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

105,915 篇 会议
684 篇 期刊文献
554 册 图书
4 篇 学位论文

馆藏范围

107,156 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

60,454 篇 工学
- 49,571 篇 计算机科学与技术...
- 39,558 篇 软件工程
- 13,351 篇 信息与通信工程
- 11,480 篇 生物工程
- 8,434 篇 控制科学与工程
- 6,702 篇 电气工程
- 6,321 篇 生物医学工程（可授...
- 3,803 篇 光学工程
- 3,314 篇 机械工程
- 2,969 篇 电子科学与技术（可...
- 2,729 篇 网络空间安全
- 2,689 篇 安全科学与工程
- 2,503 篇 动力工程及工程热...
- 2,317 篇 化学工程与技术
- 2,171 篇 仪器科学与技术
25,849 篇 理学
- 12,106 篇 生物学
- 11,963 篇 数学
- 5,129 篇 物理学
- 4,858 篇 统计学（可授理学、...
- 3,515 篇 系统科学
- 2,324 篇 化学
17,336 篇 管理学
- 10,934 篇 管理科学与工程(可...
- 7,575 篇 图书情报与档案管...
- 5,114 篇 工商管理
13,503 篇 教育学
- 13,302 篇 教育学
6,377 篇 医学
- 5,441 篇 临床医学
- 5,191 篇 基础医学(可授医学...
- 3,408 篇 药学(可授医学、理...
- 2,855 篇 公共卫生与预防医...
3,345 篇 法学
- 2,897 篇 社会学
1,965 篇 文学
1,753 篇 农学
1,654 篇 经济学
267 篇 艺术学
105 篇 军事学
11 篇 历史学
10 篇 哲学

主题

7,093 篇 machine learning
6,838 篇 deep learning
5,239 篇 accuracy
5,005 篇 training
4,787 篇 computational mo...
4,149 篇 education
3,826 篇 students
3,516 篇 feature extracti...
3,006 篇 support vector m...
2,999 篇 machine learning...
2,985 篇 predictive model...
2,907 篇 convolutional ne...
2,378 篇 real-time system...
2,297 篇 data models
2,223 篇 neural networks
1,982 篇 cloud computing
1,805 篇 teaching
1,469 篇 reinforcement le...
1,412 篇 artificial intel...
1,379 篇 internet of thin...

机构

823 篇 chitkara univers...
145 篇 lovely professio...
109 篇 computer science...
98 篇 karnataka bangal...
92 篇 college of compu...
87 篇 school of comput...
80 篇 chitkara univers...
76 篇 school of comput...
72 篇 department of co...
68 篇 department of co...
64 篇 department of co...
58 篇 department of co...
57 篇 school of comput...
49 篇 department of co...
48 篇 chitkara univers...
43 篇 department of me...
42 篇 school of automa...
42 篇 vellore institut...
41 篇 chennai
37 篇 sathyabama insti...

作者

70 篇 shiva mehta
69 篇 anjali t.
53 篇 li yang
42 篇 sharma vikrant
40 篇 kukreja vinay
39 篇 wang lei
39 篇 rakesh kumar
37 篇 gill kanwarparta...
35 篇 sharma neha
34 篇 wang jing
32 篇 vinay kukreja
31 篇 lei wang
31 篇 zhang lei
30 篇 huang yueh-min
29 篇 liu bo
29 篇 yu wang
27 篇 li xin
26 篇 amandeep nagpal
25 篇 vinay gautam
25 篇 kumar ajay

语言

102,616 篇 英文
4,178 篇 其他
687 篇 中文
27 篇 西班牙文
17 篇 葡萄牙文
5 篇 阿拉伯文
3 篇 法文
3 篇 土耳其文
2 篇 俄文

检索条件"任意字段=International Conference on Teaching and Learning in Computing and Engineering"

共 107157 条记录，以下是4791-4800 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：

ACO-Based Hyperparameter Tuning of a DL Model for Lung Cancer Prediction 5

ACO-Based Hyperparameter Tuning of a DL Model for Lung Cance...

