Sparse Autoencoder Research Articles

Deep learning based crop-type mapping using SAR and optical data fusion

Accurate crop maps are essential in various applications related to food security management activities. Remote Sensing is the primary data source for land-use and land-cover monitoring applications. However, high spectral, spatial, and temporal variations of crop types during their phonological stages make crop mapping challenging. According to the complementary behaviors of optical and radar data, a deep learning-based feature-level fusion strategy is proposed for accurate crop-type mapping. The proposed method uses Sparse Auto-Encoders (SAE) as its base classifier and can involve spatial information in two data-preparation and post-processing steps. Involving spatial information in the classification process can significantly improve crop mapping. The post-processing step uses the guided filter as an edge-aware filtering process that uses field boundary information that implicitly exists in data. Accuracy analysis was performed using two pairs of RapidEye and UAVSAR data from two agricultural areas in Canada. The suitability and higher performance of the proposed method were demonstrated compared to traditional machine learning and common decision-level fusion methods. Accuracy metrics were improved by at least 3% and 1% in two datasets. Class F-scores were also significantly enhanced in oat and wheat classes (up to 20%).

Intelligent fault diagnosis of photovoltaic systems based on deep digital twin

The energy loss and substantial costs associated with faults in photovoltaic (PV) systems impose significant limitations on their efficiency and reliability. Addressing current issues in PV fault diagnosis such as the lack of typical fault data, imbalanced data distribution, and poor diagnostic performance, this paper proposes an intelligent fault diagnosis method for PV systems, deep digital twins (DDT) with information gain stacking sparse autoencoders (IGSSAEs). Initially, the method designs a novel DDT modeling framework tailored to actual PV system specifications. This framework utilizes a mechanism simulation model to generate typical data under various states. Simultaneously, a deep data model is constructed to learn the distribution characteristics of the mechanism model and complete data diversification, achieving the fusion and complementation of data from both models. Subsequently, a diagnostic network using IGSSAE is introduced. This network utilizes information gain ratio to assess feature classification contributions, enabling automatic feature selection. Based on the input features, a stacked sparse autoencoder fault classification network is designed, incorporating multi-level feature compression to enhance the model’s stability and diagnostic accuracy. Finally, a case study is conducted using a 250 kW grid-connected PV system, thoroughly validating the method’s effectiveness with a diagnostic accuracy of 98.4%.

Enhancing Predictive Maintenance in Water Treatment Plants through Sparse Autoencoder Based Anomaly Detection

The deployment of Machine Learning (ML) for improving Water Treatment Plants (WTPs) predictive maintenance is investigated in the present article. Proactively detecting and fixing functional difficulties which might cause catastrophic effects has historically been an endeavour for reactive or schedule-based maintenance methods. Anomaly Detection (AD) in WTP predictive maintenance frameworks is the primary goal of this investigation, which recommends a novel approach based on autoencoder (AE)-based ML models. For the objective of examining high-dimensional time-series sensor data collected from a WTP over a long time, Sparse Autoencoders (SAEs) are implemented. The data collected involves an array of operational measurements that, evaluated together, describe the plant's overall performance. With the support of the AE, this work aims to develop a practical framework for WTP operation predictive maintenance. Anomalies are all system findings from testing that might result in flaws or malfunctions. The research article analyses January and July 2023 WTP data from Jiangsu Province China. The AE paradigm had been evaluated using F1-scores, recall, accuracy, and precision. SAE has substantially improved AD functionality.

Game Analysis of Financial Regulation in International Financial Crisis using Multi-view Graph Convolutional Network

