The imbalanced data classification is a challenging issue in many domains including medical intelligent diagnosis and fraudulent transaction analysis. The performance of the conventional classifier degrades due to the...
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The imbalanced data classification is a challenging issue in many domains including medical intelligent diagnosis and fraudulent transaction analysis. The performance of the conventional classifier degrades due to the imbalanced class distribution of the training data set. Recently, machine learning and deep learning techniques are used for imbalanced data classification. Data preprocessing approaches are also suitable for handling class imbalance problem. Data augmentation is one of the preprocessing techniques used to handle skewed class distribution. Synthetic Minority Oversampling Technique (SMOTE) is a promising class balancing approach and it generates noise during the process of creation of synthetic samples. In this paper, autoencoder is used as a noise reduction technique and it reduces the noise generated by SMOTE. Further, Deep one-dimensional Convolutional Neural Network is used for classification. The performance of the proposed method is evaluated and compared with existing approaches using different metrics such as Precision, Recall, Accuracy, Area Under the Curve and Geometric Mean. Ten data sets with imbalance ratio ranging from 1.17 to 577.87 and data set size ranging from 303 to 284807 instances are used in the experiments. The different imbalanced data sets used are Heart-Disease, Mammography, Pima Indian diabetes, Adult, Oil-Spill, Phoneme, Creditcard, BankNoteAuthentication, Balance scale weight & distance database and Yeast data sets. The proposed method shows an accuracy of 96.1%, 96.5%, 87.7%, 87.3%, 95%, 92.4%, 98.4%, 86.1%, 94% and 95.9% respectively. The results suggest that this method outperforms other deep learning methods and machine learning methods with respect to G-mean and other performance metrics.
Unmanned surface vehicle(USV)is currently a hot research topic in maritime communication network(MCN),where denoising and semantic segmentation of maritime images taken by USV have been rarely *** former has recently ...
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Unmanned surface vehicle(USV)is currently a hot research topic in maritime communication network(MCN),where denoising and semantic segmentation of maritime images taken by USV have been rarely *** former has recently researched on autoencoder model used for image denoising,but the existed models are too complicated to be suitable for real-time detection of *** this paper,we proposed a lightweight autoencoder combined with inception module for maritime image denoising in different noisy environments and explore the effect of different inception modules on the denoising ***,we completed the semantic segmentation task for maritime images taken by USV utilizing the pretrained U-Net model with tuning,and compared them with original U-Net model based on different ***,we compared the semantic segmentation of noised and denoised maritime images respectively to explore the effect of image noise on semantic segmentation *** studies are provided to prove the feasibility of our proposed denoising and segmentation ***,a simple integrated communication system combining image denoising and segmentation for USV is shown.
Abnormal detection plays an important role in video surveillance. LSTM encoder-decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned re...
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Abnormal detection plays an important role in video surveillance. LSTM encoder-decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder-decoder is learned from encoder, and it is crucial for decoder. However, LSTM encoder-decoder generally fails to account for the global context of the learned representation with a fixed dimension representation. In this paper, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder by the shortcut connection. The experiment shows that the hybrid model performs better than the state-of-the-art anomaly detection methods in both qualitative and quantitative ways on benchmark datasets.
The domain adaptation uses labeled source domain data to train a classifier to be used in the target domain with no or small amount of labeled data. Usually there exists discrepancy in terms of marginal and conditiona...
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The domain adaptation uses labeled source domain data to train a classifier to be used in the target domain with no or small amount of labeled data. Usually there exists discrepancy in terms of marginal and conditional distributions for both source and target domains,which is of critical importance to minimize the distribution discrepancy between domains. As a classical model in deep learning, the autoencoder is capable of realizing distribution matching and enhancing classification accuracy by extracting more abstract and effective features from data. A Domain adaptation network based on autoencoder(DANA) is proposed. The DANA structure consists of a couple of encoding layers: a feature extraction layer and a classification layer. For the feature extraction layer,the marginal distributions of source and target domains are matched by using the nonparametric maximum mean discrepancy measurement. For the classification layer, the softmax regression model is applied to encode the label information of source domains meanwhile to match the conditional distribution. Experimental results on ImageNet,Corel and Leaves datasets have shown the enhanced classification accuracy by our proposed algorithm compared with the classical methods.
Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid's safety, and economic loss....
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Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid's safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc.
To enhance the accuracy of identifying water sources in mine inrush incidents, this study, taking the Shengquan coal mine in Shandong, China, as a case study, proposes a novel water source identification model based o...
