Neural Computing & Applications最新文献_第5页

A flexible framework for anomaly Detection via dimensionality reduction. 通过降维实现异常检测的灵活框架。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-021-05839-5

Alireza Vafaei Sadr, Bruce A Bassett, M Kunz

引用次数: 6

Lightweight ResGRU: a deep learning-based prediction of SARS-CoV-2 (COVID-19) and its severity classification using multimodal chest radiography images. 轻量级ResGRU:基于深度学习的基于多模态胸片图像的SARS-CoV-2 (COVID-19)预测及其严重程度分类

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-023-08200-0

Mughees Ahmad, Usama Ijaz Bajwa, Yasar Mehmood, Muhammad Waqas Anwar

{"title":"Lightweight ResGRU: a deep learning-based prediction of SARS-CoV-2 (COVID-19) and its severity classification using multimodal chest radiography images.","authors":"Mughees Ahmad, Usama Ijaz Bajwa, Yasar Mehmood, Muhammad Waqas Anwar","doi":"10.1007/s00521-023-08200-0","DOIUrl":"https://doi.org/10.1007/s00521-023-08200-0","url":null,"abstract":"The new COVID-19 emerged in a town in China named Wuhan in December 2019, and since then, this deadly virus has infected 324 million people worldwide and caused 5.53 million deaths by January 2022. Because of the rapid spread of this pandemic, different countries are facing the problem of a shortage of resources, such as medical test kits and ventilators, as the number of cases increased uncontrollably. Therefore, developing a readily available, low-priced, and automated approach for COVID-19 identification is the need of the hour. The proposed study uses chest radiography images (CRIs) such as X-rays and computed tomography (CTs) to detect chest infections, as these modalities contain important information about chest infections. This research introduces a novel hybrid deep learning model named Lightweight ResGRU that uses residual blocks and a bidirectional gated recurrent unit to diagnose non-COVID and COVID-19 infections using pre-processed CRIs. Lightweight ResGRU is used for multi-modal two-class classification (normal and COVID-19), three-class classification (normal, COVID-19, and viral pneumonia), four-class classification (normal, COVID-19, viral pneumonia, and bacterial pneumonia), and COVID-19 severity types' classification (i.e., atypical appearance, indeterminate appearance, typical appearance, and negative for pneumonia). The proposed architecture achieved f-measure of 99.0%, 98.4%, 91.0%, and 80.5% for two-class, three-class, four-class, and COVID-19 severity level classifications, respectively, on unseen data. A large dataset is created by combining and changing different publicly available datasets. The results prove that radiologists can adopt this method to screen chest infections where test kits are limited.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 13","pages":"9637-9655"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9873217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9330135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Ensemble filters with harmonize PSO-SVM algorithm for optimal hearing disorder prediction. 基于协调PSO-SVM算法的集成滤波器优化听力障碍预测。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-023-08244-2

Tengku Mazlin Tengku Ab Hamid, Roselina Sallehuddin, Zuriahati Mohd Yunos, Aida Ali

{"title":"Ensemble filters with harmonize PSO-SVM algorithm for optimal hearing disorder prediction.","authors":"Tengku Mazlin Tengku Ab Hamid, Roselina Sallehuddin, Zuriahati Mohd Yunos, Aida Ali","doi":"10.1007/s00521-023-08244-2","DOIUrl":"https://doi.org/10.1007/s00521-023-08244-2","url":null,"abstract":"Discovering a hearing disorder at an earlier intervention is critical for reducing the effects of hearing loss and the approaches to increase the remaining hearing ability can be implemented to achieve the successful development of human communication. Recently, the explosive dataset features have increased the complexity for audiologists to decide the proper treatment for the patient. In most cases, data with irrelevant features and improper classifier parameters causes a crucial influence on the audiometry system in terms of accuracy. This is due to the dependent processes of these two, where the classification accuracy performance could be worsened if both processes are conducted independently. Although the filter algorithm is capable of eliminating irrelevant features, it still lacks the ability to consider feature reliance and results in a poor selection of significant features. Improper kernel parameter settings may also contribute to poor accuracy performance. In this paper, an ensemble filters feature selection based on Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF) with harmonize optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented to mitigate these problems. Ensemble filters are utilized so that the initial top dominant features relevant for classification can be considered. Then, PSO and SVM are optimized simultaneously to achieve the optimal solution. The results on a standard Audiology dataset show that the proposed method produces 96.50% accuracy with optimal solution compared to classical SVM, which signifies the proposed method is effective in handling high dimensional data for hearing disorder prediction.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 14","pages":"10473-10496"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9372460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation. 基于多阈值图像分割的新冠肺炎诊断Harris hawks优化。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-08078-4

