The total losses through online banking in the United Kingdom have increased because fraudulent techniques have progressed and used advanced technology. Using the history transaction data is the limit for discovering ...
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The total losses through online banking in the United Kingdom have increased because fraudulent techniques have progressed and used advanced technology. Using the history transaction data is the limit for discovering various patterns of fraudsters. autoencoder has a high possibility to discover fraudulent action without considering the unbalanced fraud class data. Although the autoencoder model uses only the majority class data, in our hypothesis, if the original data itself has various feature vectors related to transactions before inputting the data in autoencoder then the performance of the detection model is improved. A new feature engineering framework is built that can create and select effective features for deep learning in remote banking fraud detection. Based on our proposed framework [19], new features have been created using feature engineering methods that select effective features based on their importance. In the experiment, a real-life transaction dataset has been used which was provided by a private bank in Europe and built autoencoder models with three different types of datasets: With original data, with created features and with selected effective features. We also adjusted the threshold values (1 and 4) in the autoencoder and evaluated them with the different types of datasets. The result demonstrates that using the new framework the deep learning models with the selected features are significantly improved than the ones with original data.
Internet of things (IoT) and cloud computing are used in many real-time smart applications such as smart healthcare, smart traffic, smart city, and smart industries. Fog computing has been introduced as an intermediat...
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Internet of things (IoT) and cloud computing are used in many real-time smart applications such as smart healthcare, smart traffic, smart city, and smart industries. Fog computing has been introduced as an intermediate layer to reduce communication delay between cloud and IoT devices. To improve the performance of these smart applications, a predictive maintenance system needs to adopt an anomaly detection and root cause analysis model that helps to resolve anomalies and avoid such anomalies in the future. The state-of-the-art work on data-driven root cause analysis suffers from scalability, accuracy, and interpretability. In this paper, a multi-agent-based improved data-driven root cause analysis technique is introduced to identify anomalies and their root causes. The deep learning model LSTM autoencoder is used to find the anomalies, and a game theory approach called SHAP algorithm is used to find the root cause of the anomaly. The evaluation result shows the improvement in accuracy and interpretability as compared to state-of-the-art works.
Controlling milk processing steps is a crucial task as it affects the quality and safety of the final product. Using Raman spectrometer in combination with various evaluation techniques such as principal component ana...
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Controlling milk processing steps is a crucial task as it affects the quality and safety of the final product. Using Raman spectrometer in combination with various evaluation techniques such as principal component analysis and regression, Gaussian process regression, and the autoencoder were checked to define an accurate method for detection of deviations from standard procedures. For this purpose, milk with 5% fat measured at 10 degrees C was considered as the reference milk. A temperature-controlled flow cell was used in a by-pass for online measurements. While the principal component regression was not able to predict the deviations, results demonstrate the capability of Gaussian process regression and the autoencoder to detect 5% added water and cleaning solution, 0.1% difference in fat content and variation of 5 degrees C in measurement temperature. It can be concluded that both procedures display promising results, however, the autoencoder can be trained once and used immediately for online supervision. Therefore, changes can be detected promptly, enabling companies to react instantly.
This research uses deep learning to address the high peak-toaverage power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM), which is critical for wireless communications. Although a PAPRreducing netwo...
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This research uses deep learning to address the high peak-toaverage power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM), which is critical for wireless communications. Although a PAPRreducing network (PRNet), which is a deep learning model, can be used to suppress the PAPR, its computational cost is huge. In this research, the number of layers in a PRNet model is optimized and a fully connected layer is replaced with a convolution layer to reduce the computational load.
The usage of credit card has increased dramatically due to a rapid development of credit cards. Consequently, credit card fraud and the loss to the credit card owners and credit cards companies have been increased dra...
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The usage of credit card has increased dramatically due to a rapid development of credit cards. Consequently, credit card fraud and the loss to the credit card owners and credit cards companies have been increased dramatically. Credit card Supervised learning has been widely used to detect anomaly in credit card transaction records based on the assumption that the pattern of a fraud would depend on the past transaction. However, unsupervised learning does not ignore the fact that the fraudsters could change their approaches based on customers' behaviors and patterns. In this study, three unsupervised methods were presented including autoencoder, one-class support vector machine, and robust Mahalanobis outlier detection. The dataset used in this study is based on real-life data of credit card transaction. Due to the availability of the response, fraud labels, after training the models the performance of each model was evaluated. The performance of these three methods is discussed extensively in the manuscript. For one-class SVM and auto encoder, the normal transaction labels were used for training. However, the advantages of robust Mahalanobis method over these methods is that it does not need any label for its training.
Although the classification of chest radiographs has long been an extensively researched topic, interest increased significantly with the onset of the COVID-19 pandemic. Existing results are promising;however, the rad...
