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

523 篇 会议
138 篇 期刊文献
1 册 图书

馆藏范围

662 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

318 篇 工学
- 229 篇 计算机科学与技术...
- 198 篇 软件工程
- 81 篇 信息与通信工程
- 65 篇 控制科学与工程
- 48 篇 生物工程
- 44 篇 电气工程
- 35 篇 生物医学工程（可授...
- 32 篇 电子科学与技术（可...
- 31 篇 网络空间安全
- 27 篇 安全科学与工程
- 25 篇 动力工程及工程热...
- 23 篇 土木工程
- 19 篇 机械工程
- 19 篇 光学工程
- 19 篇 交通运输工程
188 篇 理学
- 78 篇 数学
- 57 篇 物理学
- 55 篇 生物学
- 24 篇 统计学（可授理学、...
- 19 篇 系统科学
- 17 篇 化学
- 16 篇 海洋科学
101 篇 管理学
- 67 篇 管理科学与工程(可...
- 41 篇 图书情报与档案管...
- 31 篇 工商管理
50 篇 医学
- 40 篇 基础医学(可授医学...
- 40 篇 临床医学
- 30 篇 公共卫生与预防医...
- 18 篇 药学(可授医学、理...
31 篇 法学
- 28 篇 社会学
18 篇 农学
12 篇 经济学
8 篇 教育学
3 篇 文学

主题

73 篇 accuracy
36 篇 convolutional ne...
29 篇 deep learning
28 篇 computational mo...
27 篇 feature extracti...
26 篇 real-time system...
25 篇 training
22 篇 support vector m...
22 篇 predictive model...
22 篇 machine learning
22 篇 data models
19 篇 internet of thin...
16 篇 optimization
16 篇 data mining
13 篇 security
13 篇 artificial intel...
13 篇 technological in...
12 篇 scalability
12 篇 medical services
12 篇 random forests

机构

16 篇 lovely professio...
14 篇 maharishi school...
12 篇 centre of interd...
12 篇 department of ma...
10 篇 department of ma...
8 篇 school of comput...
8 篇 department of ma...
8 篇 master of comput...
7 篇 master program o...
7 篇 radiology techni...
7 篇 prince shri venk...
6 篇 department of co...
6 篇 department of co...
6 篇 department of co...
5 篇 school of comput...
5 篇 kongu engineerin...
5 篇 department of co...
5 篇 school of comput...
5 篇 department of co...
5 篇 department of co...

作者

7 篇 bens pardamean
7 篇 thotad puneeth n...
7 篇 asha v.
7 篇 jayadurga r.
7 篇 hiroshi hashimot...
7 篇 pundru chandra s...
7 篇 v. asha
6 篇 pardamean bens
6 篇 r. jayadurga
6 篇 puneeth n. thota...
6 篇 binju saju
6 篇 lin jason
6 篇 ginni nijhawan
6 篇 vijayakumar p.
6 篇 tsai chia-wei
6 篇 yang chun-wei
5 篇 j. relin francis...
5 篇 ali albawi
5 篇 singh kamred udh...
5 篇 yasuhiro ohyama

语言

493 篇 英文
166 篇 其他
5 篇 中文
3 篇 日文
1 篇 德文

检索条件"机构=Master of Technology-Computer Science and Engineering"

共 662 条记录，以下是101-110 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：

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling and Fusion Approach for Underwater Net Cage Damage Detection in Aquaculture

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling ...

引用

IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Xiang-Rui Huang Yi-Cheng Chen Liang-Bi Chen Wu-Chih Hu Department of Electrical Engineering Master Program in Electrical Engineering and Computer Science National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection.

关键词： Accuracy Gears Computational modeling Sea floor computer architecture Manuals Inspection Feature extraction Personnel Aquaculture

来源：评论

学校读者我要写书评

暂无评论

Developing Instruments for Digital Talent Competence Using Partial Least Square-Based Models 11

Developing Instruments for Digital Talent Competence Using P...

