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

67,855 篇 会议
804 册 图书
452 篇 期刊文献
2 篇 学位论文
1 篇 科技报告

馆藏范围

69,113 篇 电子文献
3 种 纸本馆藏

日期分布

学科分类号

39,677 篇 工学
- 34,083 篇 计算机科学与技术...
- 28,134 篇 软件工程
- 8,367 篇 信息与通信工程
- 6,577 篇 控制科学与工程
- 6,149 篇 生物工程
- 4,733 篇 生物医学工程（可授...
- 4,648 篇 电气工程
- 2,823 篇 机械工程
- 2,217 篇 光学工程
- 2,112 篇 电子科学与技术（可...
- 2,056 篇 安全科学与工程
- 1,919 篇 动力工程及工程热...
- 1,743 篇 化学工程与技术
- 1,540 篇 交通运输工程
- 1,536 篇 仪器科学与技术
18,280 篇 理学
- 10,074 篇 数学
- 6,671 篇 生物学
- 4,386 篇 统计学（可授理学、...
- 3,610 篇 系统科学
- 3,414 篇 物理学
- 1,655 篇 化学
11,702 篇 管理学
- 6,962 篇 管理科学与工程(可...
- 5,566 篇 图书情报与档案管...
- 3,445 篇 工商管理
4,615 篇 医学
- 4,107 篇 临床医学
- 4,013 篇 基础医学(可授医学...
- 2,408 篇 药学(可授医学、理...
- 2,044 篇 公共卫生与预防医...
1,961 篇 法学
- 1,649 篇 社会学
1,476 篇 农学
- 1,436 篇 作物学
1,377 篇 教育学
1,314 篇 经济学
134 篇 文学
95 篇 军事学
21 篇 艺术学
1 篇 哲学
1 篇 历史学

主题

12,070 篇 machine learning
4,972 篇 accuracy
4,497 篇 machine learning...
4,201 篇 support vector m...
3,831 篇 deep learning
3,784 篇 real-time system...
3,275 篇 training
3,217 篇 feature extracti...
3,122 篇 predictive model...
2,285 篇 computational mo...
1,956 篇 data models
1,825 篇 neural networks
1,767 篇 learning systems
1,717 篇 convolutional ne...
1,479 篇 artificial intel...
1,449 篇 adversarial mach...
1,343 篇 prediction algor...
1,267 篇 internet of thin...
1,205 篇 random forests
1,102 篇 decision trees

机构

456 篇 chitkara univers...
87 篇 lovely professio...
68 篇 department of co...
54 篇 department of co...
47 篇 chitkara univers...
46 篇 school of comput...
46 篇 centre of resear...
42 篇 karnataka bangal...
41 篇 computer science...
38 篇 department of co...
36 篇 department of co...
34 篇 chennai
33 篇 chitkara univers...
32 篇 department of co...
31 篇 school of comput...
30 篇 cmr technical ca...
30 篇 department of ar...
29 篇 department of in...
29 篇 school of comput...
29 篇 department of me...

作者

44 篇 gill kanwarparta...
36 篇 rakesh kumar
28 篇 senthil pandi s.
26 篇 shiva mehta
26 篇 pareek piyush ku...
24 篇 amandeep nagpal
23 篇 lakshmanarao a.
20 篇 ramesh s.
20 篇 yu wang
20 篇 kumar ajay
20 篇 zheng-ou wang
19 篇 j. relin francis...
19 篇 wang lei
19 篇 kumar atul
18 篇 zhang tao
18 篇 anjali t.
17 篇 sharma neha
17 篇 deepak gerard
17 篇 ajay kumar
17 篇 swain debabrata

语言

63,020 篇 英文
5,946 篇 其他
357 篇 中文
10 篇 土耳其文
4 篇 西班牙文
2 篇 德文
2 篇 葡萄牙文
1 篇 法文
1 篇 俄文

检索条件"任意字段=International Conference on Advances in Machine Learning and Systems Engineering"

共 69114 条记录，以下是301-310 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：

Factory Floor Transformation for Achieving Operational Excellence with IoT and machine learning

Factory Floor Transformation for Achieving Operational Excel...

