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

22,565 篇 会议
3,191 篇 期刊文献
19 篇 学位论文
7 册 图书

馆藏范围

25,781 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

5,124 篇 工学
- 3,076 篇 计算机科学与技术...
- 2,516 篇 电气工程
- 1,375 篇 信息与通信工程
- 688 篇 控制科学与工程
- 594 篇 软件工程
- 283 篇 仪器科学与技术
- 216 篇 环境科学与工程（可...
- 189 篇 机械工程
- 183 篇 生物医学工程（可授...
- 164 篇 交通运输工程
- 149 篇 测绘科学与技术
- 130 篇 生物工程
- 123 篇 石油与天然气工程
- 120 篇 电子科学与技术（可...
- 117 篇 材料科学与工程（可...
- 114 篇 网络空间安全
- 99 篇 土木工程
- 92 篇 动力工程及工程热...
- 66 篇 水利工程
946 篇 理学
- 291 篇 生物学
- 189 篇 物理学
- 154 篇 化学
- 139 篇 地球物理学
- 133 篇 数学
- 84 篇 系统科学
614 篇 管理学
- 524 篇 管理科学与工程(可...
- 72 篇 图书情报与档案管...
439 篇 医学
- 284 篇 临床医学
- 97 篇 基础医学(可授医学...
227 篇 农学
- 148 篇 作物学
54 篇 经济学
53 篇 法学
49 篇 文学
47 篇 教育学
17 篇 艺术学
4 篇 军事学
2 篇 哲学

主题

25,782 篇 machine learning...
8,325 篇 machine learning
4,241 篇 support vector m...
4,153 篇 prediction algor...
4,131 篇 accuracy
3,570 篇 predictive model...
3,453 篇 training
3,194 篇 classification a...
3,150 篇 feature extracti...
1,922 篇 data models
1,790 篇 computational mo...
1,770 篇 deep learning
1,637 篇 random forests
1,463 篇 neural networks
1,386 篇 clustering algor...
1,365 篇 real-time system...
1,270 篇 data mining
1,115 篇 optimization
995 篇 decision trees
967 篇 measurement

机构

120 篇 chitkara univers...
37 篇 lovely professio...
36 篇 chitkara centre ...
30 篇 computer science...
30 篇 centre of resear...
28 篇 department of co...
26 篇 school of comput...
21 篇 univ chinese aca...
21 篇 computer science...
21 篇 school of comput...
20 篇 computer science...
18 篇 school of comput...
17 篇 school of comput...
17 篇 department of co...
16 篇 brac university ...
16 篇 khulna universit...
15 篇 symbiosis instit...
15 篇 department of co...
13 篇 school of comput...
13 篇 computer science...

作者

25 篇 ashwini kodipall...
24 篇 mahendra kumar g...
15 篇 rakesh kumar
13 篇 wei liu
13 篇 atul kumar
12 篇 qiangfu zhao
12 篇 amit kumar
12 篇 surendran r
12 篇 satvik vats
11 篇 jun zhang
11 篇 li li
11 篇 liu yang
11 篇 wei li
10 篇 sudeep tanwar
10 篇 himanshu sharma
10 篇 amiya ranjan pan...
9 篇 lei wang
9 篇 tohari ahmad
9 篇 shanmugasundaram...
9 篇 rishabh sharma

语言

25,573 篇 英文
155 篇 其他
32 篇 中文
5 篇 法文
4 篇 土耳其文
2 篇 西班牙文
2 篇 荷兰文
1 篇 德文

检索条件"主题词=Machine Learning Algorithms"

共 25782 条记录，以下是101-110 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：

Decoding machine learning algorithms for DR Detection

Decoding Machine Learning Algorithms for DR Detection

引用

IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Jagrit Ahuja Varun Sharma Raghavv Gupta Preeti Kapoor Shaveta Arora Bharati Vidyapeeth Institute of Management and Research New Delhi India Department of Computer Science and Engineering The NorthCap University Gurugram India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Diabetic retinopathy is a serious eye disease which can lead to vision defects in diabetic patients. Early detection is important for preventing vision loss. Automating the detection process makes it less laborious and helps in achieving more accurate results. Therefore, this research aims to study various machine learning methods and develop an effective model for diabetic retinopathy detection. The proposed model operates through four stages: pre-processing, feature extraction, feature optimization, and classification. Image processing techniques are thoroughly utilized to enhance the fundus images. The textual and spatial features are then extracted and most relevant features are selected using particle swarm optimization. These features are then fed into machine learning classifiers for disease diagnosis. The model is trained and validated on the MESSIDOR I and MESSIDOR II datasets. The model achieves an accuracy (85.9%), F1 score (0.8585), Sensitivity (0.8049) and Specificity (0.8958) on MESSIDOR II data with Support Vector machine (SVM) classifier. The results obtained show the effectiveness of the work in early detection.

