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

177 篇 会议
3 册 图书
1 篇 期刊文献

馆藏范围

181 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

146 篇 工学
- 121 篇 计算机科学与技术...
- 59 篇 软件工程
- 21 篇 控制科学与工程
- 16 篇 信息与通信工程
- 12 篇 生物工程
- 9 篇 生物医学工程（可授...
- 6 篇 机械工程
- 5 篇 光学工程
- 5 篇 电气工程
- 5 篇 安全科学与工程
- 4 篇 仪器科学与技术
- 4 篇 电子科学与技术（可...
- 4 篇 交通运输工程
- 4 篇 农业工程
- 3 篇 动力工程及工程热...
- 3 篇 化学工程与技术
59 篇 理学
- 40 篇 数学
- 16 篇 生物学
- 11 篇 统计学（可授理学、...
- 7 篇 物理学
- 4 篇 化学
- 3 篇 系统科学
29 篇 管理学
- 19 篇 图书情报与档案管...
- 12 篇 管理科学与工程(可...
- 6 篇 工商管理
9 篇 医学
- 9 篇 临床医学
- 8 篇 基础医学(可授医学...
- 5 篇 药学(可授医学、理...
6 篇 法学
- 6 篇 社会学
6 篇 农学
- 6 篇 作物学
2 篇 教育学
1 篇 经济学

主题

13 篇 deep learning
12 篇 machine learning
10 篇 data mining
7 篇 real-time system...
7 篇 convolutional ne...
6 篇 clustering algor...
5 篇 feature extracti...
5 篇 data models
4 篇 artificial intel...
4 篇 accuracy
4 篇 forecasting
3 篇 internet of thin...
3 篇 fuzzy sets
3 篇 support vector m...
3 篇 automation
3 篇 manuals
3 篇 database managem...
3 篇 visualization
3 篇 decision making
3 篇 classification

机构

3 篇 school of comput...
2 篇 monash universit...
2 篇 chengdu universi...
2 篇 information tech...
2 篇 department of ma...
2 篇 ajou univ div el...
2 篇 dept. of informa...
2 篇 department of da...
2 篇 intelligent comp...
2 篇 telkom universit...
2 篇 univ london roya...
2 篇 department of in...
2 篇 naval research l...
2 篇 department of co...
2 篇 the university o...
2 篇 school of comput...
2 篇 faculty of scien...
2 篇 tsing hua univ s...
2 篇 ajou univ grad s...
2 篇 department of ma...

作者

4 篇 yin hujun
3 篇 lee yuh-jye
3 篇 zhang byoung-tak
3 篇 taniar david
3 篇 huang de-shuang
2 篇 sun jg
2 篇 chan tony f.
2 篇 horváth tomáš
2 篇 pao hsing-kuo
2 篇 liu yang
2 篇 ng michael k.
2 篇 tashu tsegaye mi...
2 篇 ahmad k
2 篇 fakhrurroja hani...
2 篇 p. vishnu kumar
2 篇 bellotti t
2 篇 luo zy
2 篇 wu edmond h.
2 篇 smith james f.
2 篇 kumar b. kiran

语言

173 篇 英文
5 篇 其他
3 篇 中文

检索条件"任意字段=5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004"

共 181 条记录，以下是41-50 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：

IoT-Driven intelligent Waste Management System with Disinfectant Spray

IoT-Driven Intelligent Waste Management System with Disinfec...

引用

Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： N. S. Swapna Basutkar Kalyan Rao Kummari Netra Paradesi Abhinav Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the rapid increase in urban waste generation demands innovative solutions to improve public health and operational efficiency. this paper presents an IoT-driven intelligent waste management system that integrates real-time monitoring, dynamic route optimization, and automated disinfectant mechanisms. Smart bins equipped with ultrasonic, gas, rain, and infrared sensors transmit data via ESP8266 Wi-Fi/GSM modules to a cloud platform (thingSpeak), enabling anomaly detection and route optimization using a modified Travelling Salesman Problem (TSP) algorithm. A solenoid-based disinfectant system reduces bacterial load by 98% (verified through lab testing [5]), while the TSP algorithm reduces collection time by 40% and fuel consumption by 30% compared to traditional methods [4]. A three-level dashboard (admin, collector, user) provides actionable insights through a mobile app. Deployed in a 1 km 2 testbed with 10 bins, the system demonstrates scalability and cost-effectiveness. Future work includes integrating machine learning for predictive analytics and automated waste segregation. this solution aligns with UN Sustainable Development Goals (SDGs) 11 and 13 [1], promoting cleaner cities and climate action.