The concept of "game analysis" generally refers to the study and assessment of video games, frequently from the viewpoints of player experience, story, mechanics, design, and other relevant variables. It can be used for a variety of games, such as sports, board, card, and video games. A sudden drop in the value of assets or financial institutions is referred to as a financial crisis. It frequently causes the regular operation of financial markets to be interrupted and may have negative impacts on the economy as a whole. In this manuscript, Game Analysis of Financial Regulation in International Financial Crisis using Multi-view Graph Convolutional Network (GA-FR-IFC-MGCN). The proposed method comprises of four phases: dataset, pre-processing, feature selection, classification. Initially, the data is taken from Analcat dataset. Then, Federated Neural Collaborative Filtering (FNCF) method is used to enhancing the networks data. For feature selection phase, the ideal features are chosen by Siberian Tiger Optimization (STO). After, the Multi-view graph convolutional network (MGCN) method is used to classifying international financial crisis as bankruptcy and Non-bankruptcy. In general, MGCN does not express some adaption of optimization strategies for determining optimal parameters to promise exact classification of International Financial Crisis. Therefore, Harbor Seal Whiskers Optimization Algorithm is proposed to enhance weight parameter of MGCN classifier, which precisely predicts the International Financial Crisis. The proposed technique is executed and efficiency of GA-FR-IFC-MGCN depend classification framework is assessed by support of numerous performances evaluating metrics likes accuracy, recall, precision, FI-score, specificity. Finally the performance of proposed GA-FR-IFC-MGCN methods provides 25.32%, 29.30% and 27.32% higher accuracy, 22.41%, 29.30% and 24.31% higher specificity and 25.71%, 27.12% and 25.31% higher precision though analyzed with existing method likes game analysis of financial supervision in international financial crisis(GA-FS-IFC), performance evaluation of clustering techniques for financial crisis prediction(PE-CT-FC) and modified grey wolf optimizer with sparse auto-encoder for financial crisis prediction in small marginal firms (MGWO-SA-FCP) respectively.

A Physics-Guided MFL Deformed Defect Recovery Method

Magnetic flux leakage (MFL) testing and analysis is an effective non-destructive testing (NDT) method of pipeline health. The health status of pipelines is evaluated by analyzing MFL signals, especially the defect analysis. However, complex operating conditions and equipment deviations may cause deformation on the defects. Availability and integrity of the MFL defects are the keys to accurate signal processing. Due to the limited interpretability of abstract networks and insufficient feature extraction, it is hard to accurately recover the deformed defects. Considering the problems above, an MFL deformed defect recovery method with the guidance of physical features is proposed. The proposed method integrates a deep feature extraction model into a sparse autoencoder, whose parameters are guided by the combination of MSE loss and physics loss. First, the deep feature extraction model fully extracts the abstract features, which contains more information on deformed defects. Second, the sparse penalty factor in the sparse autoencoder reduces the feature redundancy of deformed defects. Third, the physical features are extracted to guide the MFL deformed defect recovery relying on the MFL mechanism for the first time, which enhances the robustness and accuracy of the network. Finally, several comparison experiments have been conducted on measured and simulated MFL defects. The results show that the proposed method is effective in deformed defect recovery. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is a practical problem of MFL deformed defect recovery. The deformation on MFL defects affects defect inversion and evaluation seriously, which causes great potential harm to pipeline safety transmission. In this paper, compared with traditional neural network-based methods, physical features based on MFL mechanism are extracted to guide the parameter update. In addition, a pre-trained model replaces regular convolutional layers to extract features sufficiently, while a sparse factor is applied to reduce redundant features. The proposed method not only utilizes deep features from deep networks, but also increases the guidance of physical features, so that the algorithm is more robust and accurate. The experiment results show that our proposed method is more effective than the comparative methods on MFL simulated and measured defects. In a word, the proposed method has strong theoretical research and practical value.

Is Single Enough? A Joint Spatiotemporal Feature Learning Framework for Multivariate Time Series Prediction.

A fuzzy cognitive map (FCM) is a simple but effective tool for modeling and predicting time series. This article focuses on the problem of multivariate time series prediction (TSP), which is essential and challenging in data mining. Although several FCM-based approaches have been designed to solve this problem, their feature extraction module designed for single mode falls short in capturing the nonlinear spatiotemporal dependencies among variates, thereby resulting in low prediction accuracy in forecasting multivariate time series, which shows that the single mode learning is not enough. Therefore, in this article, we propose a joint spatiotemporal feature learning framework for multivariate TSP, where a mix-resolution spatial module consisting of multiple sparse autoencoders (SAEs) is designed to extract the feature series with different spatial resolutions, and a mix-order spatiotemporal module concluding multiple high-order FCMs (HFCMs) is designed to model the spatiotemporal dynamics of these feature series. Finally, the outputs of the two modules are concatenated to predict future values. We refer to this framework as the spatiotemporal FCM (STFCM). Especially, an efficient learning algorithm is designed to update the integral weights of STFCM based on the batch gradient descent algorithm when it deems necessary. We validate the performance of the STFCM on four real-world datasets. Compared with the existing state-of-the-art (SOTA) methods, the experimental results not only show the advantages of the two designed modules in the STFCM but also show the excellent performance of the STFCM.

Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM

This study introduces an innovative approach to diagnostics, employing a unique combination of techniques including a stratified group K-fold cross-validation method and a sparse stacked autoencoder (SSAE) alongside LightGBM. By examining signatures derived from motor current, voltage, speed, and torque, the framework aims to effectively detect and classify broken rotor bars (BRBs) within inverter-fed induction machines. In this kind of cross-validation method, class labels and grouping factors are spread out across folds by distributing motor operational data attributes equally over target label stratification and extra grouping information. By integrating SSAE and LightGBM, a gradient-boosting framework, we elevate the precision and efficacy of defect diagnosis. The SSAE feature extraction algorithm proves to be particularly effective in identifying small BRB signatures within motor operational data. Our approach relies on comprehensive datasets collected from motor systems operating under diverse loading conditions, ranging from 0% to 100%. Using a sparse stacked autoencoder, the model lowers the dimensionality and noise of the motor fault data. It then sends the cleaned data to the LightGBM network for fault diagnosis. LightGBM leverages the attributes of the sparse stacked autoencoder to showcase the distinctive qualities associated with BRBs. This integration offers the potential to improve defect identification by furnishing input representations that are both more precise and more concise. The proposed model (SSAE with LightGBM) was trained using 80% of the data, while the remaining 20% was used for testing. To validate the proposed architecture, we evaluate the accuracy, precision, recall, and F1-scores of the results using motor global signals, with the help of confusion matrices with receiver operating characteristic (ROC) curves. Following the training of a new LightGBM model with refined hyperparameters through Bayesian optimization, we proceed to conduct the final classification utilizing the optimal feature subset. Evaluation of the test dataset indicates that the BRBs diagnostic framework facilitates the detection and classification of issues with induction motor BRBs, achieving accuracy rates of up to 99% across all loading conditions.

Early identification of out-of-roundness damage wheels in railway freight vehicles using a wayside system and a stacked sparse autoencoder

Early identification of wheel defects can prevent serious damage to railways, considerably lowering maintenance costs for both railway administrations and rolling stock operators. Within this context, an unsupervised methodology based on artificial intelligence techniques is presented, which allows the detection and classification of out-of-roundness damage wheels, such as wheel flats and polygonal wheels, based on dynamic responses induced on the track by crossing freight railway vehicles. The methodology involves the following steps: (i) data collection and pre-processing, (ii) feature extraction (iii) data fusion and (iv) feature discrimination. In the first phase, an FFT algorithm is applied to the acceleration track responses. Then, features are extracted after training a Stacked Sparse Autoencoder, in which the main features of the responses are obtained after a compression stage using an encoder network. This lower dimensional layer forces the model to learn a compression of the input data. Then, these extracted features are merged using the Mahalanobis distance, which enhances the sensitivity to the damage recognition. Posteriorly, an outlier analysis is performed to distinguish a healthy wheel from a defective one and a cluster analysis to discriminate the two types of out-of-roundness (OOR) damage and classify the severity of each type of damage.

A fast and accurate domain decomposition nonlinear manifold reduced order model

This paper integrates nonlinear-manifold reduced order models (NM-ROMs) with domain decomposition (DD). NM-ROMs approximate the full order model (FOM) state in a nonlinear-manifold by training a shallow, sparse autoencoder using FOM snapshot data. These NM-ROMs can be advantageous over linear-subspace ROMs (LS-ROMs) for problems with slowly decaying Kolmogorov n-width. However, the number of NM-ROM parameters that need to be trained scales with the size of the FOM. Moreover, for “extreme-scale” problems, the storage of high-dimensional FOM snapshots alone can make ROM training expensive. To alleviate the training cost, this paper applies DD to the FOM, computes NM-ROMs on each subdomain, and couples them to obtain a global NM-ROM. This approach has several advantages: Subdomain NM-ROMs can be trained in parallel, involve fewer parameters to be trained than global NM-ROMs, require smaller subdomain FOM dimensional training data, and can be tailored to subdomain-specific features of the FOM. The shallow, sparse architecture of the autoencoder used in each subdomain NM-ROM allows application of hyper-reduction (HR), reducing the complexity caused by nonlinearity and yielding computational speedup of the NM-ROM. This paper provides the first application of NM-ROM (with HR) to a DD problem. In particular, this paper details an algebraic DD reformulation of the FOM, training a NM-ROM with HR for each subdomain, and a sequential quadratic programming (SQP) solver to evaluate the coupled global NM-ROM. Theoretical convergence results for the SQP method and a priori and a posteriori error estimates for the DD NM-ROM with HR are provided. The proposed DD NM-ROM with HR approach is numerically compared to a DD LS-ROM with HR on the 2D steady-state Burgers’ equation, showing an order of magnitude improvement in accuracy of the proposed DD NM-ROM over the DD LS-ROM.

