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

1,741 篇 会议
47 册 图书
42 篇 期刊文献

馆藏范围

1,830 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,196 篇 工学
- 1,105 篇 计算机科学与技术...
- 549 篇 软件工程
- 183 篇 电气工程
- 154 篇 信息与通信工程
- 111 篇 控制科学与工程
- 82 篇 生物工程
- 54 篇 机械工程
- 43 篇 生物医学工程（可授...
- 41 篇 安全科学与工程
- 33 篇 交通运输工程
- 32 篇 网络空间安全
- 31 篇 测绘科学与技术
- 27 篇 动力工程及工程热...
- 22 篇 石油与天然气工程
- 21 篇 化学工程与技术
282 篇 理学
- 121 篇 数学
- 91 篇 生物学
- 80 篇 物理学
- 46 篇 统计学（可授理学、...
- 40 篇 系统科学
253 篇 管理学
- 192 篇 管理科学与工程(可...
- 92 篇 图书情报与档案管...
- 91 篇 工商管理
82 篇 医学
- 67 篇 临床医学
- 56 篇 基础医学(可授医学...
- 42 篇 公共卫生与预防医...
- 25 篇 药学(可授医学、理...
48 篇 教育学
- 48 篇 教育学
38 篇 法学
- 33 篇 社会学
32 篇 经济学
- 31 篇 应用经济学
13 篇 农学
2 篇 文学
1 篇 艺术学

主题

411 篇 big data
339 篇 machine learning
143 篇 deep learning
113 篇 data analytics
88 篇 data mining
75 篇 data models
73 篇 big data analyti...
66 篇 artificial intel...
60 篇 machine learning...
59 篇 predictive model...
57 篇 training
56 篇 cloud computing
47 篇 real-time system...
45 篇 internet of thin...
39 篇 feature extracti...
34 篇 accuracy
34 篇 data analysis
33 篇 analytical model...
32 篇 computational mo...
31 篇 predictive analy...

机构

7 篇 national enginee...
5 篇 widener univ sch...
5 篇 univ manitoba de...
5 篇 beijing institut...
5 篇 peking universit...
5 篇 university of ma...
4 篇 univ politecn ca...
4 篇 univ north texas...
4 篇 centre for patte...
4 篇 university of sh...
4 篇 al-rafidain univ...
4 篇 shenzhen univers...
4 篇 zhejiang univers...
4 篇 natl univ def te...
4 篇 florida atlantic...
4 篇 al mansour unive...
4 篇 uruk university ...
4 篇 national enginee...
4 篇 univ elect sci &...
4 篇 institute of adv...

作者

14 篇 cuzzocrea alfred...
10 篇 ouyang bing
9 篇 leung carson k.
6 篇 venkatesh svetha
6 篇 bailey colleen p...
5 篇 depoian arthur c...
5 篇 liu ying
5 篇 yang yang
5 篇 wills paul s.
5 篇 guturu parthasar...
5 篇 zhang wentao
4 篇 xiaomeng ma
4 篇 alfredo cuzzocre...
4 篇 john macintyre
4 篇 sharma priyanka
4 篇 xhafa fatos
4 篇 homayoun houman
4 篇 honavar vasant
4 篇 wang wei
4 篇 khoshgoftaar tag...

语言

1,778 篇 英文
44 篇 其他
17 篇 中文

检索条件"任意字段=Conference on Big Data VI - Learning, Analytics, and Applications"

共 1830 条记录，以下是1-10 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：

Design of a user electricity consumption reminder system and sensitivity analysis of credit ratings based on big data analytics 2

Design of a user electricity consumption reminder system and...

引用

2nd International conference on big data, Computational Intelligence, and applications, BDCIA 2024

作者： Liao, Fengran Han, Tao Yang, Yang Jia, Legang Li, Tianhui State Grid Shandong Electric Power Company Yantai Power Supply Company Shandong264000 China

ISBN: (纸本)9781510689053

With the continuous growth of electricity demand, the efficient management of energy consumption has become a critical issue. However, many users lack awareness of their electricity usage patterns, leading to inefficient consumption behaviors and financial burdens. This paper proposes a novel user electricity consumption reminder system integrated with big data analytics to address these challenges. The proposed system analyzes user electricity consumption patterns using clustering algorithms and machine learning models, providing personalized consumption reminders. Additionally, a sensitivity analysis of credit ratings is conducted to explore the impact of electricity payment behavior on user credit. Experimental results demonstrate the system's effectiveness in enhancing user awareness, promoting sustainable energy practices, and maintaining financial stability. The study highlights the advantages of integrating big data techniques into user-centered energy management systems and suggests directions for future research. © 2025 SPIE.

