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

3,172 篇 会议
42 册 图书
25 篇 期刊文献

馆藏范围

3,239 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,212 篇 工学
- 2,032 篇 计算机科学与技术...
- 1,327 篇 软件工程
- 418 篇 信息与通信工程
- 339 篇 电气工程
- 302 篇 控制科学与工程
- 153 篇 机械工程
- 128 篇 生物工程
- 124 篇 交通运输工程
- 116 篇 动力工程及工程热...
- 110 篇 生物医学工程（可授...
- 102 篇 安全科学与工程
- 81 篇 环境科学与工程（可...
- 72 篇 仪器科学与技术
- 70 篇 建筑学
- 67 篇 电子科学与技术（可...
675 篇 管理学
- 513 篇 管理科学与工程(可...
- 312 篇 工商管理
- 250 篇 图书情报与档案管...
498 篇 理学
- 244 篇 数学
- 138 篇 生物学
- 96 篇 统计学（可授理学、...
- 73 篇 系统科学
- 64 篇 物理学
152 篇 医学
- 142 篇 临床医学
- 132 篇 基础医学(可授医学...
- 76 篇 药学(可授医学、理...
- 75 篇 公共卫生与预防医...
138 篇 法学
- 105 篇 社会学
114 篇 经济学
- 113 篇 应用经济学
67 篇 教育学
- 67 篇 教育学
63 篇 农学
2 篇 文学
2 篇 军事学

主题

376 篇 big data
257 篇 internet of thin...
227 篇 cloud computing
133 篇 machine learning
129 篇 edge computing
123 篇 real-time system...
103 篇 data analytics
97 篇 artificial intel...
95 篇 smart cities
93 篇 intelligent sens...
84 篇 monitoring
81 篇 data models
79 篇 smart city
78 篇 data mining
74 篇 blockchain
71 篇 predictive analy...
64 篇 sensors
63 篇 deep learning
59 篇 iot
58 篇 security

机构

14 篇 jilin engineerin...
9 篇 hubei province k...
8 篇 school of comput...
8 篇 fujian provincia...
7 篇 institute for in...
7 篇 chitkara univers...
7 篇 liaoning jianzhu...
7 篇 school of econom...
6 篇 college of compu...
6 篇 fuzhou universit...
6 篇 symbiosis instit...
5 篇 china electric p...
5 篇 school of big da...
5 篇 luoyang normal u...
5 篇 smart agricultur...
5 篇 comsats univ isl...
5 篇 college of mecha...
5 篇 guangzhou colleg...
5 篇 electrical and i...
5 篇 university of ma...

作者

18 篇 javaid nadeem
9 篇 xu hongsheng
8 篇 leung carson k.
7 篇 zeng zeng
7 篇 xu xiaolong
7 篇 chang kuo-chi
7 篇 ramakrishnan ram...
6 篇 tang you
6 篇 doukovska lyubka
6 篇 wang wei
6 篇 cuzzocrea alfred...
5 篇 liu yang
5 篇 silva jesús
5 篇 vikrant bhateja
5 篇 fan ganglong
5 篇 vale zita
5 篇 qiu meikang
5 篇 cristea valentin
5 篇 stoyanov stanimi...
5 篇 wang yong

语言

3,182 篇 英文
46 篇 其他
20 篇 中文

检索条件"任意字段=International Conference on Intelligent and Smart Computing in Data Analytics, ISCDA 2020"

共 3239 条记录，以下是361-370 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：

Machine Learning Insights for Predicting Customer Behavior in Online Retail

Machine Learning Insights for Predicting Customer Behavior i...

引用

Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： N. Reena Catherine Ragul. R Deepika. M Pradeepkumar. R Shyam Prasanth. A Department of Commerce Dr.SNS Rajalakshmi College of Arts and Science Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The research work reports a comprehensive analysis of predictive analytics of customer behavior on e-commerce based on high-end data science and artificial intelligence techniques. The sourced information for customer demographics, transaction details, and purchase history was extracted from Kaggle. Machine learning models' applications were utilized to predict the attrition of the customers, which indicated that the accuracy of Random Forest was 87%, while that of XGBoost was 89%. The performance of the model was tested in terms of precision, recall, and F1-score. Precisely and recall for XGBoost were both 0.88. Additionally, different consumer types were identified by applying K-Means clustering to classify customers into different kinds based on their attributes of Recency, Frequency, and Monetary. Its findings indicate that predictive analytics can play a very significant input in making advertisements very effective and in keeping clients in this very dynamic world of e-commerce. In the last few years, there has been an impressive rise in online shopping due to more and more people gaining access to the internet, a growing preference for convenience, and technology's ever-changing nature of digital platforms.

