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

6,818 篇 会议
15 篇 期刊文献
11 册 图书

馆藏范围

6,844 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,366 篇 工学
- 3,826 篇 计算机科学与技术...
- 3,201 篇 软件工程
- 882 篇 信息与通信工程
- 710 篇 生物工程
- 588 篇 控制科学与工程
- 562 篇 生物医学工程（可授...
- 500 篇 电气工程
- 304 篇 电子科学与技术（可...
- 290 篇 光学工程
- 284 篇 安全科学与工程
- 238 篇 动力工程及工程热...
- 223 篇 机械工程
- 217 篇 交通运输工程
- 210 篇 化学工程与技术
- 199 篇 仪器科学与技术
- 176 篇 网络空间安全
2,144 篇 理学
- 1,267 篇 数学
- 766 篇 生物学
- 655 篇 统计学（可授理学、...
- 399 篇 系统科学
- 331 篇 物理学
- 234 篇 化学
1,306 篇 管理学
- 715 篇 管理科学与工程(可...
- 650 篇 图书情报与档案管...
- 393 篇 工商管理
597 篇 医学
- 531 篇 临床医学
- 502 篇 基础医学(可授医学...
- 310 篇 药学(可授医学、理...
- 282 篇 公共卫生与预防医...
200 篇 法学
190 篇 农学
- 186 篇 作物学
173 篇 经济学
137 篇 教育学
11 篇 军事学
2 篇 文学

主题

1,001 篇 machine learning
668 篇 accuracy
630 篇 deep learning
393 篇 machine learning...
382 篇 support vector m...
359 篇 predictive model...
355 篇 data models
302 篇 training
300 篇 feature extracti...
246 篇 adversarial mach...
223 篇 computational mo...
208 篇 convolutional ne...
197 篇 data mining
179 篇 real-time system...
179 篇 prediction algor...
174 篇 forecasting
144 篇 decision trees
142 篇 random forests
132 篇 analytical model...
117 篇 classification a...

机构

131 篇 chitkara univers...
69 篇 maharishi school...
53 篇 centre of interd...
22 篇 department of co...
21 篇 uttaranchal inst...
21 篇 school of electr...
20 篇 department of el...
19 篇 maharishi school...
19 篇 department of ar...
18 篇 graphic era deem...
18 篇 computer science...
17 篇 college of techn...
17 篇 department of in...
16 篇 pune
15 篇 graphic era hill...
15 篇 computer science...
14 篇 chitkara univers...
14 篇 department of co...
14 篇 graphic era hill...
13 篇 vishwakarma inst...

作者

19 篇 gill kanwarparta...
17 篇 shiva mehta
17 篇 mehta shiva
16 篇 kanwarpartap sin...
14 篇 chauhan rahul
12 篇 vinay kukreja
11 篇 kukreja vinay
10 篇 singh jagendra
10 篇 rahul chauhan
8 篇 deepak upadhyay
8 篇 akhilendra prata...
8 篇 girija shankar s...
7 篇 deepak banerjee
7 篇 kumar manish
7 篇 babu tina
7 篇 anand vatsala
7 篇 sheifali gupta
7 篇 vatsala anand
7 篇 radha d.
6 篇 banerjee deepak

语言

4,877 篇 英文
1,964 篇 其他
97 篇 中文
3 篇 土耳其文

检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6844 条记录，以下是551-560 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：

Autism Spectrum Disorder Analysis and Detection System 3

Autism Spectrum Disorder Analysis and Detection System

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ananthajothi, K. Mohanapriya, E. Mukhilan, S.S. Department of CSE Engineering College Chennai India

ISBN: (纸本)9798331527549

Autism Spectrum Disorder is a neurodevelopmental disorder characterized by social interaction, communication impairments, and repetitive behaviors. In recent years, there has been an increase in prevalence, and it is important to identify individuals early for appropriate intervention to improve their quality of life. The following study was conducted to deepen the understanding and management of the disorder by employing a comprehensive two-module approach. Module one emphasizes the detection of ASD through sophisticated machine learning techniques for early and accurate diagnosis. Module two is on improving social and communication skills of children with ASD via game-based assessments, offering them an interactive yet enjoyable learning environment. This project, not only in contributing to the scientific community, but also through practical applications contributes and offers easy solutions for caregivers and educators. Integration of technology with both detection and intervention holds huge advancement in this area, promising better outcomes for ASD individuals. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Key Technologies for Dynamic Early Warning of BIM Power Monitoring data in Bridge Operation and Maintenance 3

Key Technologies for Dynamic Early Warning of BIM Power Moni...

