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

22,562 篇 会议
3,220 篇 期刊文献
19 篇 学位论文
7 册 图书

馆藏范围

25,807 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

5,149 篇 工学
- 3,093 篇 计算机科学与技术...
- 2,542 篇 电气工程
- 1,383 篇 信息与通信工程
- 686 篇 控制科学与工程
- 593 篇 软件工程
- 285 篇 仪器科学与技术
- 217 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...
- 163 篇 交通运输工程
- 149 篇 测绘科学与技术
- 130 篇 生物工程
- 122 篇 石油与天然气工程
- 121 篇 电子科学与技术（可...
- 117 篇 材料科学与工程（可...
- 111 篇 网络空间安全
- 98 篇 土木工程
- 92 篇 动力工程及工程热...
- 66 篇 水利工程
943 篇 理学
- 289 篇 生物学
- 188 篇 物理学
- 155 篇 化学
- 139 篇 地球物理学
- 132 篇 数学
- 84 篇 系统科学
611 篇 管理学
- 524 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

25,808 篇 machine learning...
8,337 篇 machine learning
4,247 篇 support vector m...
4,161 篇 prediction algor...
4,141 篇 accuracy
3,577 篇 predictive model...
3,462 篇 training
3,199 篇 classification a...
3,156 篇 feature extracti...
1,930 篇 data models
1,793 篇 computational mo...
1,774 篇 deep learning
1,639 篇 random forests
1,463 篇 neural networks
1,386 篇 clustering algor...
1,368 篇 real-time system...
1,271 篇 data mining
1,121 篇 optimization
995 篇 decision trees
971 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

25,604 篇 英文
147 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 25808 条记录，以下是371-380 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：

Intrusion Detection System Based On machine learning algorithms In the DOUNG Ad Hoc Network

Intrusion Detection System Based On Machine Learning Algorit...

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Boubacar Tawayé Abdoul Aziz Mahamadou Issoufou Tiado Boukar Abatchia Nicolas Boureima Djibo Abasse Department of Mathematics and Computer Science Research team on Network and Telecommunication University of Abdou Moumouni Niamey Niger

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

The New Generation of Open Digital Universities (DOUNG) [1] is a model designed to cover as few human resources (professors) and material resources (lecture halls and classrooms) as possible. Its architecture offers global ubiquity and tailor-made offers to learners by relying on the IT and telecom networks already available. Thus, it inherits the vulnerabilities and threats inherent in these channels, in particular those of wireless networks and without pre-existing infrastructure. This lengthens the perimeter to be protected and exponentially increases the risk of intrusions into the synchronous course offer. To overcome these challenges, we proposed and tested an intelligent intrusion detection model based on machine learning (ML). Based on the approaches used (Fl score and confusion matrix), it appears from our experiment that logistic regression (LR) provides the best detection of MiTM attacks [1]–[3]. in the ad hoc network of the DOUNG.

关键词： Support vector machines Radio frequency Logistic regression machine learning algorithms Wireless networks Intrusion detection Ad hoc networks Telecommunications Network intrusion Random forests

来源：评论

学校读者我要写书评

暂无评论

Credit Card Fraud Detection Using KNC, SVC, and Decision Tree machine learning algorithms

Credit Card Fraud Detection Using KNC, SVC, and Decision Tre...

引用

AI in Cybersecurity (ICAIC), International Conference on

作者： Pushpanjali Pandey K K Garg Department of Management Lingaya’s Vidyapeeth Faridabad India

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Credit cards are replacing cash in shopping. With the increase in popularity of plastic money like credit cards, hackers are misusing this as an opportunity to commit fraud. In this research work, we have used three different machine-learning techniques to detect credit card fraud. We have taken a credit card fraud dataset from Kaggle and applied K Neighbor Classifiers (KNC), Support Vector Classifiers (SVC), and decision tree machine-learning methods to this dataset. We have achieved $\mathbf{9 9. 9 4 \%}$ accuracy using both KNN and Decision Tree machine learning algorithms. We have got $\mathbf{9 9. 8 4 \%}$ accuracy using the Support vector classifier (SVC) machine learning Algorithm.

