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

891 篇 会议
6 册 图书
5 篇 期刊文献

馆藏范围

902 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

611 篇 工学
- 514 篇 计算机科学与技术...
- 320 篇 软件工程
- 102 篇 信息与通信工程
- 94 篇 电气工程
- 78 篇 生物工程
- 72 篇 控制科学与工程
- 47 篇 环境科学与工程（可...
- 35 篇 机械工程
- 35 篇 安全科学与工程
- 30 篇 交通运输工程
- 29 篇 生物医学工程（可授...
- 28 篇 动力工程及工程热...
- 24 篇 建筑学
- 23 篇 光学工程
- 18 篇 电子科学与技术（可...
186 篇 理学
- 87 篇 数学
- 81 篇 生物学
- 30 篇 统计学（可授理学、...
- 29 篇 物理学
- 22 篇 系统科学
132 篇 管理学
- 99 篇 管理科学与工程(可...
- 50 篇 图书情报与档案管...
- 48 篇 工商管理
112 篇 教育学
- 111 篇 教育学
49 篇 法学
- 40 篇 社会学
36 篇 医学
- 35 篇 临床医学
- 34 篇 基础医学(可授医学...
- 25 篇 公共卫生与预防医...
21 篇 经济学
- 21 篇 应用经济学
19 篇 农学
- 19 篇 作物学
6 篇 军事学
4 篇 文学

主题

82 篇 machine learning
60 篇 deep learning
43 篇 internet of thin...
40 篇 accuracy
38 篇 ecosystems
25 篇 predictive model...
23 篇 reinforcement le...
23 篇 training
23 篇 convolutional ne...
19 篇 real-time system...
19 篇 feature extracti...
19 篇 security
19 篇 artificial intel...
19 篇 technological in...
18 篇 big data
17 篇 computational mo...
17 篇 students
17 篇 machine learning...
16 篇 sustainable deve...
16 篇 e-learning

机构

11 篇 chitkara centre ...
9 篇 department of in...
8 篇 chitkara univers...
8 篇 aslerd rome
7 篇 chitkara univers...
6 篇 centre of resear...
5 篇 department of co...
4 篇 texas a&m univer...
4 篇 czech tech univ ...
4 篇 bina nusantara u...
4 篇 lloyd institute ...
4 篇 the islamic univ...
4 篇 univ politehn bu...
4 篇 acad romanian sc...
4 篇 lloyd law colleg...
4 篇 univ innsbruck i...
3 篇 yunnan key labor...
3 篇 kkr and ksr inst...
3 篇 vishwakarma inst...
3 篇 universidad tecn...

作者

7 篇 giovannella carl...
6 篇 urban josef
5 篇 dascalu mihai
4 篇 bakken jeffrey p...
4 篇 uskov vladimir l...
4 篇 rachakonda rama
3 篇 heinemann collee...
3 篇 maranan ramya
3 篇 p. william
3 篇 refaat shady s.
3 篇 kaliszyk cezary
3 篇 olsak miroslav
3 篇 setiawan karli e...
3 篇 santos fernando ...
3 篇 leoste janika
3 篇 trausan-matu ste...
2 篇 m. vigenesh
2 篇 zhao huijuan
2 篇 toma irina
2 篇 a l n rao

语言

836 篇 英文
64 篇 其他
5 篇 中文
1 篇 法文

检索条件"任意字段=4th International Conference on Smart Learning Ecosystems and Regional Development"

共 902 条记录，以下是261-270 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：

Deep learning CNN-Based Architecture Applied to Intelligent Near-Infrared Analysis of Water Pollution from Agricultural Irrigation Resources 4th

Deep Learning CNN-Based Architecture Applied to Intelligent ...

