International Journal of Computational Intelligence Systems最新文献_第2页

Developing a Novel Long Short-Term Memory Networks with Seasonal Wavelet Transform for Long-Term Wind Power Output Forecasting 利用季节小波变换开发用于长期风电输出预测的新型长短期记忆网络

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-11-30 DOI: 10.1007/s44196-023-00371-x

Kuen-Suan Chen, Tinglong. Lin, Kuo-Ping Lin, Ping-Teng Chang, Yu-Chen Wang

引用次数: 0

Meta-analysis of Artificial Intelligence-Assisted Pathology for the Detection of Early Cervical Cancer 人工智能辅助病理学检测早期宫颈癌的元分析

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-11-27 DOI: 10.1007/s44196-023-00367-7

Di Qin, Chunmei Zhang, Huan Zhou, Xiaohui Yin, Geng Rong, Shixian Zhou, Mingming Wang, Zhigang Pei

引用次数: 0

Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data XGBoost_BiLSTM 模型的预测能力：利用电子健康数据准确检测睡眠呼吸暂停的机器学习方法

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-11-27 DOI: 10.1007/s44196-023-00362-y

Ashir Javeed, Johan Sanmartin Berglund, A. Dallora, Muhammad Asim Saleem, P. Anderberg

引用次数: 0

Dual Siamese Anchor Points Adaptive Tracker with Transformer for RGBT Tracking 用于 RGBT 跟踪的带变压器的双连体锚点自适应跟踪器

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-11-22 DOI: 10.1007/s44196-023-00360-0

Liangsong Fan, Pyeoungkee Kim

引用次数: 0

A Novel Distance Measure and CRADIS Method in Picture Fuzzy Environment 图片模糊环境中的新型距离测量和 CRADIS 方法

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-11-22 DOI: 10.1007/s44196-023-00354-y

Jiaqi Yuan, Zichun Chen, Miaofeng Wu

引用次数: 0

APT Attack Detection Based on Graph Convolutional Neural Networks 基于图卷积神经网络的 APT 攻击检测

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-11-20 DOI: 10.1007/s44196-023-00369-5

Weiwu Ren, Xintong Song, Yu Hong, Ying Lei, Jinyu Yao, Yazhou Du, Wenjuan Li

引用次数: 0

Research on the Prediction of Tire Radial Load Based on 1D CNN and BiGRU 基于一维 CNN 和 BiGRU 的轮胎径向载荷预测研究

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-11-20 DOI: 10.1007/s44196-023-00357-9

Yuanjin Ji, Junwei Zeng, L. Ren

引用次数: 0

Lexical Normalization Using Generative Transformer Model (LN-GTM) 基于生成转换模型的词法归一化

4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-11-14 DOI: 10.1007/s44196-023-00366-8

Mohamed Ashmawy, Mohamed Waleed Fakhr, Fahima A. Maghraby

{"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}

引用次数: 0

A New Image Segmentation Method Based on the YOLO5 and Fully Connected CRF 基于YOLO5和全连通CRF的图像分割新方法

4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-11-14 DOI: 10.1007/s44196-023-00365-9

Jian Huang, Guangpeng Zhang, Li juan Ren, Nina Wang

引用次数: 0

A Novel Deep Kernel Incremental Extreme Learning Machine Based on Artificial Transgender Longicorn Algorithm and Multiple Population Gray Wolf Optimization Methods 基于人工跨性别Longicorn算法和多种群灰狼优化方法的深度核增量极限学习机

4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-11-14 DOI: 10.1007/s44196-023-00323-5

Di Wu, Yan Xiao

{"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}

引用次数: 0