引用

5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Dilip, Kumar Ghantasala, G. S. Pradeep Rao, D. Nageswara Rathee, Manisha Bathla, Priyanka Alliance University Department of Computer Science and Engineering Bengaluru India Jawaharlal Nehru University School of Computer & Systems Sciences New Delhi India Chandigarh University Mohali Punjab Gharuan India

ISBN: (纸本)9798350383522

Machine learning models could provide human intelligence to perform complex tasks effectively. Among them, deep learning models have successfully been applied in several domains. However, that efficacy can be achieved only through effective design and training of deep learning models, which is a complex task to achieve and requires a lot of expertise in the domain. Finding the optimal hyperparameter values is crucial for high-performance machine learning models, especially tree-based models and neural network-based models. Estimation of the parameters such as the weight of neural networks can be done using data sets and these are internal model parameters, however the hyperparameters such as number of layers, size of layers, learning rates etc. are external to the model. The values of hyperparameters can either be determined intuitively by domain experts or by manually and randomly trying various possible configurations until we arrive at some acceptable values. Both have severe limitations for machine learning models with a lot of hyperparameters. Several attempts have been made in the direction of automatic hyperparameter optimization to save human efforts in designing machine learning models. Nature-inspired computing has gained a lot of popularity in this direction in recent years. In this paper, we have applied ant colony optimization, a nature-inspired approach for hyperparameter optimization for deep learning models for the prediction of lung cancer. © 2024 IEEE.

关键词： Ant colony optimization combinatorial optimization Deep learning model Hyperparameter optimization Metaheuristic

来源：评论

学校读者我要写书评

暂无评论

Intelligent Malware Detection System Based on Behavior Analysis in Cloud computing Environment

Intelligent Malware Detection System Based on Behavior Analy...

引用

2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Samuel, Jibu K Jacob, Mahima Thankam Roy, Melvin Sayoojya, P.M. Joy, Anu Rose Amal Jyothi College of Engineering Department of Computer Engineering Kerala India

ISBN: (纸本)9798350333244

IBMD(Intelligent Behavior-Based Malware Detection) aims to detect and mitigate malicious activities in cloud computing environments by analyzing the behavior of cloud resources, such as virtual machines, containers, and applications. The system uses different machine learning methods like deep learning and artificial neural networks, to analyze the behavior of cloud resources and detect anomalies that may indicate malicious activity. The IBMD system can also monitor and accumulate the data from various resources, such as network traffic and system logs, to provide a comprehensive view of the behavior of cloud resources. IBMD is designed to operate in a cloud computing environment, taking advantage of the scalability and flexibility of the cloud to detect malware and respond to security incidents. The system can also be integrated with existing security tools and services, such as firewalls and intrusion detection systems, to provide a comprehensive security solution for cloud computing environments. © 2023 IEEE.

关键词： Virtualization

来源：评论

学校读者我要写书评

暂无评论

Integration of Deep learning Models and Explanatory Models to Detect Anomalous Periods in Vital Signs of Mining Truck Engines 31

Integration of Deep Learning Models and Explanatory Models t...

引用

31st IEEE international conference on Electronics, Electrical engineering and computing, INTERCON 2024

作者： Lima, Ines Katia Huaman Gladys Cutipa Arapa, E. Professional School of Computer and Systems Engineering National University of San Antonio Abad de Cusco Cusco Peru

ISBN: (纸本)9798350378344

'Latex The Peruvian mining industry is beginning to adopt IoT technologies. The increasing complexity of modern engines makes it crucial to implement predictive maintenance systems to ensure reliability and performance optimization. This study is based on data obtained from an open-pit copper mine in southern Peru. The use of advanced machine learning models is proposed, evaluating between Long Short-Term Memory with Autoencoders (LSTM-AE) and Artificial Neural Network (ANN) for anomaly detection in multivariate time series for diesel engine health monitoring. This project addresses three issues of the current analysis methodology: first, point anomalies are used to monitor potential failures, generating a high rate of false alarms, while the proposed method identifies abnormal time periods;second, the threshold for alerts is manually calculated and requires expert intervention periodically, whereas the proposed methodology calculates the threshold in an unsupervised manner. Third, deep learning models are often considered 'black box' models since it is not easy to interpret how the model reached a conclusion, which is why model explainability is important for its adoption within the industry. The objective is to develop a predictive model that identifies potential failures early, allowing for the planning of preventive maintenance and reducing unplanned downtime. The results show that the combination of these models significantly improves the accuracy in failure prediction, facilitating timely and precise interventions. © 2024 IEEE.

