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

253,843 篇 会议
107,898 篇 期刊文献
2,757 册 图书
22 篇 学位论文
2 篇 科技报告

馆藏范围

364,520 篇 电子文献
9 种 纸本馆藏

日期分布

学科分类号

207,380 篇 工学
- 129,739 篇 计算机科学与技术...
- 104,234 篇 软件工程
- 51,272 篇 信息与通信工程
- 33,410 篇 控制科学与工程
- 33,024 篇 电气工程
- 32,778 篇 电子科学与技术（可...
- 22,539 篇 生物工程
- 18,164 篇 生物医学工程（可授...
- 18,023 篇 光学工程
- 15,029 篇 机械工程
- 13,027 篇 化学工程与技术
- 12,440 篇 仪器科学与技术
- 12,015 篇 动力工程及工程热...
- 10,945 篇 材料科学与工程（可...
- 7,469 篇 安全科学与工程
- 7,206 篇 网络空间安全
- 6,819 篇 交通运输工程
121,380 篇 理学
- 58,247 篇 数学
- 43,588 篇 物理学
- 25,743 篇 生物学
- 18,743 篇 统计学（可授理学、...
- 13,994 篇 化学
- 13,709 篇 系统科学
49,880 篇 管理学
- 31,131 篇 管理科学与工程(可...
- 20,718 篇 图书情报与档案管...
- 14,668 篇 工商管理
17,515 篇 医学
- 14,506 篇 临床医学
- 12,921 篇 基础医学(可授医学...
- 8,128 篇 药学(可授医学、理...
9,008 篇 法学
- 7,407 篇 社会学
5,940 篇 经济学
4,691 篇 农学
3,506 篇 教育学
1,669 篇 文学
847 篇 军事学
455 篇 艺术学
66 篇 哲学
39 篇 历史学

主题

9,977 篇 computer science
8,710 篇 accuracy
7,808 篇 computational mo...
7,671 篇 feature extracti...
6,967 篇 deep learning
6,435 篇 machine learning
6,195 篇 training
4,547 篇 predictive model...
4,542 篇 support vector m...
4,392 篇 optimization
4,208 篇 data mining
4,111 篇 neural networks
4,074 篇 convolutional ne...
4,059 篇 wireless sensor ...
4,047 篇 computer archite...
3,924 篇 bandwidth
3,922 篇 real-time system...
3,754 篇 artificial intel...
3,718 篇 testing
3,689 篇 costs

机构

3,271 篇 school of electr...
2,237 篇 chitkara univers...
2,018 篇 department of el...
1,721 篇 department of co...
1,260 篇 university of ch...
1,056 篇 cavendish labora...
981 篇 department of ph...
863 篇 oliver lodge lab...
860 篇 physikalisches i...
809 篇 department of el...
717 篇 department of co...
634 篇 department of co...
614 篇 school of comput...
610 篇 school of comput...
568 篇 school of electr...
533 篇 infn sezione di ...
523 篇 infn laboratori ...
511 篇 infn sezione di ...
498 篇 horia hulubei na...
490 篇 infn sezione di ...

作者

250 篇 barter w.
241 篇 j. wang
240 篇 s. chen
231 篇 a. gomes
223 篇 blake t.
220 篇 bowcock t.j.v.
219 篇 bellee v.
219 篇 amato s.
217 篇 betancourt c.
215 篇 borsato m.
214 篇 c. alexa
213 篇 boettcher t.
211 篇 brundu d.
210 篇 j. m. izen
205 篇 beiter a.
202 篇 bencivenni g.
194 篇 c. amelung
193 篇 g. bella
193 篇 j. strandberg
192 篇 d. calvet

语言

348,326 篇 英文
11,679 篇 其他
4,396 篇 中文
278 篇 日文
41 篇 朝鲜文
36 篇 德文
29 篇 法文
21 篇 土耳其文
17 篇 斯洛文尼亚文
7 篇 荷兰文
3 篇 阿拉伯文
3 篇 爱沙尼亚文
3 篇 葡萄牙文
3 篇 俄文
1 篇 捷克文
1 篇 西班牙文
1 篇 泰文

检索条件"机构=Computer Engineering Department and Computer Technology Institute"

共 364522 条记录，以下是431-440 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：

Ahead of the Curve: Predictive Maintenance Solutions for Modern Automobiles

Ahead of the Curve: Predictive Maintenance Solutions for Mod...

