Pub Date : 2023-11-27DOI: 10.1007/s44196-023-00362-y
Ashir Javeed, Johan Sanmartin Berglund, A. Dallora, Muhammad Asim Saleem, P. Anderberg
{"title":"Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data","authors":"Ashir Javeed, Johan Sanmartin Berglund, A. Dallora, Muhammad Asim Saleem, P. Anderberg","doi":"10.1007/s44196-023-00362-y","DOIUrl":"https://doi.org/10.1007/s44196-023-00362-y","url":null,"abstract":"","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"29 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139229474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-20DOI: 10.1007/s44196-023-00357-9
Yuanjin Ji, Junwei Zeng, L. Ren
{"title":"Research on the Prediction of Tire Radial Load Based on 1D CNN and BiGRU","authors":"Yuanjin Ji, Junwei Zeng, L. Ren","doi":"10.1007/s44196-023-00357-9","DOIUrl":"https://doi.org/10.1007/s44196-023-00357-9","url":null,"abstract":"","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"44 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139257827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.1007/s44196-023-00366-8
Mohamed Ashmawy, Mohamed Waleed Fakhr, Fahima A. Maghraby
Abstract Lexical Normalization (LN) aims to normalize a nonstandard text to a standard text. This problem is of extreme importance in natural language processing (NLP) when applying existing trained models to user-generated text on social media. Users of social media tend to use non-standard language. They heavily use abbreviations, phonetic substitutions, and colloquial language. Nevertheless, most existing NLP-based systems are often designed with the standard language in mind. However, they suffer from significant performance drops due to the many out-of-vocabulary words found in social media text. In this paper, we present a new (LN) technique by utilizing a transformer-based sequence-to-sequence (Seq2Seq) to build a multilingual characters-to-words machine translation model. Unlike the majority of current methods, the proposed model is capable of recognizing and generating previously unseen words. Also, it greatly reduces the difficulties involved in tokenizing and preprocessing the nonstandard text input and the standard text output. The proposed model outperforms the winning entry to the Multilingual Lexical Normalization (MultiLexNorm) shared task at W-NUT 2021 on both intrinsic and extrinsic evaluations.
{"title":"Lexical Normalization Using Generative Transformer Model (LN-GTM)","authors":"Mohamed Ashmawy, Mohamed Waleed Fakhr, Fahima A. Maghraby","doi":"10.1007/s44196-023-00366-8","DOIUrl":"https://doi.org/10.1007/s44196-023-00366-8","url":null,"abstract":"Abstract Lexical Normalization (LN) aims to normalize a nonstandard text to a standard text. This problem is of extreme importance in natural language processing (NLP) when applying existing trained models to user-generated text on social media. Users of social media tend to use non-standard language. They heavily use abbreviations, phonetic substitutions, and colloquial language. Nevertheless, most existing NLP-based systems are often designed with the standard language in mind. However, they suffer from significant performance drops due to the many out-of-vocabulary words found in social media text. In this paper, we present a new (LN) technique by utilizing a transformer-based sequence-to-sequence (Seq2Seq) to build a multilingual characters-to-words machine translation model. Unlike the majority of current methods, the proposed model is capable of recognizing and generating previously unseen words. Also, it greatly reduces the difficulties involved in tokenizing and preprocessing the nonstandard text input and the standard text output. The proposed model outperforms the winning entry to the Multilingual Lexical Normalization (MultiLexNorm) shared task at W-NUT 2021 on both intrinsic and extrinsic evaluations.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"43 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.1007/s44196-023-00365-9
Jian Huang, Guangpeng Zhang, Li juan Ren, Nina Wang
Abstract When manually polishing blades, skilled workers can quickly machine a blade by observing the characteristics of the polishing sparks. To help workers better recognize spark images, we used an industrial charge-coupled device (CCD) camera to capture the spark images. Firstly, the spark image region detected by yolo5, then segment from the background. Secondly, the target region was further segmented and refined in a fully connected conditional random field (CRF), from which the complete spark image obtained. Experimental results showed that this method could quickly and accurately segment whole spark image. The test results showed that this method was better than other image segmentation algorithms. Our method could better segment irregular image, improve recognition and segmentation efficiency of spark image, achieve automatic image segmentation, and replace human observation.
