Deep Sparse Autoencoder Research Articles

A detection method of oil content for maize kernels based on CARS feature selection and deep sparse autoencoder feature extraction

To achieve accurate, rapid, and non-destructive oil content determination in maize, a neural fitting model that combines feature selection and feature extraction is proposed. Competitive adaptive re-weighted sampling (CARS) is used to choose features, while the deep sparse autoencoder (DSAE) network is utilized to extract features. The 160 spectral data from the near-infrared spectra public datasets of maize served as experimental samples. Several preprocessing approaches were tested, and the first-order derivatives fared the best. To capture the various degrees of abstract features in spectral data, SSAE networks with two or three hidden layers are developed. The number of nodes in the network's hidden layer (30, 50, 70, 100, 200, 300, respectively) and the network's sparsity parameter (0.001, 0.004, 0.007, 0.01, 0.04, 0.07, 0.1, 0.2, …, 1) are tuned to generate partial least squares regression (PLSR) models for testing the feature learning effect. The highest performance comes from a three-layer network with 200 hidden layer nodes and a sparsity parameter of 0.04. This network extracts three levels of features from the preprocessed spectral data: F1, F2, and F3. Full_cars, F1_cars, F2_cars, and F3_cars are obtained using the CARS algorithm for variable selection on the preprocessed spectral data, F1, F2, and F3, respectively. They are then integrated into a final feature set to build a neural-fitting network (FNN). After training and testing, the test set has a correlation coefficient of 0.964205 and an mean square error (MSE) of 0.00636809. Comparing this model to support vector machine regression (SVR), PLSR, and gaussian process regression (GPR), the findings demonstrate that the model described in this study outperforms the others in terms of fitting accuracy and generalization performance. To summarize, this paper introduces a new regression analysis method based on near-infrared spectra.

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.

Efficient Intrusion Detection System Data Preprocessing Using Deep Sparse Autoencoder with Differential Evolution

A great amount of data is generated by the Internet and communication areas’ rapid technological improvement, which expands the size of the network. These cutting‐edge technologies could result in unique network attacks that present security risks. This intrusion launches many attacks on the communication network which is to be monitored. An intrusion detection system (IDS) is a tool to prevent from intrusions by inspecting the network traffic and to make sure the network integrity, confidentiality, availability, and robustness. Many researchers are focused to IDS with machine and deep learning approaches to detect the intruders. Yet, IDS face challenges to detect the intruders accurately with reduced false alarm rate, feature selection, and detection. High dimensional data affect the feature selection methods effectiveness and efficiency. Preprocessing of data to make the dataset as balanced, normalized, and transformed data is done before the feature selection and classification process. Efficient data preprocessing will ensure the whole IDS performance with improved detection rate (DR) and reduced false alarm rate (FAR). Since datasets are required for the various feature dimensions, this article proposes an efficient data preprocessing method that includes a series of techniques for data balance using SMOTE, data normalization with power transformation, data encoding using one hot and ordinal encoding, and feature reduction using a proposed deep sparse autoencoder (DSAE) with differential evolution (DE) on data before feature selection and classification. The efficiency of the transformation methods is evaluated with recursive Pearson correlation‐based feature selection and graphical convolution neural network (G‐CNN) methods.

Drug Repositioning Based on Deep Sparse Autoencoder and Drug-Disease Similarity.

Drug repositioning is critical to drug development. Previous drug repositioning methods mainly constructed drug-disease heterogeneous networks to extract drug-disease features. However, these methods faced difficulty when we are using structurally simple models to deal with complex heterogeneous networks. Therefore, in this study, the researchers introduced a drug repositioning method named DRDSA. The method utilizes a deep sparse autoencoder and integrates drug-disease similarities. First, the researchers constructed a drug-disease feature network by incorporating information from drug chemical structure, disease semantic data, and existing known drug-disease associations. Then, we learned the low-dimensional representation of the feature network using a deep sparse autoencoder. Finally, we utilized a deep neural network to make predictions on new drug-disease associations based on the feature representation. The experimental results show that our proposed method has achieved optimal results on all four benchmark datasets, especially on the CTD dataset where AUC and AUPR reached 0.9619 and 0.9676, respectively, outperforming other baseline methods. In the case study, the researchers predicted the top ten antiviral drugs for COVID-19. Remarkably, six out of these predictions were subsequently validated by other literature sources.