Mohammad Hashem Ryalat, Osama Dorgham, Sara Tedmori, Zainab Al-Rahamneh, Nijad Al-Najdawi, Seyedali Mirjalili

{"title":"Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation.","authors":"Mohammad Hashem Ryalat, Osama Dorgham, Sara Tedmori, Zainab Al-Rahamneh, Nijad Al-Najdawi, Seyedali Mirjalili","doi":"10.1007/s00521-022-08078-4","DOIUrl":"https://doi.org/10.1007/s00521-022-08078-4","url":null,"abstract":"Digital image processing techniques and algorithms have become a great tool to support medical experts in identifying, studying, diagnosing certain diseases. Image segmentation methods are of the most widely used techniques in this area simplifying image representation and analysis. During the last few decades, many approaches have been proposed for image segmentation, among which multilevel thresholding methods have shown better results than most other methods. Traditional statistical approaches such as the Otsu and the Kapur methods are the standard benchmark algorithms for automatic image thresholding. Such algorithms provide optimal results, yet they suffer from high computational costs when multilevel thresholding is required, which is considered as an optimization matter. In this work, the Harris hawks optimization technique is combined with Otsu's method to effectively reduce the required computational cost while maintaining optimal outcomes. The proposed approach is tested on a publicly available imaging datasets, including chest images with clinical and genomic correlates, and represents a rural COVID-19-positive (COVID-19-AR) population. According to various performance measures, the proposed approach can achieve a substantial decrease in the computational cost and the time to converge while maintaining a level of quality highly competitive with the Otsu method for the same threshold values.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 9","pages":"6855-6873"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9376951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images. 利用更快R-CNN和屏蔽R-CNN对CT图像进行检测和分类。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-023-08450-y

M Emin Sahin, Hasan Ulutas, Esra Yuce, Mustafa Fatih Erkoc

{"title":"Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images.","authors":"M Emin Sahin, Hasan Ulutas, Esra Yuce, Mustafa Fatih Erkoc","doi":"10.1007/s00521-023-08450-y","DOIUrl":"https://doi.org/10.1007/s00521-023-08450-y","url":null,"abstract":"The coronavirus (COVID-19) pandemic has a devastating impact on people's daily lives and healthcare systems. The rapid spread of this virus should be stopped by early detection of infected patients through efficient screening. Artificial intelligence techniques are used for accurate disease detection in computed tomography (CT) images. This article aims to develop a process that can accurately diagnose COVID-19 using deep learning techniques on CT images. Using CT images collected from Yozgat Bozok University, the presented method begins with the creation of an original dataset, which includes 4000 CT images. The faster R-CNN and mask R-CNN methods are presented for this purpose in order to train and test the dataset to categorize patients with COVID-19 and pneumonia infections. In this study, the results are compared using VGG-16 for faster R-CNN model and ResNet-50 and ResNet-101 backbones for mask R-CNN. The faster R-CNN model used in the study has an accuracy rate of 93.86%, and the ROI (region of interest) classification loss is 0.061 per ROI. At the conclusion of the final training, the mask R-CNN model generates mAP (mean average precision) values for ResNet-50 and ResNet-101, respectively, of 97.72% and 95.65%. The results for five folds are obtained by applying the cross-validation to the methods used. With training, our model performs better than the industry standard baselines and can help with automated COVID-19 severity quantification in CT images.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 18","pages":"13597-13611"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014413/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9502282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Linguistic methods in healthcare application and COVID-19 variants classification. 医疗保健应用中的语言方法和新冠肺炎变异分类。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2021-07-06 DOI: 10.1007/s00521-021-06286-y

Marek R Ogiela, Urszula Ogiela

引用次数: 5

Prediction of PM2.5 time series by seasonal trend decomposition-based dendritic neuron model. 基于季节趋势分解的树枝状神经元模型预测PM2.5时间序列。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2023-04-11 DOI: 10.1007/s00521-023-08513-0