关键词： machine learning algorithms Accuracy Static VAr compensators machine learning Nearest neighbor methods Credit cards Vectors Fraud Decision trees Plastics

来源：评论

学校读者我要写书评

暂无评论

Predicting Port Congestion at Busan Port Using machine learning algorithms and Temporal Variables

Predicting Port Congestion at Busan Port Using Machine Learn...

引用

International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Sang-Hyun Ha Ki-Hwan Kim Young-Jin Kang Ji Yeon Kim Seok-Chan Jeong AI Grand ICT Research Center Dong-Eui University Busan Republic of Korea Department of Artificial Intelligence Dong-Eui University Busan Republic of Korea Dept. of e-Business AI Grand ICT Research Center Dong-Eui University Busan Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Ports are vital infrastructures for global logistics and trade, with South Korea handling approximately 99.7 % of its import and export cargo via maritime routes. Busan Port, the largest in the country and the seventh-largest container port worldwide, plays a crucial role in both national logistics and regional economic growth. However, congestion in port operations leads to delays, increased costs, and reduced efficiency. This study employs actual data from Busan Port (February to September 2024) to analyze the impact of temporal variables-such as day of the week, time of day, and monthly factors-on port congestion. machine learning algorithms, including Random Forest, XGBoost, and LightGBM, were utilized to predict congestion levels using a refined dataset of approximately 1.4 million samples. Results indicate that Extra Trees and CatBoost classifiers achieved high accuracy (0.9654) and AUC (0.9952), while Extreme Gradient Boosting reached an AUC of 0.9989, demonstrating exceptional performance. These findings highlight the effectiveness of machine learning in capturing the nonlinear dynamics of port operations. Future research will aim to enhance model performance by expanding data collection and integrating external factors such as weather conditions and cargo characteristics. This study underscores the potential of machine learning tools in optimizing port management and improving logistical efficiency.

关键词： machine learning algorithms Costs Seaports Data collection Data models Nonlinear dynamical systems Delays Random forests Logistics Meteorology

来源：评论

学校读者我要写书评

暂无评论

Soft Alert Generation for student Dropout Mitigation and Proactive Management by machine learning algorithms

Soft Alert Generation for student Dropout Mitigation and Pro...

引用

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： S. Geetha Spandana A P Vijay D Vishruth M V Dept. of CSE B.N.M Institute of Technology Bangalore Karnataka India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Proactive dropout mitigation is crucial for enhancing student success and retention in schools. Dropout rates pose significant challenges in education systems globally, impacting students, communities, and societies. This paper emphasizes the necessity of proactive measures to address dropout rates and support student achievement by building an educational network. Through proactive approaches, schools can identify and address risk factors early, preventing student disengagement and dropout. Various strategies such as early interventions, personalized support systems, academic enrichment programs, and community partnerships are examined for their efficacy in promoting student engagement and retention. Additionally, the role of data-driven decision-making and predictive analytics in identifying at-risk students and tailoring interventions is explored. Implementing proactive dropout mitigation requires collaboration among educators, administrators, families, and community stakeholders. Prioritizing proactive measures can foster a supportive learning environment, empowering students to excel academically. This paper highlights the significance of proactive dropout mitigation that has been enabled using a seemingly enhanced automated full-stack student management system integrated with ML algorithms used in enhancing student success and promoting educational equity. By investing in proactive interventions, educational institutions can reduce dropout rates and foster a culture of academic excellence and lifelong learning.

关键词： machine learning algorithms Prevention and mitigation Education Decision making Buildings Collaboration machine learning Stakeholders Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of Feature Selection and machine learning algorithms for Enhanced Anomaly Detection in 5G Core Networks

A Comparative Analysis of Feature Selection and Machine Lear...