引用

International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Aishwarya Barath, Ananya Satish Kumar, T. Khushi, L. Lavanya, N. Department of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India

ISBN: (纸本)9789819613472

Predictive maintenance in the automotive sector has received a lot of attention because of its potential to improve operating efficiency and lower maintenance costs. This paper presents an approach for predictive maintenance that focuses on engine condition prediction and battery remaining usable life (RUL) predictions. The work intends to develop precise predictive models to detect anomalies in engine health and anticipate battery degeneration, allowing for proactive maintenance interventions. The research entails gathering and analyzing data from engine sensors and battery characteristics in order to train and test various machine learning models. XGBoost was the best algorithm for forecasting engine health, with an accuracy of 0.66, while Bagging Regressor was the best regression technique for engine prediction, with an MSE of 4443.04 and RMSE of 66.66. These models are highly accurate and efficient, demonstrating their potential for real-world use in automobile maintenance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

Brinjal Leaf Diseases Detection Based on Deep Convolutional Neural Network 4

Brinjal Leaf Diseases Detection Based on Deep Convolutional ...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Anu Priya, A. Rajaram, K. Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Brinjal leaf diseases, such as leaf spot, mosaic virus, and wilt, would critically reduce crop yield quality if the disease is spread during an early stage. Such reasons have made early identification imperative to ensure effective prevention and management. This article, therefore, aims at coming up with an effective technique of identifying brinjal leaf diseases using VGG16 CNN model. Traditional identification methods are based on manually examining plant diseases, an effort known to be highly labour-consuming, subjective, and even unreliable. In addition, most of the available machine learning methods fail in scalability, computational cost, and specific domain problems. However, our technique makes use of the high ability of VGG16 architecture to extract automatically relevant features from leaf images and then classify them with high accuracy. Used Eggplant Leaf Disease Dataset which contains six classes. The central idea of our approach is to employ deep convolutional layers of VGG16 to extract discriminative features from the images followed by classification using fully connected layers and softmax activation. The approach obtains 98.2% accuracy for determining of Brinjalleaf diseases. This work will impact the agricultural sector by means of providing a fast automated solution that gives accurate insights about early detection of leaf disease occurrence, hence reducing losses to crops and enhancing production. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

CropSmart-A Crop Prediction Application Using Machine Learning 12th

CropSmart-A Crop Prediction Application Using Machine Learni...

引用

12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Madugula, Vidya Perumandla, Anshu Likhitha, Singari Gupta, Manu Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819601424

India is a country whose primary sources of income are agriculture and farming. Varied soil in the nation enables farmers to grow a wide range of crops all year round. The agricultural industry has been the focus of in-depth investigation in the past several years due to recent technological advances, including machine learning and smart computing. The proposed work aims to develop a web application for crop selection supporting farmers and improving agricultural productivity. Various supervised machine learning classifiers are used for crop prediction based on environmental factors and soil conditions. Performance metrics are included in the evaluation of several machine learning models. The results attained show that the bagging classifier achieved better results for crop prediction with an accuracy of 97%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Automated Immune Cell Classification using Image Processing Techniques 6

Automated Immune Cell Classification using Image Processing ...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sathiyaprasad, B. Reddy, Arva Hari Vardhan Abdurrahman, Usama Micheal, A. Annie Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331522667

Advances in immunological research are essential for elucidating immune responses and developing targeted therapeutic approaches. This study proposes an automated method for immune cell classification leveraging machine learning and image processing techniques. A novel framework is introduced, combining high-resolution imaging with computational algorithms to accurately classify various immune cell types from biological samples. The methodology incorporated preprocessing steps, like normalization and noise reduction. Then, deep learning models, such as CNNs and SVMs, will be applied for feature extraction and classification. These models were trained on annotated datasets derived from flow cytometry and microscopy, demonstrating better accuracy and reliability. Results demonstrate an improvement in processing speed and accuracy of classification over traditional approaches, which underlines the importance of automation in immunological diagnostics. The data obtained can be used in high-throughput immunotherapy and disease diagnostic screening;it allows for the identification of immune cell populations within a short period and therefore improves research efficiency. The work provides a basis for real-time analysis and incorporation of multiscale datasets that will help to better understand immunity and its associated disorders. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Synergistic Fusion of Steganography and Cryptography in Medical Imaging: A Comparative Analysis to Enhance Image Security 4th

Synergistic Fusion of Steganography and Cryptography in M...

