Fusion Vector Research Articles

HOG transformation based feature extraction framework in modified Resnet50 model for brain tumor detection

Brain tumor happens due to the instant and uncontrolled cell growth. It may lead to death if not cured at an early stage. In spite of several promising results and substantial efforts in this research area, the real challenge is to provide the accurate classification and segmentation. The key issue in brain tumor detection develops from the irregular changes in the tumor size, shape and location. In assessing the MRI images, computer-aided diagnoses are playing an extraordinary role and can help clinicians/radiologist. Nowadays, brain tumor has become the most incursive ailment that leads to a very short life expectancy when it reaches its highest grade. This research paper has created a new model using histogram of gradient (HOG) based neural features from MRI images for tumors detection. This research has conducted the feature optimization approach to achieve additional instinctive features from the complex feature vector. We developed a Modified ResNet50 model with HOG technique. The modified ResNet50 model can accurately extract the deep feature using deep learning approach. This model is applied along with the upgraded layered architecture in order to keep the optimal computational efficiency. We have also used the augmentation and feature extraction techniques using machine learning-based ensemble classifier that further provides the optimized fusion vector to identify the tumor. Such hybrid approach provides excellent performance with the detection accuracy of 88% with HOG and modified ResNet50. The results are also compared with the recent state of art methods.

A nanotherapeutic system for gastric cancer suppression by synergistic chemotherapy and immunotherapy based on iPSCs and DCs exosomes.

Chemotherapeutic drugs, the indispensable therapy in the treatment of gastric cancer, contain many problems such as high organ toxicity and insufficient therapeutic effect. The development of nanodrug delivery carriers with both tumor targeting function and immune stimulation ability possesses the potential to remedy these practical defects. In this study, a tumor targeting nanosystem that combines chemotherapy with immunotherapy was applied to the treatment and prognosis of gastric cancer. The fusion vector of iPSCs and DCs exosomes, which simultaneously possess the ability of tumor targeting and immune factor recruitment, effectively improved the in vivo efficacy of chemotherapy drugs and released the suppressed T lymphocytes under the action of modified PD-1 antibody to dredge the immunotherapy process. In addition, extensive recruitment of immune cells to clean the environment while exposing vast tumor antigens efficiently amplified the anti-tumor immune effect and ensured the good prognosis. Nanodrug delivery system DOX@aiPS-DCexo could effectively inhibit the expansion process of gastric cancer MFC through synergistic chemotherapy and immunotherapy and demonstrated the capacity of improving prognosis. Scheme: schematic illustration of the nanostructure DOX@aiPS-DCexo and the mechanism of action.

Coherent photons induced high energy reactions in ultraperipheral heavy ion collisions

We review the recent progress in the studies of coherent photons induced high energy reactions in ultraperipheral heavy ion collisions. The strong electromagnetic field created by a fast moving charged heavy ion can be effectively viewed as a flux of quasi-real coherent photons. In this paper, we mainly discuss two different type processes that coherent photons take part in: lepton pair production via photon fusion and diffractive vector meson production in UPCs. We focus on investigating the impact parameter dependent effect and the final state soft radiation effect. On the other hand, a series of recent work have revealed that coherent photons are highly linearly polarized with its polarization vector being parallel to its transverse momentum. It has been shown that the linearly polarized photons can lead to <inline-formula><tex-math id="M2">\begin{document}$\cos 4\phi$\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="7-20230074_M2.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="7-20230074_M2.png"/></alternatives></inline-formula> azimuthal asymmetries in di-lepton production. This theoretical predication soon has been confirmed by the STAR measurement. With this new development from both theory and experiment sides, the linearly polarized photons provide a new experimental avenue to explore novel QCD phenomenology. For example, the linearly polarized photons can give rise to various different azimuthal asymmetries in diffractive vector meson production. These observables provide us unique chance to study two source interference effect in high energy scatterings, Coulomb-Nuclear interference effect as well as extracting gluon Wigner distribution. We will discuss these novel phenomenology studies and the possible future developments.

