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

22,750 篇 会议
3,246 篇 期刊文献
19 篇 学位论文
7 册 图书

馆藏范围

26,021 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

5,179 篇 工学
- 3,111 篇 计算机科学与技术...
- 2,554 篇 电气工程
- 1,395 篇 信息与通信工程
- 691 篇 控制科学与工程
- 600 篇 软件工程
- 288 篇 仪器科学与技术
- 222 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
- 150 篇 测绘科学与技术
- 131 篇 生物工程
- 124 篇 电子科学与技术（可...
- 124 篇 石油与天然气工程
- 120 篇 材料科学与工程（可...
- 115 篇 网络空间安全
- 96 篇 土木工程
- 95 篇 动力工程及工程热...
- 65 篇 水利工程
951 篇 理学
- 290 篇 生物学
- 190 篇 物理学
- 159 篇 化学
- 139 篇 地球物理学
- 135 篇 数学
- 84 篇 系统科学
616 篇 管理学
- 529 篇 管理科学与工程(可...
- 69 篇 图书情报与档案管...
437 篇 医学
- 284 篇 临床医学
- 95 篇 基础医学(可授医学...
227 篇 农学
- 148 篇 作物学
53 篇 经济学
52 篇 法学
49 篇 文学
47 篇 教育学
17 篇 艺术学
3 篇 军事学
2 篇 哲学

主题

26,022 篇 machine learning...
8,400 篇 machine learning
4,276 篇 support vector m...
4,237 篇 accuracy
4,197 篇 prediction algor...
3,618 篇 predictive model...
3,490 篇 training
3,224 篇 classification a...
3,187 篇 feature extracti...
1,955 篇 data models
1,814 篇 computational mo...
1,787 篇 deep learning
1,684 篇 random forests
1,469 篇 neural networks
1,400 篇 real-time system...
1,399 篇 clustering algor...
1,276 篇 data mining
1,147 篇 optimization
1,004 篇 decision trees
977 篇 measurement

机构

120 篇 chitkara univers...
39 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...
14 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
13 篇 surendran r
12 篇 qiangfu zhao
12 篇 amit kumar
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

25,809 篇 英文
155 篇 其他
33 篇 中文
4 篇 法文
2 篇 西班牙文
2 篇 荷兰文
2 篇 土耳其文

检索条件"主题词=Machine Learning Algorithms"

共 26022 条记录，以下是4771-4780 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 Review on Prediction and Diagnosis of Mental Health Disorders Using Quantum machine learning Classifier

A Review on Prediction and Diagnosis of Mental Health Disord...

引用

Cybernation and Computation (CYBERCOM), International Conference on

作者： Poonam Vaishali Mehta Mutiq Katib Alrashidi Monika Mangla Manik Rakhra Nonita Sharma Geeta University Panipat Haryana India Maharishi Markandeshwar University Mullana India Department of Artificial intelligence University of Ha'il Saudia Arabia Department of Information Technology Dwarkadas J Sanghvi College of Engg. Mumbai India Department of Computer Science and Engineering Lovely Professional University Phagwara Jalandhar India Department of Information Technology Indira Gandhi Delhi Technical University for Women New Delhi

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

Chronic stress may have detrimental impacts on mental health as well as an elevated risk of various diseases. Stress monitoring might become simple with the help of artificial intelligence and quantum computing. The main goal of this work is to investigate how quantum enhanced machine learning algorithms can be used to forecast the stress levels of knowledge workers in the workplace using many modalities. This paper provides a high-level overview of the most well-known quantum augmented machine learning algorithms., such as Variational Quantum Classifier (VQC)., Quantum K-Nearest Neighbor (QKNN) and Quantum Support Vector machine (QSVM)., after analysing the literature during the last ten years

关键词： Support vector machines Quantum computing machine learning algorithms Reviews Employment Mental health machine learning Nearest neighbor methods Monitoring Diseases

