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

分类表

所选分类

>> <<

限定检索结果

标题

标题
作者
主题词
出版物名称
出版社
机构
学科分类号
摘要
ISBN
ISSN
基金资助
索书号

作者

作者
标题
主题词
出版物名称
出版社
机构
学科分类号
摘要
ISBN
ISSN
基金资助
索书号

文献类型

89,152 篇 会议
38,592 篇 期刊文献
1,303 册 图书

馆藏范围

129,047 篇 电子文献
3 种 纸本馆藏

日期分布

学科分类号

72,939 篇 工学
- 46,491 篇 计算机科学与技术...
- 37,158 篇 软件工程
- 18,985 篇 信息与通信工程
- 12,507 篇 控制科学与工程
- 11,938 篇 电气工程
- 11,515 篇 电子科学与技术（可...
- 8,313 篇 生物工程
- 7,068 篇 生物医学工程（可授...
- 6,829 篇 光学工程
- 5,177 篇 机械工程
- 4,684 篇 化学工程与技术
- 4,565 篇 仪器科学与技术
- 4,448 篇 动力工程及工程热...
- 4,147 篇 材料科学与工程（可...
- 3,451 篇 网络空间安全
- 3,090 篇 安全科学与工程
43,612 篇 理学
- 19,436 篇 数学
- 16,770 篇 物理学
- 9,558 篇 生物学
- 6,365 篇 统计学（可授理学、...
- 5,287 篇 化学
- 4,683 篇 系统科学
17,902 篇 管理学
- 11,310 篇 管理科学与工程(可...
- 7,404 篇 图书情报与档案管...
- 5,398 篇 工商管理
7,305 篇 医学
- 5,947 篇 临床医学
- 5,320 篇 基础医学(可授医学...
- 3,283 篇 药学(可授医学、理...
- 3,158 篇 公共卫生与预防医...
3,535 篇 法学
- 2,902 篇 社会学
2,242 篇 经济学
1,869 篇 农学
1,155 篇 教育学
595 篇 文学
282 篇 军事学
123 篇 艺术学
14 篇 哲学
8 篇 历史学

主题

3,891 篇 accuracy
3,173 篇 deep learning
2,932 篇 feature extracti...
2,829 篇 computer science
2,793 篇 machine learning
2,666 篇 computational mo...
2,291 篇 training
1,993 篇 real-time system...
1,881 篇 internet of thin...
1,849 篇 support vector m...
1,794 篇 convolutional ne...
1,776 篇 predictive model...
1,638 篇 artificial intel...
1,470 篇 optimization
1,405 篇 neural networks
1,350 篇 wireless sensor ...
1,261 篇 security
1,254 篇 data mining
1,219 篇 bandwidth
1,216 篇 computer archite...

机构

1,093 篇 cavendish labora...
1,022 篇 department of ph...
970 篇 physikalisches i...
961 篇 oliver lodge lab...
770 篇 university of ch...
680 篇 chitkara univers...
650 篇 department of el...
615 篇 infn laboratori ...
606 篇 infn sezione di ...
585 篇 infn sezione di ...
568 篇 infn sezione di ...
561 篇 fakultät physik ...
550 篇 school of physic...
540 篇 horia hulubei na...
528 篇 department of ph...
526 篇 school of physic...
515 篇 h.h. wills physi...
510 篇 imperial college...
502 篇 nikhef national ...
501 篇 stfc rutherford ...

作者

263 篇 barter w.
256 篇 a. gomes
245 篇 c. alexa
241 篇 j. m. izen
235 篇 j. wang
234 篇 c. amelung
233 篇 g. bella
233 篇 blake t.
232 篇 j. strandberg
232 篇 d. calvet
231 篇 s. chen
229 篇 beiter a.
224 篇 bellee v.
223 篇 amato s.
222 篇 betancourt c.
221 篇 boettcher t.
220 篇 borsato m.
219 篇 bowcock t.j.v.
218 篇 brundu d.
217 篇 bencivenni g.

语言

105,757 篇 英文
21,997 篇 其他
1,509 篇 中文
119 篇 日文
26 篇 斯洛文尼亚文
25 篇 法文
24 篇 德文
8 篇 朝鲜文
6 篇 土耳其文
5 篇 阿拉伯文
4 篇 俄文
2 篇 荷兰文
1 篇 爱沙尼亚文
1 篇 意大利文
1 篇 波兰文
1 篇 葡萄牙文

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

共 129047 条记录，以下是4561-4570 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：

相关度排序

相关度排序
时效性降序
时效性升序

Predictive Maintenance Model Using Hybrid Procedure of Improved Quantum Cat Swarm Optimisation for Asset Management in Industry 4.0

Predictive Maintenance Model Using Hybrid Procedure of Impro...

