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

65,368 篇 会议
597 册 图书
335 篇 期刊文献

馆藏范围

66,299 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

39,061 篇 工学
- 35,706 篇 计算机科学与技术...
- 23,365 篇 软件工程
- 9,207 篇 信息与通信工程
- 5,480 篇 电气工程
- 5,261 篇 控制科学与工程
- 4,772 篇 生物工程
- 3,836 篇 生物医学工程（可授...
- 1,926 篇 网络空间安全
- 1,712 篇 安全科学与工程
- 1,466 篇 光学工程
- 1,413 篇 机械工程
- 1,385 篇 动力工程及工程热...
- 1,336 篇 电子科学与技术（可...
- 1,252 篇 仪器科学与技术
- 1,220 篇 交通运输工程
- 1,203 篇 化学工程与技术
14,268 篇 理学
- 8,059 篇 数学
- 5,262 篇 生物学
- 3,664 篇 统计学（可授理学、...
- 3,114 篇 系统科学
- 2,384 篇 物理学
- 1,293 篇 化学
9,125 篇 管理学
- 5,395 篇 管理科学与工程(可...
- 4,576 篇 图书情报与档案管...
- 2,255 篇 工商管理
4,536 篇 医学
- 3,733 篇 临床医学
- 3,238 篇 基础医学(可授医学...
- 2,071 篇 药学(可授医学、理...
- 1,808 篇 公共卫生与预防医...
1,914 篇 法学
- 1,661 篇 社会学
1,191 篇 农学
1,039 篇 经济学
770 篇 教育学
349 篇 文学
45 篇 艺术学
39 篇 军事学
5 篇 哲学
5 篇 历史学

主题

17,419 篇 machine learning
5,383 篇 machine learning...
4,691 篇 deep learning
4,145 篇 accuracy
4,006 篇 support vector m...
3,475 篇 computational mo...
3,330 篇 training
3,248 篇 predictive model...
2,957 篇 feature extracti...
2,054 篇 data models
1,987 篇 cloud computing
1,814 篇 real-time system...
1,731 篇 neural networks
1,605 篇 adversarial mach...
1,560 篇 prediction algor...
1,350 篇 artificial intel...
1,311 篇 data mining
1,295 篇 decision trees
1,228 篇 random forests
1,175 篇 internet of thin...

机构

209 篇 chitkara univers...
134 篇 school of comput...
80 篇 karnataka bangal...
78 篇 school of comput...
63 篇 department of co...
48 篇 chennai
47 篇 lovely professio...
44 篇 school of comput...
43 篇 univ chinese aca...
42 篇 centre of interd...
39 篇 graphic era deem...
39 篇 graphic era deem...
35 篇 shenyang institu...
33 篇 vishwakarma inst...
30 篇 department of in...
30 篇 faculty of compu...
29 篇 department of co...
29 篇 shanghai jiao to...
29 篇 department of co...
28 篇 computer science...

作者

36 篇 anjali t.
26 篇 isman kurniawan
26 篇 de-shuang huang
24 篇 rakesh kumar
23 篇 kumar anil
22 篇 kumar ajay
22 篇 hui wang
21 篇 amandeep nagpal
21 篇 gill kanwarparta...
20 篇 qin zengchang
20 篇 sharif mhd saeed
19 篇 yu wang
19 篇 ajay kumar
19 篇 babu tina
18 篇 qomariyah nunung...
18 篇 pareek piyush ku...
17 篇 zohreh doborjeh
16 篇 schuurmans dale
16 篇 andersson karl
16 篇 kumar manish

语言

51,677 篇 英文
14,449 篇 其他
729 篇 中文
3 篇 法文
2 篇 葡萄牙文
2 篇 俄文
1 篇 西班牙文
1 篇 土耳其文

检索条件"任意字段=International Conference on Machine Learning and Computing"

共 66300 条记录，以下是4581-4590 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：

Multi-Disease Detection and Classification of Sugarcane Leaves using Transfer learning 11

Multi-Disease Detection and Classification of Sugarcane Leav...

