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

592 篇 会议
2 册 图书
1 篇 期刊文献

馆藏范围

595 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

365 篇 工学
- 337 篇 计算机科学与技术...
- 265 篇 软件工程
- 94 篇 生物工程
- 64 篇 信息与通信工程
- 64 篇 生物医学工程（可授...
- 24 篇 电气工程
- 15 篇 控制科学与工程
- 14 篇 光学工程
- 14 篇 化学工程与技术
- 10 篇 机械工程
- 10 篇 动力工程及工程热...
- 10 篇 土木工程
- 10 篇 农业工程
197 篇 理学
- 99 篇 生物学
- 96 篇 数学
- 43 篇 统计学（可授理学、...
- 37 篇 系统科学
- 33 篇 物理学
- 11 篇 化学
84 篇 管理学
- 50 篇 图书情报与档案管...
- 37 篇 管理科学与工程(可...
- 32 篇 工商管理
51 篇 医学
- 51 篇 基础医学(可授医学...
- 51 篇 临床医学
- 44 篇 药学(可授医学、理...
- 12 篇 公共卫生与预防医...
26 篇 法学
- 22 篇 社会学
15 篇 农学
- 15 篇 作物学
11 篇 经济学
- 11 篇 应用经济学
10 篇 教育学
- 10 篇 教育学
1 篇 军事学

主题

157 篇 machine learning
63 篇 machine learning...
59 篇 deep learning
36 篇 support vector m...
30 篇 computational mo...
25 篇 predictive model...
22 篇 prediction algor...
19 篇 informatics
17 篇 feature extracti...
17 篇 training
15 篇 decision trees
14 篇 forecasting
13 篇 internet of thin...
13 篇 cloud computing
13 篇 supervised learn...
12 篇 intrusion detect...
11 篇 medical services
11 篇 heart
11 篇 artificial intel...
11 篇 technological in...

机构

25 篇 department of co...
21 篇 uttaranchal inst...
18 篇 graphic era deem...
9 篇 uttaranchal inst...
7 篇 department of cs...
7 篇 department of co...
6 篇 chitkara univers...
6 篇 symbiosis instit...
5 篇 amity university...
5 篇 amity university...
5 篇 uttaranchal univ...
4 篇 maharashtra pune
4 篇 department of cs...
4 篇 department of co...
4 篇 vishwakarma univ...
4 篇 department of bi...
4 篇 faculty of engin...
4 篇 biomedical syste...
4 篇 department of el...
4 篇 engineering and ...

作者

9 篇 anita gehlot
7 篇 rajesh singh
7 篇 singh rajesh
7 篇 durgaprasad gang...
7 篇 gehlot anita
5 篇 sandeep kumar
5 篇 joel alanya-belt...
5 篇 alanya-beltran j...
5 篇 ramakrishnan ram...
5 篇 bhasker pant
4 篇 pundir sumit
4 篇 wan azani mustaf...
4 篇 hiam alquran
4 篇 vikas tripathi
4 篇 ahmed alkhayyat
4 篇 arora bhavna
4 篇 raman ramakrishn...
4 篇 devesh pratap si...
4 篇 kumar sandeep
4 篇 gangodkar durgap...

语言

590 篇 英文
5 篇 其他

检索条件"任意字段=5th Asia Conference on Machine Learning and Computing, ACMLC 2022"

共 595 条记录，以下是51-60 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：

Research of Data-driven Iterative Optimization Scheme for machine learning Models 5

Research of Data-driven Iterative Optimization Scheme for Ma...

引用

5th International conference on computing and Big Data, ICCBD 2022

作者： Yang, Jinsen Zou, Zhijian Xiu, Jiapeng Wang, Peinian Yang, Yiting Beijing University of Posts and Telecommunications Software Systems and Engineering Center Beijing China Beijing Qiyuan Capital Market Development Service Co. Ltd. Beijing China Law School Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665457163

With the development of big data technology providing massive data information for machine learning, more researchers are focusing on data methods in big data technology, by constructing better data set to improve machine learning model's performance. this research based on the data-driven and human-machine collaboration methods in big data technology, continuously build data set to optimize machine learning models. We take the growth and risk assessment model of technology companies as an example, design and implement a data-driven machine learning model pipeline. the experiments show that the pipeline can effectively and continuously optimize these machine learning models, and ensure the stability of machine learning application. © 2022 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence Aspect Of Transportation Analysis Using Large Scale Systems 6

Artificial Intelligence Aspect Of Transportation Analysis Us...

