检索结果-南通市图书馆

A feature Learning-Based Model for Analyzing Students’ Performance in Supportive Learning

Intelligent Automation & Soft Computing 2023年第6期36卷 2989-3005页

作者： P.Prabhu P.Valarmathie K.Dinakaran Department of Computer Science and Engineering K.S.R College of EngineeringThiruchengodeTamil Nadu-637215India Centre for Artificial Intelligence Chennai Institute of TechnologyChennaiTamil Nadu-600069India Department of Computer Science and Engineering S.A.Engineering CollegeChennaiTamil Nadu-600077India

Supportive learning plays a substantial role in providing a quality edu-cation system.The evaluation of students’performance impacts their deeper insight into the subject knowledge.Specifically,it is essential to maintain the baseline foundation for building a broader understanding of their careers.This research concentrates on establishing the students’knowledge relationship even in reduced samples.Here,Synthetic Minority Oversampling TEchnique(SMOTE)technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy.When the initial processing is not done substantially,it leads to misleading prediction accuracy.This research concentrates on modelling an efficient classifier model to predict students’perfor-mance.Generally,the online available student dataset comprises a lesser amount of sample,and k-fold cross-validation is performed to balance the dataset.Then,the relationship among the students’performance(features)is measured using the auto-encoder.The stacked Long Short Term Memory(s-LSTM)is used to learn the previous feedback connection.The stacked model handles the provided data and the data sequence for understanding the long-term dependencies.The simula-tion is done in the MATLAB 2020a environment,and the proposed model shows a better trade-off than the existing approaches.Some evaluation metrics like pre-diction accuracy,sensitivity,specificity,AUROC,F1-score and recall are evalu-ated using the proposed model.The performance of the s?LSTM model is compared with existing approaches.The proposed model gives 89% accuracy,83% precision,86%recall,and 87%F-score.The proposed model outperforms the existing systems in terms of the earlier metrics.

关键词： Student performance quality education supportive learning feature relationship auto-encoder stacked LSTM

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Rice disease identification method based on improved CNN-BiGRU

Artificial Intelligence in Agriculture

引用

Artificial Intelligence in Agriculture 2023年第3期9卷 100-109页

作者： Yang Lu Xiaoxiao Wu Pengfei Liu Hang Li Wanting Liu Colloge of Information and Electrical Engineering Heilongjiang Bayi Agricultural UniversityDaqingHeilongjiang 163319China Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqingHeilongjiang 163318China Sanya Offshore Oil and Gas Research Institute Northeast Petroleum UniversitySanyaHainan 572025China

In the field of precision agriculture,diagnosing rice diseases from images remains challenging due to high error rates,multiple influencing factors,and unstable conditions.While machine learning and convolutional neural networks have shown promising results in identifying rice diseases,they were limited in their ability to explain the relationships among disease features.In this study,we proposed an improved rice disease classification method that combines a convolutional neural network(CNN)with a bidirectional gated recurrent unit(BiGRU).Specifically,we introduced a residual mechanism into the Inception module,expanded the module's depth,and integrated an improved Convolutional Block Attention Module(CBAM).We trained and tested the improved CNN and BiGRU,concatenated the outputs of the CNN and BiGRU modules,and passed them to the classification layer for recognition.Our experiments demonstrate that this approach achieves an accuracy of 98.21%in identifying four types of rice diseases,providing a reliable method for rice disease recognition research.

关键词： Deep learning CNN-BiGRU Rice disease feature relationship

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

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

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

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

在线全文

在线全文

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：