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Although the classification of chest radiographs has long been an extensively researched topic, interest increased significantly with the onset of the COVID-19 pandemic. Existing results are promising;however, the radiological similarities between COVID-19 and other types of respiratory diseases limit the success of conventional image classification approaches that focus on single instances. This study proposes a novel perspective that conceptualizes COVID-19 pneumonia as a deviation from a normative distribution of typical pneumonia patterns. Using a population- based approach, our approach utilizes distributional anomaly detection. This method diverges from traditional instance-wise approaches by focusing on sets of scans instead of individual images. Using an autoencoder to extract feature representations, we present instance-based and distribution-based assessments of the separability between COVID-positive and COVIDnegative pneumonia radiographs. The results demonstrate that the proposed distribution-based methodology outperforms conventional instance-based techniques in identifying radiographic changes associated with COVID-positive cases. This underscores its potential as an early warning system capable of detecting significant distributional shifts in radiographic data. By continuously monitoring these changes, this approach offers a mechanism for early identification of emerging health trends, potentially signaling the onset of new pandemics and enabling prompt public health responses.
Understanding the distribution of hydrogeological properties of the aquifers is crucial for sustainable groundwater resource development. This research explores the application of deep autoencoder neural networks (AEN...
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Understanding the distribution of hydrogeological properties of the aquifers is crucial for sustainable groundwater resource development. This research explores the application of deep autoencoder neural networks (AENN), assisted with global optimization methods for estimating hydrogeological parameters in the Quaternary aquifer system in the Debrecen area, Hungary. Traditional methods for estimating aquifer parameters typically depend on field experiments and laboratory analyses, which are both costly and time-consuming, and often fail to account for the heterogeneity of groundwater formations. In this study, deep AE-NN models are trained to extract latent space (LS) representations that capture key features from the available well logs, including spontaneous potential (SP), natural gamma ray (NGR), shallow resistivity (RS), and deep resistivity (RD). The LS log is then correlated with shale volume and hydraulic conductivity, as determined by the Larionov and Csokas methods, respectively. Regression analysis revealed a Gaussian relationship between the LS log and shale volume and a negative nonlinear relationship with hydraulic conductivity. Global optimization methods, including simulated annealing (SA) and particle swarm optimization (PSO), were used to refine the regression parameters, enhancing the predictive capabilities of the models. The results demonstrated that AE-NN assisted with global optimization methods can be effectively used to estimate shale volume and hydraulic conductivity, proposing a novel and independent approach for estimating hydrogeological parameters critical to groundwater flow and contaminant transport modeling.
In this work, a machine learning model is trained on the basis of an autoencoder. The aim of the model is to recognise faulty cuts during laser cutting, as faulty cuts lead to high reject rates. The literature shows t...
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Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in E...
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Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in European Union countries up to 40% of the total energy is consumed by households. Most residential buildings and industrial zones are equipped with smart sensors such as metering electric sensors, that are inadequately utilized for better energy management. In this paper, we develop a hybrid convolutional neural network (CNN) with an long short-term memory autoencoder (LSTM-AE) model for future energy prediction in residential and commercial buildings. The central focus of this research work is to utilize the smart meters' data for energy forecasting in order to enable appropriate energy management in buildings. We performed extensive research using several deep learning-based forecasting models and proposed an optimal hybrid CNN with the LSTM-AE model. To the best of our knowledge, we are the first to incorporate the aforementioned models under the umbrella of a unified framework with some utility preprocessing. Initially, the CNN model extracts features from the input data, which are then fed to the LSTM-encoder to generate encoded sequences. The encoded sequences are decoded by another following LSTM-decoder to advance it to the final dense layer for energy prediction. The experimental results using different evaluation metrics show that the proposed hybrid model works well. Also, it records the smallest value for mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared to other state-of-the-art forecasting methods over the UCI residential building dataset. Furthermore, we conducted experiments on Korean commercial building data and the results indicate that our proposed hybrid model is a worthy contribution to energy forecasting.
The AI community has been paying attention to submodular functions due to their various applications (e.g., target search and 3D mapping). Learning submodular functions is a challenge since the number of a function...
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The AI community has been paying attention to submodular functions due to their various applications (e.g., target search and 3D mapping). Learning submodular functions is a challenge since the number of a function's outcomes of N sets is 2N. The state-of-the-art approach is based on compressed sensing techniques, which are to learn submodular functions in the Fourier domain and then recover the submodular functions in the spatial domain. However, the number of Fourier bases is relevant to the number of sets' sensing overlapping. To overcome this issue, this research proposed a submodular deep compressed sensing (SDCS) approach to learning submodular functions. The algorithm consists of learning autoencoder networks and Fourier coefficients. The learned networks can be applied to predict 2N values of submodular functions. Experiments conducted with this approach demonstrate that the algorithm is more efficient than the benchmark approach.
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