来源：评论

学校读者我要写书评

暂无评论

Exploring the Potential of machine learning and Deep learning in ECG Image Analysis for Cardiovascular Disease Diagnosis

Exploring the Potential of Machine Learning and Deep Learnin...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Sathiya Priya S Angel Jean Vincy K Bharathi S P Sasi Gresa Manivannan D V Sathya Department of Computer Science & Engineering VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science & Engineering SRM Institute of Science and Technology Chennai-26 India Department of Information Technology Velammal Engineering College Surapet Chennai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This research proposes a novel machine learning approach to detect cardiovascular diseases (CVDs) in electrocardiogram (ECG) images. The goal is to improve diagnostic accuracy and efficiency within the healthcare system. CVDs remain the leading cause of death globally, emphasizing the importance of early and accurate diagnosis for effective treatment. ECG signals provide valuable information for assessing cardiovascular health. The methodology utilizes advanced machine learning algorithms for classification. First, it pre-processes ECG signals to extract relevant features. The results demonstrate the machine learning model's potential as a significant diagnostic tool by showcasing its accuracy in detecting various cardiovascular illnesses from ECG images. Specifically, this research leveraged the power of machine learning to predict four major heart abnormalities: arrhythmias, myocardial infarction (heart attack), heart failure, and hypertrophic cardiomyopathy. This investigation explores how different machine learning methods perform when analyzing ECG images to identify cardiovascular diseases. The primary objective is to identify the algorithm that delivers the highest accuracy in classifying ECG images for CVD detection. Decision trees are best suited for working with data organized in tables, where each entry has specific numerical values or categories. Random forests, like decision trees, are primarily designed to handle data in a tabular format with features like numbers or categories. ECG image analysis necessitates techniques adept at recognizing intricate patterns within the pixels of an image. The ultimate goal is to identify the most effective machine learning approach for accurate CVD detection using ECG images, potentially leading to improved healthcare system efficiency and patient outcomes. In the proposed system the algorithm with the highest accuracy for CVD diagnosis using ECG image analysis is the Decision Tree, achieving 59.36% accuracy.

关键词： Deep learning Accuracy Image analysis machine learning algorithms machine learning Medical services Electrocardiography Feature extraction Cardiovascular diseases Decision trees

来源：评论

学校读者我要写书评

暂无评论

learning Client Selection Strategy for Federated learning across Heterogeneous Mobile Devices

Learning Client Selection Strategy for Federated Learning ac...

引用

IEEE International Symposium on Quality Electronic Design

作者： Sai Qian Zhang Jieyu Lin Qi Zhang Yu-Jia Chen Department of Electrical Engineering and Computer Science New York University New York USA Department of Electrical and Computer Engineering University of Toronto Toronto Canada Microsoft Corporation Redmond WA Department of Communication Engineering National Central University Taoyuan Taiwan

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

The rapid growth of Internet of Things have yielded a remarkable increase in the volume of the data generated on client devices. This technological trend coincides with the rise of machine learning applications, which leverage user-generated data for large scale model training. In this context, Federated learning (FL) has become a popular model for facilitating model training across edge devices in a decentralized fashion. However, the statistical diversity presented in the client data and performance heterogeneity existed among the user mobile device can seriously impact the accuracy of the result model and system performance of FL. This article first illustrates the state-of-the-art FL algorithms and investigates the major issues presented in the FL implementation, and then presents a novel FL algorithm that jointly optimizes both the model performance and implementation efficiency for the FL systems. Specifically, we propose an intelligent FL client selection scheme by leveraging the recent advance of Reinforcement learning (RL) in solving complex control problems. The proposed solution, termed IntelliFL, can greatly improve both the accuracy performance and system performance of FL under the training environment with heterogeneous client devices.