关键词： Support vector machines Deep learning Diabetic retinopathy Accuracy machine learning algorithms Nearest neighbor methods Feature extraction Particle swarm optimization Optimization Vision defects

来源：评论

学校读者我要写书评

暂无评论

Classification of Autism Spectrum Disorder using Supervised machine learning algorithms: A Review

Classification of Autism Spectrum Disorder using Supervised ...

引用

International Conference on ent Computing and Control Systems (ICICCS)

作者： P Yugander M Jagannath School of Electronics Engineering Vellore Institute of Technology Chennai India Department of ECE Kakatiya Institute of Technology and Science Warangal India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects the cognitive, speech, and social communication of individuals. Globally, around 2% of the population suffers from ASD. The problems associated with ASD start in childhood and continue through adulthood. It can be identified using several methods, such as Autism Diagnostic Observation Schedule (ADOS), machine learning, deep learning, and computer-aided design (CAD) tools. Over the past two decades, CAD tools have become more prevalent in diagnosing diseases. The CAD tools identify abnormalities in autistic individuals using machine learning algorithms and magnetic resonance images. This paper aims to present various ASD classification algorithms and abnormalities of ASD controls. Articles are searched in various databases such as IEEE Explore, Springer, ACM Digital Library, PubMed, and Science Direct. A total of 22 complete articles from 2010 to 2024 are included in the review. Various supervised ML algorithms like Logistic regression (LR), Random Forest (RF), Decision tree (DT), K-Nearest Neighbor (KNN), and Support vector machine (SVM) are investigated. This study revealed numerous structural and functional abnormalities of ASD controls.

关键词： Support vector machines Autism Logistic regression machine learning algorithms Design automation Reviews Nearest neighbor methods Classification algorithms Decision trees Random forests

来源：评论

学校读者我要写书评

暂无评论

Identification and Classification of Medicinal Plants using machine learning algorithms

Identification and Classification of Medicinal Plants using ...

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： G Bhargavi Venkatesh Jayaraman Jabez J Deepa R Surendran R Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily accessible and credible information on plant categorization, particularly focusing on medicinal plants. This innovative project synergizes machine learning capabilities, botanical proficiency, and user-friendly technology to give users the insights required to harness the benefits of medicinal plants responsibly and sustainably. The platform facilitates precise categorization and a comprehensive understanding of the therapeutic attributes of plants.

关键词： Medicinal plants Support vector machines Visualization Renewable energy sources machine learning algorithms Biological system modeling Pressing Nearest neighbor methods Convolutional neural networks Random forests

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of machine learning algorithms and Ensemble Techniques for Diverse Image Classification Tasks

Comparative Analysis of Machine Learning Algorithms and Ense...

引用

International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Yulita Salim Athar Fathana Rakasyah Herdianti Darwis Harlinda Irawati Abdul Rachman Manga Department of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

In the era of burgeoning image data, the demand for robust image classification algorithms has never been more pressing. This research article delves into the realm of image classification, encompassing a comprehensive analysis of diverse datasets and machine learning algorithms, While simultaneously studying the efficiency of ensemble approaches in improving classification performance. The study employs five distinct datasets, spanning medical images, everyday objects, and natural scenery, ensuring a broad spectrum of classification challenges. These datasets include the Skin Cancer ISIC dataset, CIFAR-10, Flowers, Apparel Image, and Brain Tumor dataset. The image classes within these data sets vary significantly, presenting an ideal testbed for assessing algorithmic versatility. Our investigation scrutinizes five machine learning algorithms: Support Vector Classifier (SVC), Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), K-Nearest Neighbors (KNN), and Gaussian Naive Bayes (GNB). Each algorithm is evaluated individually, followed by a comprehensive ensemble approach involving a Voting Classifier. The results unveil nuanced performance variations across diverse datasets. Notably, RFC and GBC exhibit remarkable accuracy in brain tumor image classification, while KNN demonstrates strengths in classifying apparel images. Ensemble techniques, embodied by the Voting Classifier, harmonize these algorithms, yielding competitive and balanced performance across the datasets. This article contributes valuable insights into the realm of image classification, shedding light on algorithmic strengths and limitations, the efficacy of ensemble techniques, and their applicability to diverse image datasets. These findings hold significance for fields ranging from medical diagnostics to everyday object recognition, paving the way for more precise and versatile image classification solution

关键词： machine learning algorithms Accuracy Heuristic algorithms Brain tumors Nearest neighbor methods Classification algorithms Complexity theory Ensemble learning Random forests Image classification

来源：评论

学校读者我要写书评

暂无评论

Partial Discharge Source Classification Using machine learning algorithms

Partial Discharge Source Classification Using Machine Learni...

