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  • Hindawi Limited  (5)
  • 1
    Online Resource
    Online Resource
    Hindawi Limited ; 2020
    In:  Computational Intelligence and Neuroscience Vol. 2020 ( 2020-11-28), p. 1-10
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2020 ( 2020-11-28), p. 1-10
    Abstract: In view of the fact that the detection of driver’s distraction is a burning issue, this study chooses the driver’s head pose as the evaluation parameter for driving distraction and proposes a driver distraction method based on the head pose. The effects of single regression and classification combined with regression are compared in terms of accuracy, and four kinds of classical networks are improved and trained using 300W-LP and AFLW datasets. The HPE_Resnet50 with the best accuracy is selected as the head pose estimator and applied to the ten-category distracted driving dataset SF3D to obtain 20,000 sets of head pose data. The differences between classes are discussed qualitatively and quantitatively. The analysis of variance shows that there is a statistically significant difference in head posture between safe driving and all kinds of distracted driving at 95% and 90% confidence levels, and the postures of all kinds of driving movements are distributed in a specific Euler angle range, which provides a characteristic basis for the design of subsequent recognition methods. In addition, according to the continuity of human movement, this paper also selects 90 drivers’ videos to analyze the difference in head pose between safe driving and distracted driving frame by frame. By calculating the spatial distance and sample statistics, the results provide the reference point, spatial range, and threshold of safe driving under this driving condition. Experimental results show that the average error of HPE_Resnet50 in AFLW2000 is 6.17° and that there is an average difference of 12.4° to 54.9° in the Euler angle between safe driving and nine kinds of distracted driving on SF3D.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2020
    detail.hit.zdb_id: 2388208-6
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  • 2
    In: BioMed Research International, Hindawi Limited, Vol. 2020 ( 2020-03-23), p. 1-14
    Abstract: Background . Cervical cancer (CC) is one of the most common female malignant tumors. And cervical intraepithelial neoplasia (CIN) is the precancerous lesion of CC, which can progress to invasive CC. MicroRNAs (miRNAs) have been found to be potential diagnostic biomarkers for CIN or CC. However, recently, the lack of sufficient studies about the diagnostic value of miRNAs for CIN made it challenging to separately investigate the diagnostic efficacy of miRNAs for CIN. Likewise, the conclusions among those studies were discordant. Therefore, we conducted this meta-analysis, aimed at evaluating the diagnostic efficacy of miRNAs for CIN and CC patients. Methods . Literature search was performed in PubMed, Embase, and Web of Science databases. Pooled sensitivity, specificity, and other diagnostic parameters were calculated through Stata 14.0 software. Furthermore, subgroup analyses and metaregression analysis were conducted to explore the main sources of heterogeneity. Results . Ten articles covering 50 studies were eligible, which included 5,908 patients and 4,819 healthy individuals. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were 0.81 (95% CI, 0.77-0.85), 0.86 (95% CI, 0.83-0.89), 5.9 (95% CI, 4.5-7.7), 0.22 (95% CI, 0.17-0.28), 27 (95% CI, 17-44), and 0.91 (95% CI, 0.88-0.93), respectively. Additionally, the ethnicity and internal reference were the main sources of heterogeneity. Conclusions . Circulating miRNAs can be a promising noninvasive diagnostic biomarker for CIN and early CC, especially miR-9 and miR-205, which need to be verified by large-scale studies.
    Type of Medium: Online Resource
    ISSN: 2314-6133 , 2314-6141
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2020
    detail.hit.zdb_id: 2698540-8
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  • 3
    In: Contrast Media & Molecular Imaging, Hindawi Limited, Vol. 2021 ( 2021-11-24), p. 1-8
    Abstract: Anaphylaxis has rapidly spread around the world in the last several decades. Environmental factors seem to play a major role, and epigenetic marks, especially DNA methylation, get more attention. We discussed several GEO opening data classifications with TOP 100 specific methylation region values (normalized M-values on line) by machine learning, which are remarkable to classify specific anaphylaxis after monoallergen exposure. Then, we sequenced the whole-genome DNA methylation of six people (3 wormwood monoallergen atopic rhinitis patients and 3 normal-immune people) during the pollen season and analyzed the difference of the single nucleotide and DNA region. The results’ divergences were obvious (the differential single nucleotides were mostly distributed in nongene regions but the differential DNA regions of GWAS, on the other hand), which may have caused most single nucleotides to be concealed in the regions’ sequences. Therefore, we suggest that we should conduct more “pragmatic” and directly find special single-nucleotide changes after exposure to atopic allergens instead of complex correlativity. It is possible to try to use DNA methylation marks to accurately diagnose anaphylaxis and form a machine learning classification based on the single methylated CpGs.
    Type of Medium: Online Resource
    ISSN: 1555-4317 , 1555-4309
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2222967-X
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  • 4
    Online Resource
    Online Resource
    Hindawi Limited ; 2020
    In:  Computational Intelligence and Neuroscience Vol. 2020 ( 2020-11-18), p. 1-11
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2020 ( 2020-11-18), p. 1-11
    Abstract: With a focus on fatigue driving detection research, a fully automated driver fatigue status detection algorithm using driving images is proposed. In the proposed algorithm, the multitask cascaded convolutional network (MTCNN) architecture is employed in face detection and feature point location, and the region of interest (ROI) is extracted using feature points. A convolutional neural network, named EM-CNN, is proposed to detect the states of the eyes and mouth from the ROI images. The percentage of eyelid closure over the pupil over time (PERCLOS) and mouth opening degree (POM) are two parameters used for fatigue detection. Experimental results demonstrate that the proposed EM-CNN can efficiently detect driver fatigue status using driving images. The proposed algorithm EM-CNN outperforms other CNN-based methods, i.e., AlexNet, VGG-16, GoogLeNet, and ResNet50, showing accuracy and sensitivity rates of 93.623% and 93.643%, respectively.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2020
    detail.hit.zdb_id: 2388208-6
    Location Call Number Limitation Availability
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  • 5
    Online Resource
    Online Resource
    Hindawi Limited ; 2020
    In:  Computational Intelligence and Neuroscience Vol. 2020 ( 2020-12-15), p. 1-12
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2020 ( 2020-12-15), p. 1-12
    Abstract: It is necessary to improve the performance of the object detection algorithm in resource-constrained embedded devices by lightweight improvement. In order to further improve the recognition accuracy of the algorithm for small target objects, this paper integrates 5 × 5 deep detachable convolution kernel on the basis of MobileNetV2-SSDLite model, extracts features of two special convolutional layers in addition to detecting the target, and designs a new lightweight object detection network—Lightweight Microscopic Detection Network (LMS-DN). The network can be implemented on embedded devices such as NVIDIA Jetson TX2. The experimental results show that LMS-DN only needs fewer parameters and calculation costs to obtain higher identification accuracy and stronger anti-interference than other popular object detection models.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2020
    detail.hit.zdb_id: 2388208-6
    Location Call Number Limitation Availability
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