引用

6th Artificial Intelligence and Cloud computing conference (AICCC) / 5th asia Digital Image Processing conference

作者： Hu, Tiechuan Zhu, Wenbo Yan, Yuqi Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Univ Chicago Dept Comp Sci Chicago IL USA Washington Univ Olin Business Sch St Louis MO USA

ISBN: (纸本)9798400716225

Problem: the problem of the finalized exploration revolved around the inadequacy of current traffic forecasting models. Despite decades of examination in many fields, existing approaches, often relying on linear models and stationary time series assumptions, struggle to accurately predict traffic under chaotic events. the identified limitation has profound consequences, evident in the significant economic losses and time inefficiencies incurred due to traffic congestion, as exemplified by the $144 billion in losses and the 34% increase in travel time for drivers in Los Angeles County in 2013. the challenge lies in the inherently unpredictable nature of traffic events, ranging from regular rush hours causing sharp declines in traffic speed to unpredictable accidents leading to unforeseen delays. Consequently, there is a pressing need for a more effective and adaptive traffic forecasting model that can reliably operate under both normal and abnormal traffic conditions, addressing the shortcomings of traditional linear models and stationary time series assumptions. Purpose: the purpose of the completed investigation was to determine whether a traffic forecasting model that incorporates machine learning and deep learning technologies can yield effective traffic forecasts based on real-time weather and traffic data. Method: the study involved the development of a traffic forecasting model as informed by existing literature. Data collection was done through simulating data similar to the traffic data from Los Angeles County that was utilized in Yu et al.'s research on deep learning and traffic prediction in extreme weather scenarios [1]. Data analysis was done through MAE and t-test. Results: the findings demonstrated that the created traffic forecasting model outperformed the current methods in its ability to provide traffic forecasts with a better degree of accuracy. Conclusion: Regardless of the traffic volume, weather, or time of day, the developed traffic forecasting

关键词： Performance Analysis Sentiment Analysis machine learning Transfer learning

来源：评论

学校读者我要写书评

暂无评论

A Predictive Maintenance Platform for a Conveyor Motor Sensor System Using Recurrent Neural Networks 5th

A Predictive Maintenance Platform for a Conveyor Motor Senso...

引用

5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Kiangala, Kahiomba Sonia Wang, Zenghui Univ South Africa UNISA Dept Elect & Smart Syst Engn ZA-1709 Johannesburg Gauteng South Africa

ISBN: (纸本)9789819770007;9789819770014

Since the effusion of Industry 4.0 (I40) and smart manufacturing, predictive maintenance (PdM) has become critical to prevent severe system breakdowns and costly production downtime in various industries. Several state-of-the-art Artificial Intelligence (AI) approaches, such as machine learning models (ML), empower the PdM design concept to produce more accurate outcomes. In this study, we propose the development of a PdM platform that effectively detects future possible warnings and failures in the vibration speed sensor of a conveyor motor. We build the platform backend using recurrent neural networks (RNN) regression ML models to predict vibration speed values and their trends. We train our RNN models using univariate time-series historical vibration sensor data recorded over the years. the experimental results demonstrate the robustness of our platform built on RNN models compared to other traditional regression machine learning models such as Extreme Gradient Boosting (XGboost). We compare the effectiveness of the PdM platform with two RNN models: a basic RNN model and a long short-term memory (LSTM) model. Unlike other regression PdM systems focusing on predicting devices Remaining Useful Life (RUL), our PdM platform forecasts and generates new vibration sensor data based on the desired future time range. It also pinpoints set warning and failure values and indicates when these faults are more likely to occur. Factories can utilize this feature for analytics purposes.

关键词： Conveyor Motor Sensor Predictive Maintenance (PdM) Recurrent Neural Networks (RNN) Regression Models time-series data

来源：评论

学校读者我要写书评

暂无评论

Forecasting COVID-19 Total Daily Cases in Indonesia Using LSTM Networks 5

Forecasting COVID-19 Total Daily Cases in Indonesia Using LS...

引用

5th International conference on computing and Informatics (IEEE ICCI)

作者： Indriyani, Clarissa Angelita Wijaya, Claudia Rachel Qomariyah, Nunung Nurul Bina Nusantara Univ Fac Comp & Media Dept Comp Sci Jakarta 11480 Indonesia

ISBN: (纸本)9781665499736;9781665499729

the COVID-19 virus has taken over the course of the world for over two years;governments all over the world have been trying to mitigate its effects in several ways such as instilling most jobs to be done at home instead of working from the office. thus, it is important to be able to see predictions of COVID-19 cases to better plan the intervention of the virus spreading. With the use of machine learning, our paper aims to propose and evaluate an LSTM (Long Short Term Memory) model that can forecast daily COVID-19 cases in Indonesia. Several tests show that 50 epochs and a batch size of eight are the best parameters to use for our model. Furthermore, after comparison with differing amounts of lookbacks, we have decided that 10 is best for our model as it consistently does better than other numbers of lookbacks. Based on our model, there will still be an increase of COVID-19 cases in the future.

