首页 > 最新文献

International Journal of Intelligent Systems and Applications in Engineering最新文献

英文 中文
Classification of COVID-19 Cases using Deep Neural Network based on Chest Image Data through WSN 基于WSN胸部图像数据的深度神经网络新冠肺炎病例分类
Q3 Computer Science
International Journal of Intelligent Systems and Applications in Engineering
Pub Date : 2022-03-31 DOI: 10.18201/ijisae.2022.264
V. V, S. Nisha A., N. R., Amirthalakshmi T. M., B. Velan
Due to the rapid spread of corona virus disease (COVID-19), it has been considered as a pandemic throughout the world. The misclassification of COVID-19 cases may even lead the death of the patients, and hence the diagnosis at early stage is important to stop further spread of the infection and to safeguard the life of the patients. This paper proposes the Aquila tuned Deep neural network (Aquila-DNN) classifier for the classification of COVID-19 patients using the chest image data assessed through Wireless sensor Network (WSN). The extraction of important features from the chest image data is important in the diagnosis as it encloses the important data of the patients. The optimal tuning of the DNN parameters using the Aquila Optimizer (AO) assists in improving the classification accuracy of proposed model. In addition, the convergence is also boosted using the tuning process of the AO algorithm. The effectiveness of the proposed Aquila-DNN model is validated with the analysis of the model based on the performance indices, namely accuracy, ROC curve, and F1 measure. The testing accuracy and the training accuracy of Aquila-DNN model are attained to be 99.7%, and 95.4545%, respectively. © 2022, Ismail Saritas. All rights reserved.
由于冠状病毒疾病（新冠肺炎）的快速传播，它被认为是一种在世界各地流行的疾病。新冠肺炎病例的错误分类甚至可能导致患者死亡，因此早期诊断对于阻止感染的进一步传播和保障患者的生命至关重要。本文提出了Aquila调谐深度神经网络（Aquila-DNN）分类器，用于利用无线传感器网络（WSN）评估的胸部图像数据对新冠肺炎患者进行分类。从胸部图像数据中提取重要特征在诊断中很重要，因为它包含了患者的重要数据。使用Aquila Optimizer（AO）对DNN参数进行优化调整有助于提高所提出模型的分类精度。此外，使用AO算法的调谐过程也提高了收敛性。通过基于性能指标（即精度、ROC曲线和F1测度）的模型分析，验证了所提出的Aquila DNN模型的有效性。Aquila DNN模型的测试精度和训练精度分别达到99.7%和95.4545%。©2022，伊斯梅尔·萨里塔斯。保留所有权利。
{"title":"Classification of COVID-19 Cases using Deep Neural Network based on Chest Image Data through WSN","authors":"V. V, S. Nisha A., N. R., Amirthalakshmi T. M., B. Velan","doi":"10.18201/ijisae.2022.264","DOIUrl":"https://doi.org/10.18201/ijisae.2022.264","url":null,"abstract":"Due to the rapid spread of corona virus disease (COVID-19), it has been considered as a pandemic throughout the world. The misclassification of COVID-19 cases may even lead the death of the patients, and hence the diagnosis at early stage is important to stop further spread of the infection and to safeguard the life of the patients. This paper proposes the Aquila tuned Deep neural network (Aquila-DNN) classifier for the classification of COVID-19 patients using the chest image data assessed through Wireless sensor Network (WSN). The extraction of important features from the chest image data is important in the diagnosis as it encloses the important data of the patients. The optimal tuning of the DNN parameters using the Aquila Optimizer (AO) assists in improving the classification accuracy of proposed model. In addition, the convergence is also boosted using the tuning process of the AO algorithm. The effectiveness of the proposed Aquila-DNN model is validated with the analysis of the model based on the performance indices, namely accuracy, ROC curve, and F1 measure. The testing accuracy and the training accuracy of Aquila-DNN model are attained to be 99.7%, and 95.4545%, respectively. © 2022, Ismail Saritas. All rights reserved.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44809874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A Novel Screening Tool System for Depressive Disorders using Social Media and Artificial Neural Network 一种基于社交媒体和人工神经网络的抑郁症筛查工具系统
Q3 Computer Science
International Journal of Intelligent Systems and Applications in Engineering
Pub Date : 2022-03-31 DOI: 10.18201/ijisae.2022.274
A. M. Baes, A. J. Adoptante, John Carlo A. Catilo, Patrick Kendrex L. Lucero, Janice F. Peralta, Anton Louise P. de Ocampo
{"title":"A Novel Screening Tool System for Depressive Disorders using Social Media and Artificial Neural Network","authors":"A. M. Baes, A. J. Adoptante, John Carlo A. Catilo, Patrick Kendrex L. Lucero, Janice F. Peralta, Anton Louise P. de Ocampo","doi":"10.18201/ijisae.2022.274","DOIUrl":"https://doi.org/10.18201/ijisae.2022.274","url":null,"abstract":"","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43242258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Detecting Face-Touch Hand Moves Using Smartwatch Inertial Sensors and Convolutional Neural Networks 使用智能手表惯性传感器和卷积神经网络检测面部触摸手势
Q3 Computer Science
International Journal of Intelligent Systems and Applications in Engineering
Pub Date : 2022-03-31 DOI: 10.18201/ijisae.2022.275
E. Sehirli, Abdullah Alesmaeil
As per World Health Organization (WHO), avoiding touching the face when people are in public or crowded places is an effective way to prevent respiratory viral infections. This recommendation has become more crucial with the current health crisis and the worldwide spread of COVID-19 pandemic. However, most face touches are done unconsciously, that is why it is difficult for people to monitor their hand moves and try to avoid touching the face all the time. Hand-worn wearable devices like smartwatches are equipped with multiple sensors that can be utilized to track hand moves automatically. This work proposes a smartwatch application that uses small, efficient, and end-to-end Convolutional Neural Networks (CNN) models to classify hand motion and identify Face-Touch moves. To train the models, a large dataset is collected for both left and right hands with over 28k training samples that represents multiple hand motion types, body positions, and hand orientations. The app provides real-time feedback and alerts the user with vibration and sound whenever attempting to touch the face. Achieved results show state of the art face-touch accuracy with average recall, precision, and F1-Score of 96.75%, 95.1%, 95.85% respectively, with low False Positives Rate (FPR) as 0.04%. By using efficient configurations and small models, the app achieves high efficiency and can run for long hours without significant impact on battery which makes it applicable on most off-the-shelf smartwatches. © 2022, Ismail Saritas. All rights reserved.