引用

4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Ghosh, Sharmila Saha, Ashim Pal, Tannistha Department of Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (纸本)9789819751990

Image steganography is a technique for encrypting data, allowing covert communication, and enhancing data security inside image files. This study provides an in-depth analysis of image steganography, covering its core ideas, methods, uses, difficulties, and developments. Numerous embedding algorithms and methodologies, including transform domain methods and LSB-based strategies, are examined in this study. The paper highlights the need for effective defenses against steganalysis attacks, while also discussing the challenges and potential weaknesses in steganography. It examines common steganography techniques and discusses countermeasures such as adaptive embedding, key-based embedding, and algorithmic resistance. Additionally, the paper also highlights the significance of assessing the efficacy and caliber of image steganography techniques. Objective metrics such as PSNR and MSE are discussed, providing insights into the trade-offs between security and image fidelity. For those interested in understanding image steganography, its applications, and its challenges, this study offers a complete resource. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Detecting Inattentive and Aggressive Driver Behavior Using Deep Learning: Recent Advances, Challenges with Performance Evaluation 4th

Detecting Inattentive and Aggressive Driver Behavior Using D...

引用

4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Debbarma, Tina Pal, Tannistha Debbarma, Nikhil Department of Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (纸本)9789819751990

Road safety faces a grave threat from the prevalence of aggressive and inattentive driving behaviors, contributing significantly to the global rise in traffic accidents and fatalities. Identifying, tracking, and proactively addressing instances of aggressive and inattentive driving are pivotal steps toward mitigating accidents and enhancing road safety. This research project seeks to explore the potential of deep learning methods for analyzing human driving behavior, with a specific focus on identifying inattentive and aggressive driving behaviors. The ultimate goal is to develop a robust and reliable system capable of accurately recognizing and categorizing such behaviors. To achieve this objective, we harness the power of deep learning algorithm. This paper also discusses the identification and categorization of aggressive and distracted driving behaviors among human drivers, using deep learning methodologies which offer substantial promise for elevating traffic safety. The literature review uncovered several noteworthy techniques, including CNN, MTCNN, transformer-based action detection systems, and fusion models inspired by densely connected convolution networks. Comparative analysis revealed improvements in accuracy and precision through the utilization of these techniques. However, the pursuit of larger and more diverse datasets, real-time implementation capabilities, and robustness in various environmental conditions remain essential requirements. Despite these challenges, deep learning-based systems offer the potential for automatic and precise analysis of intricate driving patterns, pivotal in accident prevention and overall road safety enhancement. Future research should focus on surmounting current limitations and advancing the development of more intricate deep learning models for accurate recognition and categorization of human driving behaviors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Automated Brain Tumor Detection and Classification in MRI Scans using a Deep Convolutional Neural Network and U-Net Architecture 6

Automated Brain Tumor Detection and Classification in MRI Sc...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Devadas, Prathima Mathivanan, G. Department of Computer science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Brain tumor identification and categorization serve a precarious role in early diagnosis and treatment planning. This study suggests a new deep learning (DL)-based structure for the automatic detection and categorization of brain cancers using medical imaging technologies. The framework uses a deep convolutional neural network (DCNN) with an exclusive segmentation technique, notably the U-Net architecture, to discrete tumor areas in magnetic resonance imaging (MRI) scans. The DCNN model, which contains multiple convolutional layers, is used for feature extraction, followed by fully connected layers for tumor classification. To increase segmentation accuracy, the U-Net model integrates both encoder-decoder structures and skip connections to capture fine-grained spatial details. The suggested algorithm is trained and evaluated on a large dataset of brain MRI images, achieving high accuracy, sensitivity, and specificity for both tumor detection and classification. Experimental results show that the model outclasses standard methods, providing a dependable and operative solution for the early detection and diagnosis of brain cancers, potentially aiding clinical decision-making processes. © 2025 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Neuro-Cybernetics: Enabling Human–Machine Interaction Through Brain Signals

Neuro-Cybernetics: Enabling Human–Machine Interaction Throu...

