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

554 篇 会议
8 册 图书
2 篇 期刊文献

馆藏范围

564 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

386 篇 工学
- 370 篇 计算机科学与技术...
- 246 篇 软件工程
- 61 篇 信息与通信工程
- 58 篇 生物工程
- 43 篇 生物医学工程（可授...
- 28 篇 电气工程
- 25 篇 控制科学与工程
- 18 篇 化学工程与技术
- 13 篇 机械工程
- 13 篇 光学工程
- 13 篇 农业工程
- 13 篇 安全科学与工程
161 篇 理学
- 106 篇 数学
- 61 篇 生物学
- 57 篇 系统科学
- 39 篇 统计学（可授理学、...
- 20 篇 物理学
- 13 篇 化学
- 9 篇 大气科学
95 篇 管理学
- 52 篇 图书情报与档案管...
- 47 篇 管理科学与工程(可...
- 24 篇 工商管理
42 篇 医学
- 42 篇 临床医学
- 41 篇 基础医学(可授医学...
- 29 篇 药学(可授医学、理...
- 15 篇 公共卫生与预防医...
18 篇 农学
- 18 篇 作物学
15 篇 法学
- 11 篇 社会学
10 篇 教育学
- 10 篇 教育学
9 篇 经济学
- 9 篇 应用经济学

主题

116 篇 machine learning
50 篇 deep learning
44 篇 machine learning...
38 篇 accuracy
35 篇 support vector m...
21 篇 predictive model...
20 篇 security
18 篇 data models
15 篇 adversarial mach...
15 篇 feature extracti...
15 篇 random forests
15 篇 prediction algor...
15 篇 training
14 篇 decision trees
14 篇 classification a...
14 篇 forecasting
13 篇 computational mo...
12 篇 internet of thin...
12 篇 information tech...
11 篇 real-time system...

机构

3 篇 faculty of compu...
2 篇 faculty of compu...
2 篇 infologia techno...
2 篇 kindi center for...
2 篇 centre of resear...
2 篇 department of co...
2 篇 graphic era hill...
2 篇 greater bay area...
2 篇 vellore institut...
2 篇 department of ar...
2 篇 school of comput...
2 篇 department of ma...
2 篇 chitkara centre ...
2 篇 fraunhofer cente...
2 篇 department of co...
2 篇 sreyas institute...
2 篇 school of comput...
2 篇 ipmi internation...
2 篇 al-farabi kazakh...
2 篇 department of ma...

作者

6 篇 azar ahmad taher
3 篇 bhatnagar roheet
3 篇 foozy cik feresa...
3 篇 ammar hossam has...
2 篇 valeeva elvira
2 篇 khan ayub
2 篇 santos igor
2 篇 varun ojha
2 篇 shreya
2 篇 bringas pablo ga...
2 篇 kurniawan r.
2 篇 maragatharajan m...
2 篇 usha g.
2 篇 singh shikha
2 篇 xiao kai
2 篇 cao peng
2 篇 shyamala devi m.
2 篇 nazri m.z.a.
2 篇 m. maragatharaja...
2 篇 swathi p.

语言

544 篇 英文
18 篇 其他
2 篇 中文

检索条件"任意字段=4th International Conference on Machine Learning and Soft Computing, ICMLSC 2020"

共 564 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：

Convolutional Sequence to Sequence learning for English-Khasi Neural machine Translation 4

Convolutional Sequence to Sequence Learning for English-Khas...

引用

4th international conference on computing and Communication Systems, I3CS 2023

作者： Hujon, Aiusha Vellintihun Amitab, Khwairakpam Singh, thoudam Doren St. Anthony's College Dept. of Computer Science Meghalaya Shillong India North Eastern Hill University Dept. of Information Technology Meghalaya Shillong India NIT Silchar Dept. of Computer Science and Engineering Assam India

ISBN: (纸本)9798350323771

the convolutional neural network is relatively prevalent for visual recognition tasks. Implementing CNN for neural machine translation is still challenging compared to the predominant approach such as recurrent neural networks and transformers. this paper presents an empirical study of the convolutional sequence to sequence learning model to translate English to Khasi. the approach uses convolution for both encoders and decoders with multi-step attention. the study also includes a model using a transfer learning approach on which the parent model is trained using the English-French dataset. the quantitative and qualitative results are significant thus suggesting that the convolutional approach for neural machine translation tasks can perform remarkably well as other tasks using convolution neural networks in the case of English-Khasi language pair. © 2023 IEEE.