引用

International Conference on Data Analytics and Management, ICDAM 2023

作者： Kanchana, S. Rajan, Dhivya Mahaveerakannan, R. Sagar, K. V. Daya Subramanian, P. Rajakumar, B. Department of Computer Science Faculty of Science and Humanities SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Applications CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electronics and Computer Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of AI & DS JNN Institute of Engineering Chennai Periyapalayam Highway Tamil Nadu Thiruvallur601102 India

ISBN: (纸本)9789819965465

Contemporary manufacturing system design centres on the Industry 4.0 paradigm. A versatile platform secondary the efficient optimisation of manufacturing-related activities, such as predictive keep, is necessary for the successful mixing of cutting-edge technologies like the (IoT), cyber-physical schemes, computing. Predictive maintenance research that already exists often either develops a model without taking maintenance decisions into account or seeks to optimise maintenance in the light of the degradation replicas of the already-known scheme. With the use of predictive maintenance, a company's owner can make decisions like fixing or replacing a failing part of the manufacturing line before it actually fails. Because of this, Industry 4.0 necessitates efficient asset management in order to maximise the distribution of duties and the accuracy of predictive maintenance models. To address the issue of predictive maintenance in fog computing, this paper proposes an enhanced version of the quantum cat swarm optimisation (QCSO) algorithm by combining the quantum bit (Q-bit) and the cat swarm procedure (CSO) and updating the cats based on the quantum rotation angle site. Finally, the (MR) worth is chosen based on the sum of times the procedure is run. FogWorkflowsim uses simulated real-time datasets to evaluate the forecasting performance of QCSO and other existing models. The outcomes prove that the projected method is superior to the currently used predictive maintenance resource allocation methods. In comparison to the approximately 91% to 95% accuracy achieved by previous models, the proposed model has an accuracy of 96%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Analysis of Lung Cancer for Developing Smart Healthcare with the Help of BGWO Based TSA-XGBoost Model

Analysis of Lung Cancer for Developing Smart Healthcare with...

引用

2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Mahaveerakannan, R. Murali Dhar, M.S. Goswami, Sohan Krishna, P. Gopala Jayanthi, K. Kumar, M. Sathish The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Technique Polytechnic Institute Department of Computer Science and Technology West Bengal India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Pollachi Institute of Engineering and Technology Department of Computer Science and Engineering Pollachi Tamil Nadu Coimbatore India Karpagam College of Engineering Department of Cyber Security Tamil Nadu Coimbatore India

ISBN: (纸本)9798350300857

Given the circumstances the healthcare system, the Internet of Things (IoT) is crucial. IoT gadgets offer patient data for the framework of healthcare monitoring. IoT is a key component in every aspect of the health care management system since people can use smart devices to check on their health. Lung cancer is a fatal malignancy, and the likelihood of survival is increased by early identification. Because of the computational difficulty involved in gathering characteristics, it is imperative to design an approach using machine learning techniques for categorising cancer disease because the classification results given by the current methods are inadequate. With the use of the synthetic minority oversampling methods (SMOTE) methodology, the incoming data is pre-processed and balanced. With the help of the binary grey wolf optimisation algorithm (BGWOA), the pertinent features are best chosen. Finally, the suggested model's hyper-parameters are best chosen by the tunicate swarm optimisation (TSA) model, and the classification is carried out by the extreme gradient boosting (XGBoost) model. The experimental analysis demonstrates that the suggested model attained accuracy and recall values of 98% and 95%, respectively, compared to 95% and 95%, respectively, for the identical proposed model without the feature selection (FS) method. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

In Search of a Suitable Epoch Value for the Optimum Result in Image Captioning 3

In Search of a Suitable Epoch Value for the Optimum Result i...

引用

3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Dinda, Sourav Pal, Alok Ranjan Pal, Antara College of Engineering and Management Kolaghat Department of Computer Science and Engineering Kolaghat India National Institute of Technology Durgapur Department of Computer Science and Engineering Durgapur India

ISBN: (纸本)9781665494007

The proposed work is carried out on retrieving the content of an image using machine learning based approach. In order to gain a deeper insight of this topic, the current state-of-the-art image caption generation technique has been considered as the baseline. The goal of this work is to find a suitable Epoch value during the training phase which can maximize the performance of a system. The proposed model is composed of six major components: 1) Data pre-processing unit;2) Convolutional Neural Network (CNN) as an encoder;3) At-tention mechanism;4) Recurrent Neural Network (RNN) as a decoder;5) Beam search to find most optimal caption;and 6) Sentence Generation and evaluation. The model is trained to maximize the likelihood of the description sentence of a given training image. Experiments on the test dataset show the accuracy of the model and the fluency of the language it learns solely from image descriptions. The challenges of this work are mentioned in detail at the last of this paper. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Exploring Emotion and Sentiment Landscape of Depression: A Multimodal Analysis Approach 4

Exploring Emotion and Sentiment Landscape of Depression: A M...