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To enhance the accuracy of identifying water sources in mine inrush incidents, this study, taking the Shengquan coal mine in Shandong, China, as a case study, proposes a novel water source identification model based on an improved autoencoder-the "Masked autoencoder-based Classifier" model. This model, through a unique autoencoder framework and a custom 'masked_loss' loss function, achieves semi-supervised learning and dimensionality reduction of groundwater sample ionic data. By configuring the hidden layers, the classifier component of the model directly receives data processed by the encoder component. This not only improves the model's performance but also optimizes its complexity. Through an evaluation of the model's fitting effectiveness, our model achieved an average accuracy of 88.8% across 20 runs, with precision, recall, F1-score, and MCC reaching 88.1%, 80.6%, 0.827, and 0.833, respectively, significantly outperforming other classic models. The model successfully identified the sources of three sets of inrush water samples, with a high number of successful runs and clear average probabilities. This work contributes not only to the field of mine water inrush source identification but also offers a new perspective for the broader field of machine learning.
As a powerful soft computing tool, fuzzy cognitive maps (FCMs) have been successfully employed for time-series modeling and forecasting problems. However, both the rapid time variation and the trends are still open pr...
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As a powerful soft computing tool, fuzzy cognitive maps (FCMs) have been successfully employed for time-series modeling and forecasting problems. However, both the rapid time variation and the trends are still open problems when processing univariate non-stationary time-series forecasting problems via FCM-based models. In this paper, we propose a time-series forecasting model by composing FCMs, gated recurrent unit network (GRU), and autoencoder network (AE). The model is termed GAE-FCM. Firstly, a scheme based on gated recurrent unit networks and autoencoder networks is designed to learn the potential representations and capture the long-term trend of non-stationary time series while decomposing these univariate time series into a group of multivariate feature vectors. Then, the obtained multivariate feature vectors are modeled as a fuzzy cognitive map in which quantifying its connection matrix is regarded as a convex optimization problem. Finally, the time-series trend is predicted by the optimized fuzzy cognitive map and corresponding modeling mechanism. The performance of the proposed model has been validated by comparison with several representative methods on five non-stationary time-series datasets.
Cross-modal retrieval has gained much attention in recent years. As the research mainstream, most of existing approaches learn projections for data from different modalities into a common space where data can be compa...
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Cross-modal retrieval has gained much attention in recent years. As the research mainstream, most of existing approaches learn projections for data from different modalities into a common space where data can be compared directly. However, they neglect the preservation of feature and semantic information, so they are unable to obtain satisfactory results as expected. In this paper, we propose a two-stage learning method to learn multi-modal mappings that project multi-modal data to low dimensional embeddings that preserve both feature and semantic information. In the first stage, we combine both low-level feature and high-level semantic information to learn feature-aware semantic code vectors. In the second stage, we use encoder-decoder paradigm to learn projections. The encoder projects feature vectors to code vectors, and the decoder projects code vectors back to feature vectors. The encoder-decoder paradigm guarantees the embeddings to preserve both feature and semantic information. An alternating minimization procedure is developed to solve the multi-modal semantic autoencoder optimization problem. Extensive experiments on three benchmark datasets demonstrate that the proposed method outperforms state-of-the-art cross-modal retrieval methods. (C) 2018 Elsevier B.V. All rights reserved.
As one of the key operations in Wireless Sensor Networks(WSNs), the energy-efficient data collection schemes have been actively explored in the literature. However, the transform basis for sparsifing the sensed data i...
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As one of the key operations in Wireless Sensor Networks(WSNs), the energy-efficient data collection schemes have been actively explored in the literature. However, the transform basis for sparsifing the sensed data is usually chosen empirically, and the transformed results are not always the sparsest. In this paper, we propose a Data Collection scheme based on Denoising autoencoder(DCDA) to solve the above problem. In the data training phase, a Denoising autoencoder(DAE) is trained to compute the data measurement matrix and the data reconstruction matrix using the historical sensed data. Then, in the data collection phase, the sensed data of whole network are collected along a data collection tree. The data measurement matrix is utilized to compress the sensed data in each sensor node, and the data reconstruction matrix is utilized to reconstruct the original data in the ***, the data communication performance and data reconstruction performance of the proposed scheme are evaluated and compared with those of existing schemes using real-world sensed data. The experimental results show that compared to its counterparts, the proposed scheme results in a higher data compression rate, lower energy consumption, more accurate data reconstruction, and faster data reconstruction speed.
Electromagnetic (EM) metasurfaces have attracted great attention from both engineers and researchers due to their unique physical responses. With the rapid development of complex metasurfaces, the design and optimizat...
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Electromagnetic (EM) metasurfaces have attracted great attention from both engineers and researchers due to their unique physical responses. With the rapid development of complex metasurfaces, the design and optimization processes have also become extremely time-consuming and computational resource-consuming. Here we proposed a deep learning model (DLM) based on a convolutional autoencoder network and inverse design network, which can help to establish the complex relationships between the geometries of metasurfaces and their EM responses. As a typical example, a metasurface absorber consisting of polymethacrylimide foam/metal ring alternating multilayers is chosen to demonstrate the capability of the DLM. The relative spectral error of the two desired spectra is only 5.80 and 5.49, respectively. Our model shows great predictive power and may be used as an effective tool to accelerate the design and optimization of metasurfaces.
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