Neural Computing & Applications最新文献_第6页

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

Early detection of COPD patients' symptoms with personal environmental sensors: a remote sensing framework using probabilistic latent component analysis with linear dynamic systems. 使用个人环境传感器早期检测COPD患者症状：使用线性动态系统的概率潜在成分分析的遥感框架。

IF 4.5 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2023-04-30 DOI: 10.1007/s00521-023-08554-5

Şefki Kolozali, Lia Chatzidiakou, Roderic Jones, Jennifer K Quint, Frank Kelly, Benjamin Barratt

{"title":"Early detection of COPD patients' symptoms with personal environmental sensors: a remote sensing framework using probabilistic latent component analysis with linear dynamic systems.","authors":"Şefki Kolozali, Lia Chatzidiakou, Roderic Jones, Jennifer K Quint, Frank Kelly, Benjamin Barratt","doi":"10.1007/s00521-023-08554-5","DOIUrl":"10.1007/s00521-023-08554-5","url":null,"abstract":"In this study, we present a cohort study involving 106 COPD patients using portable environmental sensor nodes with attached air pollution sensors and activity-related sensors, as well as daily symptom records and peak flow measurements to monitor patients' activity and personal exposure to air pollution. This is the first study which attempts to predict COPD symptoms based on personal air pollution exposure. We developed a system that can detect COPD patients' symptoms one day in advance of symptoms appearing. We proposed using the Probabilistic Latent Component Analysis (PLCA) model based on 3-dimensional and 4-dimensional spectral dictionary tensors for personalised and population monitoring, respectively. The model is combined with Linear Dynamic Systems (LDS) to track the patients' symptoms. We compared the performance of PLCA and PLCA-LDS models against Random Forest models in the identification of COPD patients' symptoms, since tree-based classifiers were used for remote monitoring of COPD patients in the literature. We found that there was a significant difference between the classifiers, symptoms and the personalised versus population factors. Our results show that the proposed PLCA-LDS-3D model outperformed the PLCA and the RF models between 4 and 20% on average. When we used only air pollutants as input, the PLCA-LDS-3D forecasting results in personalised and population models were 48.67 and 36.33% accuracy for worsening of lung capacity and 38.67 and 19% accuracy for exacerbation of COPD patients' symptoms, respectively. We have shown that indicators of the quality of an individual's environment, specifically air pollutants, are as good predictors of the worsening of respiratory symptoms in COPD patients as a direct measurement.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 23","pages":"17247-17265"},"PeriodicalIF":4.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10007233","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

An enhanced PSO algorithm to configure a responsive-resilient supply chain network considering environmental issues: a case study of the oxygen concentrator device. 考虑环境问题配置响应弹性供应链网络的增强型粒子群算法:氧气浓缩器设备的案例研究。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07739-8

Soodeh Nasrollah, S Esmaeil Najafi, Hadi Bagherzadeh, Mohsen Rostamy-Malkhalifeh

{"title":"An enhanced PSO algorithm to configure a responsive-resilient supply chain network considering environmental issues: a case study of the oxygen concentrator device.","authors":"Soodeh Nasrollah, S Esmaeil Najafi, Hadi Bagherzadeh, Mohsen Rostamy-Malkhalifeh","doi":"10.1007/s00521-022-07739-8","DOIUrl":"https://doi.org/10.1007/s00521-022-07739-8","url":null,"abstract":"In recent years, the hyper-competitive marketplace has led to a drastic enhancement in the importance of the supply chain problem. Hence, the attention of managers and researchers has been attracted to one of the most crucial problems in the supply chain management area called the supply chain network design problem. In this regard, this research attempts to design an integrated forward and backward logistics network by proposing a multi-objective mathematical model. The suggested model aims at minimizing the environmental impacts and the costs while maximizing the resilience and responsiveness of the supply chain. Since uncertainty is a major issue in the supply chain problem, the present paper studies the research problem under the mixed uncertainty and utilizes the robust possibilistic stochastic method to cope with the uncertainty. On the other side, since configuring a supply chain is known as an NP-Hard problem, this research develops an enhanced particle swarm optimization algorithm to obtain optimal/near-optimal solutions in a reasonable time. Based on the achieved results, the developed algorithm can obtain high-quality solutions (i.e. solutions with zero or a very small gap from the optimal solution) in a reasonable amount of time. The achieved results demonstrate the negative impact of the enhancement of the demand on environmental damages and the total cost. Also, according to the outputs, by increasing the service level, the total cost and environmental impacts have increased by 41% and 10%, respectively. On the other hand, the results show that increasing the disrupted capacity parameters has led to a 17% increase in the total costs and a 7% increase in carbon emissions.Supplementary information: The online version contains supplementary material available at 10.1007/s00521-022-07739-8.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 3","pages":"2647-2678"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10538298","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: 6