关键词： Waste management Rain Machine learning algorithms Urban areas Real-time systems Mobile applications Predictive analytics Optimization Gas detectors intelligent sensors

来源：评论

学校读者我要写书评

暂无评论

Enhancing Natural Language Instruction Document Comprehension with Large Language Models

Enhancing Natural Language Instruction Document Comprehensio...

引用

Computer, Big data and Artificial Intelligence (ICCBD+AI), international conference on

作者： Shang Li Yang Chen Xin Zhang Beijing Institute of Tracking and Telecommunications Technology Beijing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Recent advancements in Large Language Models (LLMs) have created new avenues for automated understanding and analysis of natural language instruction documents. this paper presents an innovative approach that leverages pre-trained LLMs with specialized fine-tuning techniques to facilitate understanding and analysis of instruction documents across a range of domains. Our method capitalizes on the semantic understanding capabilities of LLMs, adapting to instruction comprehension tasks through few-shot learning and prompt engineering. the model demonstrates proficiency in identifying key steps, entities, and their logical relationships within instructions, exhibiting robust cross-domain generalization capabilities. A multi-domain instruction document dataset is constructed to evaluate the model’s performance. Experimental results indicate excellent performance across a range of instruction comprehension tasks, including step sequencing, entity recognition, and contextual interpretation. this research contributes to advancing document comprehension using LLMs, with the potential for significant impact on technical documentation management, intelligent customer service, and automated workflow processes.

关键词： Adaptation models Sequential analysis Text analysis Large language models Semantics Documentation Natural language processing data models Prompt engineering Few shot learning

来源：评论

学校读者我要写书评

暂无评论

An Effective Approach for Sonar Image Recognition with Improved Efficientdet and Ensemble learning 5

An Effective Approach for Sonar Image Recognition with Impro...

引用

2022 5th international conference on Advanced Algorithms and Control engineering, ICAACE 2022

作者： Du, Yunben Lin, Wei Zhong, Weibo Yuan, Yu. College of Electronics and Information Jiangsu University of Science and Technology Zhenjiang212000 China

Precisely recognition of underwater targets plays a critical role in the field of underwater unmanned exploration. According to the characteristics of sonar image, in order to improve the accuracy of underwater automatic target recognition, this paper proposes an underwater target sonar image recognition method combined with improved Efficientdet and ensemble learning. According to the regional dominance principle and threshold segmentation method, the sonar image is preprocessed, and preliminary effective features extracted from preprocessed data by Efficientnet backbone, so as to balance the efficiency and power consumption of the algorithm. the feature fusion adopts the improved weighted bi-directional feature pyramid ( BiFPN ) structure to aggregate the global information and strengthen the feature representation of the shallow feature map, which optimizes the problem that the small feature extraction is not ideal due to the low resolution of the sonar image and less detail information. By building a two-level classifier to complete the learning task in an ensemble way, a better classification effect is obtained than a single classifier. this method is applied to the underwater intelligent perception group of the 10th National Marine Aircraft Design and Production Competition in 2021, and won the first prize in the country. the experimental results show that the designed ensemble network model is more accurate than the conventional convolution neural network in identifying underwater targets on the measured sonar image data set while ensuring a certain prediction speed. © Published under licence by IOP Publishing Ltd.

关键词： Image recognition

来源：评论

学校读者我要写书评

暂无评论

Fast Dynamic Calibration of Millimeter-Wave Radar External Parameters: Research and Applications

Fast Dynamic Calibration of Millimeter-Wave Radar External P...

