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

106,756 篇 会议
687 篇 期刊文献
555 册 图书
4 篇 学位论文

馆藏范围

108,001 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

60,973 篇 工学
- 49,953 篇 计算机科学与技术...
- 39,802 篇 软件工程
- 13,499 篇 信息与通信工程
- 11,482 篇 生物工程
- 8,567 篇 控制科学与工程
- 6,788 篇 电气工程
- 6,343 篇 生物医学工程（可授...
- 3,861 篇 光学工程
- 3,352 篇 机械工程
- 3,006 篇 电子科学与技术（可...
- 2,782 篇 网络空间安全
- 2,726 篇 安全科学与工程
- 2,543 篇 动力工程及工程热...
- 2,342 篇 化学工程与技术
- 2,201 篇 仪器科学与技术
26,033 篇 理学
- 12,115 篇 生物学
- 12,088 篇 数学
- 5,179 篇 物理学
- 4,910 篇 统计学（可授理学、...
- 3,517 篇 系统科学
- 2,348 篇 化学
17,448 篇 管理学
- 11,006 篇 管理科学与工程(可...
- 7,620 篇 图书情报与档案管...
- 5,156 篇 工商管理
13,540 篇 教育学
- 13,339 篇 教育学
6,412 篇 医学
- 5,471 篇 临床医学
- 5,222 篇 基础医学(可授医学...
- 3,407 篇 药学(可授医学、理...
- 2,890 篇 公共卫生与预防医...
3,364 篇 法学
- 2,910 篇 社会学
1,965 篇 文学
1,765 篇 农学
1,660 篇 经济学
267 篇 艺术学
106 篇 军事学
11 篇 历史学
10 篇 哲学

主题

7,181 篇 machine learning
6,982 篇 deep learning
5,351 篇 accuracy
5,035 篇 training
4,829 篇 computational mo...
4,195 篇 education
3,832 篇 students
3,553 篇 feature extracti...
3,029 篇 support vector m...
3,028 篇 predictive model...
3,026 篇 machine learning...
2,972 篇 convolutional ne...
2,489 篇 real-time system...
2,334 篇 data models
2,234 篇 neural networks
1,986 篇 cloud computing
1,817 篇 teaching
1,487 篇 reinforcement le...
1,449 篇 artificial intel...
1,388 篇 internet of thin...

机构

826 篇 chitkara univers...
145 篇 lovely professio...
106 篇 computer science...
98 篇 karnataka bangal...
94 篇 college of compu...
89 篇 school of comput...
80 篇 chitkara univers...
76 篇 school of comput...
72 篇 department of co...
68 篇 department of co...
64 篇 department of co...
60 篇 department of co...
58 篇 school of comput...
50 篇 department of in...
49 篇 chitkara univers...
49 篇 department of co...
43 篇 department of me...
42 篇 school of automa...
41 篇 vellore institut...
41 篇 chennai

作者

70 篇 shiva mehta
70 篇 anjali t.
53 篇 li yang
42 篇 sharma vikrant
40 篇 wang lei
40 篇 kukreja vinay
39 篇 rakesh kumar
37 篇 gill kanwarparta...
35 篇 sharma neha
33 篇 wang jing
32 篇 vinay kukreja
31 篇 lei wang
31 篇 zhang lei
30 篇 huang yueh-min
29 篇 liu bo
29 篇 yu wang
27 篇 li xin
26 篇 amandeep nagpal
25 篇 vinay gautam
25 篇 shang wang

语言

100,155 篇 英文
7,484 篇 其他
773 篇 中文
27 篇 西班牙文
17 篇 葡萄牙文
5 篇 阿拉伯文
3 篇 法文
3 篇 土耳其文
2 篇 俄文

检索条件"任意字段=International Conference on Learning and Teaching in Computing and Engineering"

共 108002 条记录，以下是4481-4490 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：

Human Action Detection using Transfer learning Algorithms

Human Action Detection using Transfer Learning Algorithms

引用

2023 international conference on Sustainable computing and Smart Systems, ICSCSS 2023

作者： Martha, Sheshikala Patti, Rithika Reddy Paka, Varsha Sri Pashikanti, Saketh Shanigarapu, Saikeerthana Sr University Warangal India Computer Science and Engineering Sr Engineering College Warangal India