引用

11th international conference on Soft computing and machine Intelligence, ISCMI 2024

作者： Jain, Anand Kumar Sharma, Shivam Nain, Neeta MNIT Dept. CSE Jaipur India

ISBN: (纸本)9798331518127

The early detection of multiple diseases in sugarcane is crucial for improving crop yield and reducing losses. Traditional manual inspection methods are time-consuming, errorprone, and require expert knowledge. In this study, we propose a novel approach for detecting multiple diseases in sugarcane leaves using a combination of transfer learning and a custom hybrid model. Initially, pre-trained models like MobileNet and DenseNet are fine-tuned on a sugarcane disease dataset to leverage the feature extraction capabilities of transfer learning. We then propose a new model, the MobileNet-DenseNet Concatenation (MobileNet-DenseNet Concat), which combines the strengths of both architectures. This hybrid model fuses the lightweight, efficient nature of MobileNet with the deep feature extraction capabilities of DenseNet, creating a robust model for multidisease classification. The model is trained and evaluated on a labeled sugarcane disease dataset, and its performance is compared with individual transfer learning models. The proposed model achieves improved 96% accuracy, precision, recall, and F1 scores, outperforming existing approaches in detecting multiple diseases simultaneously. Additionally, techniques like bounding boxes are used to localize disease regions on the leaves. The results demonstrate that the MobileNet-DenseNet Concat model offers a promising solution for automating sugarcane disease detection, potentially benefiting farmers and agronomists by facilitating early intervention and treatment strategies. © 2024 IEEE.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

An Efficient Model based on machine learning Algorithms for Virtual machines Classification in Cloud computing Environment 4

An Efficient Model based on Machine Learning Algorithms for ...

引用

4th international conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Amahrouch, Abdelhadi Bouhamidi, Mehdi Saadi, Youssef El Kafhali, Said Sultan Moulay Slimane University Faculty Of Sciences And Techniques Data Science For Sustainable Earth Laboratory B.P. 523 Beni Mellal23000 Morocco Polytechnic University Laboratory Of Industrial And Human Automation Mechanics And Computer Science Hauts-de-France France Hassan First University Of Settat Faculty Of Sciences And Techniques Computer Networks Modeling And Mobility Laboratory B.P. 539 Settat26000 Morocco

ISBN: (纸本)9798350309508

In cloud computing, virtual machines consolidation (VMC) techniques are commonly used to improve resource utilization and reduce energy consumption. Task scheduling in cloud systems is a crucial aspect of VMC as it involves mapping clients' requirements to the appropriate computing resources such as Virtual machines (VMs) or Physical machines (PMs). The cloud provider must ensure that tasks are executed efficiently using the available shared computing resources while maintaining the quality of service (QoS) and minimizing the carbon footprint. Therefore, a good VM migration based on the customer's needs and IT resource capacity is required to maintain the best performance of the cloud system. In this work, we introduce an approach that leverage machine learning-based algorithms for VMs classification based on their latency sensitivity to facilitate subsequent migration into a suitable PM for better VMC. These algorithms categorize VMs into two groups: potentially inter-active machines (exhibiting periodic behavior on a daily scale) and latency-insensitive machines (for example, batch workloads, development, and test workloads). Our model demonstrated robust performance, achieving an accuracy of around 83%, thus establishing itself as the most proficient classifier in this study. © 2024 IEEE.

关键词： Virtual machine

来源：评论

学校读者我要写书评

暂无评论

Teaching AI and Data Analytics to Electrical Engineering Students

Teaching AI and Data Analytics to Electrical Engineering Stu...

引用

2024 IEEE Region 10 conference, TENCON 2024

作者： Ercan, M. Fikret School of Electrical and Electronic Engineering Singapore Polytechnic Singapore

ISBN: (纸本)9798350350821

Artificial intelligence and data analytics began to dominate all aspects of technology. Electrical Engineering graduates entering the workforce will likely rely on AI tools and systems to manage their engineering tasks, or they may be required to integrate AI features into the products and services they develop. Traditionally, these subjects were part of computer engineering curriculum rather than electrical engineering. Considering the recent advances made in AI and machine learning, and their widespread application in various fields, integrating these subjects into electrical engineering curriculum would be beneficial to engineering graduates. Nevertheless, it is a challenging task. A major challenge is to make the subject relevant to the body of electrical engineering knowledge. Another obstacle to overcome is students' shortcoming in computing knowledge and programming skills. In this paper, we present our initiatives to integrate AI, machine learning and data analytics concepts into electrical engineering curriculum and report our key findings and challenges encountered. © 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

Integration of Open-Source machine learning Operations Tools into a Single Framework 4

Integration of Open-Source Machine Learning Operations Tools...