引用

Power Engineering Conference (SAUPEC), Southern African Universities

作者： Lucas T. Thobejane Bonginkosi A. Thango Department of Electrical Engineering University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

This article proposes a machine-learning algorithm for the automatic classification of single-source partial discharge (PD) in power transformers. PD testing provides valuable information of the state and deterioration of the insulation systems of transformer windings and core. A PD testing setup based on the IEC 60270 International standard is used to test PD from transformers of varying size and operational age. PD is recorded at different voltage levels applied to the transformer under test. Where the majority of PD classification literature has focused on laboratory developed artificial PD models, this work uses practical power transformers as a basis for testing and collecting the PD database. The PD database collected from this testing is utilized for the training, validation and testing of the machine learning algorithm. In this article, a comparative analysis of various trained machine learning algorithms for classifying PD is performed. The results of the classification show very pleasing performance from the tested classifier algorithms, with Bilayered Neural Network achieving a 96.97% validation accuracy of and a test accuracy of 97%.

关键词： Partial discharges machine learning algorithms Accuracy Databases Voltage machine learning Classification algorithms Standards Testing Power transformer insulation

来源：评论

学校读者我要写书评

暂无评论

Intelligent Event Detection in Unbalance Active Distribution System Using Reinforcement machine learning algorithms

Intelligent Event Detection in Unbalance Active Distribution...

引用

Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE International Conference on

作者： Archana Tamrakar Saumendra Sarangi Electrcical Engineering MNNIT Allahabad Prayagraj India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

With integration of Distributed Energy Resources (DERs) in the distribution system (DS), the power quality has decreased causing large deviation in voltage, frequency and, especially during disturbances, the security of the system may lose due to the maloperation of the relays. However, effective event detection can reduce the outage time and initiate faster restoration of the healthy section. Furthermore, the data collected from the DS is large in volume and variety and hence, to capture the accurate information from those data during any disturbance, is difficult and demands application machine learning techniques for event identification. In this paper, a Q-learning algorithm, is proposed for accurate event detection in distribution system following a disturbance. The previous and subsequent events data is utilized to identify the event. On the basis of events, the corresponding actions include isolating faulty sections, facilitating fast restoration, and load reduction as necessary. The efficacy of these algorithms is evaluated through simulations conducted on the IEEE 33-bus system with integration of photo-voltaic, utilizing MATLAB/SIMULINK 2023 and Jupyter Notebook and necessary actions are derived following the disturbance. The results obtained are satisfactory.

关键词： Meters machine learning algorithms Accuracy Q-learning Event detection Power quality Voltage Security Sustainable development Relays

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of machine learning algorithms for Disease Prediction Using Medical Datasets

Comparative Analysis of Machine Learning Algorithms for Dise...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： C. Roopa R.S. Latha M. Dhavamani G. Bommisrilekkhaa P. Angel Shalini R. Dhanyajothi Department of Computer Science and Engineering Kongu Engineering College Erode Tamilnadu Department of Artificial Intelligence Kongu Engineering College Erode Tamilnadu Department of Mathematics Kongu Engineering College Erode Tamilnadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Application of machine learning in the analysis of medical data can be said to be one of the current transformations happening within the healthcare fraternity. This paper highlights several ways in which various machine learning techniques can be used for gaining valuable insight into disparate types of medical data, such as patient records or genetic information, toward further clinical decision-making with a view to better prediction of diseases or strategies related to personalized medicine. The study critically evaluated various algorithms by considering their performances, suitability with regard to certain medical datasets they would be set upon to analyze, and to interpret the performance, particularly within the supervised learning frameworks. It talks about methodologies through which feature selection can take place to make sure that there is effective model development. Consequential applications in early disease detection as well as the optimization of their treatments are further emphasized, representing great potential for massive improvements in their recovery outcomes. The results highlight how far reaching data-driven approaches can affect the future of healthcare. It is envisioned that, with the use of such methods, the paper foresees a paradigm shift in disease diagnosis and treatment for personalized medical care and improved health outcomes.