关键词： Consumer behavior Soft sensors Organizations Predictive models data science data models Electronic commerce History Predictive analytics Random forests

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Mix-Method Approach for Efficient Waste Management using IoT-Driven Solutions

Comprehensive Mix-Method Approach for Efficient Waste Manage...

引用

Emerging smart computing and Informatics (ESCI), conference on

作者： Neha Gupta School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad Haryana

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Escalating the global waste management crisis calls for innovative technological interventions transforming the age-old practice of traditional waste handling. The present system remains mostly inefficient, labor-intensive, and environmentally unsustainable even when rising levels of urbanization and consumption of resources increase waste production. This study examines the transformative potential of IoT technologies in developing intelligent data-driven solutions for waste management to solve critical infrastructure challenges in urban centers. This study developed IoT-enabled smart waste collection systems in three metropolitan areas using a comprehensive mixed-method approach that includes quantitative sensor data analysis, geospatial mapping, and longitudinal municipal waste tracking. Advanced sensor networks and associated machine learning algorithms were used to monitor volume, composition, and efficient collections of wastes. Predictive analytics and the edge computing framework were adapted to optimize routes for better waste collection, reduce associated operational costs, and hence also minimize the environmental footprint. The key results are possible savings of 37% in waste collection costs, an increase of 42% in route optimization for collections, and a reduction of several percent in greenhouse gas emissions due to smart routing and scheduling. IoT-led strategy outperformed conventional strategies for waste management with better operational efficiency, further signifying the revolutionary changes brought by digital technologies in managing urban infrastructure.

关键词： Waste management Waste handling Technological innovation Costs Urban planning Geospatial analysis Predictive analytics Sustainable development Optimization Edge computing

来源：评论

学校读者我要写书评

暂无评论

A Medical Pre-diagnosis Scheme Based on Neural Network and Inner Product Function Encryption 4

A Medical Pre-diagnosis Scheme Based on Neural Network and I...

引用

4th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Cui, Xu Liu, Hao Tang, Min Ma, Yihong School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin54004 China Guilin541002 China

ISBN: (纸本)9798350314496

Artificial neural networks are widely used in various fields, such as intelligent road networks, Internet of Things, and smart medical systems due to their ability to process large amounts of data in parallel, store information in a distributed manner, and self-organize and self-learn. Cloud computing technology has further expanded the development of neural network applications. However, user data often contains sensitive information, and once the data management right is transferred to the cloud, it faces serious security and privacy issues. In the medical field, privacy-preserving implementation of classification algorithms is crucial for ensuring the privacy of electronic medical diagnosis services. Current privacy-preserving medical pre-diagnosis schemes based on homomorphic encryption impose a significant computational and communication burden on users and servers. This paper proposes an efficient privacy-preserving medical pre-diagnosis scheme based on neural networks and inner product function encryption that protects user privacy during pre-diagnosis while having small computational and communication overheads. © 2023 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Multi-objective Computation Offloading in MEC-Empowered smart Warehousing 12th

Multi-objective Computation Offloading in MEC-Empowered Sma...

引用

12th international conference on Mobile Networks and Management, MONAMI 2022

作者： Yang, Liangfei Peng, Kai Zhao, Bohai Huaqiao University Quanzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Technology Co. Ltd. Quanzhou China

ISBN: (纸本)9783031324420

As a typical application of the Industrial Internet of Things (IIoT), smart warehousing has attracted widespread attention. In addition, smart warehousing is regarded as a key part of logistics and supply chain management. The main idea of smart wareshousing is to deploy a large number of smart devices (SDs) to collect large amounts of data for improving the efficiency of digital management. However, it is difficult for SDs to process large amounts of data due to their limited computing capacity, meanwhile, traditional cloud-based smart warehousing paradigm often suffers from high latency disadvantage. Fortunately, mobile edge computing (MEC) can make up for the above shortcoming. Nevertheless, it is challenge to effectively integrate edge computing and smart warehousing. In view of this, we investigate the computation offloading problem in MEC-empowered smart warehousing, and propose an intelligent computation offloading algorithm to optimize time consumption and energy consumption of SDs as well as the resource utilization of the edge server cluster in this paper. Finally, we conduct several group of experiments to prove the effectiveness of our proposed method, and the results indicate that our method outperforms the other comparison methods in the given optimization objectives. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

Integrating Predictive analytics and Deep Learning for Vehicle Safety Incident Forecasting

Integrating Predictive Analytics and Deep Learning for Vehic...

