P_VggNet: A convolutional neural network (CNN) with pixel-based attention map

Liu, Kunhua; Zhong, Peisi; Zheng, Yi; Yang, Kaige; Liu, Mei

doi:10.1371/journal.pone.0208497

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…Also, this combination presented 11.74% loss, the lowest when compared with other cases. Such results align with many previous studies and empirical evidence that evaluated the performance of the classifier by focusing on accuracy and loss ( 49, 50 ). Therefore, this combination was selected for further experimentation with the different scenarios to determine the optimal strategy in developing and applying the deep learning-based classifier.…”

Section: Methodssupporting

confidence: 91%

Extraction of Construction Quality Requirements from Textual Specifications via Natural Language Processing

Jeon

Zhang

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Construction inspection is an essential component of the quality assurance programs of state transportation agencies (STAs), and the guidelines for this process reside in lengthy textual specifications. In the current practice, engineers and inspectors must manually go through these documents to plan, conduct, and document their inspections, which is time-consuming, very subjective, inconsistent, and prone to error. A promising alternative to this manual process is the application of natural language processing (NLP) techniques (e.g., text parsing, sentence classification, and syntactic analysis) to automatically extract construction inspection requirements from textual documents and present them as straightforward check questions. This paper introduces an NLP-based method that: 1) extracts individual sentences from the construction specification; 2) preprocesses the resulting sentences; 3) applies Word2Vec and GloVe algorithms to extract vector features; 4) uses a convolutional neural network (CNN) and recurrent neural network to classify sentences; and 5) converts the requirement sentences into check questions via syntactic analysis. The overall methodology was assessed using the Indiana Department of Transportation (DOT) specification as a test case. Our results revealed that the CNN + GloVe combination led to the highest accuracy, at 91.9%, and the lowest loss, at 11.7%. To further validate its use across STAs nationwide, we applied it to the construction specification of the South Carolina DOT as a test case, and our average accuracy was 92.6%.

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Section: Methodssupporting

confidence: 91%

Extraction of Construction Quality Requirements from Textual Specifications via Natural Language Processing

Jeon

Zhang

et al. 2021

Transportation Research Record: Journal of the Transportation R

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“…The test length of CAT might be slightly higher than that of CAT in previous studies. [68][69][70] The initial question was randomly selected from a subscale similar to previous studies [23][24][25][26][27]30,59] using an item selection strategy. The provisional person measure was estimated using the iterative Newton-Raphson procedure [36,69] after 3 items were answered, avoiding all item answers corresponding to either 0 or 4 as the extreme category in PMHB26, limiting the effective estimation in CAT or MCAT.…”

Section: Mcat Rule and Calculationmentioning

confidence: 99%

“…[20] 1.3. The convolutional neural networks might be helpful Convolutional neural networks (CNNs) have been successfully used in healthcare settings in several forms, [14,[20][21][22][23][24][25][26][27] with their greatest impact being in the field of health informatics. [28] The CNN, a famous deep learning method, can improve the prediction accuracy (up to 7.14%).…”

Section: Many Constructs Surveyed In a Studymentioning

confidence: 99%

An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing

2021

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Background: The classification of a respondent's opinions online into positive and negative classes using a minimal number of questions is gradually changing and helps turn techniques into practices. A survey incorporating convolutional neural networks (CNNs) into web-based computerized adaptive testing (CAT) was used to collect perceptions on My Health Bank (MHB) from users in Taiwan. This study designed an online module to accurately and efficiently turn a respondent's perceptions into positive and negative classes using CNNs and web-based CAT. Methods: In all, 640 patients, family members, and caregivers with ages ranging from 20 to 70 years who were registered MHB users were invited to complete a 3-domain, 26-item, 5-category questionnaire asking about their perceptions on MHB (PMHB26) in 2019. The CNN algorithm and k-means clustering were used for dividing respondents into 2 classes of unsatisfied and satisfied classes and building a PMHB26 predictive model to estimate parameters. Exploratory factor analysis, the Rasch model, and descriptive statistics were used to examine the demographic characteristics and PMHB26 factors that were suitable for use in CNNs and Rasch multidimensional CAT (MCAT). An application was then designed to classify MHB perceptions. Results: We found that 3 construct factors were extracted from PMHB26. The reliability of PMHB26 for each subscale beyond 0.94 was evident based on internal consistency and stability in the data. We further found the following: the accuracy of PMHB26 with CNN yields a higher accuracy rate (0.98) with an area under the curve of 0.98 (95% confidence interval, 0.97–0.99) based on the 391 returned questionnaires; and for the efficiency, approximately one-third of the items were not necessary to answer in reducing the respondents’ burdens using Rasch MCAT. Conclusions: The PMHB26 CNN model, combined with the Rasch online MCAT, is recommended for improving the accuracy and efficiency of classifying patients’ perceptions of MHB utility. An application developed for helping respondents self-assess the MHB cocreation of value can be applied to other surveys in the future.

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“…On the other hand, 2D-CNN layers concentrated on much spatial texture and context. Liu et al [32] extended the CNN model by incorporating the attention mechanism to enhance feature extraction from HSI. More recently, Zhong et al [33] introduced the spectral-spatial residual network (SSRN).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid 3D–2D Feature Hierarchy CNN with Focal Loss for Hyperspectral Image Classification

Wen,

Yu,

Wang

et al. 2023

Remote Sensing

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Hyperspectral image (HSI) classification has been extensively applied for analyzing remotely sensed images. HSI data consist of multiple bands that provide abundant spatial information. Convolutional neural networks (CNNs) have emerged as powerful deep learning methods for processing visual data. In recent work, CNNs have shown impressive results in HSI classification. In this paper, we propose a hierarchical neural network architecture called feature extraction with hybrid spectral CNN (FE-HybridSN) to extract superior spectral–spatial features. FE-HybridSN effectively captures more spectral–spatial information while reducing computational complexity. Considering the prevalent issue of class imbalance in experimental datasets (IP, UP, SV) and real-world hyperspectral datasets, we apply the focal loss to mitigate these problems. The focal loss reconstructs the loss function and facilitates effective achievement of the aforementioned goals. We propose a framework (FEHN-FL) that combines FE-HybridSN and the focal loss for HSI classification and then conduct extensive HSI classification experiments using three remote sensing datasets: Indian Pines (IP), University of Pavia (UP), and Salinas Scene (SV). Using cross-entropy loss as a baseline, we assess the hyperspectral classification performance of various backbone networks and examine the influence of different spatial sizes on classification accuracy. After incorporating focal loss as our loss function, we not only compare the classification performance of the FE-HybridSN backbone network under different loss functions but also evaluate their convergence rates during training. The proposed classification framework demonstrates satisfactory performance compared to state-of-the-art end-to-end deep-learning-based methods, such as 2D-CNN, 3D-CNN, etc.

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P_VggNet: A convolutional neural network (CNN) with pixel-based attention map

Cited by 6 publications

References 20 publications

Extraction of Construction Quality Requirements from Textual Specifications via Natural Language Processing

Extraction of Construction Quality Requirements from Textual Specifications via Natural Language Processing

An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing

A Hybrid 3D–2D Feature Hierarchy CNN with Focal Loss for Hyperspectral Image Classification

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