{"title":"A New Image Segmentation Method Based on the YOLO5 and Fully Connected CRF","authors":"Jian Huang, Guangpeng Zhang, Li juan Ren, Nina Wang","doi":"10.1007/s44196-023-00365-9","DOIUrl":"https://doi.org/10.1007/s44196-023-00365-9","url":null,"abstract":"Abstract When manually polishing blades, skilled workers can quickly machine a blade by observing the characteristics of the polishing sparks. To help workers better recognize spark images, we used an industrial charge-coupled device (CCD) camera to capture the spark images. Firstly, the spark image region detected by yolo5, then segment from the background. Secondly, the target region was further segmented and refined in a fully connected conditional random field (CRF), from which the complete spark image obtained. Experimental results showed that this method could quickly and accurately segment whole spark image. The test results showed that this method was better than other image segmentation algorithms. Our method could better segment irregular image, improve recognition and segmentation efficiency of spark image, achieve automatic image segmentation, and replace human observation.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"43 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.1007/s44196-023-00323-5
Di Wu, Yan Xiao
Abstract Redundant nodes in a kernel incremental extreme learning machine (KI-ELM) increase ineffective iterations and reduce learning efficiency. To address this problem, this study established a novel improved hybrid intelligent deep kernel incremental extreme learning machine (HI-DKIELM), which is based on a hybrid intelligent algorithm and a KI-ELM. First, a hybrid intelligent algorithm was established based on the artificial transgender longicorn algorithm and multiple population gray wolf optimization methods to reduce the parameters of hidden layer neurons and then to determine the effective number of hidden layer neurons. The learning efficiency of the algorithm was improved through the reduction of network complexity. Then, to improve the classification accuracy and generalization performance of the algorithm, a deep network structure was introduced to the KI-ELM to gradually extract the original input data layer by layer and realize high-dimensional mapping of data. The experimental results show that the number of network nodes of HI-DKIELM algorithm is obviously reduced, which reduces the network complexity of ELM and greatly improves the learning efficiency of the algorithm. From the regression and classification experiments, its CCPP can be seen that the training error and test error of the HI-DKIELM algorithm proposed in this paper are 0.0417 and 0.0435, which are 0.0103 and 0.0078 lower than the suboptimal algorithm, respectively. On the Boston Housing database, the average and standard deviation of this algorithm are 98.21 and 0.0038, which are 6.2 and 0.0003 higher than the suboptimal algorithm, respectively.
{"title":"A Novel Deep Kernel Incremental Extreme Learning Machine Based on Artificial Transgender Longicorn Algorithm and Multiple Population Gray Wolf Optimization Methods","authors":"Di Wu, Yan Xiao","doi":"10.1007/s44196-023-00323-5","DOIUrl":"https://doi.org/10.1007/s44196-023-00323-5","url":null,"abstract":"Abstract Redundant nodes in a kernel incremental extreme learning machine (KI-ELM) increase ineffective iterations and reduce learning efficiency. To address this problem, this study established a novel improved hybrid intelligent deep kernel incremental extreme learning machine (HI-DKIELM), which is based on a hybrid intelligent algorithm and a KI-ELM. First, a hybrid intelligent algorithm was established based on the artificial transgender longicorn algorithm and multiple population gray wolf optimization methods to reduce the parameters of hidden layer neurons and then to determine the effective number of hidden layer neurons. The learning efficiency of the algorithm was improved through the reduction of network complexity. Then, to improve the classification accuracy and generalization performance of the algorithm, a deep network structure was introduced to the KI-ELM to gradually extract the original input data layer by layer and realize high-dimensional mapping of data. The experimental results show that the number of network nodes of HI-DKIELM algorithm is obviously reduced, which reduces the network complexity of ELM and greatly improves the learning efficiency of the algorithm. From the regression and classification experiments, its CCPP can be seen that the training error and test error of the HI-DKIELM algorithm proposed in this paper are 0.0417 and 0.0435, which are 0.0103 and 0.0078 lower than the suboptimal algorithm, respectively. On the Boston Housing database, the average and standard deviation of this algorithm are 98.21 and 0.0038, which are 6.2 and 0.0003 higher than the suboptimal algorithm, respectively.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"43 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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