关键词： Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

Forecasting Reference Evapotranspiration Using LSTM and Transformer 4th

Forecasting Reference Evapotranspiration Using LSTM and Tran...

引用

4th international conference on Digital Technologies and Applications (ICDTA)

作者： Mustapha, Musa Zineddine, Mhamed Majikumna, Usman Kaloma Alaoui, Ahmed El Hilali Euromed Univ Fes Sch Digital Engn & Artificial Intelligence Fes Morocco

ISBN: (纸本)9783031686498;9783031686504

Reference evapotranspiration (ET) helps manage irrigation water supply, minimize freshwater waste, maximize crop yields, and promote sustainable agriculture. However, machine learning (ML)-based ET forecasting models are only effective and accurate if localized weather/climatic data are available, which is not always the case in most cases, especially in many parts of Africa. In this study, we obtained a historical climate record of 23 years from the gridded AgERA5 dataset and calculated ET using the Penman-Monteith (PM) method. thereafter, two different ET prediction algorithms (transformer and long short-term memory network (LSTM)) were trained using the AgERA5 to overcome the challenges caused by limited localized data. According to the results, LSTM outperformed the transformer in terms of forecasting ET, with lower mean absolute error (MAE) values, root mean square errors (RMSE), and a higher correlation coefficient (R2). therefore, the proposed approach demonstrates how gridded climate observations can be integrated with soft computing techniques for precise ET forecasting within areas that lack access to climate records from local meteorological stations.

关键词： ET forecasting ERA5 Reference evapotranspiration Remote sensing LSTM machine learning

来源：评论

学校读者我要写书评

暂无评论

Human Activity Recognition in Videos Using Deep learning 4th

Human Activity Recognition in Videos Using Deep Learning

引用

4th international conference on soft computing and its Engineering Applications (icsoftComp)

作者： Kumar, Mohit Rana, Adarsh Ankita Yadav, Arun Kumar Yadav, Divakar NIT Hamirpur Dept Comp Sci & Engn Hamirpur 177005 HP India

ISBN: (纸本)9783031276088;9783031276095

Human Activity Recognition (HAR) is a challenging classification task. In the past, it traditionally involved the identification of the movement and activities of a person based on sensor inputs, apply signal processing to receive features and fit the features into a machine learning model. In recent times, deep learning methods have shown good results in automatic Human Activity Recognition. In this paper, we propose a pre-trained CNN (Inception-v3) and LSTM based methodology for Human Activity Recognition. the proposed methodology is evaluated on the publicly available UCF-101 dataset. the results show that the proposed methodology outperforms recent state-of-art methods in terms of accuracy (79.21%) and top-5 accuracy (92.92%) on the HAR task.

关键词： Human Activity Recognition HAR LSTM CNN Inception V3 UCF-101

来源：评论

学校读者我要写书评

暂无评论

FedCLUS: Federated Clustering from Distributed Homogeneous Data 4th

FedCLUS: Federated Clustering from Distributed Homogeneous D...

引用

4th international conference on soft computing and its Engineering Applications (icsoftComp)

作者： Gupta, Sargam Sharma, Shachi South Asian Univ Dept Comp Sci New Delhi India

ISBN: (纸本)9783031276088;9783031276095

An emerging trend in the field of machine learning presently is Federated learning (FL) that enables to build a global model without sharing private data distributed among multiple data owners. Extensive research conducted on FL is mostly restricted to deep learning methods utilizing labeled data. However, voluminous big data generated today is unlabeled. the paper presents a federated clustering method, called FedCLUS, that allows to learn clusters from distributed homogeneous data without sharing it. the distinguishing characteristic of the FedCLUS method is its ability to retain, merge and discard original clusters sent by data owners. Also, FedCLUS can identify any number of arbitrary shaped clusters that are robust to outliers. the feasibility and performance of the FedCLUS is tested on many diverse datasets.

关键词： Federated clustering Homogeneous data Performance analysis Unsupervised federated learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Efficient Approach for Feature Selection for Enhanced Performance in Breast Cancer Prediction 4th

A Novel Efficient Approach for Feature Selection for Enhance...