引用

Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Anil Kumar Adike S. Silvia Priscila Department of Computer Science Bharath Institute of Higher Education and Research Chennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Forecasting vehicle safety incidents is an important step towards improving road safety and reducing accidents. Rule-based systems, which often rely on historical data, can be inflexible and reactive, resulting in high false positive rates and poor accuracy. It presents an integrated predictive system that combines with Deep Learning using CNN and LSTM analysis of multimodal data channels (Traffic images, Weather reports, Vehicle sensor signals). The hybrid architecture incorporates spatial and temporal dynamics, achieving a mean prediction accuracy of 92 % and lowering the false positive rate to 10%. It is important to highlight that results show significant improvements over state-of-the-art systems with 89% precision and 85% recall. The model allows safety management in real-time so that authorities can intervene before an accident happens. By creating a shift from reactive to proactive, the new way of forecasting incidents also makes for improved road safety and ultimately saves lives through timely interventions.

关键词： Deep learning Accuracy Computational modeling Vehicle safety Safety management Predictive models Road safety Forecasting Predictive analytics Accidents

来源：评论

学校读者我要写书评

暂无评论

Water Management and Control Systems for smart City using Io Tand Artificial Intelligence

Water Management and Control Systems for Smart City using Io...

引用

2022 international conference on Edge computing and Applications, ICECAA 2022

作者： Kumar, B. Sakthi Soumiya, S. Ramalingam, S. Yogeswari, S. Balamurugan, S. Department of ECE Sri Eshwar College of Engineering Coimbatore India Department of EEE A.C.Government College of Engineering and Technology Karaikudi India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Most of the city's water system is intermittent and poorly serviced. The worst levels will be attributed to available resource constraints, minimal equipment instrumentation, inadequate system operation, and poor network maintenance. smart Water Io TManagement (SWIM) work is designed for various applications, including water level indication and water flow monitoring. This requires extensive research into leak detection techniques to prevent leak threats and minimize leaks. This article focuses on implementing and developing Io Tusing Arduino and Node MCU. Its main goal is to create a big city Io Tsystem that helps realize intelligent cities while solving the inconvenience of using online cloud computing in real-time. This article focuses on the remote control of solenoid valves using Io T(Internet of Things) devices. Get real-time water supply data anytime, anywhere through the Internet of Things. The data can be continuously visualized in real-time. Further, the local BLYNK server is used to store the data in the cloud and communicate efficiently between the server and the client. This finely divided system works more efficiently than manual operating systems, saves human resources, and improves the system's accuracy. The system design combines existing Io Tinfrastructure, big data analytics to collect and analyze user needs, and cloud computing to store data for further processing. © 2022 IEEE.

关键词： Flow of water

来源：评论

学校读者我要写书评

暂无评论

Leveraging Artificial Intelligence to Address Domestic Violence Against Women with Disabilities in India

Leveraging Artificial Intelligence to Address Domestic Viole...

引用

Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Anuj Kumar Tiwari A. Marisport Gujarat National Law University Gandhinagar Gujarat India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Women who have impairments frequently confront compounded vulnerabilities, which makes them more prone to domestic violence and less willing to accept acceptable support. This article investigates how artificial intelligence technology can improve discovery, prevention, and intervention tactics. Artificial intelligence canrecognize patterns of abuse and forecast high-risk situations by analyzing data from a wide variety of sources, such as social services, healthcare providers, and judicial systems. The research highlights the emergence of AI-driven tools such as mobile operations for reporting abuse, virtual sidekicks for providing information and support, and predictive analytics to assist law enforcement and social services. These tools are similar to mobile operations reported by abuse victims. It is also addressed in the study that ethical considerations, such as sequestration and data security, are taken into account, and it is emphasized that the implementation of AI is respectful of the rights and quality of women who have impairments. The findings indicate that artificial intelligence has the potential to play a significant role in the process of making the environment safer for these women, which will ultimately lead to a society that is more accepting and uncaring.

关键词： Ethics Law enforcement Prevention and mitigation Government Medical services Frequency measurement Safety Stakeholders Artificial intelligence Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

Optimization of renewable energy integration in smart grids using AI and data analytics

Optimization of renewable energy integration in smart grids ...