Predicting potential microbe-disease associations based on auto-encoder and graph convolution network

The increasing body of research has consistently demonstrated the intricate correlation between the human microbiome and human well-being. Microbes can impact the efficacy and toxicity of drugs through various pathways, as well as influence the occurrence and metastasis of tumors. In clinical practice, it is crucial to elucidate the association between microbes and diseases. Although traditional biological experiments accurately identify this association, they are time-consuming, expensive, and susceptible to experimental conditions. Consequently, conducting extensive biological experiments to screen potential microbe-disease associations becomes challenging. The computational methods can solve the above problems well, but the previous computational methods still have the problems of low utilization of node features and the prediction accuracy needs to be improved. To address this issue, we propose the DAEGCNDF model predicting potential associations between microbes and diseases. Our model calculates four similar features for each microbe and disease. These features are fused to obtain a comprehensive feature matrix representing microbes and diseases. Our model first uses the graph convolutional network module to extract low-rank features with graph information of microbes and diseases, and then uses a deep sparse Auto-Encoder to extract high-rank features of microbe-disease pairs, after which the low-rank and high-rank features are spliced to improve the utilization of node features. Finally, Deep Forest was used for microbe-disease potential relationship prediction. The experimental results show that combining low-rank and high-rank features helps to improve the model performance and Deep Forest has better classification performance than the baseline model.

Self-Adaptive Imbalanced Domain Adaptation With Deep Sparse Autoencoder

Domain adaptation aims to transfer knowledge between different domains to develop an effective hypothesis in the target domain with scarce labeled data, which is an effective method for remedying the problem of labeled data requirement in deep learning. In reality, it is unavoidable that the dataset has a large gap in the number of positive and negative instances across different categories in source and target domains, which is the imbalanced domain adaptation problem. However, since the imbalanced degree always varies greatly in different source and target domains datasets, most existing imbalanced domain adaptation models fix the imbalanced parameters which can not adapt to the change of the proportion between positive and negative instances in different domains. To address this problem, in this paper, we propose a self-adaptive imbalanced domain adaptation method via deep sparse autoencoder, which can adjust the model automatically according to the imbalanced extent for bridging the chasm of domains. More specifically, the self-adaptive imbalanced cross entropy loss is designed for emphasizing more on minority categories and compensating the bias of training loss automatically. In addition, to alleviate the deficient problem of labeled data, we further propose the unlabeled information incorporating method by minimizing the distribution discrepancy of high-level representation space between the source and target domains. Experiments on several real-world datasets demonstrate the effectiveness of our method compared to other state-of-the-art methods.

Predicting potential microbe-disease associations based on multi-source features and deep learning.

Studies have confirmed that the occurrence of many complex diseases in the human body is closely related to the microbial community, and microbes can affect tumorigenesis and metastasis by regulating the tumor microenvironment. However, there are still large gaps in the clinical observation of the microbiota in disease. Although biological experiments are accurate in identifying disease-associated microbes, they are also time-consuming and expensive. The computational models for effective identification of diseases related microbes can shorten this process, and reduce capital and time costs. Based on this, in the paper, a model named DSAE_RF is presented to predict latent microbe-disease associations by combining multi-source features and deep learning. DSAE_RF calculates four similarities between microbes and diseases, which are then used as feature vectors for the disease-microbe pairs. Later, reliable negative samples are screened by k-means clustering, and a deep sparse autoencoder neural network is further used to extract effective features of the disease-microbe pairs. In this foundation, a random forest classifier is presented to predict the associations between microbes and diseases. To assess the performance of the model in this paper, 10-fold cross-validation is implemented on the same dataset. As a result, the AUC and AUPR of the model are 0.9448 and 0.9431, respectively. Furthermore, we also conduct a variety of experiments, including comparison of negative sample selection methods, comparison with different models and classifiers, Kolmogorov-Smirnov test and t-test, ablation experiments, robustness analysis, and case studies on Covid-19 and colorectal cancer. The results fully demonstrate the reliability and availability of our model.

Few-shot IoT attack detection based on SSDSAE and adaptive loss weighted meta residual network

The Internet of Things (IoT) is an open and comprehensive network of smart objects. Unfortunately, it is also becoming increasingly vulnerable to security attacks during the increasing popularity of the IoT. It can lead traditional antivirus software to be less likely to prevent this threat. Therefore, it is necessary to design a model for the IoT attack detection. Current detection models are trained using massive big data samples. However, the distribution of traffic samples is few in specific scenarios. Also, existing models are also susceptible to noise interference in IoT environments, lowering detection efficiency and accuracy. In this work, we propose a few-shot IoT attack detection approach using a semi-supervised deep sparse autoencoder (SSDSAE) and an adaptive loss weighted meta residual network (ALWM-ResNet). First, an SSDSAE feature extraction model for local graph embedding is designed using local and non-local graph embedding constraints. Then, we design an ALWM-ResNet model to achieve IoT attack detection with few-shot samples under noise labels. A weighted function map is established using a weighted network and a meta-model, and weights are adaptively learned from the noise labels. Finally, we validate our approach using four IoT datasets. Several experimental results demonstrate the superior performance of our approach in IoT attack detection under few-shot samples.