引用

International Global Information Infrastructure Symposium (GIIS)

作者： Júnia Maísa Oliveira César Morais Daniel Macedo José Marcos Nogueira Computer Science Department Universidade Federal de Minas Gerais Department of Electrical Electronic and Information Engineering (DEI) University of Bologna Italy

ISBN: (数字)9798331520113

ISBN: (纸本)9798331520120

A critical challenge lies in choosing the appropriate processing techniques to achieve optimal evaluation performance in machine learning algorithms for the 5G core data analysis in anomaly detection use case. In this work, we address this challenge by focusing on the comparative analysis of machine learning (ML) algorithms combined with data processing techniques for anomaly detection in 5G core networks. ML algorithms are promising for detecting such anomalies, but their performance heavily depends on the quality of the features used. This work highlights the importance of feature selection, demonstrating that by applying techniques such as Pearson’s correlation and dimensionality reduction (PCA and LDA), we can enhance the detection accuracy of various ML models. We evaluate eight ML algorithms using the 5GAD-2022 cybersecurity dataset and show that Random Forest achieves the best performance in terms of F1-score, AUC, and accuracy, regardless of the preprocessing techniques applied. The results suggest that meticulous feature selection can significantly enhance the algorithms’ effectiveness in detecting anomalies.

关键词： machine learning algorithms Data analysis Accuracy 5G mobile communication Focusing Feature extraction Computer security Anomaly detection Random forests Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of machine learning algorithms for Enhancing Stroke Prediction in Healthcare Database

Comparative Analysis of Machine Learning Algorithms for Enha...

引用

Devices for Integrated Circuit (DevIC)

作者： Samik Basu Shubhangini Dey Prapti Ganguly Debangana Basak Pritha Banerjee Amlan Chakrabarti Dept. of Computer Science & Engg. University of Calcutta Kolkata India Dept. of Information Technology Jadavpur University Kolkata India A.K. Choudhury School of I.T. University of Calcutta Kolkata India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

Stroke is a major global cause of disability and death, often resulting from sudden blockages in the brain or heart. Recognizing early warning signs can help reduce stroke risk and is crucial for effective stroke prevention and management. This study enhances stroke prediction using machine learning and neural networks, analyzing risk factors like hypertension, heart disease, ever-married, work type, residence type, glucose levels, BMI, smoking, gender, and age. Ten machine learning algorithms are tested on a well-curated clinical dataset. The results showed high accuracy for Random Forest (98.17%), Logistic Regression (98.32%), CatBoost (98.52%), and an Ensemble Technique (98.63%). However, the Artificial Neural Network (ANN) outperformed all with 99.37% accuracy, the highest AUC score, and the lowest false positive rate. These findings highlight ANN’s effectiveness in stroke risk assessment, offering valuable insights for improved prevention and timely medical interventions.

关键词： Heart Integrated circuits Logistic regression machine learning algorithms Accuracy Prevention and mitigation Artificial neural networks Medical services Stroke (medical condition) Random forests

来源：评论

学校读者我要写书评

暂无评论

Impact Analysis of Nasdaq-100 Using machine learning algorithms

Impact Analysis of Nasdaq-100 Using Machine Learning Algorit...

引用

Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Mohd. Toushif Akhtar Shreshth Goyanka Alka Chaudhary Amity Institute of Information Technology Amity University Noida Uttar Pradesh India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

This study focuses on leveraging machine learning algorithms to predict and analyse the performance of the NASDAQ-100 index in financial markets. Our objectives encompass developing accurate predictive models, identifying the growth of the index, and determining the trends. Through extensive data preprocessing and model evaluation, our findings indicate that the index possesses great potential for a long-term investment. This research holds significance for people involved in related sectors and also provides guidance for further exploration in this field.

关键词： Analytical models machine learning algorithms Accuracy Computational modeling Biological system modeling Time series analysis Predictive models Data models Indexes Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Application of machine learning algorithms in Artificial Intelligence Image Recognition Technology

Application of Machine Learning Algorithms in Artificial Int...