关键词： Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

20th international conference on computing in Civil and Building engineering, ICCCBE 2024

20th International Conference on Computing in Civil and Buil...

引用

20th international conference on computing in Civil and Building engineering, ICCCBE 2024

ISBN: (纸本)9783031842238

The proceedings contain 34 papers. The special focus in this conference is on computing in Civil and Building engineering. The topics include: Data-Based Assessment of the Resilience of Municipal Road Infrastructure as a Component of Sustainable Asset Management;Where Project Governance and Sustainability Meet? A Review of Literature and Discussion on IPD Governance Structure;analysis of Software Solutions to Find the Most Suitable Project Delivery Model;Multi-source Transfer learning Method for Enhancing the Deployment of Deep Reinforcement learning in Multi-Zone Building HVAC Control;2D and 3D Thermal Transition Presentation of Temperature Spikes in Net-Zero Energy Residential Test Facility;a Driver Business Model: Triple Layer Business Canvas Model for Integrating Drivers of the Energy Efficiency Market in Europe;a Knowledge Graph-Based Approach for Construction Safety Hazards Management and Rectification Measures Intelligent Recommendation;Hazards Identification and Impact Reduction for At-Risk Construction Activities Using 3D BIM and VR;recurrent Neural Network-Based Workers’ Posture and Workload Estimation on Building Construction Sites;implementation of Wearable Technologies for Enhancing Safety Monitoring at Construction Jobsites: A Trend Analysis;unveiling the Spectrum of Personal Heat Stress Responses in Buildings;analyzing the Effect of Wind on Construction Schedules;image-Based Traffic Data Collection in Traffic-Related Air Pollution Analysis;prediction of Expansion Joint Life in Concrete Pavement;asset Management of Municipal Road Infrastructure;impact of Municipal Referencing Basis on Methods of Road Condition Assessment;employing the Policy Gradient Approach for Strategic Decision-Making in Road Network Infrastructures;classification of Physical Fatigue Levels in Shoveling Using Heart Rate, Electrodermal Data, and Skin Temperature.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine learning Based Hardware Accelerated Cobot Grasping in the Food Industry 11

Machine Learning Based Hardware Accelerated Cobot Grasping i...

引用

11th international conference on Electrical, Electronic and computing engineering, IcETRAN 2024

作者： Ivačko, Nikola Ćirić, Ivan Ćojbašić, Žarko Milošević, Maša Jevtić, Dušan University of Nis Faculty of Mechanical Engineering Nis18000 Serbia Fazi doo Belgrade11000 Serbia

ISBN: (纸本)9798350386998

In food industry automation, the integration of machine learning with robotic systems can introduce improved efficiency and precision in various tasks such as fruit and vegetable handling and manipulation. This paper introduces an innovative approach to robotic grasping, specifically customized for the challenge of identifying and handling apples and oranges within a dynamic and unstructured environment. Utilizing the YOLO (You Only Look Once) object detection algorithm, the system consists of a camera mounted on the robot's end effector, enabling real-time identification and localization of fruits and vegetables. Implemented on the NVIDIA Jetson platform, this solution presents the combination of machine learning techniques with hardware acceleration, ensuring optimal performance in real-time object recognition and manipulation tasks. Our experimental setup tests the robustness of our object recognition and localization approach under varying lighting and orientation scenarios and evaluates the system's overall performance in a real-world, cluttered environment typical of the food industry. The findings of this study underscore the potential of integrating ML-powered vision systems with hardware-accelerated robotics for food handling and manipulation applications. By demonstrating high levels of accuracy in recognition, classification and grasping, our research contributes to the broader field of robotic automation, offering insights into the scalability and adaptability of such systems across different sectors of the food industry. © 2024 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