关键词： Training Performance evaluation machine learning algorithms Federated learning System performance Reinforcement learning Market research

来源：评论

学校读者我要写书评

暂无评论

machine learning Technique for Hydrogen Fuel Cell Based Microgrid Energy Management

Machine Learning Technique for Hydrogen Fuel Cell Based Micr...

引用

National Power Systems Conference (NPSC)

作者： Swapnil Kumar Harshil Sajan Kassamkuttiyil Kartikey Singh Bhadauria Jeet Vijay Shah Vandit Khanna Premalata Jena Department of Electrical Engineering Indian Institute of Technology Roorkee India

ISBN: (数字)9798331519568

ISBN: (纸本)9798331519575

This study explores the application of reinforcement learning (RL) techniques to the integration of hydrogen fuel cells into microgrids, specifically focusing on the Rye Microgrid in Norway, which operates in island *** investigation centers on the seamless integration of fuel cells using reinforcement learning (RL) methods, treating the problem as a Markov Decision Process (MDP). The state space includes renewable energy production, current energy storage levels, and spot market prices, while the action space involves decisions on charging and discharging energy storage elements. Forecasted state values support the RL agent's decision-making process.A multi-agent RL framework is employed to optimize the actions for both hydrogen fuel cell and battery with the objective of minimizing costs while satisfying energy demand. The study rigorously investigates various RL algorithms, including Proximal Policy Optimization (PPO) and Deep Q-Network (DQN), identifying DQN as the most effective algorithm. Further analysis of the DQN algorithm, considering different forecasting intervals, demonstrates its robustness and adaptability. The study highlights the potential of advanced RL techniques in improving the economic viability and operational efficiency of microgrids incorporating hydrogen fuel cells.

关键词： Renewable energy sources machine learning algorithms Heuristic algorithms Hydrogen Fuel cells Microgrids Reinforcement learning Batteries Energy management Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Automated Leukemia Detection and Classification from Blood Smear Images Using machine learning Techniques

Automated Leukemia Detection and Classification from Blood S...

引用

Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Y M Manu M E Priyanka Department of Computer Science & Engineering BGS Institute of Technology Adichunchanagiri University BG Nagara Mandya

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Leukemia early detection and identification is a major problem in disease diagnosis because it requires low-cost, precise differentiation of malignant leukocytes in the early stages of the illness. Despite its high frequency, leukemia is difficult to diagnose because of the restricted availability of flow cytometer equipment and the laborious nature of current laboratory diagnostic procedures. Considering machine learning’s (ML) potential for illness diagnosis, the purpose of this systematic review is to look into research that uses ML methods for leukemia detection and classification. Leukemia smear image analysis using machine learning (ML) algorithms can improve diagnostic accuracy, shorten diagnosis times, and provide quicker, safer, and more economical diagnostic services. Clinical and laboratory personnel may also use machine learning techniques into their tools and applications in the lab in addition to their present diagnostic techniques. One method that has been suggested is using a machine learning technique, such artificial neural networks, to diagnose and categorize patients’ conditions early on. As a result after collection of data model development should be done then data training and at last model should be tested and Ml algorithms should be used to compare results. Tests are conducted on samples from blood images.

关键词： Training machine learning algorithms Microscopy machine learning Artificial neural networks Data models Classification algorithms Personnel Medical diagnosis Blood

来源：评论

学校读者我要写书评

暂无评论

Detecting Schizophrenia from Healthy control using machine learning and EEG Signals

Detecting Schizophrenia from Healthy control using Machine L...