引用

11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Wahyudi, Mochamad Chakim, Mochamad Heru Riza Henderi Sudaryono Si, Muhamad Viktor A Saputra, Dimas Bagus Rahardja, Reymund Master of Computer Science University of Bina Sarana Informatika Tangerang Indonesia University of Raharja Master of Retail Management Tangerang Indonesia University of Raharja Master of Informatics Engineering Tangerang Indonesia University of Raharja Master of Digital Business Tangerang Indonesia University of Raharja Department of Information Technology Tangerang Indonesia University of Raharja Department of Digital Business Tangerang Indonesia

ISBN: (纸本)9798350305968

Research on measuring digital talent competencies using explainable artificial intelligence is lacking in depth. The study aimed to address this gap by developing a reliable and valid measurement instrument for digital talent. SmartPLS 4.0 was employed for calculations and quality assessment. The measurement model was assessed from both reflective and formative perspectives, using Google Forms to gather responses from 600 participants. The 60 questionnaire items exhibited high validity and reliability, as evidenced by loading factors and composite reliability exceeding 0.70. Direct effects were found in various competencies: adaptability and flexibility had a 0.744 effect on learning dexterity, achievement orientation showed a 0.782 effect on innovative creation skills, customer service orientation had a 0.810 effect on digital networking, and digital communication skills exhibited a 0.824 effect on continual improvement. The measurement model met various criteria for reliability and validity, including loading factors, composite reliability, average variance extracted, cross loadings, Fornell Lacker Criterion, and Heterotrait Monotrait Ratio. These findings supported the acceptance of the measurement model's quality and validity. SmartPLS analysis yielded an average communality of 0.624 and an average R Square of 0.592, resulting in a GoF index of 0.608. This GoF index, according to Wetzel et al. (2018), falls in the high fit category, indicating the model's appropriateness for measuring digital talent competencies. In summary, the study successfully developed a robust measurement model for digital talent competencies using explainable artificial intelligence and demonstrated its high fit and reliability for assessing digital talent in various contexts. © 2023 IEEE.

关键词： Reliability

来源：评论

学校读者我要写书评

暂无评论

Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

引用

2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

来源：评论

学校读者我要写书评

暂无评论

RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

引用

Prognostics and System Health Management Conference (PHM)

作者： Congrui Cai Zhonghai Lu Embedded Systems Master Program School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Electrical Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms.

关键词： Performance evaluation Semiconductor device modeling Recurrent neural networks Computational modeling Estimation Power electronics Data models

来源：评论

学校读者我要写书评

暂无评论

Model using GAN for Text to Image synthesis

Model using GAN for Text to Image synthesis

引用

2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Patil, Kumari Reshma Biradar, Vidyadevi G Mohan, S.G. Dileep, M.R. Clara Kanmani, A. Pramanick, Monalisa Information Science Engineering Nitte Meenakshi Institute of Technology Bengaluru India Nitte Meenakshi Institute of Technology Master of Computer Applications Bengaluru India Information Science Engineering Presidency University Bengaluru India

ISBN: (纸本)9781665453424

Due to its remarkable data-generating capabilities, generative adversarial networks have gained significant relevance in unsupervised learning. Generative adversarial networks are very much useful in generating synthetic images from the given text description. The research work presented in this paper aims at developing a text-image synthesis model to generate high resolution synthetic images based on the scenario description given in input text. The paper proposes a novel model Df-GAN which is derived from VQGAN and CLIP using transfer learning. This model is evaluated on the COCO dataset. the experimental results show that the model can generate high resolution images. The model achieves FID score of 18.26 which is satisfactory. © 2022 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

RS-S3VM: Combining Rough Sets with S3VM for Image Classification

RS-S3VM: Combining Rough Sets with S3VM for Image Classifica...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Fariha Tasnim Anannya Chowdhury M. Jamshed Alam Patwary Department of Computer Science & Engineering Chittagong University of Engineering & Technology Master of Information Technology Central Queensland University Australia Institute of Information & Communication Technology Chittagong University of Engineering & Technology