引用

Sensors and Information Technology (ICSI), international conference on

作者： Jie Yang Xiaohui Li Xianjin He Qin Xia Juncheng Huang China Automotive Engineering Research Institute Co. Ltd Chongqing China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

With the rapid development of intelligent connected technologies, the application of millimeter-wave radar in roadside perception systems has become increasingly widespread [1], [2]. Known for its excellent anti-interference capabilities and resistance to adverse weather conditions, millimeter-wave radar has become a key sensor in these systems [3]. However, the accuracy of radar data is still limited by the calibration of the coordinate system, especially in dynamic environments [4]. Traditional calibration methods rely on manual adjustments, which are inefficient [5]. this paper proposes an automated dynamic millimeter-wave radar calibration method based on deep reinforcement learning, combined with multi-sensor data fusion, to address the high-precision calibration issue between the radar coordinate system and the world coordinate system in dynamic environments. Experiments on open roads show that both the new method and the method incorporating deep reinforcement learning (DQN) significantly improve calibration performance. the calibration accuracy increased by 11.11% and 48.15%, respectively, compared to traditional methods, while the calibration speed was also greatly improved. the proposed method effectively reduces human intervention, lowers system deployment costs, and demonstrates strong scalability, making it highly valuable for practical applications in intelligent connected vehicles and roadside perception systems.

关键词： Accuracy Roads Manuals Millimeter wave radar Sensor fusion Deep reinforcement learning Calibration Trajectory Vehicle dynamics Optimization

来源：评论

学校读者我要写书评

暂无评论

Advancing Android Security: Leveraging Stacking Ensemble and Bioinspired Feature Selection for Efficient Malware Detection

Advancing Android Security: Leveraging Stacking Ensemble and...

引用

Emerging Technology (INCET), international conference for

作者： V. Jyothsna Peddesugari Mokshitha Shaik Khulud Lakshmireddy Gari Premanath Reddy Nare Jagannath Reddy Bhasha Pydala Department of Data Science Mohan Babu University(Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupati India Dept. of Information Technology Sree Vidyanikethan Engineering College Tirupati India Department of Data Science Mohan Babu University Tirupati India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Nowadays, the use of smartphones and related applications is increasing rapidly due to their “easy to use” features, the functionality of many applications, and the constant development of software and hardware of smart devices. Research in this field shows that 4.5 billion people will have mobile phones by 2024. the most used smartphone operating system among these devices is Android. Android accounts for 75.5% of the market. Because Android is so widely used, it is more vulnerable to viruses and malware, making it an ideal target for hackers. Android anti-malware technology, which can quickly detect and classify different malware to create a rapid response plan, has become popular in recent years. the usefulness of combining deep learning and machine learning to offer automated and self-learning services has been shown by numerous applications; nevertheless, the shortage of malware samples has been identified as a barrier to the development of deep learning-based solutions. To solve this problem, this work introduces new machine-learning techniques that can classify malware into different groups and identify malware attacks. the proposed method used the Drebin dataset divided into hazardous and non-hazardous substances.

关键词： Machine learning algorithms Accuracy Stacking Feature extraction Malware Classification algorithms Ensemble learning

来源：评论

学校读者我要写书评

暂无评论

Leveraging Ensemble Deep learning Model for Fruit Image Recognition with Explainable AI

Leveraging Ensemble Deep Learning Model for Fruit Image Reco...

引用

Big data & Artificial Intelligence & Software engineering (ICBASE), international conference on

作者： Yaoqing Wang Chang Yu Xiyue Lin Goodness Temofe Mgbejime Grace U. Nneji Happy N. Monday Chengdu University of Technology Chengdu China School of Electronic Science and Engineering University of Electronic Science and Technology China Intelligent Computing Lab HACE SOFTTECH Lagos Nigeria

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

Precise classification of fruit images is crucial for a range of agricultural and retail purposes. Although there have been improvements, current methods have limits when it comes to dealing with complex feature extraction and ensuring model resilience. this paper introduces a new framework for categorizing fruits using a combination of advanced deep learning models, namely AlexNet, ResNet50, and EfficientNet. the collection of 43,912 photos across 44 fruit classifications underwent thorough pre-processing and data augmentation procedures. Grad-CAM, SHAP, and LIME were employed to achieve model interpretability, offering valuable insights into the decision-making process of the ensemble model. the experimental results indicate that the ensemble model surpasses the performance of individual models, obtaining $96.9 \%$ accuracy, $\mathbf{9 6. 2 \%}$ precision, $94.5 \%$ recall, and $95.4 \%$ F1-score. the proposed ensemble model provides a highly efficient solution for automated fruit classification, with substantial implications for agricultural technology and retail automation.