ISBN: (纸本)9798350333602

In the past few years, Human Action Detection has become more popular due to its applications in various fields. In this scenario, if a model can be created to detect human action then it will be very much useful to the application in Human Action Detection. Training a neural network from scratch may not be a feasible option. The main challenge to develop a neural networking model form scratch is the complexity of the model. The results of each model can be analyzed efficiently with Transfer learning approach and also the results for the transfer learning models are much more promising than building the model from scratch when there is less amount of data. A comparative study is done to determine the best architecture for a transfer learning model. © 2023 IEEE.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Improving Long Short-Term Memory (LSTM)-Based Stock Market Price Predictions in the Machine learning Era 5

Improving Long Short-Term Memory (LSTM)-Based Stock Market P...

引用

5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, Sarvesh Pal, Nitin Tripathi, Aanjey Mani Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Post covid, world especially India has seen enormous rise in stock market investor. Profits in stock market solely depends upon prediction - right prediction can help gain money. And predicting stock price can be very tedious task if done manually. Traditional methods are using stock price charts, exponential moving average (EMA), oscillators, indicators etc. While predicting stock price manually, human greed nature can lead to wrong decisions while choosing right stock. Latest technologies like machine learning and artificial intelligence can be used to predict future of stocks. Several research and works with different techniques have been already carried out to do the same. In this research explores a method, for predicting stock prices using machine learning, Long Short-Term Memory (LSTM) networks. These networks, which fall under the umbrella of learning provide a solution, by successfully capturing complex patterns from past stock data. As we explore the existing body of research and real-world implementations our goal is to participate in the discussion surrounding the utilization of machine learning, specifically LSTM networks to improve the precision and dependability of stock price forecasts. Looking ahead we anticipate a future where financial decision making becomes more informed and accurate. © 2024 IEEE.

关键词： deep learning financial forecasting lstm machine learning stock price prediction

来源：评论

学校读者我要写书评

暂无评论

Brain Tumour Prediction Using MRI Images with MobileNet and Transfer learning: A Deep learning Approach 7

Brain Tumour Prediction Using MRI Images with MobileNet and ...

引用

7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Rastogi, Deependra Singh, Anand Dhanda, Sumit Singh Bindewari, Shantanu Yadav, Suman Avdhesh Iilm University School of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

Brain tumour detection and classification are crucial for early diagnosis, improving patient survival rates. Magnetic Resonance Imaging (MRI) offers non-invasive, high-resolution brain images, but manual interpretation is time-consuming and error prone. This research proposes a deep learning approach for brain tumour prediction using MRI images, leveraging the MobileNet architecture with transfer learning, incorporating Flatten, Dense, and Dropout layers to enhance model generalization. MobileNet, a lightweight Convolutional Neural Network (CNN), efficiently processes large-scale MRI datasets. The pre-trained MobileNet is integrated with a Flatten layer to reduce feature map dimensions, followed by Dense layers for learning complex patterns, and a Dropout layer to prevent overfitting. Transfer learning allows leveraging pre-trained weights, reducing the need for large datasets. The model was finetuned on an MRI brain tumour dataset, achieving an accuracy of 0.9845 with a loss of 0.0015, outperforming traditional deep learning methods in tumour classification. This architecture is computationally efficient, making it suitable for real-time clinical applications, providing medical professionals with a reliable, automated tool for brain tumour diagnosis. © 2024 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Identification and Prediction of Hepatitis B and NAFLD using Machine learning 2

Identification and Prediction of Hepatitis B and NAFLD using...

引用

2nd international conference on Sustainable computing and Data Communication Systems, ICSCDS 2023

作者： Garg, Umang Urmila Pundir, Rahul Singh Manchanda, Mahesh Gupta, Neha Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9781665491990

Hepatitis B and Non-Alcoholic Fatty Liver Disease (NAFLD) are the most frequent and well-known liver disorders worldwide. Over time, these disorders cause liver cirrhosis, which finally leads to failure and major consequences, which can be fatal. More than a third of the world's population has Hepatitis B and NAFLD. Blood tests are utilized to identify hepatitis B, and while the liver biopsy is the gold standard in diagnosing NAFLD, ultrasound scan pictures are employed in this work. The proposed method efficiently identifies or predicts Hepatitis B and NAFLD using the findings of this study, namely the application of machine learning and deep learning neural networks. Keyword: Fatty liver, Hepatitis B, Machine learning, NAFLD. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Identification of Thyroid Disorder using Deep learning Techniques- A Survey 10

Identification of Thyroid Disorder using Deep Learning Techn...

