kumar Valaparla Rohini Jyothi Yadla Jonnalagadda VNRaju Department of IT Dhanekula Institute of Engineering & Technology Ganguru Vijayawada Department of CSE Sir C R Reddy College of Engineering

Heart disease problems are rapidly increasing day to day. Humanslose their lives at an early stage. Consequently, themain purpose of this projectis to employ supervised machine learning methods for heart disease early prediction. For the prediction and diagnosis of cardiac diseases, different techniques are used like data mining and machine learning. This would be tremendously useful to human life since, owing to a lack of cardiovascular competency and quick development in improperly diagnosed instances, heart diseases in people might develop at an early stage. As a result, developing robust and effective early-stage cardiac illness prediction by using analytical decision-making and digital patient data might alleviate this problem. To predict heart diseases, numerous supervised machine-learning techniques were used to learn about the illness, and their efficiency and accuracy were evaluated. This study used a Kaggle dataset on heart disease and found that three classification methods-, K-Nearest Neighbor (KNN), Support Vector Classifier, and Multi-Layer Perception (neural network) could accurately classify heart disease. TheKNN is given 91.8% accuracy. As a result, we discovered that KNN results can more accurately forecast the chance of patients developing heart disease.

关键词： Heart Training Machine learning algorithms Neural networks Supervised learning Support vector machine classification prediction algorithms

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 << < 59 60 61 62 63 64 65 66 67 68 > >>

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：