Deep Learning-Based Active Jamming Suppression for Radar Main Lobe

Due to the development of digital radio frequency memory (DRFM), active jamming against the main lobe of the radar has become mainstream in electronic warfare. The jamming infiltrates the radar receiver via the main lobe, covering up the target echo information. This greatly affects the detection, tracking, and localization of targets by radar. In this study, we consider jamming suppression based on the independence of RF features. First, two stacked sparse auto-encoders (SSAEs) are built to extract the RF characteristics and signal features carried out by the actual radar signal for subsequent jamming suppression. This method can effectively separate RF features from signal features, making the extracted RF features more efficient and accurate. Then, an SSAE-based jamming suppression auto-encoder (JSAE) is proposed; the mixed signal, including the radar signal, jamming signal, and noise, is input to JSAE for dimensionality reduction. Therefore, the radar signal and RF features, extracted by the two SSAEs in the previous step, are used to constrain the features of the reduced mixed signal. Moreover, we integrate the feature level and signal level to jointly achieve jamming suppression. The original radar signal is used to assist the radar signal reconstructed by the decoder. By first filtering out interference-related features and then reconstructing the signal, we can achieve better jamming suppression performance. Finally, the effectiveness of the proposed method is verified by simulating the actual collected data.

Deep self-taught learning framework for intrusion detection in cloud computing environment

&lt;p&gt;Cloud has become a target-rich environment for malicious attacks by cyber intruders. Security is a major concern and remains an obstacle to the adoption of cloud computing. The intrusion detection system (IDS) is regarded as defense-in-depth. Unfortunately, most machine learning approaches designed for cloud intrusion detection require large amounts of labeled attack samples, but in real practice, they are limited. Therefore, the key impetus of this work is to introduce self-taught learning (STL) combining stacked sparse autoencoder (SSAE) with long short-term memory (LSTM) as a candidate solution to learn the robust feature representation and efficiently improve the performance of IDS with respect to false alarm rate (FAR) and detection rate (DR). Accordingly, the proposed approach as a first step employs SSAE to achieve dimensional reduction by learning the discriminative features from network traffic. The approach adopts LSTM to recognize the intrusion with the features encoded by SSAE. To evaluate the detective performance of our model, a comprehensive set of experiments are conducted on NSL-KDD. Also, ablation experiments are conducted to show the contribution of each component of our approach. Further, the comparative analysis shows the efficacy of our approach against the existing approaches with an accuracy of 86.31%.&lt;/p&gt;

Tracking element-mineral associations with unsupervised learning and dimensionality reduction in chemical and optical image stacks of thin sections