引用

3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Peng, Yu Wang, Tian Chongqing College of Electronic Engineering Chongqing401331 China

ISBN: (纸本)9798350376173

Bridge operation and maintenance is an important solution to how to ensure the safe operation of bridges and extend their service life. In this paper, a dynamic early warning technology of BIM (Building Information Modeling) power monitoring data was proposed to supervise the safety of bridges. Through the layout of sensors and monitoring equipment, the power data of the bridge was obtained in real-time. The quality control and data correction of the collected power data were carried out, and data cleaning, data correlation and other technologies were used to process the power data and extract useful characteristic information. An early warning model was also constructed, and machine learning, data mining and other methods were used to analyze and model power data to realize dynamic early warning of bridge power systems. After experiments and analysis, it was concluded that the stability of the method in this paper was maintained at 89%-98%. Through the dynamic early warning of BIM power monitoring data, real-time monitoring and fault diagnosis of the bridge power system can be realized and abnormal conditions and potential problems can be found in a timely manner. © 2024 IEEE.

关键词： bridge operation and maintenance building information modeling data dynamic warning power monitoring

来源：评论

学校读者我要写书评

暂无评论

DVC in Open Source ML-development: The Action and the Reaction 24

DVC in Open Source ML-development: The Action and the Reacti...

引用

IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Pacheco, Lorena Barreto Simedo Rahman, Musfiqur Rabbi, Fazle Fathollahzadeh, Pouya Abdellatif, Ahmad Shihab, Emad Chen, Tse-Hsun (Peter) Yang, Jinqiu Zou, Ying Concordia Univ Montreal PQ Canada Queens Univ Kingston ON Canada Univ Calgary Calgary AB Canada

ISBN: (纸本)9798400705915

machine learning (ML) systems are gaining popularity, reshaping various domains ranging from customer services to software engineering. The effectiveness of ML systems is dependent on the quality of their training data. Therefore, practitioners invest substantial time experimenting with different data, parameters, and models to guarantee the quality of the end system. Prior work highlighted unique challenges of developing ML systems, particularly concerning versioning data and models. Recently, various tools such as DVC and MLFlow have emerged to aid developers in the storage and tracking of data. Despite their growing popularity, very little is known about their usage patterns and impact on open-source software (OSS) systems. To address this gap, we conducted an empirical study on 56 GitHub OSS projects that use DVC to understand the DVC usage pattern and the impact of using DVC on the software development process. We found that Versioning and tracking is the most adopted DVC feature, being utilized by all 56 projects and being the only adopted feature in 85.7% of them. Furthermore, we found that DVC has a significant impact on the software development process indicators such as the number of created pull requests (PRs), and the number of bug-fix commits. For instance, our findings showed that DVC causes a peak in the number of commits and PRs at the moment of the adoption, followed by a long-term decrease. We believe that our findings can assist practitioners in tailoring tools to better meet user requirements and help organizations realize potential outcomes of adopting such tools.

关键词： Empirical Software engineering data Version Control Software Evolution SE4AI

来源：评论

学校读者我要写书评

暂无评论

Plant Diseases Detection Using Deep learning and machine Vision 3

Plant Diseases Detection Using Deep Learning and Machine Vis...

引用

3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Singh, Nidhi School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9798350361186

This research delves into deep learning and machine vision applications for plant leaf disease detection in agricultural settings, focusing on farm village datasets. Utilizing a blend of authentic farm village data and synthetic data from Generative Adversarial Networks (GANs), three advanced convolutional neural network (CNN) models VGG16, ResNet50, and InceptionNet V3 are employed with transfer learning. Leveraging transfer learning enhances model performance through fine-tuning pre-trained networks. The study systematically evaluates models based on key metrics like accuracy, precision, recall, and F1 score. Results showcase the methodology's robustness, with ResNet50 emerging as the leading performer at 83.23%, contributing to precision agriculture's advancements with promising implications for sustainable farming and crop yield optimization. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Supervised learning Approaches for Vibration Analysis and Anomaly Detection in Rotating machines 3

Supervised Learning Approaches for Vibration Analysis and An...