引用

4th international conference on Advanced Intelligent Technologies, ICAIT 2023

作者： Zhang, Yi Xia, Guofeng Taoli, Lulu Zhou, Zhongyuan Lin, Dongqi Ou, Shengyang Chongqing Three Gorges University Chongqing China

ISBN: (纸本)9789819732098

Agricultural irrigation is a key component of agricultural production;however, inappropriate water management may lead to water pollution, affecting crop growth and ecological environment. the aim of this study is to use the advanced technique of deep learning convolutional neural network (CNN) in combination with near-infrared (NIR) spectroscopy to achieve intelligent monitoring and analysis of water quality in agricultural irrigation resources. We collected water samples from different agricultural fields and measured the NIR spectral data of these samples using a NIR spectrometer. these data were used to train and validate our designed CNN model. Our CNN model has multiple convolutional and pooling layers to efficiently capture feature information in the spectra. this study provides a new and efficient approach for water resource management and pollution monitoring in agricultural production, which can help to achieve the goals of sustainable agricultural development and ecosystem protection. Future research could further optimise the model performance and consider its application to water quality monitoring systems in real agricultural fields. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Near infrared spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Systematic Literature Review on Machine learning Predictive Models for Indoor Climate in smart Solar Dryer Dome 4

Systematic Literature Review on Machine Learning Predictive ...

引用

4th international conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Setiawan, Karli Eka Elwirehardja, Gregorius Natanael Pardamean, Bens Bina Nusantara University BINUS Graduate Program Master of Computer Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

Precise predictions of indoor climate conditions are required in the implementation of smart Solar Dryer Dome (SDD). Trend development of prediction models is discussed in this review from 15 selected research papers (2018-2022) on indoor climate prediction which was obtained from research paper databases the output shows that the most used model for predicting indoor climate is Artificial Neural Network (ANN), especially Recurrent Neural Network (RNN) such as LSTM and GRU. However, there are some potential methods such as Transformer, Combined Support Vector Machines (SVM)-Deep learning, and sequence-to-sequence which could outperform other commonly used models. Based on findings various opportunities exist to improve the precision of indoor climate prediction, which can bring power consumption efficiency and others benefit to smart SDD users. Such studies may further be explored to produce more accurate machine learning models. © 2022 IEEE.

关键词： Domes

来源：评论

学校读者我要写书评

暂无评论

Machine learning in Renewable Energy Systems 4

Machine Learning in Renewable Energy Systems

引用

4th international conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Munir, Muhammad Sohail Shahid, Usama Nasir, Usama Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Fatima, Hoor Yaqub, Muhammad Atif Punjab Institute of Management and Professional Development Lahore Pakistan School of Business Computing and Social Sciences University of Gloucestershire Cheltenham United Kingdom Bahria University Lahore Campus Lahore Pakistan University of Central Punjab Lahore Faculty of Information Technology Lahore Pakistan Bahria University Lahore Campus Department of Computer Science Lahore Pakistan National College of Business Administration and Economics Lahore Pakistan Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350391183

From solar and wind power to hydropower and biomass, smart grids, the catalytic industry, and power storage and distribution, this paper covers the gamut of ML's present-day uses. Among the many domains covered by machine learning are classification issues and the methods used to solve them. Classification schemes have recently attracted a lot of interest as a tool for improving the deployment, administration, and optimization of Renewable Energy Systems. the integration of new data sources (such as innovative sensors) and improved algorithms for creating trustworthy data will improve the data flow between ML and systems. the growth of other important areas of data science, such big data analytics, will determine the significance of unsupervised and reinforcement learning in the energy industry. the development of sustainable uses of ML in non-industrial applications for energy management will be aided by massive implementations, specialized algorithms, and new technologies like 5G. this work aims to survey the state-of-the-Art classification algorithms for Renewable Energy situations, including both traditional and cutting-edge methods. Wind speed/power prediction, fault diagnostics in Renewable Energy Systems, power quality disturbance categorization, and other applications in alternative Renewable Energy Systems are some of the particular Renewable Energy topics covered in the paper's extensive literature analysis and discussion. Researchers and practitioners in the area may benefit from the paper's description of categorization algorithms and metrics used to Renewable Energy situations. © 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

development of mobile indoor flight test rig for VTOL UAV application. 4

Development of mobile indoor flight test rig for VTOL UAV ap...