关键词： machine learning algorithms Precision medicine Supervised learning machine learning Predictive models Feature extraction Prediction algorithms Medical diagnostic imaging Optimization Diseases

来源：评论

学校读者我要写书评

暂无评论

Comparative Performance Analysis of machine learning algorithms for Non-Performing Loan Prediction

Comparative Performance Analysis of Machine Learning Algorit...

引用

Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Tanmay Chaturvedi Sukanta Halder Uppalapati Sudheer kumar Nilanjan Das SK Bittu Department of Electrical Engineering Sardar Vallabhbhai National Institute of Technology Surat India Department of Electrical Engineering Indian Institute of Technology (ISM) Dhanbad India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

This paper investigates the application of machine learning models for Non-Performing Loan (N. P. L) prediction in the banking sector. The study evaluates the accuracy of diverse models, including traditional algorithms and advanced machine learning approaches such as Random Forest, Gradient Boost, XGBoost, LSTM, Decision Tree, Gaussian Naive Bayes and LightGBM. Training of the models are done on the dataset 'Training Data,’ available on Kaggle under the notebook named “Loan Prediction Based on Customer Behaviour”. Data processing and analysis of the given dataset was done on Kaggle's Jupyter Notebook cloud environment by using Python programming libraries. Through rigorous experimentation, it has been attested that Random Forest classifiers stands out to be best performing model when tested across all the performance benchmarks. This information thereby offers valuable guidance for institutions aiming to strengthen N. P. L prediction mechanisms and overall risk management strategies.

关键词： Training Accuracy machine learning algorithms Computational modeling Predictive models Vectors Bayes methods Decision trees Random forests Long short term memory

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Analysis of machine learning algorithms for Diabetes Prediction: Comparative Performance and Insights

A Comprehensive Analysis of Machine Learning Algorithms for ...

引用

International Conference on Trends in Electronics and Informatics (ICEI)

作者： A. Akilandeswari SAVEETHA SCHOOL OF ENGINEERING SIMATS UNIVERSITY

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Diabetes is one of the most chronic conditions seen worldwide. Its prevalence has been causing many complications, such as cardiovascular diseases, kidney failure, and neuropathy. Early prediction is crucial in helping prevent these from occurring or, at worst, to be affected minimally. In this work, we implemented various machine learning techniques to predict the onset of diabetes based on demographic and health-related data like age, body mass index (BMI), hypertension, heart disease, and blood glucose levels. The used algorithms were XGBoost, Random Forest, Stacked Ensemble, Gradient Boosting machine (GBM), and Support Vector machine (SVM). XGBoost could reach a maximum AUC of 0.979653 and a log loss of 0.0789284 with an F1-score of 0.811485. These really demonstrated strong power in the prediction of diabetes. High performance was also highlighted with the Stacked Ensemble Model, reaching an AUe of 0.979579 and an accuracy of 97.22%. These outcomes suggest that the superior ensemble methods, XGBoost and Stacked Ensembles, will provide quality prediction such that earlier diagnosis of patients with diabetes might be achieved consequently and, therefore, good recovery.

关键词： Support vector machines Analytical models machine learning algorithms Medical services Predictive models Boosting Prediction algorithms Market research Diabetes Random forests

来源：评论

学校读者我要写书评

暂无评论

Optimization and Improvement of Reliability of machine learning algorithms Based on Regularization Methods

Optimization and Improvement of Reliability of Machine Learn...

引用

International Russian Smart Industry Conference (SmartIndustryCon)

作者： Sherzodjon Yarmatov Xudaykul Bustanov Lola Safarova Samarkand State University named after Sharof Rashidov Samarkand Uzbekistan Samarkand State University of Veterinary Medicine Samarkand Uzbekistan

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Nowadays, optimizing the results of machine learning algorithms is of great importance in the field of artificial intelligence. This article analyzes the effectiveness of Lasso, Ridge, and Elastic Net regression methods to increase the reliability of the results of machine learning algorithms in assessing economic indicators. These methods help to eliminate the problems of overfitting and multicollinearity, and are aimed at improving models based on machine learning algorithms. The study tests the effectiveness of these methods on a data set of economic indicators, and uses the Grid Search method to determine the optimal values for model parameters. As a result, it is shown that these approaches can improve the accuracy and generalizability of economic indicator assessments.

关键词： machine learning algorithms Accuracy Economic indicators Biological system modeling Estimation Prediction algorithms Search problems Data models Reliability Overfitting

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 << < 7 8 9 10 11 12 13 14 15 16 > >>

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：