引用

4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Bansal, Arin Lohan, Vishal Kumar Khanna, Munish Agnihotri, Shantanu UPES Dehra Dun Uttarakhand India Galgotias Univ Dankaur India

ISBN: (纸本)9783031837920;9783031837937

Modern research relies on computer-aided diagnostic (CAD) tools for efficient utilisation of time and resources. these technologies help medical practitioners make decisions by swiftly identifying anomalies and providing reliable information. the effective selection of features (characteristics) is crucial for these systems to appropriately categorise high-dimensional biological data. these traits may help diagnose linked medical issues. Biomedical data has many unnecessary or redundant information, making pattern identification difficult. therefore, a feature selection (FS) technique should be implemented and used to remove these characteristics from the training data. Eliminating irrelevant or uninformative characteristics is the main objective of FS techniques to enhance classification accuracy along with reduction of training and testing time of machine learning (ML) classifiers. In ML-driven CAD systems, the FS phase is crucial to success. Adding efficient features during training may boost ML model performance. this empirical research proposes an FS-based biological data categorisation approach based on integration of Water Wave Optimization (WWO) soft-computing algorithm with efficient ML classifiers. Using the publicly accessible Wisconsin Diagnostic Breast Cancer (WDBC) standard dataset, this study classifies benign and malignant tumours. the 70:30 train-test split approach is implemented and the suggested strategy's competency is assessed using accuracy, sensitivity (Recall), specificity, precision, F1-Score, Kappa Score, Matthews Correlation Coefficient (MCC), and ROC AUC Score. Best accuracy calculated using the proposed method is 97.96%. the suggested clinical decision support system has a good classification performance, making it a useful secondary opinion tool and lowering the workload of experienced medical physicians.

关键词： Feature Selection soft-computing algorithms Medical data machine learning Optimization

来源：评论

学校读者我要写书评

暂无评论

Novel Framework for Quality Crop Predictions Using Data Mining and soft computing Techniques

Novel Framework for Quality Crop Predictions Using Data Mini...

引用

2023 international conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Maurya, Rajesh Kumar Jain, Himani Sharma, Tarun Kumar Sharma, Surbhi Dublish, Mani Abes Engineering College Department of Computer Applications NH-9 Near Crossing Republic Ghaziabad India

ISBN: (纸本)9798350322309

Data mining (DM) and soft computing (SC) are a vital computational approach that offers good competence of flexible agricultural data processing systems to solve farmer's problems. Recently, soft computing has emerged as a powerful technique for solving and analyzing complex real-world problems. this article suggests an approach of smart crop predictions is presented through DM &SC in the field of agricultural quality crop prediction. A five-level framework is proposed namely 1) Collection of data from different repositories, 2) Pre-processing of data, 3) Appropriate Classifier Selection, 4) Prediction and Estimation 5) Draw AUC & ROC curve. Method proposed here focuses on analyzing agricultural yield, soil for crop, rainfall required based on chemical property of soil. Agricultural data analysis and cataloging is one of the best applications of new computing tools such as soft computing, machine learning (ML) approach, became a burning area for the reason that of the massive development of farming data. DM&SC approaches for accomplishing applied research will give effective answers for this type of problem. ML is a working tool to study multiple learners and combine their assessments for accomplishing greater forecasting accurateness. In this investigation, we had recapitulated ML methods which can be applied as an essential tool by the farmer and agriculture scientist for timely prediction of crop production. © 2023 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Clustering and Autoencoder-Based Cybersecurity for EVs in a Blockchain-Enabled Smart Cloud Environment 4

Fuzzy Clustering and Autoencoder-Based Cybersecurity for EVs...

引用

4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Subha, B. Nagarajan, Sivakumar thangam, A. Somasekhar Srinivas, V.K. Praveen, R.V.S. Bakshi, Kingshuk Das Kristu Jayanti College Department of Professional Management Studies Kothanur India I & I Software Bethlehem United States Pondicherry University Community College Department of Mathematics Pondicherry India Mohan Babu University Department of Mathematics Tirupati India Skyview Department of Digital Engineering and Assurance Hyderabad India Ideal Institute of Engineering Department of Computer Science and Engineering Nadia India

ISBN: (纸本)9798350352931

the rise of electric vehicles (EVs) has revealed several cybersecurity weaknesses, especially in data transmission inside cloud-based systems. Current cybersecurity algorithms for anomaly detection frequently struggle to manage the intricacy and magnitude of electric vehicle network data. this study presents an innovative cybersecurity system that integrates fuzzy clustering for soft anomaly detection, autoencoders for deep learning-driven feature extraction, and blockchain for secure data validation. the suggested model effectively tackles critical issues in anomaly detection, attaining an accuracy rate of 97.2%, an F1 score of 0.96, and a recall of 0.95, therefore surpassing current methodologies substantially. the results underscore the model's potential to identify and address cyber risks with enhanced precision and recall, providing a scalable and resilient solution for EV networks. Future improvements may concentrate on minimising computation time and augmenting detection efficacy in real-time systems, establishing this method as a viable option for bolstering EV cybersecurity in blockchain-integrated smart settings. © 2024 IEEE.