引用

2nd international conference on Power, Communication, computing and Networking Technologies (PCCNT 2024)

作者： Jiashang Chen Lan Li Wuhan University of Technology People's Republic of China Sinounited Investment Group Corporation Limited Postdoctoral Programme X038 Pang'an Road Weishanzhuang Town Daxing District Beijing 102611 People's Republic of China

ISBN: (数字)9781837242405

This paper aims to optimize the integration of renewable energy sources into smart grids using artificial intelligence (AI) and data analytics, addressing the challenges posed by the intermittency and variability of renewable energy. The research methodology involves designing an AI-based energy management system that incorporates data analytics, optimization techniques, and renewable energy technologies. The system architecture includes modules for energy generation, storage, and transmission, with key technologies such as smart control strategies and real-time demand response systems. The study employs AI optimization algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Deep Learning (DL), to dynamically adjust the system based on real-time data inputs. The results demonstrate that Deep Learning outperforms GA and PSO across all metrics, achieving the highest energy efficiency (95%), cost savings (80%), and CO 2 reduction (70%). The significance of this research lies in its contribution to the reliability and sustainability of energy systems by effectively optimizing energy efficiency, cost savings, and environmental impact. The innovation of this paper is the introduction of a multi-objective optimization framework that simultaneously considers energy efficiency, economic cost, and environmental impact, enabling real-time optimization of the energy management system based on intelligent algorithms. This research underscores the importance of advanced AI-based optimization techniques in the design and operation of smart grids, particularly as the share of renewable energy sources continues to increase.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Bayesian Belief Network Framework for Protecting Generative AI-driven Attack in smart Grid Communication

A Bayesian Belief Network Framework for Protecting Generativ...

引用

international conference on computing, Networking and Communications (ICNC)

作者： Md Shirajum Munir Sravanthi Proddatoori Manjushree Muralidhara Sachin Shetty School of Computing Analytics and Modeling Univesity of West Georgia Carrollton GA USA Dept. of Computer Science Old Dominion University Norfolk VA USA Dept. of ECE Old Dominion University Norfolk VA USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

In the era of artificial intelligence (AI), native generative AI (GenAI) has become prominent in generating intelligent cyber-attacks in a smart grid environment by imitating operational parameters and metrics. As a result, it is imperative to understand the attack generation capabilities of native GenAI models, such as Generative Adversarial Networks (GANs) and Autoencoders in a smart grid environment. This research aims to explore the potential of GenAI models to accurately imitate and analyze the behavior of cyberattacks within a smart grid environment. The goal is to understand the risks posed by these intelligent attacks while developing strategies to protect against disruptive Distributed Energy Resources (DER) events, such as incorrect load shifting, imbalanced demand supply, and unstable price forecasting. First, this work leverages two customized native GenAI models, namely GAN and Autoencoder, to synthesize DERs control message parameters such as nominal power consumption, price elasticity coefficients, and communication data packets, among others, to introduce highly unpredictable new intelligent attack vectors. Second, this research proposes and develops a new Bayesian Belief Networks (BBNs) framework by creating correlation dependency nodes among DER control message parameters to understand the risks of intelligent attack vectors better while monitoring and mitigating their impact on smart grid operations. In particular, by leveraging the customized BBN, the system can observe and analyze the uncertain behaviors in DER operations that GenAI introduces, and the proposed framework can help mitigate grid vulnerabilities by understanding the poisonous parameters through mutual information. Finally, the experiment results show that the Autoencoder outperformed GAN in reproducing intelligent attacks with an MSE about 98.984% lower than that of GANs. Additionally, BBN can explain prominent parameters of the intelligent attacks’ by quantifying dependencies thro

关键词： Generative AI Autoencoders Generative adversarial networks Vectors data models smart grids Distributed power generation Bayes methods Mutual information Load modeling

来源：评论

学校读者我要写书评

暂无评论

College Admissions Counseling Using intelligent Question Answering System 1

引用

international conference on intelligent Systems and Sustainable computing, ICISSC 2021

作者： Hung, Bui Thanh Data Analytics and Artificial Intelligence Laboratory Engineering—Technology School Thu Dau Mot University 6 Tran Van On street Phu Hoa District Binh Duong Thu Dau Mot Viet Nam

ISBN: (数字)9789811900112

ISBN: (纸本)9789811900105

College admission is successful based on admission counseling for college entrance examinations. To understand the user’s intentions and provide students and parents with all necessary information about school admissions in a more rapid and efficient manner, the college admissions counseling system plays an important role. In this research, we build an intelligent question answering system to support enrollment counseling based on the question classification methodology using deep learning approach. In order to classify the question, we then use pre-train word embedding and bi-directional long short-term memory. Based on the outcomes of question classification, we will be able to get the answer for the question. As a practical experiment, we have built this application and applied for the college admissions counseling of Thu Dau Mot University. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Embeddings

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324页 << < 33 34 35 36 37 38 39 40 41 42 > >>

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：