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In the landscape of data mining applications, the difficult task of manually labeling extensive datasets presents formidable challenges owing to its inherent difficulties, costliness, and time consuming nature. To address the limitations associated with manual labeling, semi-supervised learning emerges as a promising paradigm, harnessing the potential of both Data with and without labels in the course of training. SVM based semi-supervised learning (S3VM), one of the several semi-supervised learning approaches, has shown potential in improving performance of classification. This research paper introduces a novel approach that combine rough sets with SVM based Semi-supervised learning for image classification. This innovative methodology involves employing a Convolutional Neural Network (CNN) model to undergo training on labeled data, make predictions on unlabeled data, and subsequently undergo retraining. Feature extraction is then executed for classification. Following this, Rough Set theory based feature selection technique is used, generating a reduct that functions as a rule for SVM. This comprehensive method not only improves the field of image classification but also offering a novel and pragmatic solution to problems in semi-supervised learning. This procedure concludes in an evaluation of the models final out come using experiments on the CIFAR-10 benchmark dataset, where it achieved an impressive accuracy of 97.30%.

关键词： Support vector machines Training Accuracy Rough sets Semisupervised learning Benchmark testing Feature extraction Convolutional neural networks Labeling Image classification

来源：评论

学校读者我要写书评

暂无评论

Crop Yield Prediction Using Deep Reinforcement Learning 2

Crop Yield Prediction Using Deep Reinforcement Learning

引用

2nd International Conference on Trends in Electrical, Electronics and computer engineering, TEECCON 2023

作者： Deivasigamani, Sudhakar Jaya Mabel Rani, A. Natchadalingam, R. Vijayakarthik, P. Bala Sendhil Kumar, G. Reddy, Pundru Chandra Shaker Digipulse Technologies Inc Scrum Master NJ United States Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India R.L.Jalappa Institute of Technology CSE-Data Science Karnataka Bengaluru India R.L.Jalappa Institute of Technology Computer Science and Engineering Karnataka Bengaluru India Sri Manakula Vinayagar Engineering College Master of Business Administration Puducherry India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350339949

Predicting crop production using information about the environment, soil, water, and crops themselves is an area ripe for investigation. Deep learning-based models often extract crop attributes for prediction. These techniques have the following drawbacks: The quality of the extracted characteristics substantially affects the efficacy of those models, and there is no simple non-linear or linear mapping between raw data and crop production estimates. Deep RL guides and inspires to compensate. Deep reinforcement learning uses RL and DL to accurately forecast agricultural yields from raw data. A Deep Recurrent Q-Network model predicts agricultural yield. This model uses Q-Learning reinforcement learning and a Recurrent Neural Network deep learning algorithm. Data parameters feed the Recurrent Neural network's layers. Q-learning networks forecast agricultural yields from data. Q-values are linear mappings from Recurrent Neural Network output values. A threshold and parametric features help the reinforcement learning agent predict crop productivity. Finally, the agent is awarded a final score that takes into account the steps taken to both reduce error and improve forecast accuracy. By maintaining the original data distribution, the suggested model efficiently predicts the crop production with an accuracy of 93.7%, exceeding competing models. © 2023 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Block Chain technology for Securing IoT Devices and Data 9

Block Chain Technology for Securing IoT Devices and Data

引用

9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Aancy, H. Mickle Kodgire, Shilpa Prashant Mohitha, MaddulaRatna Manimegalai, V. Vijay, G. Dhotre, Sunita Panimalar Engineering College Department of Master of Business Administration Tamil Nadu Chennai600123 India Maharashtra Institute of Technology Aurangabad Department of Electronics And Telecommunication Engineering Maharashtra India Aditya Institute of Technology And Management Department of Computer Science And Engineering Andhra Pradesh Tekkali India Sri Krishna College of Technology Department of Electrical And Electronics Engineering Tamil Nadu Coimbatore641042 India Department of English Tamil Nadu Dindigul624622 India College of Engineering Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350365092