PS-Mixer: A Polar-Vector and Strength-Vector Mixer Model for Multimodal Sentiment Analysis

Multimodal sentiment analysis aims to judge the sentiment of multimodal data uploaded by the Internet users on various social media platforms. On one hand, existing studies focus on the fusion mechanism of multimodal data such as text, audio and visual, but ignore the similarity of text and audio, text and visual, and the heterogeneity of audio and visual, resulting in deviation of sentiment analysis. On the other hand, multimodal data brings noise irrelevant to sentiment analysis, which affects the effectness of fusion. In this paper, we propose a Polar-Vector and Strength-Vector mixer model called PS-Mixer, which is based on MLP-Mixer, to achieve better communication between different modal data for multimodal sentiment analysis. Specifically, we design a Polar-Vector (PV) and a Strength-Vector (SV) for judging the polar and strength of sentiment separately. PV is obtained from the communication of text and visual features to decide the sentiment that is positive, negative, or neutral sentiment. SV is gained from the communication between the text and audio features to analyze the sentiment strength in the range of 0 to 3. Furthermore, we devise an MLP-Communication module (MLP-C) composed of several fully connected layers and activation functions to make the different modal features fully interact in both the horizontal and the vertical directions, which is a novel attempt to use MLP for multimodal information communication. Finally, we mix PV and SV to obtain a fusion vector to judge the sentiment state. The proposed PS-Mixer is tested on two publicly available datasets, CMU-MOSEI and CMU-MOSI, which achieves the state-of-the-art (SOTA) performance on CMU-MOSEI compared with baseline methods. The codes are available at: https://github.com/metaphysicser/PS-Mixer.

Open-ended remote sensing visual question answering with transformers

ABSTRACT Visual question answering (VQA) has been attracting attention in remote sensing very recently. However, the proposed solutions remain rather limited in the sense that the existing VQA datasets address closed-ended question-answer queries, which may not necessarily reflect real open-ended scenarios. In this paper, we propose a new dataset named VQA-TextRS that was built manually with human annotations and considers various forms of open-ended question-answer pairs. Moreover, we propose an encoder-decoder architecture via transformers on account of their self-attention property that allows relational learning of different positions of the same sequence without the need of typical recurrence operations. Thus, we employed vision and natural language processing (NLP) transformers respectively to draw visual and textual cues from the image and respective question. Afterwards, we applied a transformer decoder, which enables the cross-attention mechanism to fuse the earlier two modalities. The fusion vectors correlate with the process of answer generation to produce the final form of the output. We demonstrate that plausible results can be obtained in open-ended VQA. For instance, the proposed architecture scores an accuracy of 84.01% on questions related to the presence of objects in the query images.

Fusion of Hand-crafted and Deep Features for Automatic Diabetic Foot Ulcer Classification

This paper proposes to combine both the texture and deep features to build a robust diabetic foot ulcer recognition system since both features represent valuable information about the disease. The proposed system consists of three stages: feature extraction, feature fusion, and DFU classification. The feature extraction is performed by extracting the handcrafted and deep features. The feature fusion is performed by concatenating both feature vectors into a single vector. The DFU classification is performed by training a random forest classifier on the fusion vectors and the resulting classifier is used then for classification. Experimental results showed that the proposed approach provides satisfactory performance in DFU, ischaemia, and infection classification.

Feature extraction of rotor-stator rubbing faults based on harmonic fusion vector bispectrum, ITD and Hjorth parameters

To solve the difficulty in extracting the characteristics of rotor–stator rubbing faults, the paper has proposed the combined method of Complexity parameter, the harmonic fusion vector bispectrum (HFVB), and intrinsic time-scale decomposition (ITD). First, to fully embody the characteristic information of fault, the HFVB is used to blend the information of signals collected from sensors installed in different positions. Second, taking in mind that the ITD algorithm can embody the effective separation of nonstationary and nonlinear signals, the ITD algorithm makes the separation of blended signals. Third, regarding the important influence of signal complexity on fault characteristic extraction, the complexity parameter of Hjorth parameters can provide very great embodiment of signal complexity. Complexity parameter of Hjorth parameters is introduced as a characteristic parameter index to make a option of proper rotation component (PRC) which can show the characteristics of rubbing fault better. Fourth, signals are reconstructed based on chosen signal components. Meanwhile, to reduce the influence of noise, reconstructed signals are denoised accordingly. Finally, implement the characteristic extraction and fault identification of rubbing faults according to the square demodulation spectrum (SDF) of denoised signals. The result indicates that the harmonic fusion vector bispectrum method can embody the effective blending of fault information; the complexity parameter in Hjorth parameter can serve as the index parameter for option of sensitive characteristic components of rubbing faults. Based on the proposed method, in the square demodulation spectrum of reconstructed signals, it can effectively and precisely provide the characteristics of rotor–stator rubbing fault and successfully identify a fault type.8714542030

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning.

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.