In-situ chemical analysis on thin sections is a cornerstone of geochemical research. Despite massive advances in digital image analysis, the interpretation of such data within the optical petrological context of the thin section has largely remained an analogue task in geochemistry. In this contribution, we registered optical and micro-chemical images from large thin section areas. Chemical datasets from scanning electron microscopy energy dispersive spectrometry (SEM-EDX) and Synchrotron X-ray fluorescence microscopy (S-XFM) were exported from proprietary software. We then evaluated two dimensionality reduction techniques against false-colour images and phase maps to test whether previously unnoticed features became discoverable. Principal component analysis (PCA) and deep sparse autoencoder (DSA) neural networks were used to summarise the multi-channel SEM-EDX and S-XFM into one single red-green-blue (RGB) representation each.Applied to an oceanic gabbro, a cratonic peridotite, and a pelagic limestone, the PCA-based dimensionality reduction was found to produce crisp RGB maps of phases with less noise than false-colour three-element RGB maps. They were registered with optical images to form a simple multi-channel input for pixel-based classification into classes (i.e., semantic segmentation). This was fast and worked well for typical igneous and metamorphic rocks (1–3 h routine). In the chemically quite homogenous limestone, the combined optical and S-XFM PCA input was successfully classified into many phases that were unclassifiable on the conventional SEM-EDX phase map. From the segmented optical and S-XFM PCA map, we exported pixel locations of classified accessory phases back into the original proprietary S-XFM software. This allowed quantification of trace elements whose X-ray peaks were invisible in the original S-XFM quantification.The DSA-based approach yielded phase maps with greater noise, but the technique was very strong at detecting subtle features. In the gabbro sample, the DSA image identified cryptic relict cores in clinopyroxene more clearly than in the previously used Cr concentration map. A DSA training set can be obtained on a small ROI to generate a consistent large area DSA image from which more fragmental cores were observed. For a ROI on the gabbro thin section, the DSA representation map and the blended optical image were segmented and inspected with a loupe tool. This allowed us to generate interactive histograms and bin-scatter concentration plots of selected phases and to search for co-variations of element concentrations. This approach can be expanded to include data from any in situ geochemical acquisition tools and may help discover previously unseen features in complex elemental imagery.

Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm.

Brain tumor classification is one of the most difficult tasks for clinical diagnosis and treatment in medical image analysis. Any errors that occur throughout the brain tumor diagnosis process may result in a shorter human life span. Nevertheless, most currently used techniques ignore certain features that have particular significance and relevance to the classification problem in favor of extracting and choosing deep significance features. One important area of research is the deep learning-based categorization of brain tumors using brain magnetic resonance imaging (MRI). This paper proposes an automated deep learning model and an optimal information fusion framework for classifying brain tumor from MRI images. The dataset used in this work was imbalanced, a key challenge for training selected networks. This imbalance in the training dataset impacts the performance of deep learning models because it causes the classifier performance to become biased in favor of the majority class. We designed a sparse autoencoder network to generate new images that resolve the problem of imbalance. After that, two pretrained neural networks were modified and the hyperparameters were initialized using Bayesian optimization, which was later utilized for the training process. After that, deep features were extracted from the global average pooling layer. The extracted features contain few irrelevant information; therefore, we proposed an improved Quantum Theory-based Marine Predator Optimization algorithm (QTbMPA). The proposed QTbMPA selects both networks' best features and finally fuses using a serial-based approach. The fused feature set is passed to neural network classifiers for the final classification. The proposed framework tested on an augmented Figshare dataset and an improved accuracy of 99.80%, a sensitivity rate of 99.83%, a false negative rate of 17%, and a precision rate of 99.83% is obtained. Comparison and ablation study show the improvement in the accuracy of this work.

Status Recognition of Marine Centrifugal Pumps Based on a Stacked Sparse Auto-Encoder

Marine centrifugal pumps (MCPs) are widely used in ships, so it is important to identify their status accurately for their maintenance. Due to the influence of load, friction, and other non-linear factors, the vibration signal of an MCP shows non-linear and non-stationary characteristics, and it is difficult to extract the state characteristics contained in the vibration signal. To solve the difficulty of feature extraction of non-linear non-stationary vibration signals generated by MCPs, a novel MCP frequency domain signal feature extraction method based on a stacked sparse auto-encoder (SSAE) is proposed. The characteristic parameters of MCP frequency domain signals are extracted via the SSAE model for classification training, and different statuses of MCPs are identified. The vibration signals in different MCP statuses were collected for feature extraction and classification training, and the MCP status recognition accuracy based on the time domain feature and fuzzy entropy feature was compared. According to the test data, the accuracy of MCP status recognition based on the time domain feature is 71.2%, the accuracy of MCP status recognition based on the fuzzy entropy feature is 87.7%, and the accuracy of MCP status recognition based on the proposed method is 100%. These results show that the proposed method can accurately identify each status of an MCP under test conditions.