引用

2024 international conference on advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Nagalakshmi, T.J. Santhuja, P. Rangarajan, J. Muthukumarasamy, S. Mohankumar, N. Velmurugan, S. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Department Of Electronics And Communication Engineering Tamil Nadu Chennai India Cmr Technical Campus Department Of Computer Science And Engineering Telangana Hyderabad India Department Oj Etectronics And Communication Engineering Tamil Nadu Rasipuram India S.A.Engineering College Department Of Computer Science And Engineering Tamil Nadu Chennai India Maharashtra Nagpur India T.J.S. Engineering College Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371567

In today's competitive industry, manufacturers seek novel approaches to improve operations and enhance efficiency. To achieve Operational Excellence, this abstract provides a Factory Floor Transformation plan that uses the Internet of Things (IoT), machine learning, and the Random Forest algorithm. The Factory Floor Transformation employs IoT sensors and devices throughout production. These sensors monitor machine operation, product quality, and energy usage in real-time. Using data preprocessing, feature engineering, and predictive modeling, a strong machine learning framework is created. The Random Forest algorithm, which handles complicated, multi-dimensional data, powers machine learning. Random Forest is appropriate for manufacturing complex problems because its ensemble learning technique yields reliable and accurate forecasts. Predictive maintenance, quality control, and resource optimization are enabled by the model's previous training and real-time updates. Factory Floor Transformation provides enterprises with a complete Operational Excellence solution. Companies may minimize production bottlenecks, downtime, defects, and resource allocation by using IoT data and machine learning. This abstract illustrates how this method may transform production and make it more efficient and competitive. © 2024 IEEE.

关键词： Sensor networks

来源：评论

学校读者我要写书评

暂无评论

machine learning on Public Intrusion Datasets: Academic Hype or Concrete advances in NIDS? 53

Machine Learning on Public Intrusion Datasets: Academic Hype...

引用

53rd Annual IEEE/IFIP international conference on Dependable systems and Networks (DSN)

作者： Catillo, Marta Pecchia, Antonio Villano, Umberto Univ Sannio Dipartimento Ingn Benevento Italy

ISBN: (纸本)9798350325454

The number of papers on network intrusion detection based on machine and deep learning is growing at an unprecedented rate. Most of these papers follow a well-consolidated pattern: (i) proposal of an intrusion detection system based on machine (deep) learning, (ii) learning-testing with one (more) public intrusion dataset(s), (iii) achievement of outstanding detection performance. Is the intrusion detection problem solved? Unfortunately, no. This paper shares a deep reflection on the major limitations of public intrusion datasets and related machine learning experiments, which greatly diminish the findings documented by the literature. At the end of the day, in spite of the academic hype and the increasingly-complex machine and deep learning exercises around, the role of public datasets in advancing intrusion detection of real-world networks remains questionable. The way existing intrusion datasets are collected, released and used by the community should be approached with extreme caution. This paper provides concrete hints for the construction of future intrusion detection datasets and more rigorous machine learning experiments.

关键词： deep learning intrusion detection NIDS public datasets cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Acoustic Fault Detection in Brushless Direct Current Motors Using machine learning for Predictive Maintenance 4

Acoustic Fault Detection in Brushless Direct Current Motors ...

引用

4th IEEE international conference on Recent advances in systems Science and engineering, RASSE 2024

作者： Hsiao, Chun-Yu Htet, Soe Min Department of Electrical Engineering Tatung University Taipei Taiwan

ISBN: (纸本)9798331528263

Performance maintenance of Brushless Direct Current (BLDC) motors is crucial due to their widespread applications, ranging from household appliances like fans to critical systems in unmanned aerial vehicles (UAVs). As these motors are integral to various operations, ensuring their reliability and detecting faults in an early stage is essential to prevent failures and maintain optimal performance. Numerous studies have been conducted to develop methods for fault detection in BLDC motors, focusing on different aspects such as vibration analysis, current monitoring, and sound analysis. This paper proposes a reliable approach for detecting faults by developing a system that identifies bearing and propeller faults based on the audio analysis of the operational sound of the BLDC motor. Acoustic features were meticulously extracted from the motor’s operation sound, and five different machine learning models were trained and evaluated for their effectiveness in fault detection. Among these models, the K-Nearest Neighbors (KNN) and Extreme Gradient Boosting (XGBoost) models achieved a remarkable accuracy of 100%, outperforming the other models. Furthermore, the study identified the top five acoustic features that significantly contributed to the models’ high performance, providing valuable insights for further improvements in fault detection systems. This research offers a robust and efficient solution for maintaining the performance of BLDC motors. © 2024 IEEE.

关键词： Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

Elderly Fall Detection System Using mm-Wave Radar Sensor 4

Elderly Fall Detection System Using mm-Wave Radar Sensor

引用

4th international conference on advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Joy, Jeslet John, Amalda Theresa Anna Alex, Angel Nair, Adityakrishna S Sreesh, P.R. Manuel, Anto St Joseph's College of Engineering and Technology Dept. of Electronics and Communication Engineering Palai India

ISBN: (纸本)9798350343670

This project focuses on the implementation of an elderly fall detection system using millimeter-wave radar technology, prioritizing privacy preservation within indoor environments. By harnessing mm-wave radar, our system offers technical advantages over camera-based solutions. Radar operates in the radio frequency spectrum, ensuring privacy, as it does not capture visual data, addressing concerns regarding surveillance and consent. Technical aspects encompass mm-wave radar sensor deployment, signal processing algorithm development for fall detection, and real-time data analysis integration. Radar mitigates issues posed by low lighting, occlusions, and line-of- sight limitations common in camera-based systems. Additionally, machine learning enhances fall detection accuracy, reducing false alarms while maintaining high sensitivity. © 2024 IEEE.