根据世界卫生组织（世界卫生组织）的说法，当人们在公共场所或拥挤的地方时，避免触摸面部是预防呼吸道病毒感染的有效方法。随着当前的健康危机和新冠肺炎大流行在全球范围内的传播，这一建议变得更加重要。然而，大多数面部触摸都是在无意识的情况下进行的，这就是为什么人们很难监控自己的手部动作，并尽量避免一直触摸面部。智能手表等手持可穿戴设备配备了多个传感器，可以用来自动跟踪手的移动。这项工作提出了一种智能手表应用程序，该应用程序使用小型、高效、端到端的卷积神经网络（CNN）模型对手部运动进行分类并识别面部触摸动作。为了训练模型，为左手和右手收集了一个大型数据集，其中有超过28k个训练样本，代表了多种手部运动类型、身体位置和手部方向。该应用程序提供实时反馈，并在用户尝试触摸面部时用振动和声音提醒用户。所获得的结果显示了最先进的人脸触摸准确率，平均召回率、准确率和F1得分分别为96.75%、95.1%和95.85%，低误报率（FPR）为0.04%。通过使用高效的配置和小模型，该应用程序实现了高效率，可以长时间运行，不会对电池产生重大影响，这使其适用于大多数现成的智能手表。©2022，伊斯梅尔·萨里塔斯。保留所有权利。
{"title":"Detecting Face-Touch Hand Moves Using Smartwatch Inertial Sensors and Convolutional Neural Networks","authors":"E. Sehirli, Abdullah Alesmaeil","doi":"10.18201/ijisae.2022.275","DOIUrl":"https://doi.org/10.18201/ijisae.2022.275","url":null,"abstract":"As per World Health Organization (WHO), avoiding touching the face when people are in public or crowded places is an effective way to prevent respiratory viral infections. This recommendation has become more crucial with the current health crisis and the worldwide spread of COVID-19 pandemic. However, most face touches are done unconsciously, that is why it is difficult for people to monitor their hand moves and try to avoid touching the face all the time. Hand-worn wearable devices like smartwatches are equipped with multiple sensors that can be utilized to track hand moves automatically. This work proposes a smartwatch application that uses small, efficient, and end-to-end Convolutional Neural Networks (CNN) models to classify hand motion and identify Face-Touch moves. To train the models, a large dataset is collected for both left and right hands with over 28k training samples that represents multiple hand motion types, body positions, and hand orientations. The app provides real-time feedback and alerts the user with vibration and sound whenever attempting to touch the face. Achieved results show state of the art face-touch accuracy with average recall, precision, and F1-Score of 96.75%, 95.1%, 95.85% respectively, with low False Positives Rate (FPR) as 0.04%. By using efficient configurations and small models, the app achieves high efficiency and can run for long hours without significant impact on battery which makes it applicable on most off-the-shelf smartwatches. © 2022, Ismail Saritas. All rights reserved.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41463441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
A Novel Approach for ABO Blood Group Prediction using Fingerprint through Optimized Convolutional Neural Network 基于优化卷积神经网络的指纹ABO血型预测新方法
Q3 Computer Science
International Journal of Intelligent Systems and Applications in Engineering
Pub Date : 2022-03-31 DOI: 10.18201/ijisae.2022.268
Vijaykumar Patil, D. Ingle
{"title":"A Novel Approach for ABO Blood Group Prediction using Fingerprint through Optimized Convolutional Neural Network","authors":"Vijaykumar Patil, D. Ingle","doi":"10.18201/ijisae.2022.268","DOIUrl":"https://doi.org/10.18201/ijisae.2022.268","url":null,"abstract":"","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43642674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Land use Land cover change Assessment at Cement Industrial area using Landsat data-hybrid classification in part of YSR Kadapa District, Andhra Pradesh, India 印度安得拉邦YSR Kadapa区部分地区土地利用土地覆盖变化评估
Q3 Computer Science
International Journal of Intelligent Systems and Applications in Engineering
Pub Date : 2022-03-31 DOI: 10.18201/ijisae.2022.270
C. Sudhakar, G. Reddy
{"title":"Land use Land cover change Assessment at Cement Industrial area using Landsat data-hybrid classification in part of YSR Kadapa District, Andhra Pradesh, India","authors":"C. Sudhakar, G. Reddy","doi":"10.18201/ijisae.2022.270","DOIUrl":"https://doi.org/10.18201/ijisae.2022.270","url":null,"abstract":"","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48173217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Automatic Pancreas Segmentation using A Novel Modified Semantic Deep Learning Bottom-Up Approach 一种新的改进语义深度学习自下而上的胰腺自动分割方法