引用

4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Anekar, Devata Deshpande, Yogesh Suryawanshi, Ranjeetsingh Waman, Rushikesh Divekar, Vishal Salunke, Rushikesh Department of Computer Engineering Vishwakarma University Pune India Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Computer Engineering Vishwakarma Institute of Technology Pune India Department of Information Technology Sinhgad Academy of Engineering Pune India

ISBN: (纸本)9798350305258

Conventionally depression detection was done through extensive clinical interviews, wherein the subject's responses are studied by the psychologist to determine his/her mental state. In our model, we try to imbibe the sentiment analysis and emotion detection approach by capturing video and analyzing facial and audio input to identify the symptoms of depression. The system is built with a video capturing containing an emotion recognition, utilizing Haar Cascade classifier, CNN models, and an audio processing model using Google API, NLP forwarded with Naïve Bayes Classifier. We have used FER 2013 dataset. The multimodal approach gives almost 77% accuracy, which can be integrated with the self-assessment questionnaire to improve the accuracy of depression detection. Many depressed individuals cannot receive the right care since there are few objective ways to assess depression. The results of this model with minor modifications will be incorporated into the system for early detection along with intervention for depression and anxiety amongst youth well-being in our society. © 2023 IEEE.

关键词： CNN depression emotion Haar multimodal NLP sentiment

来源：评论

学校读者我要写书评

暂无评论

Two-Stage, Global-Local Approach for Cell Nuclei Segmentation in Histopathology Images 8

Two-Stage, Global-Local Approach for Cell Nuclei Segmentatio...

引用

8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Shakya, Amit Saharan, Revat Gupta, Chetan Kumar, Rupesh Sharma, Lalit Naidu, Srivatsava Murala, Subrahmanyam Arora, Chetan Yamaha Motor Solutions Emerging Technology and Innovation Lab India Indian Institute of Technology Ropar Department of Biomedical Engineering India School of Computer Science and Statistics Trinity College Dublin Ireland Indian Institute of Technology Delhi Department of Computer Science and Engineering India

ISBN: (纸本)9798331531539

Effective management of high-resolution, and spatially wide contextual cues is fundamental to the accurate semantic segmentation. Traditional approaches like multi-resolution feature maps, and skip-connection are effective but require changes in the backbone architecture, restricting utilization of newer models and architectures for the problem. In this work we propose an architecture-agnostic, two-stage, global-local frame-work, called GoLo, for the semantic segmentation, which can use arbitrary semantic segmentation models within its two stages. We focus on segmenting cell nuclei in histopathology image analysis, where accurate segmentation of cell nuclei boundaries is one of the key issues. The proposed framework consists of first stage with Global and second stage with Local learning approach. The first stage is proposed to process the image globally and provide the coarse nuclei segmentation map. In the second stage, to process the image locally, coarse segmentation map and input image is first converted into patches. These patches are then fed as input to the second stage to get the fine- grained segmentation map. Both stages are trained with a combination of dice and binary cross entropy loss. To show the effectiveness of our approach, we test 4 state-of-the-art segmentation architectures (ACC-UNet, UCTransnet, Swin-UNet, and Vanilla U-Net), on 4 different benchmark datasets (MoNuSeg, CPM-17, CoNSep, and TNBC). We evaluate performance of each technique before and after using our framework. We report an average improvement of 4.82 % in mIoU, and 4.52% mDSC score, across techniques, and datasets. © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Mobility Stick with Haptic Feedback for People with Vision Impairments

Mobility Stick with Haptic Feedback for People with Vision I...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Raghul, J. Durga Devi, K. Sreya, M. Maithreye, S. SRM Institute of Science and Technology Department of Electronic and Computer Engineering Chennai Ramapuram India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Ramapuram India

ISBN: (纸本)9798331540685

In this research, haptic motor, Node MCU, ultrasonic sensors are used to address the problem associated with the current walking stick for visually disabled people. These problems include disturbing their main sense of survival which is hearing. Hence, a system containing ultrasonic sensors, haptic motor and Internet of Things has been recommended in this study. This study illustrates that the implementation of haptic motors and ultrasonic sensors can successfully address the major issues that current alternatives face and it has a positive ad significant impact on visually disabled people. Using this method, one can monitor the person's movement and whether they fell down. This system makes use of IoT it integrate the data from sensors and update it onto the website for other person monitoring. © 2024 IEEE.