Objectivity meets subjectivity: A subjective and objective feature fused neural network for emotion recognition

Using multimodal fusion method to deal with emotion recognition task has become a trend. The fusion vector can more comprehensively reflect the subject’s emotional change state, so as to obtain a more accurate emotion recognition effect. However, different fusion input or feature fusion methods have different effects on the final fusion results. In this paper, we propose a subjective and objective feature fused neural network model (SOFNN) for emotion recognition, which can effectively learn spatial–temporal information from EEG signals and dynamically integrate EEG signals with eye movement signals. Specifically, we extract more abundant spatial and temporal information from the original EEG signal through a series of 1-D convolution kernels of different sizes and we verify the effectiveness of the extracted features through experiments. The size of the 1-D convolution kernels is determined by the characteristics (such as sampling rate and number of channels) of the original EEG signal. Then, we design a subjective and objective feature fusion framework to adjust the proportion of the two features through the dynamic learning of the weight vector, so as to fully exploit their respective advantages. We evaluate the performance of our model on the SEED-IV dataset, which is a common dataset. For the recognition task of four emotions (happy, sad, fear and neutral), our model achieves 86.27% accuracy and 10.16% standard deviation, which are better than the existing methods. In addition, we design a variety of ablation experiments to verify the effectiveness of each module in our model. The experiment results show that our model can make better use of the complementary relationship between subjective and objective features, which can achieve better emotion recognition effect.

Research on lithology identification based on multi-sensor hybrid domain information fusion and support vector machine

Research on lithology identification based on multi-sensor hybrid domain information fusion and support vector machine

Automated Teller Machine Authentication Using Biometric

This paper presents a novel method of a secured card-less Automated Teller Machine (ATM) authentication based on the three bio-metrics measures. It would help in the identification and authorization of individuals and would provide robust security enhancement. Moreover, it would assist in providing identification in ways that cannot be impersonated. To the best of our knowledge, this method of Biometric_ fusion way is the first ATM security algorithm that utilizes a fusion of three biometric features of an individual such as Fingerprint, Face, and Retina simultaneously for recognition and authentication. These biometric images have been collected as input data for each module in this system, like a fingerprint, a face, and a retina module. A database is created by converting these images to YIQ color space, which is helpful in normalizing the brightness levels of the image hence mainly (Y component’s) luminance. Then, it attempt to enhance Cellular Automata Segmentation has been carried out to segment the particular regions of interest from these database images. After obtaining segmentation results, the featured extraction method is carried out from these critical segments of biometric photos. The Enhanced Discrete Wavelet Transform technique (DWT Mexican Hat Wavelet) was used to extract the features. Fusion of extracted features of all three biometrics features have been used to bring in the multimodal classification approach to get fusion vectors. Once fusion vectors ware formulated, the feature level fusion technique is incorporated based on the extracted feature vectors. These features have been applied to the machine learning algorithm to identify and authorization of multimodal biometrics for ATM security. In the proposed approach, we attempt at useing an enhanced Deep Convolutional Neural Network (DCNN). A hybrid optimization algorithm has been selected based on the effectiveness of the features. The proposed approach results were compared with existing algorithms based on the classification accuracy to prove the effectiveness of our algorithm. Moreover, comparative results of the proposed method stand as a proof of more promising outcomes by combining the three biometric features.

Joint Learning With BERT-GCN and Multi-Attention for Event Text Classification and Event Assignment

Government hotline is closely related to people’s lives and plays an important role in solving social problems and maintaining social stability in China. However, the event text of the hotline is inconsistent in length and unclear in elements, so it is a challenge for the operator to manually complete the assignment tasks of hotline event. To address these problems, we propose a joint learning method for event text classification and event assignment for Chinese government hotline. Firstly, graph convolution network (GCN) and BERT are used to process the event text respectively to obtain the corresponding representation vector. Then, the obtained two representation vectors are fused by the dynamic fusion gate to get fusion vector and classified the fusion vector through the text classification. Secondly, we use multi-attention mechanism to process the GCN result vector, BERT result vector and the “sanding” vector to obtain attentive “event-sanding” representation vector and calculate the corresponding department probability distribution. Finally, the historical prior knowledge based reorder model is used to sort the results of the “event-sanding” matching module and output the optimal assignment department of government hotline event. Experimental results show that our method can achieve better performance compared with several baseline approaches. The ablation experiments also demonstrate the validity of each proposed module in our model.

Ginsenoside Rh2 Regulates the CFAP20DC-AS1/MicroRNA-3614-3p/BBX and TNFAIP3 Axis to Induce Apoptosis in Breast Cancer Cells.

