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Deep learning approach based hybrid fine-tuned Smith algorithm with Adam optimiser for multilingual opinion mining 基于深度学习的混合微调Smith算法和Adam优化器的多语种意见挖掘
IF 1.1 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-01 DOI: 10.1504/ijcat.2023.10057079
A. Shahade, K. Walse, V. Thakare
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引用次数: 0
Connection between UML use case diagrams and UML class diagrams: a matrix proposal UML用例图和UML类图之间的连接:一个矩阵建议
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-01 DOI: 10.1504/ijcat.2023.133294
Bráulio Alturas
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引用次数: 0
Web service and internet of things: a systematic literature review Web服务和物联网:系统的文献综述
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-01 DOI: 10.1504/ijcat.2023.134774
Henderson Acosta Bragança, Paulo Caetano Da Silva, Nacles Bernardino Pirajá Gomes
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引用次数: 0
Lossless EEG data compression using clustering and encoding for fog computing based IoMT networks 基于雾计算的IoMT网络的聚类和编码无损脑电数据压缩
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-01 DOI: 10.1504/ijcat.2023.132553
Ali Kadhum Idrees, Marwa Saieed Khlief
{"title":"Lossless EEG data compression using clustering and encoding for fog computing based IoMT networks","authors":"Ali Kadhum Idrees, Marwa Saieed Khlief","doi":"10.1504/ijcat.2023.132553","DOIUrl":"https://doi.org/10.1504/ijcat.2023.132553","url":null,"abstract":"","PeriodicalId":46624,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135734792","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
Link prediction analysis based on Node2Vec embedding technique 基于Node2Vec嵌入技术的链路预测分析
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-01 DOI: 10.1504/ijcat.2023.134091
Salam Jayachitra Devi, Buddha Singh
The paper focuses on analysing link prediction using the Node2Vec embedding technique, which is based on the Random Walk algorithm. In addition to this, several machine learning models have been employed to assess the effectiveness of the embedding technique. Node2Vec employs various embedding operators, including Hadamard, Concatenation, Average, Weighted L1, and Weighted L2. The comparative analysis of this embedding technique is done on real world network data sets using various machine learning models with state of the art link prediction algorithms. Performance assessment of Node2Vec's embedding technique is based on the AUC metric. According to the simulation results, it has been determined that the concatenation operator with the bagging classifier yields mean AUC value of 0.939, outperforming the other operators, which produce AUC values below 0.91. Furthermore, the study has also revealed that the embedding technique provides superior results when applied to networks with a low ratio of nodes to edges.
本文重点研究了基于随机漫步算法的Node2Vec嵌入技术的链路预测分析。除此之外,还使用了几个机器学习模型来评估嵌入技术的有效性。Node2Vec采用了多种嵌入算子,包括Hadamard、concatation、Average、Weighted L1和Weighted L2。这种嵌入技术的比较分析是在现实世界的网络数据集上进行的,使用各种机器学习模型和最先进的链接预测算法。Node2Vec嵌入技术的性能评估基于AUC度量。根据仿真结果,确定使用套袋分类器的串接操作的平均AUC值为0.939,优于其他操作,其产生的AUC值低于0.91。此外,研究还表明,当嵌入技术应用于低节点边缘比的网络时,可以提供更好的结果。
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引用次数: 0
FER to FFR: a deep-learning-based approach for robust fatigue detection 基于深度学习的鲁棒疲劳检测方法
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-01 DOI: 10.1504/ijcat.2023.133292
Rachana Yogesh Patil, Yogesh H. Patil, Sheetal U. Bhandari
Automatic detection of fatigue from the face provides non-intrusive passive identification of fatigue. The traditional approach of fatigue detection has focused on detecting yawning and eyelid closure. However, fatigue is manifested in the face through various minute facial features. In this paper, we propose a fatigue detection model, which can learn facial expression features through a deep learning-based facial expression recognition model and provide the same to the fatigue recognition model. Experiments indicate that the proposed approach achieves a qualitative improvement of facial features used for fatigue detection and improves the accuracy quantitatively on the custom Indian fatigue data set. The approach also allows mitigation of limitations of fatigue data sets of significantly fewer subjects and allows for training fatigue models suitable for unconstrained real-world settings.
面部疲劳自动检测提供非侵入性的疲劳被动识别。传统的疲劳检测方法主要集中在检测打哈欠和眼睑闭合。然而,疲劳是通过各种细微的面部特征表现在脸上的。本文提出了一种疲劳检测模型,该模型可以通过基于深度学习的面部表情识别模型学习面部表情特征,并为疲劳识别模型提供相同的特征。实验表明,该方法对人脸特征进行了定性改进,在自定义印度人疲劳数据集上提高了人脸特征的检测精度。该方法还可以减少受试者数量明显减少的疲劳数据集的局限性,并允许训练适合无约束现实环境的疲劳模型。
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引用次数: 0
Modelling and simulation of portable solar Scheffler reflector water heater using soft computing techniques 便携式太阳能舍弗勒反射式热水器的软计算建模与仿真
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-01 DOI: 10.1504/ijcat.2023.134773
Mangesh Phate, Shraddha Toney, Vikas Phate
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引用次数: 0
A systematic review of factors in augmented reality studies: a visualisation approach 增强现实研究中因素的系统回顾:一种可视化方法
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-01 DOI: 10.1504/ijcat.2023.134775
Vinh T. Nguyen
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引用次数: 0
Modelling and simulation of portable solar Scheffler reflector water heater using soft computing techniques 便携式太阳能舍弗勒反射式热水器的软计算建模与仿真
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-01 DOI: 10.1504/ijcat.2023.10059367
Mangesh Phate, Shraddha Toney, Vikas Phate
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引用次数: 0
The compound iterative algorithm for rational models based on the Coyote optimisation algorithm 基于Coyote优化算法的有理模型复合迭代算法
Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-01 DOI: 10.1504/ijcat.2023.133881
Fei Xu, Jing Chen, Xia Yin
This article proposes a Coyote Optimisation Compound Iterative Algorithm (CO-CIA) for rational models. Particularly, the parameters in the numerator and denominator of rational models make the derivative equation hard to solve. To deal with this problem, the Coyote Optimisation Algorithm (COA) is applied to estimate the parameters in the denominator. Compared with the Bias Compensation-based Least Squares (BCLS) algorithm and the Particle Swarm Optimisation Compound Iterative Algorithm (PSO-CIA), the proposed method has higher accuracy and faster convergence rates. Finally, a simulation example is utilised to verify the effectiveness of the proposed algorithm.
本文提出了一种合理模型的Coyote优化复合迭代算法(cocia)。特别是有理模型的分子和分母中的参数使微分方程难以求解。为了解决这一问题,采用COA算法对分母中的参数进行估计。与基于偏差补偿的最小二乘(BCLS)算法和粒子群优化复合迭代算法(PSO-CIA)相比,该方法具有更高的精度和更快的收敛速度。最后,通过仿真算例验证了算法的有效性。
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引用次数: 0
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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY
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