Exploring density rectification and domain adaption method for crowd counting. 探索人群计数的密度校正和域自适应方法。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07917-8

Sifan Peng, Baoqun Yin, Qianqian Yang, Qing He, Luyang Wang

{"title":"Exploring density rectification and domain adaption method for crowd counting.","authors":"Sifan Peng, Baoqun Yin, Qianqian Yang, Qing He, Luyang Wang","doi":"10.1007/s00521-022-07917-8","DOIUrl":"https://doi.org/10.1007/s00521-022-07917-8","url":null,"abstract":"Crowd counting has received increasing attention due to its important roles in multiple fields, such as social security, commercial applications, epidemic prevention and control. To this end, we explore two critical issues that seriously affect the performance of crowd counting including nonuniform crowd density distribution and cross-domain problems. Aiming at the nonuniform crowd density distribution issue, we propose a density rectifying network (DRNet) that consists of several dual-layer pyramid fusion modules (DPFM) and a density rectification map (DRmap) auxiliary learning module. The proposed DPFM is embedded into DRNet to integrate multi-scale crowd density features through dual-layer pyramid fusion. The devised DRmap auxiliary learning module further rectifies the incorrect crowd density estimation by adaptively weighting the initial crowd density maps. With respect to the cross-domain issue, we develop a domain adaptation method of randomly cutting mixed dual-domain images, which learns domain-invariance features and decreases the domain gap between the source domain and the target domain from global and local perspectives. Experimental results indicate that the devised DRNet achieves the best mean absolute error (MAE) and competitive mean squared error (MSE) compared with other excellent methods on four benchmark datasets. Additionally, a series of cross-domain experiments are conducted to demonstrate the effectiveness of the proposed domain adaption method. Significantly, when the A and B parts of the Shanghaitech dataset are the source domain and target domain respectively, the proposed domain adaption method decreases the MAE of DRNet by <math><mrow><mn>47.6</mn> <mo>%</mo></mrow> </math> .","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 4","pages":"3551-3569"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9568950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10638062","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

Automatic detection of the mental state in responses towards relaxation. 对放松反应的心理状态的自动检测。

IF 6 3区计算机科学

Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07435-7

Nagore Sagastibeltza, Asier Salazar-Ramirez, Raquel Martinez, Jose Luis Jodra, Javier Muguerza

{"title":"Automatic detection of the mental state in responses towards relaxation.","authors":"Nagore Sagastibeltza, Asier Salazar-Ramirez, Raquel Martinez, Jose Luis Jodra, Javier Muguerza","doi":"10.1007/s00521-022-07435-7","DOIUrl":"https://doi.org/10.1007/s00521-022-07435-7","url":null,"abstract":"Nowadays, considering society's highly demanding lifestyles, it is important to consider the usefulness of relaxation from the perspective of both psychology and clinical practice. The response towards relaxation (RResp) is a mind-body interaction that relaxes the organism or compensates for the physiological effects caused by stress. This work aims to automatically detect the different mental states (relaxation, rest and stress) in which RResps may occur so that complete feedback about the quality of the relaxation can be given to the subject itself, the psychologist or the doctor. To this end, an experiment was conducted to induce both states of stress and relaxation in a sample of 20 university students (average age of <math><mrow><mn>25.76</mn> <mo>±</mo> <mn>3.7</mn></mrow> </math> years old). The electrocardiographic and electrodermal activity signals collected from the participants produced a dataset with 1641 episodes or instances in which the previously mentioned mental states take place. This data was used to extract up to 50 features and train several supervised learning algorithms (rule-based, trees, probabilistic, ensemble classifiers, etc.) using and not using feature selection techniques. Besides, the authors synthesised the cardiac activity information into a single new feature and discretised it down to three levels. The experimentation revealed which features were most discriminating, reaching a classification average accuracy of up to <math><mrow><mn>94.01</mn> <mo>±</mo> <mn>1.73</mn></mrow> </math> % with the 6 most relevant features for the own-collected dataset. Finally, being restrictive, the same solution/subspace was tested with a dataset referenced in the bibliography (WESAD) and scored an average accuracy of <math><mrow><mn>90.36</mn> <mo>±</mo> <mn>1.62</mn></mrow> </math> %.","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 8","pages":"5679-5696"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10868777","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: 2