Neural Architecture Search for 1D CNNs—Different Approaches Tests and Measurements

Cordeiro, João Rala; Raimundo, Antonio; Postolache, Octavian; Sebastião, Pedro

doi:10.3390/s21237990

Cited by 35 publications

(20 citation statements)

References 33 publications

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“…In this set of experiments, the proposed SSFE was compared with RF, SVM, KNN, Adaboost [47], LeNet-5 [48], 1DCNN [49], and 2DCNN [50]. Table 5 shows the accuracies related to the OA and Kappa for different methods for the Pavia University data set while maintaining the minimum standard deviation.…”

Section: B Classification Results and Analysismentioning

confidence: 99%

Spectral-Spatial Features Extraction of Hyperspectral Remote Sensing Oil Spill Imagery Based on Convolutional Neural Networks

et al. 2022

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Marine oil spills accidents has caused a large amount of crude oil to leak into the marine environment and threaten marine ecological environment. Hyperspectral remote sensing images (HRSI) technology can quickly and accurately identify oil film of different thickness on marine surface. In order to overcome the traditional spectrum analysis method and space extraction method of long time sampling, calculation, analysis and other shortcomings. On account of the advantages of the spectral and spatial information in the field of HRSI classification, a spectral-spatial features extraction (SSFE) method based convolutional neural networks (CNNs) was proposed to analyse oil spills. In this way, one and two dimensional models based on convolutional neural networks (1D-CNN,2D-CNN) have been introduced as the spectral and spatial features extractor. When extracting spatial features, double-two convolution layers are connected to increasing nonlinearity and reduce the number of parameters. Furthermore, in order to address overfitting and imbalance samples, L2 regularization, class_weight and drouput is added to classes data modeling. More importantly, principal component analysis (PCA) is applied to data dimension reduction, 1D-CNN and 2D-CNN is combined into a unified model further extract the joint spatial-spectral features. To evaluate the effectiveness of the proposed approach, three hyperspectral datasets were utilized, including: University of Pavia dataset, oil spill area 1, oil spill area 2. Experimental results reveal that the proposed method have a very satisfactory performance and better distinguish oil spills.INDEX TERMS Marine oil spills, hyperspectral remote sensing images (HRSI), convolutional neural networks (CNNs), spectral-spatial features extraction (SSFE).

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Section: B Classification Results and Analysismentioning

confidence: 99%

Spectral-Spatial Features Extraction of Hyperspectral Remote Sensing Oil Spill Imagery Based on Convolutional Neural Networks

et al. 2022

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“…Strid [ 36 ]: The strid value defines how the kernel moves in the input data. The most common value is 1, meaning that the kernel moves over one column of the input data at each iteration.…”

Section: Methodsmentioning

confidence: 99%

“…The aim of this layer is to decrease the size of the convolved features map to reduce computational costs. There are several types of pooling operations (max pooling, average pooling, sum pooling) [ 36 ]. In this work, we used 1D max pooling, which consists of running the input with a defined spatial neighborhood or specified pool size and strid, taking the maximum value from the considered region.…”

Section: Methodsmentioning

confidence: 99%

The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN)

Guessoum

Belda

Ferrándiz

et al. 2022

Sensors

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Accurate Earth orientation parameter (EOP) predictions are needed for many applications, e.g., for the tracking and navigation of interplanetary spacecraft missions. One of the most difficult parameters to forecast is the length of day (LOD), which represents the variation in the Earth’s rotation rate since it is primarily affected by the torques associated with changes in atmospheric circulation. In this study, a new-generation time-series prediction algorithm is developed. The one-dimensional convolutional neural network (1D CNN), which is one of the deep learning methods, is introduced to model and predict the LOD using the IERS EOP 14 C04 and axial Z component of the atmospheric angular momentum (AAM), which was taken from the German Research Centre for Geosciences (GFZ) since it is strongly correlated with the LOD changes. The prediction procedure operates as follows: first, we detrend the LOD and Z-component series using the LS method, then, we obtain the residual series of each one to be used in the 1D CNN prediction algorithm. Finally, we analyze the results before and after introducing the AAM function. The results prove the potential of the proposed method as an optimal algorithm to successfully reconstruct and predict the LOD for up to 7 days.

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“…The learning rate was set to 0.0001, and the number of iterations was set to 200. After the model was trained, the test set samples were input into the model for prediction [26]. The area under ROC curve (AUC) was used to evaluate the predictive discriminability of the model [27].…”

Section: D-cnn Model Building and Trainingmentioning

confidence: 99%

Deep learning-based multifeature integration robustly predicts central lymph node metastasis in papillary thyroid cancer

Wang

Chen

et al. 2023

BMC Cancer

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Background Few highly accurate tests can diagnose central lymph node metastasis (CLNM) of papillary thyroid cancer (PTC). Genetic sequencing of tumor tissue has allowed the targeting of certain genetic variants for personalized cancer therapy development. Methods This study included 488 patients diagnosed with PTC by ultrasound-guided fine-needle aspiration biopsy, collected clinicopathological data, analyzed the correlation between CLNM and clinicopathological features using univariate analysis and binary logistic regression, and constructed prediction models. Results Binary logistic regression analysis showed that age, maximum diameter of thyroid nodules, capsular invasion, and BRAF V600E gene mutation were independent risk factors for CLNM, and statistically significant indicators were included to construct a nomogram prediction model, which had an area under the curve (AUC) of 0.778. A convolutional neural network (CNN) prediction model built with an artificial intelligence (AI) deep learning algorithm achieved AUCs of 0.89 in the training set and 0.78 in the test set, which indicated a high prediction efficacy for CLNM. In addition, the prediction models were validated in the subclinical metastasis and clinical metastasis groups with high sensitivity and specificity, suggesting the broad applicability of the models. Furthermore, CNN prediction models were constructed for patients with nodule diameters less than 1 cm. The AUCs in the training set and test set were 0.87 and 0.76, respectively, indicating high prediction efficacy. Conclusions The deep learning-based multifeature integration prediction model provides a reference for the clinical diagnosis and treatment of PTC.

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Neural Architecture Search for 1D CNNs—Different Approaches Tests and Measurements

Cited by 35 publications

References 33 publications

Spectral-Spatial Features Extraction of Hyperspectral Remote Sensing Oil Spill Imagery Based on Convolutional Neural Networks

Spectral-Spatial Features Extraction of Hyperspectral Remote Sensing Oil Spill Imagery Based on Convolutional Neural Networks

The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN)

Deep learning-based multifeature integration robustly predicts central lymph node metastasis in papillary thyroid cancer

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