关键词： Ultrasonic sensors

来源：评论

学校读者我要写书评

暂无评论

A Review on Unconstrained Real-Time Rotation-Invariant Face Detection 3

A Review on Unconstrained Real-Time Rotation-Invariant Face ...

引用

3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Agrwal, Shubh Lakshmi Sharma, Sudheer Kumar Kant, Vibhor Manipal University Jaipur Lnm Institute of Information Technology Department of Computer Science & Engineering Jaipur India Lnm Institute of Information Technology Department of Computer Science & Engineering Jaipur India Banaras Hindu University Department of Computer Science & Engineering Rgsc Varanasi India

ISBN: (纸本)9781665453578

With the amazing growth of image and video databases, there is a vast need for intelligent systems to automatically understand and look at information since doing it by hand is getting very hard. Faces are significant in social interactions because they show the feelings and identity of a person. People are not much better than machines at recognizing different faces. The automatic face detection system is a key in head pose tracking, face verification, face recognition, face tracking, face animation, face modeling, facial expression recognition, age and gender recognition, and behavior analysis in a crowd. Face detection is a way for a computer to find out the size and location of a face in an image. Face detection has been an outstanding issue in computer vision literature. This paper provides an overview of pose and rotation invariant face detection approaches with architecture designs and performance on popular benchmark datasets. The benchmark datasets used for face detection are listed as their key features. This paper also talks about different applications and challenges with face detection. Also, we set up special discussions on the practical aspects of making a face-detection system that works well. We end this paper by suggesting a few promising directions for future research. © 2023 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Subject-Verb Agreement Error Handling using NLP: A Literature Review 15

Subject-Verb Agreement Error Handling using NLP: A Literatur...

引用

15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Gautam, Prakhar Thakur, Jitendra Singh Mishra, Ashish Jabalpur Engineering College Department of Computer Science and Engineering Jabalpur India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences Jabalpur India

ISBN: (纸本)9798350324433

Subject-Verb Agreement errors in English language are a common problem in Natural Language Processing. These errors occur when the Subject and Verb in a sentence do not agree in number or person, leading to a grammatically incorrect sentence. Over the past few decades, researchers have developed various approaches to automatically detect and correct these errors in text. In this paper, a comprehensive review has been provided of the existing literature on Subject-Verb Agreement error detection and correction in Natural Language Processing. This review study focuses on key techniques that are used in Rule-based, Machine learning, and Deep learning approaches to detect and correct the errors. The performance of the approaches is compared using various evaluation metrics, such as Accuracy, Precision, Recall, and F1 score on standard benchmark datasets. Additionally, the limitations and challenges of each approach are discussed, and potential directions for future research are suggested. This review will be helpful for researchers and practitioners in the field of Natural Language Processing, particularly those interested in Grammar error detection and correction in the English language. © 2023 IEEE.

关键词： Error detection

来源：评论

学校读者我要写书评

暂无评论

Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Optimizing Real Estate Prediction - A Comparative Analysis of Ensemble and Regression Models 4th

Optimizing Real Estate Prediction - A Comparative Analysis o...

引用

4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Prasad, Runkana Durga Varma, Vemulamanda Jaswanth Jyothi, Uppalapati Padma Shankar, Sarakanam Sai Deenakonda, Mamatha Narasimharao, Kandula Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India Department of Electrical and Electronics Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031488870

Valuation is a fundamental aspect of real estate for businesses. Land and property serve as factors of production, and their value is derived from the use to which they are put. This value is influenced by the demand and supply for the product or service produced on the property. Valuation involves determining the specific amount for which a property would transact on a given date. Accurate prediction of real estate prices is crucial for investors, house owners and industry professionals. In this article, analysis of USA real estate prediction using regression and ensemble models was presented, also evaluating the best model out of all the models that have been applied. The objective of this article is to provide accurate predictions for the real estate market, by making use of Multi-Variate Regression, Random Forest Regressor, Decision Tree Regressor, XGB Regressor and CatBoost Regressor. This analysis offers valuable insights for making wise and right choices in the real estate market. © 2024, ICST institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Forecasting

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 << < 453 454 455 456 457 458 459 460 461 462 > >>

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

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：