While a number of coding genes have explained the anticancer activity of ginsenoside Rh2, little is known about noncoding RNAs. This study was performed to elucidate the regulatory activity of long noncoding RNA (lncRNA) CFAP20DC-AS1, which is known to be downregulated by Rh2. MiR-3614-3p, which potentially binds CFAP20DC-AS1, was screened using the LncBase Predicted program, and the binding was verified by assaying the luciferase activity of a luciferase/lncRNA recombinant plasmid construct. The competitive endogenous RNA (ceRNA) relationship of the two RNAs was further validated by quantitative PCR after deregulation of each RNA using siRNA. The effect of miRNA and target genes on the MCF-7 cancer cell growth was determined by monitoring proliferation and apoptosis in the presence of Rh2 after deregulating the corresponding gene. The miRNA decreased the luciferase activity of the luciferase/CFAP20DC-AS1 fusion vector, confirming the binding. SiRNA-based deregulation of CFAP20DC-AS1 attenuated the expression of miR-3614-3p and vice versa. In contrast to CFAP20DC-AS1, miR-3614-3p was upregulated by Rh2, inhibiting proliferation but stimulating apoptosis of the MCF-7 cells. Target genes of miR-3614-3p, BBX and TNFAIP3, were downregulated by Rh2 and the miRNA but upregulated by the lncRNA. Rh2 inhibits CFAP20DC-AS1, which obscures the association of the lncRNA with miR-3614-3p, resulting in the suppression of oncogenic BBX and TNFAIP3. Taken together, the Rh2/CFAP20DC-AS1/miR-3614-3p/target gene axis contributes to the antiproliferation activity of Rh2 in cancer cells.

GCN-BERT and Memory Network Based Multi-Label Classification for Event Text of the Chinese Government Hotline

In order to automatically generate multiple labels for the event text of the Chinese government hotline, this paper propose a multi-label classification framework based on graph convolutional network (GCN), BERT, and memory network. The framework consists of three modules: label count prediction module, label semantic insert module, and label selection module. In the label count prediction module, this paper constructs the event graph with the abstract meaning representation (AMR) and extract the event topic information vector with GCN. To predict the label count, this paper first use BERT to extract the event semantic information vector and then fuse it with the event topic information vector (GCN-BERT fusion vector) with a dynamic fusion gate. In the label semantic insert module, to obtain the event label candidate set, this paper uses a multi-hop memory network to store the event label semantic information, and then use the answer selection framework, which matches the GCN-BERT fusion vector with the event label semantic memory vector. In label selection module, this paper uses the label count based multi-label selection to sort the event label candidate set and guide to output the optimal multi-label set of the event. Comparison experimental results show that the proposed framework outperforms all baselines and ablation studies demonstrate the effectiveness of each module.

DeepCyto: a hybrid framework for cervical cancer classification by using deep feature fusion of cytology images

<abstract> <p>Cervical cancer is the second most commonly seen cancer in women. It affects the cervix portion of the vagina. The most preferred diagnostic test required for screening cervical cancer is the pap smear test. Pap smear is a time-consuming test as it requires detailed analysis by expert cytologists. Cytologists can screen around 100 to 1000 slides depending upon the availability of advanced equipment. Due to this reason Artificial intelligence (AI) based computer-aided diagnosis system for the classification of pap smear images is needed. There are some AI-based solutions proposed in the literature, still an effective and accurate system is under research. In this paper, the deep learning-based hybrid methodology namely DeepCyto is proposed for the classification of pap smear cytology images. The DeepCyto extracts the feature fusion vectors from pre-trained models and passes these to two workflows. Workflow-1 applies principal component analysis and machine learning ensemble to classify the pap smear images. Workflow-2 takes feature fusion vectors as an input and applies an artificial neural network for classification. The experiments are performed on three benchmark datasets namely Herlev, SipakMed, and LBCs. The performance measures of accuracy, precision, recall and F1-score are used to evaluate the effectiveness of the DeepCyto. The experimental results depict that Workflow-2 has given the best performance on all three datasets even with a smaller number of epochs. Also, the performance of the DeepCyto Workflow 2 on multi-cell images of LBCs is better compared to single cell images of other datasets. Thus, DeepCyto is an efficient method for accurate feature extraction as well as pap smear image classification.</p> </abstract>

IR-MF-SVMe: Image Retrieval using Multiple Features Extraction with Support Vector Machine ensemble