关键词： Power consumption analytics Machine learning Lithium Sustainability Analytical research data modeling Monte Carlo methods Systems modeling data integration

来源：评论

学校读者我要写书评

暂无评论

Business analytics: Prospectivness and Opportunities 5th

Business Analytics: Prospectivness and Opportunities

引用

5th International conference on data Science, Machine learning and applications

作者： Atwadkar, Sudhir A. Patil, Umeshwari P. SNBP Coll Arts Commerce Sci & Management Studies Pune Maharashtra India Inst Ind & Comp Management & Res Pune Maharashtra India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

At present, Business analytics (BA) is getting more momentum and radical changes. Today business analytics is a popular and attractive field in academic as well as commercial units. By considering this truth the present paper highlights the development of business analytics and its conceptual patterns. The changes in ICT technologies and their impact on business analytics are also considered in the present study. The study elaborated on the classical concept and new emerging concepts of business analytics. The essential foundation of BA and changes in ICT shapes to business analytics is discussed and how business analytics works. The paper focuses on the big data phenomenon and its implications and the digital economy. This paper highlights the future scope of Business analytics and its applications.

关键词： Business analytics (BA) Information-Communication Technologies (ICT) big data Digital Economy Decision making

来源：评论

学校读者我要写书评

暂无评论

CONTINUOUS learning OF big data analytics OF EMPLOYEES: INTEGRATING UTAUT AND SITUATED EXPECTANCY-VALUE BELIEFS 18

CONTINUOUS LEARNING OF BIG DATA ANALYTICS OF EMPLOYEES: INTE...

引用

18th International conferences IADIS Information Systems 2025 and 23rd International conference on e-Society 2025

作者： Lee, Seula Lee, Hyewon Cho, Youngsang Management of Technology Yonsei University Seoul03722 Korea Republic of Gyeongi-do 16824 Korea Republic of Department of Industrial Engineering College of Engineering Yonsei University Seoul03722 Korea Republic of

ISBN: (纸本)9789898704658

Despite recognizing the importance of big data analytics capabilities for competitive advantage, the understanding of employees’ skill development remains limited. While situated expectancy-value theory (SEVT) is useful for identifying factors influencing learning intention, it lacks actionable strategies for organizations to foster learning environments. Integrating the unified theory of acceptance and use of technology (UTAUT) with SEVT, this ongoing study addresses the gap in understanding how employees enhance their skills to use big data analytics effectively among semiconductor manufacturing engineers. data were collected from 144 engineers at a semiconductor manufacturing company in South Korea. Partial least squares structural equation modeling was used to analyze the relationships between the constructs. The results show that performance expectancy, effort expectancy, and social influence positively affect continued intention to use big data analytics applications, except facilitating conditions. Secondly, continued intention to use and social norms as supportive environments of learning positively influence intrinsic motivation, mediated by factors such as self-efficacy and perceived usefulness. Understanding these factors provides actionable insights into how technological investment creates learning environments that facilitate the continuous development of big data analytics capabilities. By focusing on mechanisms that foster continuous learning, this study offers strategic guidance for organizations that aim to drive data-driven innovation and maintain a competitive advantage in high-tech industries. © 2025 Proceedings of the International conferences IADIS Information Systems 2025 and e-Society 2025. All rights reserved.

关键词： Competition

来源：评论

学校读者我要写书评

暂无评论

Integrating big data analytics in Soil Quality Assessment: Techniques, Challenges, and applications 3

Integrating Big Data Analytics in Soil Quality Assessment: T...

引用

3rd International conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Hanif, Md Abu Lovely Professional University Department of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Department of Computer Science 365 5th Ave New YorkNY110016 United States

ISBN: (纸本)9798331515911

This review paper investigates the application of big data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. big data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for big data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between big data and conventional soil science practices.. © 2025 IEEE.

关键词： data quality

来源：评论

学校读者我要写书评

暂无评论

An Efficient Predictive big data analytics for Supply Chain Demand Forecasting Through Ensemble learning 12th

An Efficient Predictive Big Data Analytics for Supply Chain...