Q3 Computer Science
International Journal of Intelligent Systems and Applications in Engineering
Pub Date : 2022-03-31 DOI: 10.18201/ijisae.2022.272
Pradip M. Paithane, Dr. S. N. Kakarwal
{"title":"Automatic Pancreas Segmentation using A Novel Modified Semantic Deep Learning Bottom-Up Approach","authors":"Pradip M. Paithane, Dr. S. N. Kakarwal","doi":"10.18201/ijisae.2022.272","DOIUrl":"https://doi.org/10.18201/ijisae.2022.272","url":null,"abstract":"","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48946173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Solving Arithmetic Word Problems Using Natural Language Processing and Rule-Based Classification 利用自然语言处理和基于规则的分类解决算术单词问题
Q3 Computer Science
International Journal of Intelligent Systems and Applications in Engineering
Pub Date : 2022-03-31 DOI: 10.18201/ijisae.2022.271
Sourav Mandal, Swagata Acharya, Rohini Basak
{"title":"Solving Arithmetic Word Problems Using Natural Language Processing and Rule-Based Classification","authors":"Sourav Mandal, Swagata Acharya, Rohini Basak","doi":"10.18201/ijisae.2022.271","DOIUrl":"https://doi.org/10.18201/ijisae.2022.271","url":null,"abstract":"","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41727880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators 基于改进专利绩效指标的核心专利分类模型构建研究
Q3 Computer Science
International Journal of Intelligent Systems and Applications in Engineering
Pub Date : 2022-03-31 DOI: 10.18201/ijisae.2022.261
Youngho Kim, Sangsung Park, Junseok Lee, J. Kang
{"title":"A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators","authors":"Youngho Kim, Sangsung Park, Junseok Lee, J. Kang","doi":"10.18201/ijisae.2022.261","DOIUrl":"https://doi.org/10.18201/ijisae.2022.261","url":null,"abstract":"","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45626549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SettingsAn Intelligent Assignment Problem Using Novel Heuristic: The Dhouib-Matrix-AP1 (DM-AP1) 设置一个使用新启发式的智能分配问题：Dhouib-Matrix-AP1（DM-AP1）
Q3 Computer Science
International Journal of Intelligent Systems and Applications in Engineering
Pub Date : 2022-03-31 DOI: 10.18201/ijisae.2022.277
S. Dhouib
{"title":"SettingsAn Intelligent Assignment Problem Using Novel Heuristic: The Dhouib-Matrix-AP1 (DM-AP1)","authors":"S. Dhouib","doi":"10.18201/ijisae.2022.277","DOIUrl":"https://doi.org/10.18201/ijisae.2022.277","url":null,"abstract":"","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46495778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Comparison of Machine Learning Algorithms for Recognizing Drowsiness in Drivers using Electroencephalogram (EEG) Signals 利用脑电图(EEG)信号识别驾驶员睡意的机器学习算法比较
Q3 Computer Science
International Journal of Intelligent Systems and Applications in Engineering
Pub Date : 2022-03-31 DOI: 10.18201/ijisae.2022.266
Rüya Akinci, E. Akdogan, Mehmet Emin Aktan
{"title":"Comparison of Machine Learning Algorithms for Recognizing Drowsiness in Drivers using Electroencephalogram (EEG) Signals","authors":"Rüya Akinci, E. Akdogan, Mehmet Emin Aktan","doi":"10.18201/ijisae.2022.266","DOIUrl":"https://doi.org/10.18201/ijisae.2022.266","url":null,"abstract":"","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44831924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
首页 上一页 下一页 尾页
类型
全部 化学•材料 生命科学 医学 物理 工程技术 环境•农林 材料科学 地球科学 法学 管理学 化学 环境科学与生态学 计算机科学 教育学 经济学 农林科学 人文科学 生物学 数学 物理与天体物理 心理学 综合性期刊 其他 工业工程 理学 历史学 农学 文学 信息工程
数据库
全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley
期刊
International Journal of Intelligent Systems and Applications in Engineering
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
﹀
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们：info@booksci.cn
Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1