Fishing is a crucial worldwide activity as it provides a source of food and economic income. A challenge in ecology and conservation is decreasing overfishing and illegal, unreported, and unregulated fishing (IUUF). O...
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Fishing is a crucial worldwide activity as it provides a source of food and economic income. A challenge in ecology and conservation is decreasing overfishing and illegal, unreported, and unregulated fishing (IUUF). One strategy to decrease those issues is to track vessels for detecting fishing behaviors through monitory systems. In this letter, we present an approach to classify fishing behaviors, specifically, for four fishing gear types (trawl, purse seine, fixed gear, and longline) using automatic identification systems (AISs) data from the Global Fishing Watch platform. Thus, our main contribution is how we propose data processing by including a supervised autoencoder dimensional reduction (SA-DR) processing data step. This step allows removing redundant features and noise, avoiding overfitting, decreasing data complexity, and preserving the differences between classes. Specifically, we propose to use IVIS and centroid encoder (CE) methods. The experimental results show how our approach applying SA-DR over the vessel trajectory feature representation reduces the variation results among different classifiers and achieves a high classification accuracy of up to 95%. This result could help prevent IUUF, overfishing, and improve fishery management strategies.
Personalized modeling usually trains a predictive model for a new point using only observations similar to the new point. However, existing methodologies have limitations that do not reflect the target variable in the...
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Personalized modeling usually trains a predictive model for a new point using only observations similar to the new point. However, existing methodologies have limitations that do not reflect the target variable in the similarity calculation nor the density of neighbors. Thus, this paper proposes a new personalized modeling method. The proposed methodology transforms the input variables into the latent variables through a supervised autoencoder and calculates the similarity measure between observations in the transformed latent space. The proposed method also considers the neighborhood density around the test point. As a result of the experiments with real datasets, it was found that the proposed method outperformed other benchmark methods and showed the interpretability of the predictive model.(c) 2022 Elsevier B.V. All rights reserved.
This interdisciplinary work focuses on the interest of a new auto-encoder for supervised classification of live cell populations growing in a thermostated imaging station and acquired by a Quantitative Phase Imaging (...
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This interdisciplinary work focuses on the interest of a new auto-encoder for supervised classification of live cell populations growing in a thermostated imaging station and acquired by a Quantitative Phase Imaging (QPI) camera. This type of camera produces interferograms that have to be processed to extract features derived from quantitative linear retardance and birefringence measurements. QPI is performed on living populations without any manipulation or treatment of the cells. We use the efficient new autoencoder classification method instead of the classical Douglas-Rachford method. Using this new supervised autoencoder, we show that the accuracy of the classification of the cells present in the mitotic phase of the cell cycle is very high using QPI features. This is a very important finding since we demonstrate that it is now possible to very precisely follow cell growth in a non-invasive manner, without any bias. No dye or any kind of markers are necessary for this live monitoring. Any studies requiring analysis of cell growth or cellular response to any treatment could benefit from this new approach by simply monitoring the proportion of cells entering mitosis in the studied cell population.
Multi-label learning has been applied in various areas. One crucial research issue in multi-label learning is how to find a complex nonlinear mapping between instances and multi-label such that the instances with unkn...
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ISBN:
(纸本)9789881563958
Multi-label learning has been applied in various areas. One crucial research issue in multi-label learning is how to find a complex nonlinear mapping between instances and multi-label such that the instances with unknown labels can be predicted with the mapping. In this paper, we propose a multi-label learning method based on supervised deep autoencoder, and study the effects of the various combinations of the constraints of hidden layers and output layers. In the output layer, the sum-square error of the true labels and estimating labels is minimized through backpropagation-through-time learning algorithm. Extensive experiments conducted on eight real-world datasets demonstrate the effectiveness of our proposed method compared with several slate-of-art baseline methods.
Multi-label learning has been applied in various areas. One crucial research issue in multi-label learning is how to find a complex nonlinear mapping between instances and multi-label such that the instances with unkn...
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Multi-label learning has been applied in various areas. One crucial research issue in multi-label learning is how to find a complex nonlinear mapping between instances and multi-label such that the instances with unknown labels can be predicted with the mapping. In this paper, we propose a multi-label learning method based on supervised deep autoencoder, and study the effects of the various combinations of the constraints of hidden layers and output layers. In the output layer, the sum-square error of the true labels and estimating labels is minimized through backpropagation-through-time learning algorithm. Extensive experiments conducted on eight real-world datasets demonstrate the effectiveness of our proposed method compared with several state-of-art baseline methods.
In recent years, deep learning techniques have been applied to the diagnosis of pulmonary nodules. In order to improve the pulmonary nodule diagnostic performance effectively, we propose a novel pulmonary nodule diagn...
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In recent years, deep learning techniques have been applied to the diagnosis of pulmonary nodules. In order to improve the pulmonary nodule diagnostic performance effectively, we propose a novel pulmonary nodule diagnosis method using dual-modal deep supervised autoencoder based on extreme learning machine for which discriminative features are automatically learnt from the input data. The network is fed with nodule images in pairs obtained from computed tomography and positron emission tomography respectively. For each pair image, the high-level discriminative features of nodules in computed tomography and positron emission tomography are extracted from stacked supervised autoencoder layers. The outputs of the proposed architecture are combined using an ideal fusion method to get the final classification. In the experiments, 5-fold cross-validation method is used to validate the proposed method on 1,600 pulmonary nodule images and our method reaches high-classification sensitivities of 91.75% at 1.58 false positives per scan. Meanwhile, compared with other deep learning diagnosis methods, our method achieves better discriminative results and is highly suited to be used for pulmonary nodule diagnosis.