GeoAI: A Systematic Review on Change Detection of Geospatial Data with Different Methodologies 6

GeoAI: A Systematic Review on Change Detection of Geospatial...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Nandhakumar, C. Pradeep, D. M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

Geospatial data science offers significant insights derived from spatial information. It is employed to examine various locations within urban areas, facilitating advancements in transportation, healthcare, disaster management, and urban planning. Geospatial information encompasses both spatial and temporal data obtained from satellite imagery and street view images. A diverse array of spatial data can be input into the system to identify valuable patterns. This research study presents a comprehensive review of geospatial data science, detailing its methodologies and applications. Image processing serves as a crucial technique for segmenting regions within satellite images. Additionally, deep learning algorithms are utilized to develop models that analyze the data, yielding efficient information. © 2025 IEEE.

关键词： Satellite imagery

来源：评论

学校读者我要写书评

暂无评论

Connecting Cloud computing and Machine learning through Functional Situation-awareness: A User-centric Smart Monitoring Application

Connecting Cloud Computing and Machine Learning through Func...

引用

1st IEEE international conference on Software Services engineering (IEEE SSE)

作者： Millan, Miguel Liu, Ruochen Ming, Hua Oakland Univ Dept Comp Sci & Engn Rochester MI 48307 USA

ISBN: (纸本)9798350340754

For many parents, the time between their children leaving home and the ensuing arrival at school is a grey area with uncertainty. The need of being aware of the highly critical safetyness situations there- fore arises. Situation-aware computing serves as a promising technique, both theoretically and pragmatically, to bring smart monitoring to the field. It seamlessly integrates the benefits offered by machine learning techniques and cloud computing technologies with an emphasis on user centric situations. In this paper we present a real world smart monitoring application: its underlying model, its human computer interactive design and design principles, its support from the theory and practice of situation-awareness computing, as well as its implementation details and viability test. This application also serves the purpose of elaborating important attributes of safety-aware situation computing, with a strong linkage to safety critical human computer interaction. The goal of this application is to accurately recognize and identify the faces of children, under relevant environmental contexts, when they enter or exit a bus, and then promptly alert their parents. Two main components, one being a distributed bus subsystem and the other being a central cloud-based subsystem, are built into this application highlighting lightweight to effectively serve the users. The novelty of our approach lies in the fact that we deeply embrace functional programming paradigm to tackle the specification of the two subsystems at the design level with rigorous computational semantics, which in turn contributes to the quality implementation of the lower level details.

关键词： Situation Situation-awareness Human Computer Interaction Intelligent Monitoring Machine learning Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Accelerating Sustainability through Leveraging Machine learning to Analyze CSR Spending in the Indian Automobile Industry 7

Accelerating Sustainability through Leveraging Machine Learn...

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Yogi, Kottala Sri Dankan Gowda, V. Pati, Anwesha Srinivas, Ved Aghav, Santosh Abdullah, Ibrahim International University Symbiosis Institute of Business Management Hyderabad Symbiosis Department of Operations Pune India BMS Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Symbiosis Institute of Business Management Hyderabad India Thiagarajar School of Management Department of Human Resources Tamil Nadu Madurai India Symbiosis Law School Hyderabad Pune India Al-Turath University College Department of Computer Science Baghdad Iraq

ISBN: (纸本)9798350372816

This paper uses machine learning to assess Corporate Social Responsibility spending trends on the Indian automobile sector between 2016 and 2021. Simply put, the aim of the research is to recognize trends and patterns in sustainability expenditures. More specifically, the study examines how businesses allocate money across the three important aspects of the sustainability report: People, Planet, and Profit Purcell, 2020. The research is based on a largely industry-based database obtained from public companies' records. The use of advanced statistical techniques, such as machine learning and random forest modeling, enables one to examine the impact of financial allocations on sustainability achievements. Increments in CSR spending are related to enhanced company sustainability performance in terms of correlation outcomes. The paper also recognizes areas where cost cuts have the best returns, as well as shortfalls in the existing CSR approach, offering a strategy for possible development's. © 2024 IEEE.