引用

12th International conference on big data and Artificial Intelligence, BDA 2024

作者： Navadersh, S. Vengadeswaran, S. Indian Institute of Information Technology Kottayam Kerala Kottayam India

ISBN: (纸本)9783031818202

The increasing attention to big data analytics (BDA) in Supply Chain Management (SCM) stems from its versatile applications, encompassing customer behaviour analysis, trend analysis, and demand prediction. This paper aims to explore the potential of predictive BDA applications in supply chain demand forecasting and to pinpoint gaps in existing knowledge. In this proposed work, a time series predictive big data analytics using SARIMA, Prophet and ensemble model is proposed. Further to achieve an improved prediction accuracy, optimal parameters were identified using manual and grid search methods. In addition, geographical feature is extracted using KMeans clustering to understand any underlying patterns. Finally, an ensemble model is proposed that integrates heterogeneous models (SARIMA, Prophet), to achieve improved performance. The experiments were evaluated using five node Spark clusters deployed in the cloud. The results exhibit that the proposed ensemble approach using Linear regression achieves the lowest RMSE of 30.76. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

International conference on Machine learning and big data analytics, ICMLBDA 2023

International Conference on Machine Learning and Big Data An...

引用

International conference on Machine learning and big data analytics, ICMLBDA 2023

ISBN: (纸本)9783031513374

The proceedings contain 58 papers. The special focus in this conference is on Machine learning and big data analytics. The topics include: A TTIG-Based Deep Convolution Combined GAN and CLS for Text-to-Image Synthesis;smart Agricultural Greenhouse System: A Context-Aware Application;Improving Performance of Plant Disease Detection Using YOLOv7 and YOLOv8;Detection of Congenital Heart Disease from Heart Sounds Using 2D CNN-BiLSTM with Attention Mechanism;automated Reviewer Assignment Process Using Machine learning Technique;A Method for Detecting Retinal Microaneurysms in the Fundus Using CR-SF and RG-TF;Pilot Super-Resolution Network (PSRN)-Based Mango Fruit Classification;Performance Evaluation of QoS in MAODV Routing Protocol in MANETS;minimize the Energy Consumption to Increase the Network Lifetime for Green IoT Environment;unveiling Hate Speech: Identifying Toxic Comments Targeting Women in Online Social Media Posts;identification of Retinal Fundus in Diabetic Patients Using Deep learning Algorithms;smart Health Prediction Using Random Forest;vulnerability Assessment and Penetration Testing Using Parrot Operating System;Assessing NSAID Threat Degree of Unfavorable Medical Reactions Using Machine learning;searchable Encryption for Privacy Preserving with Fine-Grained Access Control;classification for Disease Gene Association;machine learning-Based Air Pollution Monitoring and Forecasting;a Novel Parasitic Mushroom-Like Structure with High Gain Microstrip Patch Antenna for Broadband applications;facial Emotion Recognition Using Artificial Intelligence;a Hybrid Machine Intelligence Demographic Feature Selection Approach to Improve Recommendation System in Social Domain;an Exploratory Review of Machine learning and Deep learning applications in Healthcare Management;bone Fracture Prediction Using Machine learning and Deep learning Techniques;session-Based News Recommendation System.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Advanced HLS for Reconfigurable Image Processing in big data Systems 12th

Advanced HLS for Reconfigurable Image Processing in Big Da...

引用

12th International conference on big data and Artificial Intelligence, BDA 2024

作者： Bhagirath, K. Pant, Dheeraj Tiwari, Abhishek Khaneja, vivek Noida201309 India

ISBN: (纸本)9783031818202

This paper introduces novel HLS techniques for reconfigurable and memory-efficient image processing within deep learning frameworks, addressing inherent limitations of current deep learning accelerators (DLAs) due to their dependency on the host processor, leading to pipeline delays and reduced throughput. The proposed approach emphasizes parallelization, on-chip buffering, and scalability, enhancing computational efficiency and throughput for AI applications in big data and edge computing environments. Methodologically, the paper emphasizes mathematical analysis, hardware architecture specification, HLS modeling, Verilog RTL implementation. By addressing these challenges, this work contributes to advancing the performance capabilities of DLAs in deep learning inference tasks. The algorithms integrates seamlessly with DLA IP subsystems, such as AXI interfaces, external (DRAM) and internal (SRAM) memory interfaces, and activation/pooling engines. Key contributions include advanced algorithm design to enhance memory efficiency and reconfigurability, essential for adapting to diverse hardware architectures. Experimental validation demonstrates substantial improvements in computational speed and resource utilization compared to traditional methods, making it suitable for intensive AI tasks in big data analytics. In conclusion, this research presents a framework for integrating HLS techniques into hardware accelerators, facilitating efficient image processing across diverse applications in big data and edge computing. These advancements aim to bolster DLAs’ capabilities in handling complex AI workloads effectively, promising enhanced solutions for AI-driven domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

An Exploratory Review of Machine learning and Deep learning applications in Healthcare Management

An Exploratory Review of Machine Learning and Deep Learning ...

