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Zhou H, Yin L, Su R, Zhang Y, Yuan Y, Xie P, Li X. STCGRU: A hybrid model based on CNN and BiGRU for mild cognitive impairment diagnosis. Comput Methods Programs Biomed 2024; 248:108123. [PMID: 38471292 DOI: 10.1016/j.cmpb.2024.108123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/28/2023] [Accepted: 03/07/2024] [Indexed: 03/14/2024]
Abstract
BACKGROUND AND OBJECTIVE Early diagnosis of mild cognitive impairment (MCI) is one of the essential measures to prevent its further development into Alzheimer's disease (AD). In this paper, we propose a hybrid deep learning model for early diagnosis of MCI, called spatio-temporal convolutional gated recurrent unit network (STCGRU). METHODS The STCGRU comprises three bespoke convolutional neural network (CNN) modules and a bi-directional gated recurrent unit (BiGRU) module, which can effectively extract the spatial and temporal features of EEG and obtain excellent diagnostic results. We use a publicly available EEG dataset that has not undergone pre-processing to verify the robustness and accuracy of the model. Ablation experiments on STCGRU are conducted to showcase the individual performance improvement of each module. RESULTS Compared with other state-of-the-art approaches using the same publicly available EEG dataset, the results show that STCGRU is more suitable for early diagnosis of MCI. After 10-fold cross-validation, the average classification accuracy of the hybrid model reached 99.95 %, while the average kappa value reached 0.9989. CONCLUSIONS The experimental results show that the hybrid model proposed in this paper can directly extract compelling spatio-temporal features from the raw EEG data for classification. The STCGRU allows for accurate diagnosis of patients with MCI and has a high practical value.
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Affiliation(s)
- Hao Zhou
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Liyong Yin
- The First Hospital of Qinhuangdao, Qinhuangdao, PR China
| | - Rui Su
- Hebei Medical University, Shijiazhuang, PR China
| | - Ying Zhang
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Yi Yuan
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Ping Xie
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Xin Li
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China.
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Wang X, Wang M, Liu X, Mao Y, Chen Y, Dai S. Surveillance-image-based outdoor air quality monitoring. Environ Sci Ecotechnol 2024; 18:100319. [PMID: 37841651 PMCID: PMC10569950 DOI: 10.1016/j.ese.2023.100319] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 10/17/2023]
Abstract
Air pollution threatens human health, necessitating effective and convenient air quality monitoring. Recently, there has been a growing interest in using camera images for air quality estimation. However, a major challenge has been nighttime detection due to the limited visibility of nighttime images. Here we present a hybrid deep learning model, capitalizing on the temporal continuity of air quality changes for estimating outdoor air quality from surveillance images. Our model, which integrates a convolutional neural network (CNN) and long short-term memory (LSTM), adeptly captures spatial-temporal image features, enabling air quality estimation at any time of day, including PM2.5 and PM10 concentrations, as well as the air quality index (AQI). Compared to independent CNN networks that solely extract spatial features, our model demonstrates superior accuracy on self-constructed datasets with R2 = 0.94 and RMSE = 5.11 μg m-3 for PM2.5, R2 = 0.92 and RMSE = 7.30 μg m-3 for PM10, and R2 = 0.94 and RMSE = 5.38 for AQI. Furthermore, our model excels in daytime air quality estimation and enhances nighttime predictions, elevating overall accuracy. Validation across diverse image datasets and comparative analyses underscore the applicability and superiority of our model, reaffirming its applicability and superiority for air quality monitoring.
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Affiliation(s)
- Xiaochu Wang
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
- Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China
| | - Meizhen Wang
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
- Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China
| | - Xuejun Liu
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
- Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China
| | - Ying Mao
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
- Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China
| | - Yang Chen
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
- Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China
| | - Songsong Dai
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
- Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, 210023, China
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Yenurkar G, Mal S. Future forecasting prediction of Covid-19 using hybrid deep learning algorithm. Multimed Tools Appl 2022; 82:22497-22523. [PMID: 36415331 PMCID: PMC9672606 DOI: 10.1007/s11042-022-14219-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/27/2022] [Accepted: 11/01/2022] [Indexed: 06/01/2023]
Abstract
Due the quick spread of coronavirus disease 2019 (COVID-19), identification of that disease, prediction of mortality rate and recovery rate are considered as one of the critical challenges in the whole world. The occurrence of COVID-19 dissemination beyond the world is analyzed in this research and an artificial-intelligence (AI) based deep learning algorithm is suggested to detect positive cases of COVID19 patients, mortality rate and recovery rate using real-world datasets. Initially, the unwanted data like prepositions, links, hashtags etc., are removed using some pre-processing techniques. After that, term frequency inverse-term frequency (TF-IDF) andBag of Words (BoW) techniques are utilized to extract the features from pre-processed dataset. Then, Mayfly Optimization (MO) algorithm is performed to pick the relevant features from the set of features. Finally, two deep learning procedures, ResNet model and GoogleNet model, are hybridized to achieve the prediction process. Our system examines two different kinds of publicly available text datasets to identify COVID-19 disease as well as to predict mortality rate and recovery rate using those datasets. There are four different datasets are taken to analyse the performance, in which the proposed method achieves 97.56% accuracy which is 1.40% greater than Linear Regression (LR) and Multinomial Naive Bayesian (MNB), 3.39% higher than Random Forest (RF) and Stochastic gradient boosting (SGB) as well as 5.32% higher than Decision tree (DT) and Bagging techniques if first dataset. When compared to existing machine learning models, the simulation result indicates that a proposed hybrid deep learning method is valuable in corona virus identification and future mortality forecast study.
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Affiliation(s)
- Ganesh Yenurkar
- School of Computing Science & Engineering, VIT Bhopal University, Bhopal, India
- Yeshwantrao Chavan College of Engineering, Wanadongri, Nagpur, India
| | - Sandip Mal
- School of Computing Science & Engineering, VIT Bhopal University, Bhopal, India
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