关键词： CSR Impact Sustainability Analysis Indian Automobile Industry learning Machine Social Responsibility Environmental Spending Sustainability Investments Corporate

来源：评论

学校读者我要写书评

暂无评论

Enhanced Lung Cancer Detection Using CNNs for Accurate and Efficient CT Image Classification 1

Enhanced Lung Cancer Detection Using CNNs for Accurate and E...

引用

1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Bdair, Mohammad Ruby, E D Kanmani Juyal, Prachi Taseen, Rakheeba Abirami, A. Soujanya, A. School of Architecture Computing & Engineering University of East London London United Kingdom Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of ECE Tamil Nadu Chennai India Uttarakhand Dehradun India School of computer science and Engineering Presidency university Bangalore India Sona College of Technology Department of Mathematics Tamil Nadu Salem India Ibrahimpatnam India

ISBN: (纸本)9798350353648

Lung cancer must be detected as early as possible, but it may be difficult with existing methods since they often depend on human judgment and outdated image processing. These techniques take a lot of time and are prone to errors, which produces uneven outcomes. The research provides a unique deep learning-based method for classifying lung nodules from CT images using convolutional neural networks (CNNs). By automating feature extraction and categorization, the suggested technique greatly increases diagnostic precision. The result shows superior performance over existing systems is demonstrated by evaluation measures such as AUC-ROC, accuracy, sensitivity, and specificity. With a training duration of 12 hours, an AUC-ROC of 0.95, accuracy of 92%, sensitivity of 94%, specificity of 90%, and inference speed of 0.5 seconds per scan, the results demonstrate notable improvements in diagnostic efficiency and accuracy. Deep learning has the potential to revolutionize the diagnosis of lung cancer and enhance patient care, as demonstrated by the study. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

A Multi-Channel Composite Recommendation Method in Federated learning

A Multi-Channel Composite Recommendation Method in Federated...

引用

2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Chen, Shi Li, Qianmu Wang, Qi Zhao, Xinjian Ge, Hao Li, Nianzhe State Grid Jiangsu Information & Telecommunication Company Nanjing China School of Computer Science Ana Engineering Nanjing University of Science and Technology Nanjing China State Grid Smart Grid Research Institute Co. Ltd. State Grid Key Laboratory of Information & Network Security Nanjing China

ISBN: (纸本)9798350304602

There are various data security risks in power grid systems, and recommendation systems are a good solution to the selection of security strategies. Federated learning is a machine learning framework that can protect the data privacy of each participant and can effectively solve the data security external collaborative training modeling challenges. This paper proposed a flexible implantable recommendation algorithm based on knowledge graph and multi-channel composite to enhance federated learning on demand. The algorithm designs three different feature extraction modules to learn to construct multi-dimensional features on multi-attribute collaborative knowledge graphs. Among them, the distance translation module utilizes the Trans R recommendation algorithm to learn the internal semantic structure information in knowledge graph triples and terminal-security policy selection history;the random walk module uses the Node2Vec algorithm to learn the homogeneous and Structured information in the multi-attribute collaborative knowledge graph;the graph convolutional network module learns the high-order neighborhood multi-attribute collaborative information of terminal/security policy nodes through multi-layer feature propagation and aggregation. The composite network module adopts a convolutional neural network-like operation to first locally optimize the three modules, and then extracts the highly informative elements to obtain the final feature representation of the terminal/security policy. Finally, the algorithm merges the vectors of terminal-security policy pairs into the multi-layer perceptron to learn and calculate the prediction score for recommendation. © 2023 IEEE.

关键词： 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 << < 476 477 478 479 480 481 482 483 484 485 > >>

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：