Fault Detection and Identification in an Acid Gas Removal Unit Using Deep Autoencoders.

An acid gas removal unit (AGRU) in a natural gas processing plant is designed specifically to remove acidic components, such as carbon dioxide (CO2) and hydrogen sulfide (H2S), from the natural gas. The occurrence of faults, such as foaming, and to a lesser extent, damaged trays and fouling, in AGRUs is a commonly encountered problem; however, they are the least studied in the open literature. Hence, in this paper, shallow and deep sparse autoencoders with SoftMax layers are investigated to facilitate early detection of these three faults before any significant financial loss. The dynamic behavior of process variables in AGRUs in the presence of fault conditions was simulated using Aspen HYSYS Dynamics. The simulated data were used to compare five closely related fault diagnostic models, i.e., a model based on principal component analysis, a shallow sparse autoencoder without fine-tuning, a shallow sparse autoencoder with fine-tuning, a deep sparse autoencoder without fine-tuning, and a deep sparse autoencoder with fine-tuning. All models could distinguish reasonably well between the different fault conditions. The deep sparse autoencoder with fine-tuning was best able to do so with very high accuracy. Visualization of the autoencoder features yielded further insight into the performance of the models, as well as the dynamic behavior of the AGRU. Foaming was relatively difficult to distinguish from normal operating conditions. The features obtained from the fine-tuned deep autoencoder in particular can be used to construct bivariate scatter plots as a basis for automatic monitoring of the process.

Intrusion Detection Using Chaotic Poor and Rich Optimization with Deep Learning Model for Smart City Environment

Artificial intelligence (AI) techniques play a vital role in the evolving growth and rapid development of smart cities. To develop a smart environment, enhancements to the execution, sustainability, and security of traditional mechanisms become mandatory. Intrusion detection systems (IDS) can be considered an effective solutions to achieve security in the smart environment. This article introduces intrusion detection using chaotic poor and rich optimization with a deep learning model (IDCPRO-DLM) for ubiquitous and smart atmospheres. The IDCPRO-DLM model follows preprocessing, feature selection, and classification stages. At the initial stage, the Z-score data normalization system is exploited to scale the input data. Additionally, the IDCPRO-DLM method designs a chaotic poor and rich optimization algorithm-based feature selection (CPROA-FS) approach for selecting feature subsets. For intrusion detection, butterfly optimization algorithm (BOA) with a deep sparse autoencoder (DSAE) is used. The simulation analysis of the IDCPRO-DLM technique is studied on the benchmark CICIDS dataset and the comparison results show the better performance of the IDCPRO-DLM algorithm over recent state-of-the-art approaches with a maximum accuracy of 98.53%.

A novel motor bearing fault diagnosis method based on a deep sparse binary autoencoder and principal component analysis

Due to the complex and variable operating conditions of motor bearings, it is difficult for a deep autoencoder (DAE) to effectively extract valuable fault features from the raw vibration signal, which makes it difficult to identify faults. To enhance the extraction ability of the deep features of a network model and improve the accuracy of fault identification, this paper proposes a fault diagnosis method for motor bearings based on a deep sparse binary autoencoder and principal component analysis (PCA). Firstly, a deep sparse binary autoencoder is constructed by combining an autoencoder with a binary processor to improve the ability to extract deep features. Secondly, principal component analysis is used to fuse high-dimensional features to reduce dimensionality and eliminate redundant information existing in the deep features. Finally, fused deep features are input into a Softmax classifier to train the intelligent fault diagnosis model. The proposed method is validated on a rolling bearing dataset. Compared with existing methods, the experimental results show that this method can effectively extract robust features from the original vibration signals and improve the fault diagnosis results.

The WSN intrusion detection method based on deep data mining

ABSTRACT Aiming at the defects that wireless sensor network is vulnerable to intrusion attacks and affects communication performance, a WSN intrusion detection method based on deep data mining is presented. The network traffic capture tool is used to capture the data stream of the wireless sensor network, and the captured data stream is cleaned and normalized. An improved deep sparse autoencoder is used to extract WSN intrusion features from the normalized data stream. The extracted intrusion features are taken as the initial data of K-means clustering, to optimize the k-means clustering algorithm (KMCA) by genetic algorithm. The reasonable initial center point searched by the genetic algorithm is taken as the input parameter of the improved KMCA. The proposed method can detect internal attacks, external attacks and other intrusion behaviors in wireless sensor networks, and the false detection rate is less than 1%.