With excellent feature representation capabilities, deep autoencoder networks have attracted attention in process monitoring. However, it cannot take into account the quality indicators to identify whether the faults ...
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ISBN:
(纸本)9781665493130
With excellent feature representation capabilities, deep autoencoder networks have attracted attention in process monitoring. However, it cannot take into account the quality indicators to identify whether the faults are quality-relevant. To address this issue, an orthogonal feature separation autoencoder (OFSAE) method is developed for quality-relevant fault monitoring. The proposed OFSAE mainly consists of the quality-relevant encoder network, quality-irrelevant encoder network, decoder network, and regression network. Through parallel learning and orthogonal projection for process variables, quality-relevant and quality-irrelevant variations can be isolated while maintaining good prediction performance. Finally, in comparison with conventional monitoring methods, the superiority of OFSAE is validated by the Tennessee Eastman process.
Purpose A person's healthy activities are determined by the state of his or her brain. The brain is in charge of all of a person's activities. If a small abnormality develops in the brain, it will have a negat...
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Purpose A person's healthy activities are determined by the state of his or her brain. The brain is in charge of all of a person's activities. If a small abnormality develops in the brain, it will have a negative impact on the person regardless of whether the other organs are in good condition. As a result, early detection of any abnormal growth in the brain is essential. Methods In this work, the authors have utilized data pre-processing using discrete wavelet transform (DWT) and segmentation, whereas, for detection, an ensemble learning technique is proposed. DWT and segmentation help in increasing the dataset size that is used to train the deep learning model. Segmentation using supervised Auto-encoder (AE) is used for data enhancement to strengthen the training process. The original data, outputs of DWT, and segmented images are utilized for the training of the ensemble model designed with three parallel-connected convolutional neural networks (CNNs). Results The detection results obtained from the ensemble of these recurrent models are then passed through the Multilayer Perceptron (MLP) for final detection. Kaggle brain MRI image dataset is used to complete the proposed method. Test accuracy, F1-score, precision, sensitivity, and specificity provided by this method are 98.08%, 0.9836, 1.0000, 0.9677, and 1.0000 respectively. In comparison to state-of-the-art models, the proposed model produces competitive outcomes. Conclusion In time detection of the tumor may lead to the survival of the patient. Automatic and accurate detection is another perspective of this field. For this purpose, we have proposed a deep ensemble model with wavelet features. The ensemble model provides increased performance in comparison to single models due to the parallel training.
Speech representations which are robust to pathology-unrelated cues such as speaker identity information have been shown to be advantageous for automatic dysarthric speech classification. A recently proposed technique...
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Speech representations which are robust to pathology-unrelated cues such as speaker identity information have been shown to be advantageous for automatic dysarthric speech classification. A recently proposed technique to learn speaker identity-invariant representations for dysarthric speech classification is based on adversarial training. However, adversarial training can be challenging, unstable, and sensitive to training parameters. To avoid adversarial training, in this paper we propose to learn speaker-identity invariant representations exploiting a feature separation framework relying on mutual information minimization. Experimental results on a database of neurotypical and dysarthric speech show that the proposed adversarial-free framework successfully learns speaker identity-invariant representations. Further, it is shown that such representations result in a similar dysarthric speech classification performance as the representations obtained using adversarial training, while the training procedure is more stable and less sensitive to training parameters.
According to the psychological literature, implicit motives allow for the characterization of behavior, subsequent success, and long-term development. Contrary to personality traits, implicit motives are often deemed ...
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According to the psychological literature, implicit motives allow for the characterization of behavior, subsequent success, and long-term development. Contrary to personality traits, implicit motives are often deemed to be rather stable personality characteristics. Normally, implicit motives are obtained by Operant Motives, unconscious intrinsic desires measured by the Operant Motive Test (OMT). The OMT test requires participants to write freely descriptions associated with a set of provided images and questions. In this work, we explore different recent machine learning techniques and various text representation techniques for facing the problem of the OMT classification task. We focused on advanced language representations (e.g, BERT, XLM, and DistilBERT) and deep supervised autoencoders for solving the OMT task. We performed an exhaustive analysis and compared their performance against fully connected neural networks and traditional support vector classifiers. Our comparative study highlights the importance of BERT which outperforms the traditional machine learning techniques by a relative improvement of 7.9%. In addition, we performed an analysis of how the BERT attention mechanism is being modified. Our findings indicate that the writing style features acquire higher importance at the moment of accurately identifying the different OMT categories. This is the first time that a study to determine the performance of different transformer-based architectures in the OMT task is performed. Similarly, our work propose, for the first time, using deep supervised autoencoders in the OMT classification task. Our experiments demonstrate that transformer-based methods exhibit the best empirical results, obtaining a relative improvement of 7.9% over the competitive baseline suggested as part of the GermEval 2020 challenge. Additionally, we show that features associated with the writing style are more important than content-based words. Some of these findings show strong conn
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