引用

Expert Clouds and Applications (ICOECA), International Conference on

作者： Tamilarasi K Pavitra S Anuj Kumar School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

A brain condition called schizophrenia has a significant impact on people's thinking, behaviors, and lives on a global state. The prevalence of the condition rises along with the population, and those who suffer from schizophrenia lose their capacity for thought and behavior with time. All these issues arise because it is challenging to identify schizophrenia patients. As a result, proper care and quality patient treatment require an accurate and quick diagnosis procedure. This article aims to improve the accuracy of. detection by using EEG signals and machine learning algorithms. It is tested using the Resting state EEG dataset, which demonstrates a high level of capability in distinguishing between schizophrenia patients and healthy controls. The EEG data is converted to different forms, where the given machine learning techniques extract the EEG signals to identify patients with schizophrenia and healthy control. We are utilizing the 28-file public dataset in this study. In the file, there are 14 healthy individuals as well as 14 SCZ sufferers. In accordance with the findings, (1) the decision tree successfully distinguished SCZ patients from healthy individuals using EEG signals with an accuracy of 89.12%, and (2) logistic regression was also used to successfully identify the affected patient using EEG signals with an accuracy of 69.78%. (3) The SCZ patient was correctly identified by SVM with an accuracy of 65.51 %. Our research demonstrates the potential for machine learning to play a significant role in aiding in the diagnosis of schizophrenia.

关键词： Support vector machines Logistic regression Accuracy machine learning algorithms Mental disorders Sociology machine learning

来源：评论

学校读者我要写书评

暂无评论

Optimizing Marketing Strategies: Predicting Customer Personality using Advanced machine learning Models

Optimizing Marketing Strategies: Predicting Customer Persona...

引用

Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Adla Aryan Sada Preetham Sagar Nadimpalli Madana Kailash Varma Rangisetti Manikanta Ramojula Rajashekar Gagan Deep Arora Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

In the competition of modern marketing, it is highly important to foresee the correct personality profiles of customers because this may further improve the result of marketing campaigns. Therefore, in this research, three major models of machine learning are used, including Random Forest Classifier, Support Vector machine (SVM), and Gradient Boosting Classifier, to discuss the challenges of predicting customer personalities. This study is helpful in finding unique customer segments and appropriate marketing strategies. These algorithms are valid to use for classifying customer personalities, and then determine the efficiency of it using the context also proves that through the more complex applications of such approaches, the efficacy of targeted marketing might be enhanced appropriately. Therefore, even more advanced deep learning techniques would be used at the next stage of research to further improve the forecasting accuracy and applicability in various marketing scenarios.

关键词： Support vector machines Deep learning Accuracy machine learning algorithms Predictive models Boosting Prediction algorithms Forecasting Random forests Optimization

来源：评论

学校读者我要写书评

暂无评论

Gene Expression Data Analysis using Supervised machine learning Algorithm in Data Mining for Breast Cancer Prediction

Gene Expression Data Analysis using Supervised Machine Learn...

引用

Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： S. Nathiya J. Sumitha Department of Computer Science Dr.SNS Rajalakshmi College of Arts and Science Coimbatore Tamil Nadu India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

The human body is composed of cells, and each cell has a variety of components. The human body’s cells are essential for both the construction and operation of living things. Cells in the human body are capable of dividing and self-destructing as necessary. This natural functioning can occasionally alter; for example, certain cells may grow uncontrollably or improperly, giving rise to tumors. When these tumors develop into malignant ones and spread to nearby body parts, the condition is known as cancer. Specifically, if the cells in the breast can grow uncontrollably then this condition is known as breast cancer. A gene is a piece of genetic material that is present in every human cell. The genes that code for the proteins that determine how each cell functions. Gene expression is the name given to this function that produce protein. Occasionally, variations in the genetic material, or mutated genes, produce alterations in the values of gene expression. Breast cancer is caused by these altered gene expression levels. Some diagnostic techniques are necessary to detect this altered gene expression for breast cancer, and they are highly effective in helping medical professionals to pinpoint the disease’s biomarker. Thus, the goal of this research is to use gene expression data to create a model that can accurately identify breast cancer biomarkers. The suggested model in this study, a pruned neural network method, is designed to predict breast cancer while requiring less memory and execution time and offering the best accuracy. This suggested model is evaluated against current techniques, including support vector machines and decision trees, and it outperforms them with a $\mathbf{98 \%}$ accuracy rate. Having a larger number of gene characteristics and fewer samples is a drawback of using gene expression data. Therefore, the wrapper approaches are employed as a preprocessing strategy in this research work to remove the undesirable genes. The gene expression data is pr

关键词： Proteins Accuracy machine learning algorithms Biological system modeling Neural networks Predictive models Breast cancer

来源：评论

学校读者我要写书评

暂无评论

Interactive machine learning Framework for Predicting Asthma Health Conditions Using XGBoost

Interactive Machine Learning Framework for Predicting Asthma...