引用

4th international conference on Science, Engineering, and Technology, ICSET 2024

作者： Makhtar, Siti Noormiza Samsuddin, Muhammad Danish Adha Mohd Sani, Fareisya Zulaikha Mohd Nor, Elya Mohd Bohari, Baizura Department of Electrical and Electronics Engineering Engineering Faculty National Defence University of Malaysia Kuala Lumpur Malaysia Dept of Industrial Revolution Centre for Cyber Security & Digital Industrial Revolution National Defence University of Malaysia Kuala Lumpur Malaysia Dept of Aeronautic Engineering and Aviation Faculty of Engineering National Defence University of Malaysia Kuala Lumpur Malaysia

Vertical take-off and landing (VTOL) Unmanned aerial vehicles (UAVs) significantly contribute to various industries, such as agriculture, geospatial mapping and logistic services. the flying condition of this type of drone is affected by various factors, such as wind disturbance and battery performance. It should be in stable condition to achieve full performance during operation. Flying condition monitoring ensures efficient, high-quality, and reliable operation. Prediction of flying health conditions will reduce catastrophic failures that may cause severe damage, prolonged downtime, harmful incidents, and loss due to higher repair costs and major maintenance services. the rising complexity of VTOL UAV maintenance mechanisms necessitates smart diagnosis and prediction systems. this paper describes the design and implementation of a mobile flight test rig for indoor monitoring VTOL UAV flying conditions using motion detection systems. the primary aim is to utilise motion signals captured from the monitoring setup to develop an intelligent VTOL UAV fault detection and identification system using machine learning algorithms. the emergence of machine learning techniques and signal processing methods exposed research opportunities for constructing high-accuracy learning algorithms for smart VTOL UAV flying health diagnoses. Comprehensive utilisation of massive flying data will increase the accuracy of the learning algorithm, significantly reducing unnecessary maintenance tasks and the high cost of corrective maintenance. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

international conference on 4th Industrial Revolution and Beyond, IC4IR 2021

International Conference on 4th Industrial Revolution and Be...

引用

international conference on 4th Industrial Revolution and Beyond, IC4IR 2021

ISBN: (纸本)9789811980312

the proceedings contain 53 papers. the special focus in this conference is on Industrial Revolution and Beyond. the topics include: A Method and Design for Developing an IoT-Based Auto-Sanitization System Powered by Sustainable Earth-Battery;Remote Measurement of Nitrogen and Leaf Chlorophyll Concentration Using UAV-Based Multispectral Imagery from Rice Crop Field in Sri Lanka;DIIGMCS—Design and Implementation of IoT-Based Greenhouse Monitoring and Controlling System;toward Devising a Soil Parameters Monitoring System to Improve Plant Irrigation;ioT-Based smart Health Monitoring System: Design, development, and Implementation;Identification of Molecular Signatures and Pathways of Nasopharyngeal Carcinoma (NPC) Using Network-Based Approach;Public Sentiment Analysis on COVID-19 Vaccination from Social Media Comments in Bangladesh;automated Detection of Diabetic Foot Ulcer Using Convolutional Neural Network;An Empirical Analysis of IT-Software Job Skill Requirements During COVID-19 Pandemic Period in Bangladesh;Deep learning-Based Skin Disease Detection Using Convolutional Neural Networks (CNN);Obesity and Mental Health During the COVID-19 Pandemic: Prediction and an Exploration of their Relationship;classifying Humerus Fracture Using X-Ray Images;design of Novel Feature Union for Prediction of Liver Disease Patients: A Machine learning Approach;toward Embedding Hyperparameters Optimization: Analyzing their Impacts on Deep Leaning-Based Text Classification;automatic Multi-document Summarization for Bangla News Text Using a Novel Unsupervised Approach;explainable Sentiment Analysis for Textile Personalized Marketing;use of Genetic Algorithm and Finite-Difference Time-Domain Calculations to Optimize "Plasmonic" thin-Film Solar Cell Performance;IoT-Based smart Energy Metering System (SEMS) Considering Energy Consumption Monitoring, Load Profiling and Controlling in the Perspective of Bangladesh;adaptation of Blockchain Technology in the Mobile Banking System in the Contex

关键词：

来源：评论

学校读者我要写书评

暂无评论

smart Visual Identities: A Design Challenge for smart learning Environments 5th

Smart Visual Identities: A Design Challenge for Smart Learni...