引用

Congress on Smart Computing Technologies, CSCT 2023

作者： Kulkarni, Mukund Lautawar, Tanmay Bhalgat, Kunal Nandankar, Kuntesh Lad, Yogita Langade, Niranjan Department of Computer Engineering BRACT’s Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819780952

In an era where human–machine interaction is at the forefront of technological advancement, the search for a seamless and responsive interface between human cognition and robotic control is supreme. This paper delves into the creation of an IoT-enabled mind-controlled robot, an innovation poised to redefine the boundaries of human–robot collaboration. The challenge addressed here is the establishment of Brain-computer Interface (BCI) which is real-time, having precise control over a robotic platform using EEG-based brain signals. Leveraging NodeMcu devices and wireless communication, this study pioneers a Server-Client Model for facilitating uninterrupted communication between the user and the robot. Within this framework, the user interface NodeMcu captures EEG signals via specialized sensors, while signal processing algorithms extract meaningful patterns. These patterns are mapped to predefined commands, which, when transmitted to the robotic platform NodeMcu, are transformed into motor control signals, driving the robot's movements. This dynamic interaction not only showcases the potential of mind-controlled robotics but also opens new horizons for intuitive and efficient human–robot collaboration. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Brain computer interface

来源：评论

学校读者我要写书评

暂无评论

Cloud-Based Tracker for Prioritizing Rescue Operations Using IoT-Based Drones with Advanced Thermal-Imaging Using YOLO V3 5th

Cloud-Based Tracker for Prioritizing Rescue Operations Using...

引用

5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Sangeetha, J. Kumaran, Shubeeksh Raj, V. Aditya Raman, V. R. Monish Department of Computer Science and Engineering Ramaiah Institute of Technology Karnataka Bangalore India

ISBN: (纸本)9789819771776

During disasters, such as natural catastrophes the immediate survival of victims is at stake. There have been instances where victims who were genuinely in dire need of assistance were not provided with it when they should have, resulting in their extreme injury or death. This happened because the rescue team was occupied helping other individuals who did not need immediate assistance. By prioritizing victims for rescue, rescue teams can focus on locating and aiding those most vulnerable or in life-threatening situations, increasing the chances of their survival. With a limited number of resources, categorizing individuals based on their condition and need for help, disaster management teams can strategically deploy specialized resources, making the most of available expertise. Our research introduces a drone-based solution that surveils the disaster-stricken region to identify victims and launches a priority tracker that tags them and gives their real-time situation details to the concerned authorities. The systems involved in this process are the Deployment System and the Priority Management System. The Deployment System utilizes a drone equipped with advanced thermal cameras that are continuously rotated in all directions to identify humans. Upon identification, the system initiates a priority tracker launcher that targets and tracks the identified human. The Priority Management System includes a priority tracker with sensors that provides real-time data on tagged individuals. This data allows the rescue personnel to analyze and prioritize rescue efforts using Priority UI which displays the sensor readings of multiple trackers deployed. The priority tracker also has a panic button to increase the individual's priority and ensure a quick response. Overall, the paper demonstrates a new system for rescue operations that emphasizes prioritizing victims, allocating resources effectively, and improving disaster response efficiency. © The Author(s), under exclusive lice

关键词： Disasters

来源：评论

学校读者我要写书评

暂无评论

Enhancing IoT ECO System - Smart Parking System

Enhancing IoT ECO System - Smart Parking System

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Avinash, S. Muthukrishnan, S. Aishwarya, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529833

The Smart Parking System is an innovative solution designed to tackle the challenges of parking in densely populated urban areas. By integrating advanced technologies such as RFID (Radio Frequency Identification) sensors, automated gate systems, and proximity sensors, the system delivers a streamlined and efficient parking experience. RFID technology automates entry and exit processes by recognizing vehicles equipped with RFID tags, reducing wait times and enhancing security through controlled access. Proximity sensors at each parking spot monitor occupancy in real-time, feeding data to a central management system that directs drivers to available spaces via digital signage or a mobile app. This real-time guidance decreases search time, reduces congestion, and improves overall parking organization. Designed with scalability and future-readiness, the system's central management unit can integrate additional features like payment processing, reservation systems, and predictive analytics powered by machine learning. This adaptability ensures the system remains relevant as technology advances and user needs evolve. The Smart Parking System also promotes sustainability by minimizing vehicle idling and carbon emissions, contributing to a reduced environmental impact. For users, the system offers a more convenient, secure, and efficient parking experience. For operators, it enhances operational efficiency, reduces manual labor, and provides valuable insights into parking patterns, aiding in pricing, expansion, and maintenance decisions. The system's modular design allows for cost-effective implementation, making it suitable for various facilities from small lots to large complexes. In summary, the Smart Parking System represents a forward-thinking approach to urban parking, combining technology, efficiency, and sustainability to improve urban living. © 2025 IEEE.

关键词： Proximity sensors

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 40 41 42 43 44 45 46 47 48 49 > >>

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：