引用

10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Dency Flora, G. Shanmathi, S. Gowtham, G. Nikil, S. Rajarajan, M. Mahindhar, K.S. Sri Eshwar College of Engineering Department of Cce Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Thyroid diseases are a global health concern that require precise diagnosis in order to initiate and maintain appropriate treatment. Conventional diagnostic techniques frequently depend on laboratory testing and clinical evaluations, which can be error-prone and time-consuming. In recent years, deep learning techniques have shown promise in the identification of thyroid disorders among other medical uses. An detailed review of the most recent advancements in the use of deep learning techniques to thyroid problem detection is given in this study. We go over the use of convolutional, recurrent, and hybrid neural networks to the processing of thyroid-related text, images, and clinical records.. Then we compared various deep learning methods and we are using the Hybrid Deep Convolutional neural networks in which it has the accuracy value as 93.8%. We emphasize how crucial extensive, annotated datasets are to the efficient training of deep learning models. We go over the importance of feature extraction approaches, model optimization tactics, and data preprocessing methods in enhancing the robustness and effectiveness of thyroid problem diagnostic systems. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement learning-Based Task Offloading for Multi-User Distributed Edge computing 30

Deep Reinforcement Learning-Based Task Offloading for Multi-...

引用

30th international conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Shi, Zhengtian Zhang, Zhisheng Dai, Min Xia, Zhijie Wen, Haiying Huang, Fan Southeast University School of Mechanical Engineering Nanjing China JiangSu Nan Gao Intelligent Equipment Innovation Center Nanjing China Southeast University Nanjing China

ISBN: (纸本)9798350391916

Edge computing (EC) deploys multidimensional resources, including computation, storage, and communication, at the network edge to fulfill certain applications with stringent latency requirements. In the edge environment, computing tasks can be offloaded to mobile edge computing servers (MECS) for reduced task processing latency. However, traditional methods of task offloading in edge computing are inadequate for complex environments demanding high real-time performance. This paper focuses on studying the computational task processing problem in a multi-user distributed edge environment and propose a deep reinforcement learning-based multi-user distributed edge computing task offloading method aimed at minimizing task processing latency. The proposed method effectively addresses the computational task processing challenge in dynamic and complex edge environments with high real-time demands. Simulation results show that the proposed method has superior performance in terms of both task processing delay and algorithm execution time. © 2024 IEEE.

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

Online learning Motivation and Dilemma of Secondary Vocational Students 13th

Online Learning Motivation and Dilemma of Secondary Vocation...

引用

13th international conference on Frontier computing, FC 2023

作者： Wu, Jun Liu, Hsiao-Fen National Taipei University of Technology No. 81 Section 1 Jianguo South Road Daan District Taipei106 Taiwan

ISBN: (纸本)9789819994151

Online learning has been widely adopted in different levels of education. In the field of secondary vocational education, the application of online learning in education and teaching is also becoming more popular. However, previous studies on online learning among secondary vocational students mainly focused on the technical aspects and often centered on the domain of mandatory online learning led by schools, meanwhile also neglecting the voluntary learning through various online resources among secondary vocational students. Therefore, based on cyberethnography and a voluntary online learning community formed by secondary vocational students, this study conducted in-depth interviews with some members of the community to gain insights into their motivations for participating in online learning voluntarily and the difficulties they faced in the process. Through the analysis of the interview data, the results showed that the main motivations for secondary vocational students to participate in online learning voluntarily included improving learning efficiency, enriching learning methods, and expanding learning content. However, during the voluntary online learning process, students also encountered many difficulties, such as insufficient learning resources, lack of learning motivation, and time management issues. Therefore, this study suggests that secondary vocational education should actively explore the application of online learning and provide more support and guidance from both policy and practical perspectives to cultivate students' ability to discern learning resources and plan their self-learning, so that they can better demonstrate autonomy and initiative in online learning and achieve better learning outcomes. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Custom Transformer-Based Approach for Enhanced Bengali Automatic Speech Recognition 1

Custom Transformer-Based Approach for Enhanced Bengali Autom...