关键词： fall detection machine learning millimeter wave mm-wave Radar sensors Point Cloud radar sensor signal processing

来源：评论

学校读者我要写书评

暂无评论

Cardiovascular Disease Detection Using machine learning Algorithms

Cardiovascular Disease Detection Using Machine Learning Algo...

引用

2024 international conference on advances in Computing, Communication, Electrical, and Smart systems, iCACCESS 2024

作者： Mondal, Henreata Talukder, Kamrul Hasan Khulna University Computer Science & Engineering Discipline Khulna Bangladesh

ISBN: (纸本)9798350350289

Cardiovascular diseases (CVDs) are considered as the prime cause of death across the globe. Early and highly accurate detection of heart diseases can really be helpful for the world population. Addressing this problem on a large scale is essential for keeping the world healthy. machine learning (ML) algorithms play a vital role in detecting cardiovascular diseases, offering the potential for higher accuracy in identification. Achieving precise detection with ML algorithms holds significant benefits for people worldwide. Our research utilizes various ML algorithms including Gaussian Naive Bayes (GNB), Random Forest (RF) and Logistic Regression (LR) to identify cardiovascular diseases. After assessing individual model accuracies, we combine GNB, RF and LR using hard voting, creating the 'voting-model'. Additionally, a stacking classifier is applied using GNB, RF, LR as base estimators and LR as the final estimator. The ensemble approach demonstrates maximum accuracy compared to individual models highlighting the potential of combining diverse algorithms through ensemble learning for enhanced predictive models in cardiovascular health. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

RaiDen: An IOT-based automated and integrated system for improvement of plant growth

RaiDen: An IOT-based automated and integrated system for imp...

引用

2024 international conference on advances in Computing, Communication, Electrical, and Smart systems, iCACCESS 2024

作者： Aurpita, Sumyea Nazifa Momo, Anika Tahsin Rahman, Mohammed Ashikur Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798350350289

Utilizing Internet of Things (IoT) technology, the proposed system is an automated system that establishes connectivity among various hardware components to enhance plant growth. The system is aimed for its use in agriculture, to make the process more sustainable and productive. This research focuses on controlling the pH level of water to improve plant growth. In Bangladesh, about 2,200 millimeters of rainfall every year on average. Nevertheless, a significant challenge arises from the polluted atmosphere resulting from rapid urbanization, where harmful gases such as sulfur dioxide, carbon and nitrogen dioxide combine with rainwater, leading to the formation of acidic precipitation. The system is to develop an automated, integrated system that utilizes acidic(low pH) rainwater to maintain pH between 5-6, enhancing plant growth. Furthermore, after data collection through IoT devices and analysis via machine learning algorithms like linear regression, random forest regression, and gradient boosting regression so the system can autonomously adjust the water pH and watered plants. © 2024 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

ROAR: Routing Packets in P4 Switches With Multi-Agent Decisions Logic 1

ROAR: Routing Packets in P4 Switches With Multi-Agent Decisi...

引用

1st IEEE international conference on machine learning for Communication and Networking (IEEE ICMLCN)

作者： Angi, Antonino Sacco, Alessio Esposito, Flavio Marchetto, Guido Politecn Torino Dept Control & Comp Engn Turin Italy St Louis Univ Dept Comp Sci St Louis MO 63103 USA

ISBN: (纸本)9798350343205;9798350343199

The soaring complexity of networks has led to more complex methods to efficiently manage and orchestrate the multitude of network environments. Recent advances in machine learning (ML) have opened new opportunities for network management automation, exploiting existing advances in software-defined infrastructures. Advanced routing strategies have been proposed to accommodate the traffic demand of interactive systems, where the common architecture is composed of a data-driven network management schema collecting network data that feed a reinforcement learning (RL) algorithm. However, the overhead introduced by the SDN controller and its operations can be mitigated if the networking architecture is redesigned. In this paper, we propose ROAR, a novel architectural solution that implements Deep Reinforcement learning (DRL) inside P4 programmable switches to perform adaptive routing policies based on network conditions and traffic patterns. The network devices act independently in a multi-agent reinforcement learning (MARL) framework but are able to learn cooperative behaviors to reduce the queuing time of transmitting packets. Experimental results show that for an increasing amount of traffic in the network, there is both a throughput and delay improvement in the transmission compared to traditional approaches.