Ensemble Deep Learning with Chimp Optimization Based Medical Data Classification

Eye state classification acts as a vital part of the biomedical sector, for instance, smart home device control, drowsy driving recognition, and so on. The modifications in the cognitive levels can be reflected via transforming the electroencephalogram (EEG) signals. The deep learning (DL) models automated extract the features and often showcased improved outcomes over the conventional classification model in the recognition processes. This paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classification (EDLCOA-ESC). The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing step. Besides, wavelet packet decomposition (WPD) technique is employed for the extraction of useful features from the EEG signals. In addition, an ensemble of deep sparse autoencoder (DSAE) and kernel ridge regression (KRR) models are employed for EEG Eye State classification. Finally, hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classification results to a maximum extent. An extensive range of simulation analysis on the benchmark dataset is carried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.

An explainable autoencoder with multi-paradigm fMRI fusion for identifying differences in dynamic functional connectivity during brain development

Multi-paradigm deep learning models show great potential for dynamic functional connectivity (dFC) analysis by integrating complementary information. However, many of them cannot use information from different paradigms effectively and have poor explainability, that is, the ability to identify significant features that contribute to decision making. In this paper, we propose a multi-paradigm fusion-based explainable deep sparse autoencoder (MF-EDSAE) to address these issues. Considering explainability, the MF-EDSAE is constructed based on a deep sparse autoencoder (DSAE). For integrating information effectively, the MF-EDASE contains the nonlinear fusion layer and multi-paradigm hypergraph regularization. We apply the model to the Philadelphia Neurodevelopmental Cohort and demonstrate it achieves better performance in detecting dynamic FC (dFC) that differ significantly during brain development than the single-paradigm DSAE. The experimental results show that children have more dispersive dFC patterns than adults. The function of the brain transits from undifferentiated systems to specialized networks during brain development. Meanwhile, adults have stronger connectivities between task-related functional networks for a given task than children. As the brain develops, the patterns of the global dFC change more quickly when stimulated by a task.

Remaining Useful Life Prediction of Gear Pump Based on Deep Sparse Autoencoders and Multilayer Bidirectional Long–Short–Term Memory Network

Prediction of remaining useful life is crucial for mechanical equipment operation and maintenance. It ensures safe equipment operation, reduces maintenance costs and economic losses, and promotes development. Most of the remaining useful life prediction studies focus on bearings, gearboxes, and engines; however, research on hydraulic pumps remains limited. This study focuses on gear pumps that are commonly used in the hydraulic field and develops a practical method of predicting remaining useful life. The deep sparse autoencoder is used to extract multi–dimensional features. Subsequently, the feature vectors are inputted to the support vector data description to calculate the machine degradation degree at the corresponding time and obtain the health indicator curve of the machine’s life cycle. In building the health state degradation curve, data are processed in an unsupervised manner to avoid the influence of artificial feature selection on the test. The method is validated on the public bearing and self–collected gear pump datasets. The results are better than those of the comparative algorithms: (1) commonly used time–frequency characteristics with principal component analysis and (2) deep sparse autoencoder with self–organizing mapping. Next, the multilayer bidirectional long–short–term memory network is trained as a prediction model using the gear pump health indicator curves obtained previously and applied to the test data. Finally, the proposed method is compared with two others of the same type and the evaluation indexes are calculated based on the prediction results of the three algorithms. From the evaluation indexes, the mean absolute error of the proposed method is reduced by 2.53, and the normalized mean squared error is reduced by 0.36. This result indicates that the prediction results of the method for the remaining useful life of the gear pump are closer to the actual situation.

Prediction method of wind farm power generation capacity based on feature clustering and correlation analysis

• A wind power forecasting method based on deep sparse autoencoder is proposed. • An improved fuzzy C-means clustering algorithm is proposed. • Combining IFCM with PCC optimizes the input data for model training. • A DSAE model is constructed to effectively improve the prediction accuracy. Accurate wind power forecasting can help power systems achieve economical operation and dispatch management. This paper proposes a short-term wind power forecasting method based on feature clustering and correlation analysis, improving forecasting accuracy through data feature clustering, variable correlation analysis, and building forecasting models. More specifically, the improved fuzzy C-means (IFCM) algorithm is used to cluster the wind power dataset; Pearson correlation coefficient (PCC) is used to explore the correlation between meteorology and wind power variables; Based on the deep sparse auto-encoder (DSAE) model, the predicted value of short-term wind power is obtained. Combined with the actual wind power data, the effectiveness and superiority of the proposed method are verified by comparison.