IoT detector data can be covered and authenticated using blockchain armature to avoid data duplication. It also enables secure device data transfer. IoT can handle scalability and trust challenges with blockchain, keeping data private and secure. Blockchain technology improves authentication, fraud resistance, and decentralized data security. It can polarize device authentication and communication, barring a central authority. The processing power demanded for blockchain mining and the vulnerability of present blockchains to mining monopolization are obstacles. Hyundai has patronized IoT security blockchain startups. Blockchain technology could secure IoT networks and sensitive data. Blockchain and IoT give a strong answer to linked device security issues. Blockchain's decentralized and inflexible tally can secure IoT bias and their data, according to this abstract. Blockchain technology allows secure, peer- to- peer connectivity and data sharing, guarding IoT bias from manipulation and unauthorised access. Blockchain's cryptographic algorithms guarantee data integrity, while smart contracts automate device relations to misbehave with security rules. Blockchain's practical counteraccusations in IoT security could alter how we guard our decreasingly linked world, as this exploration shows. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Facial Emotion Recognition Using a Local Binary Pattern based Deep Learning 2

Facial Emotion Recognition Using a Local Binary Pattern base...

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Tayubi, Iftikhar Aslam Pawar, Dilip N. Kiran, Ajmeera Reddy, Pundru Chandra Shaker Sharma, Nipun Chitra, D. King Abdulaziz University Faculty of Computing and Information Technology Rabigh Jeddah Saudi Arabia Bharati Vidyapeeth College of Engineering Instrumentation Engineering Maharashrra Navi Mumbai India Mlr Institute of Technology Dundigal Department of Computer Science and Engineering Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Presidency University Electronics and Communication Engineering Karnataka Bangalore India Panimalar Engineering College Master of Business Administration Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Facial-emotion-recognition(FER) is being conducted with the goals of analyzing the psychological characteristics of juvenile offenders and promoting the use of deep learning to the extraction of psychological features. Initially, we talk about how certain personality traits are linked to being good at reading facial expressions. To build a facial emotion identification model from this data, we add more convolutional layers to a CNN and combine it with other neural networks like VGGNet, AlexNet, and LeNet-5. Next, a Central-Local-Binary-Pattern(CLBP) method, which is optimized, is incorporated into the CNN to build a CNN-CLBP framework for FER. After face photos have been preprocessed and key parameters have been optimized, the algorithm is subjected to a validity study. The average detection rate for the CNN-CLBP is 88.16%, which is greater than the recognition rate for other approaches. In addition, image preparation and parameter tuning boost the recognition accuracy of this approach without resorting to poor-tting. In addition, the CNN-CLBP system accurately identifies 97% of pleased and startled expressions, but 22.54% of sad ones. The study's findings can be used as a starting point for further investigations into the mental health of teen offenders. © 2024 IEEE.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

The performance analysis of image resolution model using deep residual algorithm 15

The performance analysis of image resolution model using dee...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Chandi Priya, K.G. Savitha, P. Sujatha, V. Sabarivani, A. Dinesh, M. R.M.K College of Engineering and technology Department of General Engineering Chennai India Nandha Engineering College Department of CSE Perundurai Erode India S.A. Engineering College Master of Computer Applications Chennai India Sathyabama Institute of Science and Technology Department of Biomedical Engineering Chennai India Saveetha University Saveetha School of Engineering Department of VLSI Microelectronics Chennai India

ISBN: (纸本)9798350370249

Deep residual algorithms succeeded wildly in different computer vision problems (e.g., Image Super resolution). Deep neuraenablersraredesign and residual learning are great enablers that define how these architectures overcome limitations in traditional image resolution models. This article implements a deep residual algorithm representing the image resolution model performance research. This paper aims to synthesize HR images from their low-resolution (LR) counterparts by training the proposed model using large-scale image pairs across different resolutions. The model is tested using multiple evaluation criteria and compared with classical resolution models. Experimental results show that the deep residual model performs better in peak signal-to-noise ratio(PSNR), structural similarity index(SSIM), and visual quality metrics. It examines the influence of various network architectures, training strategies, and datasets on how well this algorithm can recognize dialog acts. Experiments demonstrate the superiority of the proposed model with a deep residual algorithm in terms of improving Resolution compared to traditional models. © 2024 IEEE.

关键词： Network architecture

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67页 << < 7 8 9 10 11 12 13 14 15 16 > >>

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：