引用

Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Qi Ouyang Siyang Dai Kexin Xie Zeyu Sun Yao Wang Sustainable Development of Architecture and Landscape Polytechnic of Milan Milan Italy Faculty of Engineering and Information Technology The University of Melbourne Victoria Australia College of Business and Public Management Wenzhou-Kean University Wenzhou China School of Environment and Science Griffith University Brisbane Australia Nanjing Stomatological Hospital Affiliated Hospital of Medical School Institute of Stomatology Nanjing University Nanjing China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In smart city applications such as medical image analysis, autonomous driving, and security monitoring, image recognition faces challenges like complex backgrounds, low-quality images, and diverse targets, affecting accuracy and robustness. This study explores machine learning algorithms to enhance image recognition performance by addressing noise interference, intricate backgrounds, and feature extraction difficulties. It combines Convolutional Neural Networks (CNN) with transfer learning, starting with data preprocessing to reduce noise, using pre-trained CNN models to extract high-level features, and fine-tuning with a ResNet transfer learning strategy for specific tasks. Additionally, ensemble learning methods are employed to further improve model robustness and accuracy. Experimental results show that the ensemble model maintains around 85% accuracy even with high background complexity, and transfer learning achieves 90% accuracy when the sample size reaches 1000. These findings demonstrate that transfer and ensemble learning effectively enhance image recognition accuracy and resilience in complex environments.

关键词： Image recognition Accuracy machine learning algorithms Transfer learning Noise Interference Feature extraction Robustness Ensemble learning Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Alzheimer's Disease Detection Using Various machine learning algorithms

Alzheimer's Disease Detection Using Various Machine Learning...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Parthvi Patel Krishi Thakkar Prince Valani Krishna Patel Parth Goel Vaishali Vadhavana Computer Science & Engineering Department Devang Patel Institude of Advance Technology and Research Charotar University of Science and Technology (CHARUSAT) Changa Anand India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Alzheimer's disease (AD) is a brain-degenerative mental illness defined by growing forgetfulness. It also includes the loss of cognitive and behavioural characteristics. This research aims at developing a machine learning based tool that can work on early recognition of AD. This paper intends to develop an accurate, non-invasive machine learning model in detecting AD. Classification for AD has been done using behavioural, clinical, and lifestyle characteristics. The idea is to get such patterns from the data in a model, which will help in early detection of AD. For the detection of the AD dataset has been taken from Kaggle. This paper delivers an in-depth comparison of various algorithms, including Support Vector machine (SVM), Random Forest, Decision Tree, Logistic Regression, and Artificial Neural Network (ANN), with the goal of improving the detection of Alzheimer's disease (AD). The algorithms were evaluated using various performance metrics, such as F1 score, Precision, Accuracy, and Recall. The results indicate that the Random Forest algorithm attained the highest Accuracy, reaching 95%.

关键词： Support vector machines Measurement Logistic regression machine learning algorithms Accuracy Artificial neural networks Alzheimer's disease Informatics Random forests Mobile computing

来源：评论

学校读者我要写书评

暂无评论

Forecasting of Reliability Indicators of Base Stations of Cellular Communication Networks Using machine learning algorithms

Forecasting of Reliability Indicators of Base Stations of Ce...

引用

Systems of Signals Generating and Processing in the Field of on Board Communications

作者： D. S. Chirov N. D. Petrov Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

The article discusses the issues of forecasting the reliability of base stations of cellular communication networks using machine learning algorithms. This task is relevant, as forecasting the reliability of base stations allows timely measures to be taken to prevent equipment failures. For example, replacing the corresponding base station nodes or performing maintenance. All this ensures an increase in QoS in cellular communication systems. An analysis of existing developments in this field has shown that the use of machine learning algorithms is a promising way to predict reliability indicators. The article discusses two of the most popular machine learning algorithms for solving forecasting problems: decision trees and recurrent neural networks. To assess the applicability of these algorithms, a special dataset has been developed that simulates the process of failures in the main elements of the base station: the power supply unit and the RF power amplifier. The simulation results showed high quality forecasting of base station reliability indicators using these algorithms. In particular, the LSTM network provides about 92% accuracy in correctly predicting base station failure.

关键词： Cellular networks Base stations machine learning algorithms Recurrent neural networks Prediction algorithms Reliability Decision trees Communication networks Forecasting Long short term memory

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 << < 34 35 36 37 38 39 40 41 42 43 > >>

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：