引用

4th IEEE international conference on computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Vishwambari, T. Agrawal, Sonali Amrita School of Engineering Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350306118

machine learning lifecycle management is a cyclic process followed by most data science projects. It involves various steps, from understanding the business objectives to monitoring the model once deployed. Various open-source tools are present to maintain the accuracy of the deployed model by performing experiment tracking, model monitoring, feature storing, model registry, etc. Most open-source tools focus on completing a particular task at a time. Integrating all these tools into a single framework increases productivity and scalability. It reduces the cost for the organization. This paper provides a method to integrate open-source tools for model monitoring, feature storing, and machine learning experiment tracking operations into a single framework. All these tools are integrated into a single framework in MLflow. The results obtained for the integrated framework are coded in Python using Jupyter Notebook. © 2023 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Liver disease Prediction using Ensemble learning 3

Liver disease Prediction using Ensemble Learning

引用

3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Pilankar, Atharva Shyamala, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The increasing prevalence of liver disease necessitates efficient identification methods to alleviate the diagnostic burden on healthcare providers. machine learning offers a promising solution by evaluating vital parameters to predict liver disease, thereby reducing the risk of incorrect diagnoses and subsequent financial burdens on patients. Ensuring that predictive models generalize well across diverse populations and demographics is crucial for their wide applicability. Standardized evaluation metrics for liver disease prediction models are essential for unbiased comparisons and benchmarking. Demonstrating the superiority of new models over existing clinical methods is key to their adoption in clinical practice. This research explores the use of ensemble learning techniques to enhance the accuracy of liver disease predictions. By combining various supervised machine learning models, including Support Vector machines (SVM), logistic regression, gradient boosting, decision trees, random forests, and artificial neural networks, the predictive outcomes have been improved. Each model undergoes extensive hyperparameter tuning to identify the optimal parameters for the given dataset. Additionally, feature selection techniques are employed to determine the most significant features contributing to the prediction. The study implements both stacking and bagging classification methodologies, achieving an accuracy rate of 79%. This hybrid approach demonstrates significant potential in improving the reliability and accuracy of liver disease predictions, offering a robust tool for early diagnosis and management of liver disease in diverse patient populations. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Trace-based Performance Analysis for Deep learning in Edge Container Environments 8

Trace-based Performance Analysis for Deep Learning in Edge C...

引用

8th IEEE international conference on Fog and Mobile Edge computing (FMEC)

作者： Park, Soyeon Bahn, Hyokyung Ewha Womans Univ Dept Comp Engn Seoul South Korea

ISBN: (纸本)9798350316971

In recent years, container-based deep learning has emerged as a trending technology in edge environments. Containers offer several advantages over traditional virtual machines, including improved resource utilization and mobility. However, containers still hinder efficient use of system resources in deep learning workloads, so it is important to adopt effective resource management techniques to avoid resource conflicts. In this paper, we extract system call and event traces while executing deep learning workloads in a container and compare the results with those of running the same workloads on a host machine. By comparing system calls invoked between the two environments, we quantify the overhead of containers with respect to resource consumption and interference. We then explore the impact of running multiple containers concurrently and highlight the issues that arise in a multi-tenant environment. Our findings show that container-based deep learning can be a viable solution for deep learning workloads, but it is important to carefully consider the resource requirements and performance impact of containerization. We recommend that cloud or edge providers use a combination of resource management techniques, such as resource quotas and limits, to avoid resource wastes of containers and interference with each other.

关键词： container deep learning virtual machine system call system resource

来源：评论

学校读者我要写书评

暂无评论

A Novel Algorithm and System of Customer Value Evaluation based on Telecom Operator Big Data 22

A Novel Algorithm and System of Customer Value Evaluation ba...

引用

IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Cheng, Chen Cheng, Xinzhou Dai, Jinyou Xia, Xu Yang, Bin Lyu, Feibi Gao, Jie Zhang, Wei Xiao, Tian Wang, Tianyi China United Network Commun Corp Res Inst Beijing Peoples R China China Informat Commun Technol Grp Wuhan Peoples R China China Telecom Res Inst Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

With the increasing market competition, telecom operators need to improve the level of services, ensure the quality of experience, as well as reduce the cost of enterprise. Therefore it is crucial to evaluate the value of telecom customers accurately. The traditional method of telecom customer value assessment is mainly based on ARPU (Average Revenue Per User), which is one-dimensional and cannot evaluate customer value comprehensively. This paper proposes a multidimensional customer value assessment method, including two perspectives, Current value and Potential value to improve the accuracy and comprehensiveness of evaluation. Also, an intelligent method based on swarm intelligence algorithm is presented to calculate the indicator weight for each characteristic field of customer value. The practical results show that the algorithm, which is applied to realistic scenarios of network operation, enables telecom operator to achieve a comprehensive and accurate customer value assessment in many issues such as customer churn warning and accurate recommendation, ultimately increasing the effectiveness and efficiency of decision-making closed loop for telecom operators. At last but not the least, the algorithm and system can benefit other industries to improve their intelligence level of customer service.