引用

5th international conference on smart learning ecosystems and regional development (SLERD)

作者： Lelis, Catarina Univ West London London England

ISBN: (纸本)9789811573835;9789811573828

smart environments in the context of education have been embedding technologies easily adoptable by all and that support users in optimising life, greatly resorting in data visualisation tactics. this means every citizen is also expected to develop their design literacy in order to effectively be a data contributor, an information coder/decoder and an evolving knowledgeable human being. this paper speculates on the relevance of investing in brand design activities, in particular within "smart visual identities", which configure as a meaningful and holistic branding resource: besides serving as a customisable window of the meaningful data collected on the user within a smart learning ecosystem (in this case university campuses), it is also a remarkable asset for the development of bonds between the user and said environment (increased brand loyalty via belonging). Seven brands of campuses that have been used in research of smart learning environments were analysed and four workshops were delivered at two different universities. the results show that the analysed campuses do not rely on smart visual identities and the workshops allowed the identification of features that a customisable smart brand identity should entail. It is argued that by having to customise a highly relatable brand identity (such as the one of a University is to its learners), individuals will increasingly develop their design literacy, while using the campus in a sustainable way.

关键词： Visual identity Design literacy learning ecosystems smart branded campuses

来源：评论

学校读者我要写书评

暂无评论

Intelligent Voice Question Answering System for Agricultural Production Based on Deep learning 4th

Intelligent Voice Question Answering System for Agricultural...

引用

4th international conference on Advanced Intelligent Technologies, ICAIT 2023

作者： Liu, Jun Li, Ni Zhao, Shizhao Yu, Kai School of Economics and Management Anshun University Anshun561000 China Financial Management Office of Guizhou Provincial Company of China National Tobacco Corporation Guiyang550002 China

ISBN: (纸本)9789819732098

Agricultural production is the main source of food, providing humans with various grains, vegetables, fruits, meat, and other nutrients. Ensuring sufficient food supply is the basic condition for maintaining human health and survival. Agriculture is one of the important pillars of the national economy. It provides the country with abundant agricultural products and raw materials, creates employment opportunities, promotes the development of rural economy, and promotes the increase of farmers’ income and the development of rural areas. therefore, agricultural production is important in many aspects and has a profound impact on social stability and human survival and development. But, the current agricultural production has important flaws in terms of intelligence. In recent years, artificial intelligence technology represented by machine learning and deep learning has advanced by leaps and bounds. While these technologies have changed our lives, they have also caused an explosive growth of various information. How can we quickly and accurately find what we need from a large amount of information? the answer to the question has become a widespread concern of relevant researchers. At present, the question answering technology basically adopts the retrieval-reading comprehension interactive framework, but there are still many defects in the agricultural question answering system based on this architecture, which makes that the answers provided by the model may not be concise and clear, and this paper will improve the problems existing in the current agricultural question answering technology. this paper proposes a question answering algorithm based on multi-task learning. this paper first enables the model to capture multiple. At the same time, it can better learn some features that are difficult to capture in a single task and improve the ability of the model through attention, so that the answers generated by it are more concise and clear. © the Author(s), under exclu

关键词： Agricultural production Attention Deep learning Intelligent question answering Multi-task learning

来源：评论

学校读者我要写书评

暂无评论

MangoDB - A TJC Mango Dataset for Deep-learning-Based on Classification and Detection in Precision Agriculture 4

MangoDB - A TJC Mango Dataset for Deep-Learning-Based on Cla...