引用

International conference on Machine learning and big data analytics, ICMLBDA 2023

作者： Vajjhala, Narasimha Rao Eappen, Philip University of New York Tirana Tirana Albania Cape Breton University Cape BretonNS Canada

ISBN: (纸本)9783031513374

The growing interest in machine learning (ML) and deep learning (DL) applications for healthcare management has led to a plethora of research outputs. In this exploratory study, the state-of-the-art ML and DL applications in healthcare management are synthesized to provide an overview of the advancements, challenges, and future directions. A total of 65 studies were analyzed, focusing on four main categories: patient care, medical imaging, electronic health records, and healthcare operations. The review highlights the potential of ML and DL in improving healthcare outcomes and efficiency while emphasizing the need for further research to address concerns such as data privacy, model interpretability, and integration into clinical practice. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Leveraging big data analytics and Applied Mathematics for Predictive Maintenance in Industrial Automation Systems 4

Leveraging Big Data Analytics and Applied Mathematics for Pr...

引用

4th International conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Lungu, Nelson Patra, Sudhansu Shekhar Dash, Bibhuti Bhusan De, Utpal Chandra National Institute of Public Administration Information Communication Technology Lusaka Zambia Deemed to Be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798331523923

Predictive maintenance of industrial automation systems has attracted much interest recently, owing to the potential benefits of reducing downtime and improving production efficiency. However, existing solutions are affected by many different types of problems, such as managing heterogeneous and huge-scale data from dispersed industrial sensors, realising real-time analysis in high-speed data streams, and, at the same time, preserving interpretability while employing advanced techniques like deep learning and distributed machine learning. The recent approaches also use deep neural networks for fault detection and anomaly-based models for early warning signal identification with federated learning to ensure privacy-preserving data sharing. These have inherent complications such as a high rate of false positives and data imbalance handling difficulty;besides that, it is not flexible enough with the changing dynamics of the industrial processes. This paper addresses these limits by introducing an innovative architecture that merges big data analytics with applied mathematics to enhance predictive maintenance tasks effectively. To this end, one integrates machine-learning models with physics-based models across diverse datasets to enhance prediction accuracy while reducing false alarms and thus enabling proactive maintenance scheduling. In so doing, this approach will aim at shattering the present system's weaknesses by delivering robust anomaly detection along with decreased operational expenses in a scalable manner, thereby playing an important role towards further smarter, self-configurable manufacturing within the Industry 4.0 movement. © 2025 IEEE.

关键词： Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

Intelligent evaluation method for carbon reduction pathways in prefabricated buildings based on data analytics and mining 2

Intelligent evaluation method for carbon reduction pathways ...

引用

2nd International conference on big data, Computational Intelligence, and applications, BDCIA 2024

作者： Li, Weiyi Prasetyo, Yogi Tri Zhengzhou Construction Investment Group Co. Ltd. Zhengzhou China School of Industrial Engineering & Management Mapua University Manila Philippines

ISBN: (纸本)9781510689053

With the growing global focus on sustainable construction practices, reducing carbon emissions in the building industry has become a critical challenge. Prefabricated buildings (PB), known for their efficiency and sustainability, have emerged as a promising solution to minimize the environmental impact of construction. However, optimizing carbon reduction in prefabricated buildings requires an integrated, data-driven approach. This paper proposes an intelligent evaluation method for carbon reduction paths in PBs, based on advanced data analysis and machine learning techniques. By leveraging big data from real-world construction projects, the method dynamically evaluates multiple carbon reduction strategies throughout the entire lifecycle of a building. Carbon emissions are predicted using a machine learning model that takes construction parameters into account. The most efficient solutions for reduction are then identified using optimization algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). The suggested method is a priceless asset for the environmentally responsible creation of prefabricated buildings, as experimental findings show that it greatly improves the precision of carbon emission estimates and offers very efficient options for reducing carbon emissions. © 2025 SPIE.

关键词： Carbon capture and utilization

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183页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：