Gear fault diagnosis based on complex network theory and error-correcting output codes: Multi class support vector machine

Gearbox failures have a detrimental effect on the machine performance that affects the production capacity and economic benefits of a manufacturing industry in an adverse manner. Early detection of faults in gears helps to prevent sudden machine failures. Previously, many signal processing and artificial intelligence techniques have been successfully applied to diagnose gear faults by analyzing the machine vibrations. These techniques have repeatedly emphasized on the need to extract quality features from gear vibration signals and using artificial intelligence techniques that can identify multiple types of faults simultaneously. As such, in this article, new features called as visibility graph features based on complex network theory have been proposed to extract fault information from vibration signals. In addition, a new artificial intelligence framework for multifault classification known as error-correcting output codes-multiclass support vector machine is proposed to detect gear faults. To the best of authors’ knowledge, these features along with the error-correcting output codes-artificial intelligence framework have been rarely utilized for fault diagnosis purpose. The proposed approach has been applied on experimental gear vibration data to validate its effectiveness. First, the vibration signals are transformed to graphs, and various graph properties are computed to serve as fault features. Then, the fault features are supplied to train the error-correcting output codes-multiclass support vector machine model and learn the fault patterns. Finally, the gear fault classification accuracies are determined for various gear test conditions and compared with those of existing methods. It is observed that the proposed approach provides an average improvement of 4.22%, 9.93% and 11.8% over the time-domain features, time–frequency domain features and voting-based multiclass support vector machine classifier, respectively, for different operating conditions. Furthermore, the suggested technique augments the classification accuracies by 10%, 7.5% and 6.7%, respectively, as compared with the deep-learning models, namely standard stacked sparse autoencoder, deep neural network and convolution neural network.

Research on Mushroom Image Classification Algorithm Based on Deep Sparse Dictionary Learning

The traditional mushroom feature extraction method has low classification efficiency and unsatisfactory effect. Dictionary learning is widely used in image classification. However, the previous work is to learn dictionaries in the original space, which limits the performance of sparse representation classification. In order to solve the problem of spatial redundancy in traditional convolutional neural networks and the weak performance of deep learning in small samples, an improved dictionary learning algorithm, Deep Sparse Dictionary learning (DSDL), is proposed. The input to DSDL is not a matrix gathered from the original grayscale image or a hand-created feature, but rather a relatively deeper feature extraction via a stack autoencoder. Then, a structured dictionary is designed to reconstruct the deep features according to different categories of distinguishing features. In addition, it is necessary to learn the associated structured projection sparse dictionary to ensure that the decoder updates in the direction of the deconvolution operator error is minimal. By utilizing sparse dictionary learning loss functions and autoencoder loss functions, DSDL can simultaneously learn deep latent features and corresponding dictionary pairs. In the testing phase of DSDL, the minimum errors of deep feature and structured projection components for different classes can be directly represented by basic matrix multiplication operations. Experimental results show that the proposed method achieves a good classification effect on mushroom images, which shows the effectiveness of the method.

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Accurate tool wear monitoring is crucial for increasing tool life and machining productivity. Although many prediction models can achieve high prediction accuracy, there are problems such as poor stability in the face of different working conditions or tool signals. A tool wear prediction method based on improved deep extreme learning machines (DELMs) was proposed as a solution to this issue; it uses the sparrow search algorithm (SSA) to upgrade the input weight of DELM to improve the model, and then extracts the time-domain, frequency-domain, and time-frequency domain characteristics from multi-sensor signals to construct and test the improved model SSA-DELM. The verification results show that the proposed model accurately reflects the tool wear. Meanwhile, the RMSE of the proposed model decreased by 53.39%, 19.95%, 43.86%, 23.80%, 24.80%, and 3.72%, respectively, and the MAE decreased by 67.81%, 17.87%, 32.70%, 29.90%, 30.30%, and 6.78%, respectively, compared to the with unimproved DELM, particle swarm optimization-least squares support vector machine, long short-term memory, stacked sparse autoencoder, recurrent neural network, and dung beetle optimizer-DELM.