引用

4th international conference on Advanced Research in Computing, ICARC 2024

作者： Jayaweera, Sasadara Sewwandi, Piyumika tharaka, Dimuthi Pallewatta, Pandula Halloluwa, thilina Wickramasinghe, Manjusri Karunanayaka, Kasun Arachchi, Samantha Mathara University of Colombo School of Computing Colombo Sri Lanka

ISBN: (纸本)9798350384864

TJC Mango (Mangifera Indica L) was discovered by Tom Ellawala in his mango orchard in Sri Lanka in the early 1990s. the Department of Agriculture recognized it in 2003. this variety is now grown in Sri Lanka's Dry and Intermediate zones. Artificial Intelligence (AI) can assist agriculture producers in making more informed decisions. the study covers prospective mango farming applications of the TJC Mango Dataset (MangoDB) contributed by AI, including yield prediction, disease detection, and fruit quality evaluation. MangoDB is a unique post-harvest TJC Mango fruit image collection. Five TJC mango label classifications have 200 photographs each, totaling 1200 images. Machine learning (ML) models and neural networks trained on the MangoDB can classify and evaluate mango fruit quality, helping Farmers and stakeholders to make better harvesting and post-harvest decisions. A TJC Mango Dataset could boost mango farming's profitability and sustainability with AI. In agriculture, grading significantly will benefit from detailed TJC mango images. Photos were taken with a NIKON D5500 DSLR at ISO 2500, 1/100 sec shutter speed, and 24mm focal length. the photos were in NEF format. Classification algorithms like Fast Forward Neural Network (FFNN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) can be used qualitatively to analyze the TJC mango's quality descriptors (colour, surface texture). In addition, MangoDB can be used to implement a rule-based classifier with a combination of ML algorithms to determine the mango's quality status. this study examined TJC mango dataset qualities and compared them to others. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

the impact of "Made in China 2025" on innovation behavior in China's manufacturing industry-Based on the perspective of systems theory 4

The impact of "Made in China 2025" on innovation behavior in...

引用

4th international conference on Big Data Economy and Information Management, BDEIM 2023

作者： Xie, Tao Li, Yanan School of Economics Shenyang University China School of Economics and Management Weifang University of Science and Technology China

ISBN: (纸本)9798400716669

the paper takes the Made in China 2025 strategy and its impact on Chinese manufacturing enterprises as an open system, using data from A-share listed companies in 2015 and 2017 as research objects, and chooses the DID model to test the impact of Made in China 2025 strategy on enterprise innovation. this paper finds that implementing the Made in China 2025 strategy can affect the innovation behavior of Chinese manufacturing enterprises, but it will have different impacts due to different stages of enterprise innovation. the dispensation of this strategy is negatively correlated with research and development intensity, but significantly positively correlated with innovation performance. this paper plays an important role in how China's manufacturing industry can leverage the "east wind"of policies to more effectively improve enterprise research and enhance innovative development. © 2023 ACM.

关键词： smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

ContraPonzi: smart Ponzi Scheme Detection for Ethereum via Contrastive learning 4

ContraPonzi: Smart Ponzi Scheme Detection for Ethereum via C...

引用

4th Asia Service Sciences and Software Engineering conference, ASSE 2023

作者： Wu, Jiajing Liu, Jieli Chen, Jinze Chen, Ting Li, Jingwei Song, Shuwei He, Jiahao School of Software Engineering Sun Yat-sen University China School of Computer Science and Engineering Sun Yat-sen University China School of Cyber Security University of Electronic Science and Technology of China China

ISBN: (纸本)9798400708534

In recent years, blockchain technology has witnessed rapid development and received considerable attention. However, its decentralized and pseudonymous nature has also attracted many criminal activities. Among them, Ponzi schemes, a classic form of financial fraud, also hide their true face in smart contracts, causing huge losses to blockchain users. Although numerous methods have been proposed to detect Ponzi contracts, these methods still have limitations in terms of generalization and feature learning. To address this issue, we conduct research on Ethereum, the currently largest blockchain platform enabling smart contracts, and propose a novel contrastive learning-based smart Ponzi scheme detection method named ContraPonzi. this method first extracts control flow graph information from bytecodes and models it as attribute graphs that preserve both semantic and structural information. Next, by augmenting the bytecode data of multi-version compilers and maximizing the graph representation similarity of multi-version bytecodes of the same contract, a pre-training graph encoder is obtained and then can be used in Ponzi contract detection. Experimental results on real-world data demonstrate that ContraPonzi is significantly superior to the state-of-the-art in Ethereum Ponzi scheme detection. © 2023 ACM.

关键词： smart contract

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91页 << < 23 24 25 26 27 28 29 30 31 32 > >>

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：