A blockchain-orchestrated deep learning approach for secure data transmission in IoT-enabled healthcare system

The integration of the Internet of Things (IoT) with traditional healthcare systems has improved quality of healthcare services. However, the wearable devices and sensors used in Healthcare System (HS) continuously monitor and transmit data to the nearby devices or servers using an unsecured open channel. This connectivity between IoT devices and servers improves operational efficiency, but it also gives a lot of room for attackers to launch various cyber-attacks that can put patients under critical surveillance in jeopardy. In this article, a Blockchain-orchestrated Deep learning approach for Secure Data Transmission in IoT-enabled healthcare system hereafter referred to as “BDSDT” is designed. Specifically, first a novel scalable blockchain architecture is proposed to ensure data integrity and secure data transmission by leveraging Zero Knowledge Proof (ZKP) mechanism. Then, BDSDT integrates with the off-chain storage InterPlanetary File System (IPFS) to address difficulties with data storage costs and with an Ethereum smart contract to address data security issues. The authenticated data is further used to design a deep learning architecture to detect intrusion in HS network. The latter combines Deep Sparse AutoEncoder (DSAE) with Bidirectional Long Short-Term Memory (BiLSTM) to design an effective intrusion detection system. Experiments on two public data sources (CICIDS-2017 and ToN-IoT) reveal that the proposed BDSDT outperformed state-of-the-arts in both non-blockchain and blockchain settings and have obtained accuracy close to 99% using both datasets.

Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal

Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal

Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition.

Recently, emotional electroencephalography (EEG) has been of great importance in brain–computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG signal has the disadvantages of being non-smooth, non-linear, stochastic, and susceptible to background noise. Additionally, EEG signal processing network models have the disadvantages of a large number of parameters and long training time. To address the above issues, a novel model is presented in this paper. Initially, a deep sparse autoencoder network (DSAE) was used to remove redundant information from the EEG signal and reconstruct its underlying features. Further, combining a convolutional neural network (CNN) with long short-term memory (LSTM) can extract relevant features from task-related features, mine the correlation between the 32 channels of the EEG signal, and integrate contextual information from these frames. The proposed DSAE + CNN + LSTM (DCRNN) model was experimented with on the public dataset DEAP. The classification accuracies of valence and arousal reached 76.70% and 81.43%, respectively. Meanwhile, we conducted experiments with other comparative methods to further demonstrate the effectiveness of the DCRNN method.

Automated Design Error Debugging of Digital VLSI Circuits

As the complexity and scope of VLSI designs continue to grow, fault detection processes in the pre-silicon stage have become crucial to guaranteeing reliability in IC design. Most fault detection algorithms can be solved by transforming them into a satisfiability (SAT) problem decipherable by SAT solvers. However, SAT solvers consume significant computational time, as a result of the search space explosion problem. This ever- increasing amount of data can be handled via machine learning techniques known as deep learning algorithms. In this paper, we propose a new approach utilizing deep learning for fault detection (FD) of combinational and sequential circuits in a type of stuck-at-faults. The goal of the proposed semi-supervised FD model is to avoid the search space explosion problem by taking advantage of unsupervised and supervised learning processes. First, the unsupervised learning process attempts to extract underlying concepts of data using Deep sparse autoencoder. Then, the supervised process tends to describe rules of classification that are applied to the reduced features for detecting different stuck-at faults within circuits. The FD model proposes good performance in terms of running time about 187 × compared to other FD algorithm based on SAT solvers. In addition, it is compared to common classical machine learning models such as Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB) classifiers, in terms of validation accuracy. The results show a maximum validation accuracy of the feature extraction process at 99.93%, using Deep sparse autoencoder for combinational circuits. For sequential circuits, stacked sparse autoencoder presents 99.95% as average validation accuracy. The fault detection process delivers around 99.6% maximum validation accuracy for combinational circuits from ISCAS’85 and 99.8% for sequential circuits from ISCAS’89 benchmarks. Moreover, the proposed FD model has achieved a running time of about 1.7x, compared to DT classifier and around 1.6x, compared to RF classifier and GB machine learning classifiers, in terms of validation accuracy in detecting faults occurred in eight different digital circuits. Furthermore, the proposed model outperforms other FD models, based on Radial Basis Function Network (RBFN), achieving 97.8% maximum validation accuracy.