Empowering EEG motor imagery classification with deep transfer learning approach

AbstractThe brain‐computer interface (BCI) enables individuals with impairments to interact with the real world without relying on the neuromuscular pathway. BCI leverages artificial intelligence (AI) models for control. It can capture brain activity patterns associated with mental processes and convert them into commands for actuators. One promising application of BCI is in rehabilitation centres. When compared to traditional methods integrated with machine learning (ML) approaches that were initially explored in the past decade, there is a relatively limited number of studies utilizing deep learning (DL) approaches in BCI applications. A novel approach has been developed for the automatic recognition of motor imagery (MI) tasks. This article introduces the improved sparrow search algorithm with deep transfer learning‐based EEG motor imagery classification (ISSADTL‐EEGMIC) algorithm for BCIs. The presented ISSADTL‐EEGMIC algorithm primarily focuses on the automated classification of motor imagery tasks. To achieve this, the ISSADTL‐EEGMIC method first preprocesses EEG signals using the wavelet packet decomposition (WPD) technique. Additionally, it employs the neural architecture search network (NASNet) technique for feature extraction. Moreover, the ISSADTL‐EEGMIC model performs the classification process using the stacked sparse autoencoder (SSAE) model. Furthermore, hyperparameter optimization of the SSAE model is performed using the ISSA technique, which helps achieve maximum classification performance. The results from the simulation of the ISSADTL‐EEGMIC algorithm have been examined using two benchmark datasets. The experimental findings suggest that it outperforms recent approaches in terms of performance.

Research on the remaining useful life prediction method for lithium-ion batteries by fusion of feature engineering and deep learning

Lithium-ion batteries age continuously during usage due to their characteristics and the influence of various external factors, but as degradation deepens, it can lead to an apparent decrease in battery safety and reliability. Therefore, predicting remaining useful life from the current to end state and preventing possible dangerous incidents are essential for battery health management. A novel method from the perspective of providing aging features reference and balance prognostics speed and precision is proposed. Firstly, the used dataset is preprocessed in many ways. Then, through feature engineering, 79 features are extracted in the dataset from three perspectives: direct, evolution, statistics, and most representative 16 features are filtered to form the final feature set based on the method of combining discretization and importance. Feature engineering is established to find crucial information within the dataset that correlates highly with remaining life and represents most battery recession paths. Finally, the sparse autoencoder and Transformer integrated approach is proposed to build the life prediction model, and it can learn the temporal relationship between the feature set and remaining cycles quickly and accurately. According to the forecasting results of 62 batteries with different aging conditions, the error can reach 7.43% by only using the feature sequence under 30 cycle lengths. For early life prediction, the error of using the first 100 cycles data is at most 2.6%.

Prediction of miRNAs and diseases association based on sparse autoencoder and MLP.

Introduction: MicroRNAs (miRNAs) are small and non-coding RNA molecules which have multiple important regulatory roles within cells. With the deepening research on miRNAs, more and more researches show that the abnormal expression of miRNAs is closely related to various diseases. The relationship between miRNAs and diseases is crucial for discovering the pathogenesis of diseases and exploring new treatment methods. Methods: Therefore, we propose a new sparse autoencoder and MLP method (SPALP) to predict the association between miRNAs and diseases. In this study, we adopt advanced deep learning technologies, including sparse autoencoder and multi-layer perceptron (MLP), to improve the accuracy of predicting miRNA-disease associations. Firstly, the SPALP model uses a sparse autoencoder to perform feature learning and extract the initial features of miRNAs and diseases separately, obtaining the latent features of miRNAs and diseases. Then, the latent features combine miRNAs functional similarity data with diseases semantic similarity data to construct comprehensive miRNAs-diseases datasets. Subsequently, the MLP model can predict the unknown association among miRNAs and diseases. Result: To verify the performance of our model, we set up several comparative experiments. The experimental results show that, compared with traditional methods and other deep learning prediction methods, our method has significantly improved the accuracy of predicting miRNAs-disease associations, with 94.61% accuracy and 0.9859 AUC value. Finally, we conducted case study of SPALP model. We predicted the top 30 miRNAs that might be related to Lupus Erythematosus, Ecute Myeloid Leukemia, Cardiovascular, Stroke, Diabetes Mellitus five elderly diseases and validated that 27, 29, 29, 30, and 30 of the top 30 are indeed associated. Discussion: The SPALP approach introduced in this study is adept at forecasting the links between miRNAs and diseases, addressing the complexities of analyzing extensive bioinformatics datasets and enriching the comprehension contribution to disease progression of miRNAs.