关键词： Deep learning Analytical models Image recognition Explainable AI Decision making Feature extraction data augmentation Software engineering Resilience Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Using Artificial Intelligence and Deep learning Methods to Analysis the Marketing Analytics and Its Impact on Human Resource Management Systems 5th

Using Artificial Intelligence and Deep Learning Methods to A...

引用

5th international conference on Emerging Technologies in Computer engineering: Cognitive Computing and intelligent IoT, ICETCE 2022

作者： Gambhir, Vinima Asnate-Salazar, Edwin Prithi, M. Alvarado-Tolentino, Joseph Tongkachok, Korakod SVIMS Business School Wadala Maharashtra Mumbai India Computer Science and Engineering Universidad Nacional Santiago Antunez de Mayolo Huaraz Peru Computer Science Periyar University Tamil Nadu Vellore India Science and Engineering with a Major in Audit and Computer Security Universidad Nacional Santiago Antunez de Mayolo Huaraz Peru Faculty of Law Thaksin University Songkhla Thailand

ISBN: (纸本)9783031070112

In recent years, all firms have been concerned with marketing analytics. they’re using numerous advanced technologies to analyse marketing analytics. Artificial intelligence (AI) and Deep learning (DL) technology are highly capable of examining large databases for patterns and insights. these technologies enable the marketing function to encompass its reach and analytics empower a deeper understanding of how the market responds to actions. With the help of Deep learning, businesses are now able to connect a wide range of datasets to better understand what customers want with greater sophistication and analytic capacity, and then use that information to gain a competitive advantage. In addition, Deep learning uses numerous technical tools that excel at extracting perceptions and patterns from huge amounts of data and then predicting the future for marketing. thus, Deep learning can potentially be used to create products that are tailored to what customers want. It has been found that marketing analytics play a significant role in HRM. It helps in examining the employees’ skill sets and developing a training programme based on the market demands. AI assists firms in determining target audiences and devising a strategy to meet its objectives. Also, AI technology adapts and learns from data to make data-driven decisions. Many time-consuming and managerial chores will be automated by HR software that includes artificial intelligence. A lot of administrative activities are automated and speeded up using AI. Moreover, to gain in-depth knowledge of Artificial Intelligence and Deep learning this paper is conducted. Furthermore, the article examines how artificial intelligence and deep learning technologies are utilised to assess marketing statistics, as well as their impact on human resource management systems. For this paper, descriptive research methodology has been used for this study, and secondary data has been used to obtain reliable conclusions. © 2022, the Author(s),

关键词： Marketing

来源：评论

学校读者我要写书评

暂无评论

intelligent Smart Waste Management Using Regression Analysis: An Empirical Study 5th

Intelligent Smart Waste Management Using Regression Analysis...

引用

5th international conference on Emerging Technologies in Computer engineering: Cognitive Computing and intelligent IoT, ICETCE 2022

作者： Rath, Abinash Das Gupta, Ayan Rohilla, Vinita Balyan, Archana Mann, Suman Lavasa Pune India Department of Geography WBES Chandernagore Government College affiliated to the University of Burdwan West Bengal Hooghly India Department of Computer Science Maharaja Surajmal Institute of Technology New Delhi India ECE Department Maharaja Surajmal Institute of Technology New Delhi India Department of IT Maharaja Surajmal Institute of Technology New Delhi India