Zijing Yuan, Shangce Gao, Yirui Wang, Jiayi Li, Chunzhi Hou, Lijun Guo

{"title":"Prediction of PM2.5 time series by seasonal trend decomposition-based dendritic neuron model.","authors":"Zijing Yuan, Shangce Gao, Yirui Wang, Jiayi Li, Chunzhi Hou, Lijun Guo","doi":"10.1007/s00521-023-08513-0","DOIUrl":"10.1007/s00521-023-08513-0","url":null,"abstract":"The rapid industrial development in the human society has brought about the air pollution, which seriously affects human health. PM2.5 concentration is one of the main factors causing the air pollution. To accurately predict PM2.5 microns, we propose a dendritic neuron model (DNM) trained by an improved state-of-matter heuristic algorithm (DSMS) based on STL-LOESS, namely DS-DNM. Firstly, DS-DNM adopts STL-LOESS for the data preprocessing to obtain three characteristic quantities from original data: seasonal, trend, and residual components. Then, DNM trained by DSMS predicts the residual values. Finally, three sets of feature quantities are summed to obtain the predicted values. In the performance test experiments, five real-world PM2.5 concentration data are used to test DS-DNM. On the other hand, four training algorithms and seven prediction models were selected for comparison to verify the rationality of the training algorithms and the accuracy of the prediction models, respectively. The experimental results show that DS-DNM has the more competitive performance in PM2.5 concentration prediction problem.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 21","pages":"15397-15413"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10107594/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9570243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Academic performance warning system based on data driven for higher education. 基于数据驱动的高等教育学习成绩预警系统。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07997-6

Hanh Thi-Hong Duong, Linh Thi-My Tran, Huy Quoc To, Kiet Van Nguyen

{"title":"Academic performance warning system based on data driven for higher education.","authors":"Hanh Thi-Hong Duong, Linh Thi-My Tran, Huy Quoc To, Kiet Van Nguyen","doi":"10.1007/s00521-022-07997-6","DOIUrl":"https://doi.org/10.1007/s00521-022-07997-6","url":null,"abstract":"Academic probation at universities has become a matter of pressing concern in recent years, as many students face severe consequences of academic probation. We carried out research to find solutions to decrease the situation mentioned above. Our research used the power of massive data sources from the education sector and the modernity of machine learning techniques to build an academic warning system. Our system is based on academic performance that directly reflects students' academic probation status at the university. Through the research process, we provided a dataset that has been extracted and developed from raw data sources, including a wealth of information about students, subjects, and scores. We build a dataset with many features that are extremely useful in predicting students' academic warning status via feature generation techniques and feature selection strategies. Remarkably, the dataset contributed is flexible and scalable because we provided detailed calculation formulas that its materials are found in any university or college in Vietnam. That allows any university to reuse or reconstruct another similar dataset based on their raw academic database. Moreover, we variously combined data, unbalanced data handling techniques, model selection techniques, and research to propose suitable machine learning algorithms to build the best possible warning system. As a result, a two-stage academic performance warning system for higher education was proposed, with the F2-score measure of more than 74% at the beginning of the semester using the algorithm Support Vector Machine and more than 92% before the final examination using the algorithm LightGBM.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 8","pages":"5819-5837"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10815475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Internet of things-enabled real-time health monitoring system using deep learning. 物联网实现了使用深度学习的实时健康监测系统。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2021-09-15 DOI: 10.1007/s00521-021-06440-6

Xingdong Wu, Chao Liu, Lijun Wang, Muhammad Bilal

{"title":"Internet of things-enabled real-time health monitoring system using deep learning.","authors":"Xingdong Wu, Chao Liu, Lijun Wang, Muhammad Bilal","doi":"10.1007/s00521-021-06440-6","DOIUrl":"10.1007/s00521-021-06440-6","url":null,"abstract":"Smart healthcare monitoring systems are proliferating due to the Internet of Things (IoT)-enabled portable medical devices. The IoT and deep learning in the healthcare sector prevent diseases by evolving healthcare from face-to-face consultation to telemedicine. To protect athletes' life from life-threatening severe conditions and injuries in training and competitions, real-time monitoring of physiological indicators is critical. In this research work, we present a deep learning-based IoT-enabled real-time health monitoring system. The proposed system uses wearable medical devices to measure vital signs and apply various deep learning algorithms to extract valuable information. For this purpose, we have taken Sanda athletes as our case study. The deep learning algorithms help physicians properly analyze these athletes' conditions and offer the proper medications to them, even if the doctors are away. The performance of the proposed system is extensively evaluated using a cross-validation test by considering various statistical-based performance measurement metrics. The proposed system is considered an effective tool that diagnoses dreadful diseases among the athletes, such as brain tumors, heart disease, cancer, etc. The performance results of the proposed system are evaluated in terms of precision, recall, AUC, and F1, respectively.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 20","pages":"14565-14576"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8442525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9572895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

An automatic improved facial expression recognition for masked faces. 一种用于蒙面人脸的自动改进的面部表情识别。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2023-04-01 DOI: 10.1007/s00521-023-08498-w

Yasmeen ELsayed, Ashraf ELSayed, Mohamed A Abdou

引用次数: 3