关键词： COVID-19 long short term memory machine learning artificial intelligence forecasting predicting

来源：评论

学校读者我要写书评

暂无评论

Comparative study of machine learning techniques based on TQWT for EMG signal classification 5

Comparative study of machine learning techniques based on TQ...

引用

5th International conference on computing and Informatics (IEEE ICCI)

作者： Abdel-Maboud, Nahla F. Parusheva, Silvia Stoyanova Alfonse, Marco Salem, Abdel-Badeeh M. Ain Shams Univ Fac Comp & Informat Sci Dept Comp Sci Cairo Egypt Univ Econ Varna Dept Comp Sci Varna Bulgaria

ISBN: (纸本)9781665499736;9781665499729

machine learning methods can be used to diagnose neuromuscular illnesses using electromyographic (EMG) signals. this research examines the tunable-Q factor wavelet transform (TQWT) for feature extraction and analyses various learning methods for classifying EMG signals in order to detect neuromuscular diseases. TQWT decomposes each type of EMG signal into sub-bands first. From each sub-band, statistical parameters such as mean absolute values (MAV), inter quartile range (IQR), kurtosis, mode, standard deviation, skewness, and ratio are calculated. Finally, the extracted features are fed into classifiers to differentiate between ALS, myopathy, and normal EMG data. the random forest classifier with TQWT achieved higher classification results in neuromuscular disorders diagnosis than the other classifiers tested in this study, according to experimental results. the accuracy of the random forest approach using TQWT was 98.64%, with an F-measure of 0.986 and a kappa value of 0.979.

关键词： neuromuscular disorders Tunable Q-factor Wavelet Transform EMG signal random forest

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence Techniques for Classification of Eye Tumors: A Survey 5

Artificial Intelligence Techniques for Classification of Eye...

引用

5th International conference on computing and Informatics (IEEE ICCI)

作者： Allam, Esraa Alfonse, Marco Salem, Abdel-Badeeh M. Ain Shams Univ Fac Comp & Informat Sci Comp Sci Dept Cairo Egypt

ISBN: (纸本)9781665499736;9781665499729

Tumors that have migrated to other regions of the body, particularly the breast, lung, bowel, or prostate, usually develop secondary tumors in the eyes. Retinoblastoma in children and melanoma in adults are two forms of primary cancers that develop within the eye. In this paper, we review the recent works of the artificial intelligence techniques that are applied for classification of ophthalmology tumors. the researchers had proposed different diagnosis systems of eye cancer;iris tumor, iris nevus, uveal melanoma and metastatic, malignant choroidal melanoma and retinoblastoma. the techniques used in these papers can be divided into three main methodologies. the main methodology depends on the Artificial Neural Network (ANN) and deep learning;Back Propagation Neural Networks (BPNN), Radial Basis Function Networks (RBFN), Auto Encoder (AE) Neural Network, hybrid Stacked Auto Encoder (SAE) Network, Deep Belief Network (DBN),Convolutional Neural Network (CNN) and Extreme learning machine (ELM). the second methodology depends on the machine learning (ML) approaches;decision tree, Fuzzy C-Means (FCM), Alternative Fuzzy C-Mean (AFCM), Support Vector machine (SVM) and Decision Tree classifier. the third one depends on different image processing techniques and Apriori based algorithm. the highest recognition rate is achieved by applying different image processing techniques and BPNN with 98.5% and 95%, respectively.

关键词： Artificial Intelligence Deep learning Healthcare informatics Ophthalmology Eye tumors

来源：评论

学校读者我要写书评

暂无评论

Analysis of Metaheuristic Algorithms for Optimized Extreme learning machines in Various Sectors 5th

Analysis of Metaheuristic Algorithms for Optimized Extreme L...

引用

5th EAI International conference on Big Data Innovation for Sustainable Cognitive computing, BDCC 2022

作者： Devikanniga, D. Stalin Alex, D. University Bangalore India State University of Bangladesh Dhaka Bangladesh

ISBN: (纸本)9783031283239

Decision-making is a critical task in our day-to-day applications. Any problem in this real world needs accurate solution at any point of time. there are several machine learning algorithms that can be adopted specifically for particular purpose and extreme learning machine (EML) is one among these algorithms. this paper presents a brief study of extreme learning machine and its combination with various metaheuristic algorithms, and its application to many problems specific domain. the performance achieved by these metaheuristic algorithms fused extreme learning machines are discussed in terms of relevant metrics. the details of the dataset used in the applications are also discussed. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Image Enhancement and Deep learning in Predicting the Gleason Score of Transcrectal Ultrasound Images of Prostate Cancer 5

Image Enhancement and Deep Learning in Predicting the Gleaso...