引用

1st international conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Surendran, R. SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Anna University St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Automated speech recognition (ASR) systems struggle with Bengali, which is the fifth most spoken language. Bengali has a varied morphology, many dialects, and limited high-quality annotated voice data. Traditional voice recognition techniques work well however struggle with Bengali phonetics and syntax. This method eliminates the need for large amounts of tagged data and the difficulties of differentiating sounds in different languages, both issues are addressed by this procedure. The research in this paper offers an Intellectual Bengali Speech Recognition System using Deep learning Techniques (IBSRS-DLT). The model in this system uses transformer architecture and attention processes to manage Bengali speech pattern long-range dependencies. The IBSRS-DLT outperforms standard models in recognition accuracy using a pre-trained transformer model modified for phonetic and lexical correctness and fine-tuned on Bengali datasets. After training on a vast Bengali speech corpus, real-world datasets and rigorous simulations confirm the system. IBSRS-DLT can be used for automatic transcription, voice-controlled devices, and Bengali virtual assistants, such applications are efficient and accessible. The simulation showed a considerable boost in recognition speed and a decrease in keyword mistakes compared to current methods. This paper shows that deep learning can solve linguistic issues in ASR systems, enabling scalable regional language detection. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Analyzing and researching the intermediate layer of Alliance medical data combined with edge computing

Analyzing and researching the intermediate layer of Alliance...

引用

1st IEEE international conference on Software Services engineering (IEEE SSE)

作者： Hsu, Tse Chuan Chu, William Cheng-Chung Tseng, Yao-Hsien Lee, Shou-Yu Soochow Univ Dept Comp Sci & Informat Management Taipei Taiwan Tunghai Univ Dept Comp Sci Taichung Taiwan Tungs Taichung MetroHarbor Hosp Div Endocrinol & Metab Dept Internal Med Taichung Taiwan

ISBN: (纸本)9798350340754

Through big data analytics and deep learning, users can uncover unseen hidden information from personal data in the cloud and derive health improvements from it - including genomic, microbial, medical history, comprehensive blood analysis chemistry, proteins and metabolites, and daily data from exercise devices and scales. The analysis and integration of this data has the potential to improve health and prevent disease. In the research, a set of tools to collect, compute and analyze data through the middle layer, combined with alliance learning and deep learning technologies to conduct sampling comparison training on medical data, while combining edge computing nodes and data hidden features of alliance learning for data construction and model verification. The outcomes can provide early warning of disease agents through data analysis, early symptom detection, and long-term monitoring before the disease becomes widespread. Daily behavior and health can be improved by supporting the P4 medical model, and by integrating these capabilities, many chronic diseases can be prevented from further devastating patients' health. In the experiments, we propose a framework for designing proxies through system construction, data quantification, description and analysis of patient-specific chronic disease risk, and data learning to validate the model construction.

关键词： Data Analysis Healthcare Edge computing Alliance learning Federated learning Service Agent

来源：评论

学校读者我要写书评

暂无评论

Transforming Agriculture with AI: Automated Crop Insect Identification Using InceptionV3 Transfer learning 1

Transforming Agriculture with AI: Automated Crop Insect Iden...

引用

1st international conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Thyagharajan, K.K. Kalaiarasi, G. Nirmala, G. Saravanan, P. Vignesh, T. Makashkumar, V. R.M.D Engineering College Department of Electronics and Communication Engineering Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.D Engineering College Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350342796

Identifying crop insects and classifying them is a challenging task for agriculturalists. A transfer learning method is suggested in this paper to address this issue. The proposed approach uses InceptionV3 as a base deep learning model and it is given additional custom layers. The model is fine-tuned using a custom dataset made up of five classes with 1000 insects each. The insect and pest images available from the National Bureau of Agricultural Insect Resources datasets and the Xie1 dataset are used for testing the model. Preprocessing techniques are applied to resize the images, and data augmentation is employed to prevent overfitting. The model was analyzed on the validation dataset with Adam, Adagrad, and Nadam optimizers, and obtained recognition accuracies of 86.6%, 77.8%, and 88.56% respectively. The outcomes confirms that the model performs well with Nadam optimizer for insect identification. © 2023 IEEE.

关键词： Deep learning InceptionV3 Insect Detection Smart Agriculture Transfer learning

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 << < 445 446 447 448 449 450 451 452 453 454 > >>

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：