ISBN: (纸本)9783031070112

the term deep learning is seen as an important part of artificial intelligence that allows the system to understand and make decisions without special human intervention. In-depth learning uses a variety of statistical models and programs that allow different computational properties to reach the highest point. It is estimated that the market development of artificial intelligence and technology for deep learning will amount to USD 500 billion by 2026. the use of advanced technology, such as neural networks, enables better image recognition and the use of automated processes for deep operations. the main purpose of the study is to understand the critical determinants of Deep learning in Creating a better City through intelligent Smart Waste Management, the major determinants cover: System usability scale, Implementation of RFID sensors and Optimizing route selection. the proposed work is that implementation of advanced tools like deep learning methodologies and machine learning tools can support in managing the waste in a smart way, this will enable in creating better cities, enhance the environment and support sustainable living. Smart cities today need to use tools like deep learning and other artificial intelligence to effectively manage waste. Smart vessels are mainly controlled and implemented, which makes it easier for users to open vessels, it is also suitable for storing solid and dry waste, but provides information on the total degree of filling, can share data and information with central waste management service, you can collect waste quickly and avoid flooding. To achieve this, governments, administrators and communities are introducing sensors that transmit data and information to the waste management company in real-time and take appropriate action. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Regression analysis

来源：评论

学校读者我要写书评

暂无评论

Research on Automation Control of intelligent Logistics Assistant

Research on Automation Control of Intelligent Logistics Assi...

引用

Machine learning and Computer Application (ICMLCA), international conference on

作者： Ziwen Li Dun Lin Kaitao Deng Weijia Li Yezhao Li Libin Feng College of Electronic Information Engineering Zhuhai College of Science and Technology Zhuhai Guangdong China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

this paper investigates an intelligent logistics assistant based on the YOLOv5 algorithm, using a three-wheel structure design with a master control system of STM32F407ZGT6. the task information is displayed through the LCD display, and the system is equipped with an MG996 robotic arm servo to control the clamping of the material while the servo of the material tray realizes the rotation of the material tray. To ensure the stability of the attitude, the system uses HW101 gyroscope for attitude correction, while the laser distance measurement module is responsible for the accurate calibration of the position. openMV vision camera runs the YOLOv5 algorithm to realize the real-time recognition and processing of the environment, and the RFID scanning module is used to obtain and identify the task code. For motion control, Emm _ V5.0closed-loop stepper motors drive three omnidirectional wheels for flexible movement, while a gimbal closed-loop stepper motor is used to regulate the rotation of the robotic arm. the lifting slide rail cooperates with the transmission belt to realize the lifting and lowering transmission of the robotic arm. through the synergistic work of the above modules, the designed intelligent logistics robot has an efficient automated material handling capability.

关键词： YOLO Accuracy Wheels Laser stability Manipulators Robot sensing systems data models Mobile robots Servomotors Logistics

来源：评论

学校读者我要写书评

暂无评论

An automated Face Mask Detection Using Ensemble learning Approach

An Automated Face Mask Detection Using Ensemble Learning App...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： B. Kiran Kumar Guggilam Mounika RVS Harish Valeti Nagarjuna Embedded Systems St. Ann's College of Engineering & Technology Chirala Andhra Pradesh India St. Ann's College of Engineering & Technology Chirala Andhra Pradesh India Dept of CSE Kallam Haranadhareddy Institute of Technology Chowdavaram Andhra Pradesh India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

the COVID-19 pandemic and other infectious diseases are spreading quickly, which has brought attention to how essential face masks are in stopping the spread of respiratory disorders. To ensure compliance with mask-wearing rules, automatic face mask detection devices are becoming increasingly important. the application of automated Convolutional Neural Networks (ACNN) in conjunction with Internet of things (IoT) devices for an automated Face Mask Detection is the main emphasis of this research. the suggested method correctly detects people wearing or not wearing face masks in real-time video and picture data by leveraging ACNN, a deep-learning architecture created for image segmentation. A wide range of labeled photos of people wearing and not wearing masks are used to train the FCNN. the model is ideal for real-world applications in public areas, healthcare facilities, and educational institutions due to its high accuracy and efficiency. Real-time monitoring and notifications are made possible by the integration of IoT devices, which improves the operation of the system. In order to collect images and videos data, IoT sensors and cameras are positioned strategically in specific regions. A central processing unit (CPU) that executes the trained FCNN model is connected to these devices. Using email, SMS, or a mobile app, the system instantly notifies the appropriate authorities or persons when it finds someone not donning a face mask.

关键词： COVID-19 Pandemics Streaming media Real-time systems Sensor systems Sensors Central Processing Unit Safety Internet of things Open source software

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19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：