关键词： deep reinforcement learning P4 routing

来源：评论

学校读者我要写书评

暂无评论

Banking Fraud Detection Using machine learning Algorithms 5

Banking Fraud Detection Using Machine Learning Algorithms

引用

5th international conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Balasankula, Upagna Rao Poojitha, Bonthu Chekurtha, Sravanthi Buyya, Kruthika Bala, Harika Punna Rao, V. B v Raju Institute of Technology Dept. of Computer Science Engineering Medak Telangana Narsapur India

ISBN: (纸本)9798350379945

As technology grows rapidly, new innovations have emerged in our generation. Unfortunately, the rate of fraudulent activities has also increased, and as a result great deal of innocent individuals are in misery. Prior to the internet revolutionizing the world, banks employed traditional methods, including manual reviews, data analytics, transaction monitoring in reporting suspicious activities. However, as technology advances, bank fraud cases continue to rise. Banks must use caution when looking for banking system fraud. This project aims to adapt to these technological changes, detect abnormal behaviors in the dataset, and improve the overall performance of the trained model to protect against evolving malpractices. Combining machine learning algorithms such as logistic regression and random forest results in improve the performance of these systems which gives significant result in detecting the fraudulent activities. Implementing these technologies will improve overall security, speed up response time, and improve accuracy by recognizing suspicious activity. In conclusion, using AI technologies, specifically machine learning helps banks stay one step ahead of fraudsters, by recognizing the need of early detection of malpractice, the study can prevent such tragedies from happening in the future. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Explainable Models to Interpret Malware Detection Using Agnostic Method

Explainable Models to Interpret Malware Detection Using Agno...

引用

2024 international conference on advances in Computing, Communication, Electrical, and Smart systems, iCACCESS 2024

作者： Al Masud, Abdulla Abdullah, Anas Bin Chowdhury, Linkon East Delta University Computer Science & Engineering Chittagong Bangladesh

ISBN: (纸本)9798350350289

Malware threats have been increasing very rapidly in today's world. Since everyone uses the internet in the modern world, users are more vulnerable to cyberattacks. Traditional methods are not capable of finding the malware accurately so this research focuses on building an effective solution using machine learning to detect malicious software and interpret the model using Explainable AI to understand why a certain decision was made by the ML classifier. The dataset is preprocessed in several steps which include Min-Max Scalar for Feature Scaling, Minimum Redundancy Maximum Relevancy (MRMR) for Feature Selection, and Principal Component Analysis (PCA) for Dimensionality Reduction. Then, the algorithms are trained using multiple machine learning algorithms such as Naive Bayes, AdaBoost, Logistic Regression, Decision Tree, Random Forest, Long Short-Term Memory (LSTM) and XGBoost. Among all the classifiers, we achieved the highest accuracy of 99.825% with XGBoost. Additionally, for model interpretability, we have used Explainable AI methods such as LIME and SHAP, to understand which feature was responsible for the instance being Malware or Benign. © 2024 IEEE.

关键词： Lime

来源：评论

学校读者我要写书评

暂无评论

Facial Emotion Recognition Through Quantum machine learning

Facial Emotion Recognition Through Quantum Machine learning

引用

2024 international conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Kaur, Geet Kiran Seram, Arvind Ansari, Kamruddin Patel, Devashish Singh, Pushpendra Singla, Sanjay Chandigarh University Computer Science and Engineering Mohali India

ISBN: (纸本)9798350359688

This Facial emotion recognition plays a pivotal role in human-computer interaction, with far-reaching implications spanning psychology, healthcare, and human computer interaction. Conventional approaches, primarily utilizing classical machine learning methods, frequently face challenges in effectively interpreting intricate facial expressions under diverse environmental circumstances. However, the burgeoning paradigm of quantum machine learning (QML) presents a transformative approach, leveraging the principles of quantum computing to potentially revolutionize the landscape of emotion detection from facial expressions. This abstract explores the convergence of quantum computing and facial emotion recognition, proposing a novel framework wherein facial features undergo encoding into quantum states, undergo processing through quantum circuits, and ultimately translate into predictions of emotional states. Within this context, quantum algorithms like variational algorithms and quantum neural networks emerge as promising avenues for both training and inference tasks, presenting unparalleled opportunities to improve the accuracy and efficiency of emotion recognition systems. Despite the immense potential offered by QML, practical implementation encounters formidable challenges, including noise, decoherence, and scalability constraints inherent in existing quantum devices. However, ongoing research endeavors persist in addressing these obstacles, driven by the promise of transforming emotion recognition systems into more nuanced and robust entities. As the integration of quantum computing principles advances, it holds the promise of facilitating deeper insights into human emotions, thereby fostering more seamless and intuitive human-computer interaction experiences. By bridging the gap between quantum computing and facial emotion recognition, this abstract underscores the transformative potential of QML in reshaping the future of emotion recognition systems, paving the wa

关键词： Quantum computers

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 << < 27 28 29 30 31 32 33 34 35 36 > >>

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：