引用

3rd international conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Ali, Syed Zulfiqar Ali, Syed Hasnat Javeed, Anees Ullah, Ihsan Memon, Sufyan Ali Manzoor, Sajjad Dept. of Electrical and Computer Engineering CUI Abbottabad Campus Abbottabad Pakistan Dept. of Defence Systems Engineering Sejong University Seoul Korea Republic of Dept. of Electrical Engineering MUST AJK Mirpur Pakistan

ISBN: (纸本)9798331504861

Safety and reliability are crucial for modern systems and technologies. This paper presents an approach for real-time anomaly detection in rotating machines using vibration analysis and machine learning algorithms for predictive maintenance. Rotating machines such as motors, engines, gearboxes and turbines are widely used systems and faults in these systems can result in complete shutdowns. Preventing unexpected downtime while ensuring equipment reliability and safety is essential. In this context, anomaly detection plays a vital role in monitoring the healthy operation of rotating machines. A test-bed was designed to generate datasets for both balanced (healthy) and unbalanced (faulty) motor conditions. The experimental setup consists of a three-phase induction motor, a signal acquisition system (Raspberry Pi), a vibration sensor (MPU6050) and a coupling system capable of adjusting motor balance. Supervised machine learning algorithms, Support Vector machine (SVM) and Random Forest (RF) were applied to classify and detect anomalies in the collected datasets. A grid search algorithm was employed for hyperparameter tuning of the SVM and RF models. The proposed technique achieved a maximum accuracy of approximately 98% using SVM and 100% using the RF algorithm leveraging nine optimally selected temporal features. ©2024 IEEE.

关键词： Rotating machinery

来源：评论

学校读者我要写书评

暂无评论

Enhancing Sugarcane Leaf Disease Detection with CNNs: Leveraging Advanced machine learning Techniques 3

Enhancing Sugarcane Leaf Disease Detection with CNNs: Levera...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Soni, Dhruv Kumar Taneja, Ashu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350354171

This paper introduces a unique method for precise detection and classification of sugarcane leaf diseases based on Convolutional Neural Networks (CNNs). Using the 'Sugarcane Leaf Disease dataset' obtained from Kaggle, trained to categorize five specific leaf conditions: Healthy, Mosaic, RedRot, Rust, and Yellow. By means of intensive testing and use of innovative machine learning techniques including ensemble learning and transfer learning, our model generated interesting results. It received a macro accuracy of 97.22%, a macro recall of 97.18%, and a macro precision of 97.22%. The performance of the model was thoroughly assessed and demonstrated to have great capacity to regularly distinguish between many disease categories by means of a confusion matrix and several criteria. The favorable outcomes underline the possibility of our approach to significantly help in the identification of agricultural illnesses, therefore enabling early detection and effective management of crop health. The validation of the model utilizing bigger and more varied datasets will take front stage in further studies. It will also look at the possibilities for the concept in other spheres of agricultural imaging. The ultimate aim is to produce an all-encompassing diagnostic instrument for pragmatic farming environments. © 2024 IEEE.

关键词： Agricultural Disease Diagnosis Confusion Matrix Convolutional Neural Networks (CNNs) Crop Health Monitoring Deep learning Ensemble learning F1 Score Kaggle dataset machine learning Precision Recall Sugarcane Leaf Diseases Transfer learning

来源：评论

学校读者我要写书评

暂无评论

A Detection Method for Typical Component of Space Aircraft Based on YOLOv3 Algorithm 3

A Detection Method for Typical Component of Space Aircraft B...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： He, Bian Jianzhong, Cao Cheng, Li Junpeng, Dong Zhongling, Ruan Chao, Mei Xi'an Institute of Optics and Precision Mechanics of Cas Xi'an China University of Chinese Academy of Sciences Beijing China Xi'an Key Laboratory of Spacecraft Optical Imaging and Measurement Technology Xi'an China

ISBN: (纸本)9798350380989

A solar panel recognition method based on YOLOv3 deep learning algorithm is proposed to address issues such as inaccurate recognition of traditional algorithms in space solar panel detection. First, this paper scales the dataset images to 416 × 416, then uses Labelme to annotate the data and transform the bounding box position information, and finally uses the YOLOv3 algorithm framework for model training. The results show that the recall, F1 score and accuracy of YOLOv3 algorithm are all above 80%. The YOLOv3 deep learning algorithm meets the requirements for real-time detection of solar panels in terms of accuracy. © 2024 IEEE.