引用

International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)

作者： Ambrose A. Azeta Chimba S. Edward Raphael H. Ekpo Department of Software Engineering Namibia University of Science and Technology Windhoek Namibia Department of Computer and Mathematical Sciences Crawford University Faith City Igbesa Ogun State Nigeria

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Asthma is a far reaching non-communicable sickness that affects individuals of any age and is a significant danger to human wellbeing worldwide, especially in low and middle-income countries. Its prevalence is substantial, and it remains one of the major contributors to global death rate. A substantial number of asthma development prediction models, and machine learning based approaches, exist. The problem associated with conventional methods of asthma prediction is low predictive accuracy of the model. The objective of this study is to develop an interactive machine learning (iML) framework for predicting Asthma Health conditions in selected African countries. The model was tested using dataset obtained from National Survey of Children's Health (NSCH). The machine learning technique used was XGBoost, and model development including preprocessing, training, and tested were carried out using Python programming language on Google Collaboratory (Collab) environment. The results of benchmarking of the performance accuracy of XGBoost shows 90.6% which is higher than the existing results in literature for Decision Tree (90.49%), KNN (90.28%), Naïve Bayes (82.7%). The study found out that the model developed by the researchers performed comparatively well in predicting asthma when compared with some existing state-of-the-art machine learning algorithms.

关键词： Training Surveys Pediatrics Accuracy machine learning algorithms machine learning Predictive models Prediction algorithms Asthma Python

来源：评论

学校读者我要写书评

暂无评论

Boosting Agricultural Productivity Using IoT and machine learning-Based Crop Recommendation

Boosting Agricultural Productivity Using IoT and Machine Lea...

引用

International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Asish Kumar Patnaik Neelamadhab Padhy Archana Patnaik Rasmita Panigrahi Department of Computer Science and Engineering GIET University Gunupur Odisha

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Crop production is crucial to the country's economic development by promoting sustainable agriculture. Integrating IoT and machine learning into crop recommendation systems widely contributes towards enhancing product yield, and minimizing farmers' efforts. The correct forecasting of crop recommendation may give a perfect outcome in analyzing the crop features based on its parameters. Objectives: This work aims to improve agricultural productivity using classification models. Our prime objective is to identify the specific parameters like soil productivity, humidity, wind velocity, rainfall, and other climatic conditions that affect the growth of crops. Classification-based prediction provides accurate results based on the optimality of crop characteristics. Materials and Methods: Our hybrid research model shows the efficiency of machine learning and IoT in the domain of crop recommendation systems. We have taken the real-time data of Nitrogen(N), Phosphorus(P), and Potassium(K) values of soil by using the NPK Soil Sensor. The real-time data collection was done at GIET University Agricultural Fields (School of Agriculture), India. The data is further converted into a. csv file for further processing. We have implemented five different types of classification techniques which include Gradient Boosting, Decision Tree, Random Forest, Logistic Regression, and Support Vector machine(SVM). Result Analysis: The effectiveness of all the different kinds of classification algorithms is assessed using the obtained results. We have estimated the performance metrics (accuracy, precision, F-measure, and recall) of all the classifiers.

关键词： Productivity machine learning algorithms Crops Signal processing algorithms Soil Boosting Real-time systems Classification algorithms Random forests Recommender systems

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 << < 474 475 476 477 478 479 480 481 482 483 > >>

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：