期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到3篇文章
< 1 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Automated Red Deer Algorithm with Deep Learning Enabled Hyperspectral Image Classification 认领 引用
1
作者 B.Chellapraba D.Manohari +1 位作者 K.Periyakaruppan M.S.Kavitha 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期2353-2366,共14页
Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is importa... Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is important for effective classification outcomes.Additionally,the recent advancements of deep learning(DL)models make it possible in several application areas.In addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics.In this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classification(RDADL-HIC)technique.The proposed RDADL-HIC technique aims to effectively determine the HSI images.In addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimi-zer.Moreover,RDA with gated recurrent unit(GRU)approach is used for the identification and classification of HSIs.The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively.The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures.The comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches. 展开更多
关键词 Hyperspectral images image classification deep learning adagrad optimizer nasnetlarge model red deer algorithm
暂未订购 下载PDF
基于RDA优化1D-CNN-BiLSTM的矿井多组分危险气体快速识别 认领 引用
2
作者 刘海平 段良松 +2 位作者 张森 何流 焦明之 《工矿自动化》 CSCD 北大核心 2026年第4期141-148,共8页
矿井多组分危险气体的快速精准识别是实现矿井危险气体泄漏早期预警的核心前提。传统的人工特征提取方法仅能反映响应过程中的局部离散信息,在矿井多组分气体泄漏等复杂场景下,识别性能低;目前基于机器学习与深度学习的气体识别算法可... 矿井多组分危险气体的快速精准识别是实现矿井危险气体泄漏早期预警的核心前提。传统的人工特征提取方法仅能反映响应过程中的局部离散信息,在矿井多组分气体泄漏等复杂场景下,识别性能低;目前基于机器学习与深度学习的气体识别算法可自动从气体传感器响应数据中提取时空维度的深层特征,但需等待传感器响应达到稳态,无法满足气体快速识别需求。针对上述问题,提出了一种基于红鹿算法(RDA)优化一维卷积神经网络-双向长短期记忆网络的混合神经网络模型(1D-CNN-BiLSTM模型)。该模型通过1D-CNN提取气体响应的局部瞬态特征,利用BiLSTM刻画长时序数据依赖关系,可对气体传感器响应数据进行端到端学习,避免人工特征提取的主观性与局限性;引入RDA对模型核心超参数进行自适应寻优,从而提升模型性能。实验结果表明,RDA寻优效率与稳定性优于传统粒子群优化算法(PSO)和遗传算法(GA);1D-CNN-BiLSTM模型能够从气体传感器响应数据中有效提取具有强判别力的气体类别特征,对单一气体和二元混合气体的识别精度高于三元混合气体;所提模型识别准确率达96.43%,优于传统机器学习模型与单一结构深度学习模型;模型仅使用气体注入后前10 s的气体传感器响应数据,即可实现高精度气体识别,兼顾了识别实时性与精度。 展开更多
关键词 矿井多组分气体识别 一维卷积神经网络 双向长短期记忆网络 红鹿算法
暂未订购 下载PDF
Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification 认领 引用 被引量：1
3
作者 R.Bhaskaran S.Saravanan +4 位作者 M.Kavitha C.Jeyalakshmi Seifedine Kadry Hafiz Tayyab Rauf Reem Alkhammash 《Computer Systems Science & Engineering》 SCIE EI 2023年第1期235-247,共13页
Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so... Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so on.SA has the ability to handle the drastically-increasing unstructured text by transform-ing them into structured data with the help of NLP and open source tools.The current research work designs a novel Modified Red Deer Algorithm(MRDA)Extreme Learning Machine Sparse Autoencoder(ELMSAE)model for SA and classification.The proposed MRDA-ELMSAE technique initially performs pre-processing to transform the data into a compatible format.Moreover,TF-IDF vec-torizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments.Furthermore,optimal parameter tuning is done for ELMSAE model using MRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced effi-ciency of MRDA-ELMSAE technique against other recent techniques. 展开更多
关键词 Sentiment analysis data classification machine learning red deer algorithm extreme learning machine natural language processing
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