关键词： Deep learning object detection Space debris YOLOv3

来源：评论

学校读者我要写书评

暂无评论

Early-Stage Diabetes Risk Prediction using machine learning Techniques 3

Early-Stage Diabetes Risk Prediction using Machine Learning ...

引用

3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Raj, Aparajita Jena, Junali Jasmine Ghosh, Debolina Jain, Sonal Sardar, Tanvir Habib Gourisaria, Mahendra Kumar School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India Department of Information Technology Manipal University Jaipur Rajasthan Jaipur India School of Economics and Commerce KIIT Deemed to be University Odisha Bhubaneshwar India CSE School of Engineering Dayanand Sagar University Karnataka Bangalore India

ISBN: (纸本)9798331527549

Diabetes is a chronic and metabolic disease caused by an increment in glucose level in the blood, which leads overtime to major serious damage to the heart and kidneys. Millions of people are affected by diabetes in the world. The main aim of this study is the prediction of the risk of diabetes at an early stage. The data set contains information on the symptoms of newly diagnosed diabetic patients, those who are at risk of developing diabetes. The data was collected through direct questionnaire's administer to patients at the hospital in Bangladesh. In this work, 10 supervised machine learning algorithms such as Random Forest (RF), Naive Bayes (NB), etc. are implemented by taking accuracy, precision, F1 score etc. as evaluation criteria. This diabetic risk prediction model has a higher accuracy of 99.12% with Random Forest and Gradient boosting than other models. © 2025 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Effective machine learning-Based Predictive Analytics for Loan Default Prediction in Banking Sector 3

Effective Machine Learning-Based Predictive Analytics for Lo...

引用

3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kumar Jain, Yogesh Mannepalli, Praveen Kumar Kaur, Kirat Maheshwari, Abhishek Singh, Jagriti Ranka, Monica Finance Dr.D.Y.Patil Vidyapeeth Centre for Online Learning Maharashtra Pune India Chandigarh University Department of Computer Science and Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India University School of Commerce Finance and Accountancy Ghaziabad India Manipal University Jaipur India Dayananda Sagar College of Arts Science and Commerce Bengaluru India

ISBN: (纸本)9798331528201

These days, a crucial topic is the early prediction of loan failure in banks. Customer loan default may be predicted using a variety of statistical and AI classification methods. This paper introduces a machine learning-based predictive analytics model aimed at improving loan default prediction in the banking sector by leveraging open P2P loan data from Lending Club. To improve the precision and consistency of predictions, the model uses state-of-the-art ML methods, namely RF. By analyzing critical borrower information such as credit history, loan purpose, and debt status, the model identifies high-risk borrowers with greater precision. The study evaluates a performance of different ML models, including LR, DT, and SVM, but identifies RF as the most effective, demonstrating superior metrics with 89% accuracy, 99.5% sensitivity, and 80.3% specificity. These results highlight the model's ability to not only predict defaults accurately but also reduce false positives and negatives. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

machine learning-based Intrusion Detection System for IoT using Contiki-OS 3

Machine Learning-based Intrusion Detection System for IoT us...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Vora, Vinit Sinha, Rishit Palan, Jignesh Kulkarni, Vikram B.Tech CSE- Cyber Security MPSTME SVKM’s NMIMS Mumbai India SMIEEE Department of Information Technology MPSTME SVKM’s NMIMS Mumbai India

ISBN: (纸本)9798331532420

The rapid expansion of the Internet of Things (IoT) is a serious concern in terms of security due to the resource-constrained environment. Classical IDS cannot work properly in such scenarios because computation cost is too high. This paper proposed a machine learning-based IDS for Contiki-OS on IoT networks that adopts the AdaBoost algorithm and a Random Forest approach in the detection of cyberattacks such as DDoS, sinkhole, and MITM attacks. The system is trained on the Edge-IIoTset dataset with data preprocessing to realize optimal performance. The simulated results in the Cooja network simulator are complemented by validation on real IoT hardware to reveal the effectiveness of the IDS. Results in 91.08% accuracy and 93.0% precision with 92.0% recall for the ability to improve IoT security with low resource usage. Future enhancement involves integrating deep learning with enhanced attack coverage. © 2024 IEEE.

关键词： Network intrusion

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 << < 52 53 54 55 56 57 58 59 60 61 > >>

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：