关键词： anomaly detection autoencoders blockchain cloud computing cyber threat detection cybersecurity electric vehicles ev networks fuzzy clustering machine learning

来源：评论

学校读者我要写书评

暂无评论

machine learning Based Prediction of Psychiatric Disorder 4

Machine Learning Based Prediction of Psychiatric Disorder

引用

4th IEEE international conference on computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Sharma, Mayank Goswami, Puneet SRM University Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350306118

the foundation of the education systems all around the world were shaken up when Covid-19 started spreading its roots worldwide. India of course was no exception to it. While the education sector in India was already undergoing a transformation with the NEP 2020 policy, all the schools and colleges had to be shut down urgently due to the rapid spread of Covid-19. Almost overnight students and teachers had to switch to online learning. At the onset of lockdown around 250 million students were affected due to closure of educational institutes in India. While there was a massive decline in learning outcomes, the physical and mental well-being of the students was also severely impacted. Students lost touch with being socially physical and alternated to being present on social media. However humans are social animals. With deprivation, came a sense of loneliness and that's when physical and mental wellness issues started taking over leading to psychiatric disorders. Our generation are the leaders of the next and ensuring the wellbeing of young minds is a must. thus there is a need to study and find out just how much Covid-19 is impacting our students and their lives using Artificial Intelligence cum machine learning and furthermore act upon it to find solutions to help them. In this paper, we have approached the above problem using the Binary Classification Predictive Model with different logical neural networks. © 2023 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

machine learning-based prediction of sorghum biomass from UAV multispectral imagery data 4

Machine learning-based prediction of sorghum biomass from UA...

引用

4th international conference on computing and Communication Systems, I3CS 2023

作者： Gano, Boubacar Ahmed, Nurzaman Shakoor, Nadia Donald Danforth Plant Science Center Saint LouisMO63132 United States

ISBN: (纸本)9798350323771

Unmanned aerial vehicle (UAV)-based remote sensing applications in plant phenotyping have received attention in modern plant breeding programs that increasingly have the need to automate time-consuming manual measurements of agronomic traits. this paper focuses on the prediction of sorghum biomass using machine learning algorithms such as Linear Regression, KNeighbors Regressor, and the XGBoost Regressor. Results from a field experiment of 344 sorghum genotypes conducted at the Donald Danforth Plant Science Center (Saint Louis, MO, USA) showed accurate prediction models. the K-Neighbors Regression model performed better than the other two models (R2=0.65, RMSE =4968.60kg/ha). the developed approach in this study could be used as a decision support tool for sorghum biomass phenotyping in breeding programs. © 2023 IEEE.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Deep learning Based Model for Fundus Retinal Image Classification 4th

Deep Learning Based Model for Fundus Retinal Image Classific...

引用

4th international conference on soft computing and its Engineering Applications (icsoftComp)

作者： thanki, Rohit IEEE Gujarat Sect Rajkot Gujarat India

ISBN: (纸本)9783031276088;9783031276095

Several types of eye diseases can lead to blindness, including glaucoma. For better diagnosis, retinal images should be assessed using advanced artificial intelligence techniques, which are used to identify a wide range of eye diseases. In this paper, support vector machine (SVM), random forest (RF), decision tree (DT), and convolutional neural network (CNN) methods are used to classify fundus retinal images of healthy and glaucomatous patients. this study tests various models on a small dataset of 30 high-resolution fundus retinal images. To classify these retinal images, the proposed CNN-based classifier achieved a classification accuracy of 80%. Furthermore, according to the confusion matrix, the proposed CNN model was 80% accurate for the healthy and glaucoma classes. In the glaucoma case, the CNN-based classifier proved superior to other classifiers based on the comparative analysis.

关键词： Convolutional neural network Fundus retinal image Glaucoma Deep learning Image classification

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57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：