关键词： Custormer Value Evaluation Telecom Big Data machine learning Swarm Intelligence

来源：评论

学校读者我要写书评

暂无评论

Media Haze Classification in Retinal Images Using Transfer learning with Convolutional Neural Networks 23

Media Haze Classification in Retinal Images Using Transfer L...

引用

23rd IEEE international conference on machine learning and Applications, ICMLA 2024

作者： O'Berry, Jonathan Liu, Xudong School of Computing University of North Florida JacksonvilleFL United States

ISBN: (纸本)9798350374889

Media haze (MH) is a serious eye condition that can lead to various issues. This is especially true since MH is often an early warning sign of more severe conditions. If not treated quickly, it can potentially cause discomfort, blurred vision, or even blindness. Currently, MH is often detected by manually examining fundus images. This process allows for human error, where MH may not be detected. This study attempts to increase the detection and diagnosis of MH through our methods of preprocessing fundus images being examined, as well as the use of machine learning models. The models examined include LeNet-5, MLeNet (a modified LeNet), AlexNet, MobileNet, ResNet152v2, and VGG-16, where the former three models were trained on the RFMiD dataset, and the latter three were trained using pre-trained models fine-tuned on RFMiD. We found that all models using the proposed preprocessing demonstrated better performance than using the original images. The most effective model in this study was MobileNet, achieving 97.2% accuracy and 99.2% AUC score on the test set, outperforming the state-of-the-art model we found in the literature. © 2024 IEEE.

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Neuro-Fuzzy System for Spatial Prediction based on Hybrid Models of Artificial Intelligence and Meta-Heuristic Optimization Algorithm 2

Neuro-Fuzzy System for Spatial Prediction based on Hybrid Mo...

引用

2nd international conference on Edge computing and Applications, ICECAA 2023

作者： Rahul Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350347579

machine learning (ML) experts are becoming more conscious of the significance of their study in addressing the climate dilemma as the part performed by statistical and computational sciences in environmental analysis and estimation becomes more significant. ML methods are effective at assessing and analyzing spatially and temporally variable environmental data because they are instruments for general nonlinear function estimation. Through their autonomous feature representation learning, Deep learning (DL) methods have demonstrated the ability to acquire spatial, temporal, and spatio-temporal relations;however, the issue of the extrapolation of consistent spatio-temporal disciplines evaluated on a set of erroneous places in space is still unresolved. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Bibliographic Exploration of Application of machine learning and Artificial Intelligence in Solar Energy

Bibliographic Exploration of Application of Machine Learning...

引用

2024 international conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ajibade, Samuel-Soma M. Bashir, Faizah Mohammed Dodo, Yakubu Aminu Oyebode, Oluwadare Joshua Culpable, Rex V. De La Calzada, Limic M. Adediran, Anthonia Oluwatosin Ajibade, Temiloluwa Iyanuoluwa Sunway University Dept of Computing and Information Systems Selangor Malaysia Istanbul Ticaret Univ Dept of Comp. Engineering Turkey University of Hail Department of Decoration and Interior Design Hail55467 Saudi Arabia Najran University Architectural Engineering Department Najran66426 Saudi Arabia Afe Babalola University Civil and Environmental Engineering Ekiti State Ado-Ekiti Nigeria Cebu Technological University College of Technology Cebu Philippines Cebu Technological University College of Engineering Cebu Philippines Federal University of Uberlandia Faculty of Architecture and Urban Design Brazil Scholar School System Dept of Business Management Birmingham United Kingdom

ISBN: (纸本)9798350358155

Solar energy could mitigate global warming and climate change. Solar energy faces economic, environmental, and technical challenges. machine learning solves these technical issues. Despite several studies, machine learning in photovoltaics and solar energy is understudied. This study examines publishing patterns and bibliometrics to critically evaluate machine learning applications in photovoltaics and solar energy research. Scopus uses PRISMA. international publishing, citations, and collaboration are high. The Chinese Ministry of Education employs famous scholars like G. E. Georghiou and Haibo Ma. China is most active due to funding schemes like the National Natural Science Foundation and the National Key Research and Development Programme. This study examines publication patterns by country, institution, and funding organisation from 2014 to 2022, spanning topic categories and indicators. Examining author-keyword data to group publishing themes and identify influential journals. Increasing understanding of machine learning applications in photovoltaics and solar energy research. This project will examine the potential for significant development and the hurdles that must be overcome to leverage Cognitive computing's benefits in cancer and tumour research. In response to the rising amount of malware, phishing, and intrusion attacks on global energy and grid infrastructure, photovoltaic and solar energy system cybersecurity may be studied. © 2024 IEEE.

关键词： Malware

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 << < 455 456 457 458 459 460 461 462 463 464 > >>

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：