引用

5th asia-Pacific conference on Image Processing, Electronics and Computers, IPEC 2024

作者： Li, Xueri Liu, Zhiyong Yang, Lei Guangdong University of Science and Technology Department of Computer Science Dongguan China Guangdong Polytechnic Normal University School of computer science Guangzhou China

ISBN: (纸本)9798350374407

the Gleason grading system for prostate cancer offers a crucial point of reference for clinical evaluation of available treatment alternatives. However, there are few studies on pathological grading of prostate cancer ultrasound images, and how to accurately describe its aggressiveness is a hot issue. therefore, this paper proposes a deep learning-based network model for Gleason grading of prostate cancer. the model consists of three modules: lesion region labelling, image enhancement, and pathological grading. Specifically, the model firstly uses the improved S-Mask R-CNN network model to label the prostate cancer lesion region, then performs image enhancement by morphological image processing and PCNN algorithm, and finally performs pathological grading of prostate ultrasound images using Xception. the experimental results on the prostate ultrasound image dataset collected in the hospital show that the prediction accuracy of the Gleason grading model in this paper is higher than that of other classical grading models and manual diagnosis by doctors. © 2024 IEEE.

关键词： Urology

来源：评论

学校读者我要写书评

暂无评论

Clothes Image Retrieval via Learnable FashionCLIP 5th

Clothes Image Retrieval via Learnable FashionCLIP

引用

5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Sun, Yuan Zhao, Mingbo Donghua Univ Coll Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9789819770007;9789819770014

With the rapid development of e-commerce platforms and the continuous influx of massive clothing data such as the Internet and mobile apps, clothing has become an indispensable way of consumption in people's daily lives. Clothes image retrieval is especially useful when the user is interested in a cloth that emerged in social media videos (Instagram, TikTok), and this is a so-called video-to-shop retrieval task, where the target clothes and query clothes dressed in a short video are within different domains. In this paper, to handle the above problem, we propose a cross-domain clothing retrieval method based on massive data in the context of e-commerce live streaming. Our method includes two components: object detection and re-identification (Re-ID). the object detection branch is an anchor-free detector. the Re-ID branch is pre-trained by large-scale image-text pairs and prompts to learn joint embedding space, cross-domain, and fine-grained information for improving retrieval performance. this network enhances visual representation by integrating prompt learning and capitalizing on the cross-modal descriptive capability of FashionCLIP. We conducted experiments on a large-scale multimodal Watch and Buy dataset and achieved good results.

关键词： Clothing Retrieval Contrastive learning Pre-trained Model Cross-modal Retrieval

来源：评论

学校读者我要写书评

暂无评论

Green computing for Big Data and machine learning 2022

Green Computing for Big Data and Machine Learning

引用

5th ACM India Joint International conference on Data Science and Management of Data (CoDS-COMAD) / 9th ACM IKDD CODS conference / 26th COMAD conference

作者： Barua, Hrishav Bakul Mondal, Kartick Chandra Khatua, Sunirmal TCS Res Robot & Autonomous Syst Kolkata W Bengal India Jadavpur Univ Dept Informat Technol Kolkata W Bengal India Univ Calcutta Dept Comp Sci & Engn Kolkata W Bengal India

ISBN: (纸本)9781450385824

the current decade has beheld a tremendous spike in data volume, velocity, variety and many other such aspects which we call as Big Data and which gave birth to a new kind of science commonly known as "Data Science". With the "Data Apocalypse" in progress, it is evident that the conventional methods to handle these data would not suffice. We need distributed and parallel architectures like Cloud services (IaaS, PaaS, SaaS, STaaS, etc.). But is that enough to satisfy our needs? Here, we propose a tutorial in a very different direction when we are talking about Data Science, that is, bringing greenness in Big Data and machine learning (ML). We divide the tutorial into two parts primarily assuming that we are using cloud backbone for analytic and prediction tasks. the first part speaks about the techniques and tools to bring energy efficiency/greenness in the algorithmic and code level for Big Data and ML using Approximate computing. the second part talks about the green techniques and power models at the infrastructural level for the cloud.

关键词： Green computing Approximate computing Cloud computing Big Data Data Science machine learning Resource Scheduling Power Modelling Heuristics

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60页 << < 2 3 4 5 6 7 8 9 10 11 > >>

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：