Image representation is a widespread strategy of image retrieval based on appearance, shape information. The traditional feature representation methods ignore hidden information that exists in the dataset samples; it reduces the discriminative performance of the classifier and excludes various geometric and photometric variations consideration in obtaining the features; these degrade retrieval performance. Hence, proposed multiple features fusion and Support Vector Machines Ensemble (IR-MF-SVMe); an Image Retrieval framework to enhance the performance of the retrieval process. The Color Histogram (CH), Color Auto-Correlogram (CAC), Color Moments (CM), Gabor Wavelet (GW), and Wavelet Moments (WM) descriptors are used to extract multiple features that separate the element vectors of images in representation. The multi-class classifier is constructed with the aggregation of binary Support Vector Machines, which decrease the count of false positives within the interrelated semantic classes. The proposed framework is validated on the WANG dataset and results in the accuracy of 84% for the individual features and 86% for the fused features related to the state-of-the-arts.

Surface display of carbonic anhydrase on Escherichia coli for CO2 capture and mineralization

Mineralization catalyzed by carbonic anhydrase (CA) is one of the most promising technologies for capturing CO2. In this work, Escherichia coli BL21(DE3) was used as the host, and the N-terminus of ice nucleation protein (INPN) was used as the carrier protein. Different fusion patterns and vectors were used to construct CA surface display systems for α-carbonic anhydrase (HPCA) from Helicobacter pylori 26695 and α-carbonic anhydrase (SazCA) from Sulfurihydrogenibium azorense. The surface display system in which HPCA was fused with INPN via a flexible linker and intermediate repeat sequences showed higher whole-cell enzyme activity, while the enzyme activity of the SazCA expression system was significantly higher than that of the HPCA expression system. The pET22b vector with the signal peptide PelB was more suitable for the cell surface display of SazCA. Cell fractionation and western-blot analysis indicated that SazCA and INPN were successfully anchored on the cell's outer membrane as a fusion protein. The enzyme activity of the surface display strain E-22b-IRLS (11.43 U·mL−1OD600−1) was significantly higher than that of the intracellular expression strain E-22b-S (8.355 U·mL−1OD600−1) under optimized induction conditions. Compared with free SazCA, E-22b-IRLS had higher thermal and pH stability. The long-term stability of SazCA was also significantly improved by surface display. When the engineered strain and free enzyme were used for CO2 mineralization, the amount of CaCO3 deposition catalyzed by the strain E-22b-IRLS on the surface (241 mg) was similar to that of the free SazCA and was significantly higher than the intracellular expression strain E-22b-S (173 mg). These results demonstrate that the SazCA surface display strain can serve as a whole-cell biocatalyst for CO2 capture and mineralization.

CSA6D: Channel-Spatial Attention Networks for 6D Object Pose Estimation

6D object pose estimation plays a crucial role in robotic manipulation and grasping tasks. The aim to estimate the 6D object pose from RGB or RGB-D images is to detect objects and estimate their orientations and translations relative to the given canonical models. RGB-D cameras provide two sensory modalities: RGB and depth images, which could benefit the estimation accuracy. But the exploitation of two different modality sources remains a challenging issue. In this paper, inspired by recent works on attention networks that could focus on important regions and ignore unnecessary information, we propose a novel network: Channel-Spatial Attention Network (CSA6D) to estimate the 6D object pose from RGB-D camera. The proposed CSA6D includes a pre-trained 2D network to segment the interested objects from RGB image. Then it uses two separate networks to extract appearance and geometrical features from RGB and depth images for each segmented object. Two feature vectors for each pixel are stacked together as a fusion vector which is refined by an attention module to generate a aggregated feature vector. The attention module includes a channel attention block and a spatial attention block which can effectively leverage the concatenated embeddings into accurate 6D pose prediction on known objects. We evaluate proposed network on two benchmark datasets YCB-Video dataset and LineMod dataset and the results show it can outperform previous state-of-the-art methods under ADD and ADD-S metrics. Also, the attention map demonstrates our proposed network searches for the unique geometry information as the most likely features for pose estimation. From experiments, we conclude that the proposed network can accurately estimate the object pose by effectively leveraging multi-modality features.

A Protein-Engineered, Enhanced Yeast Display Platform for Rapid Evolution of Challenging Targets.

Here, we enhanced the popular yeast display method by multiple rounds of DNA and protein engineering. We introduced surface exposure-tailored reporters, eUnaG2 and DnbALFA, creating a new platform of C and N terminal fusion vectors. The optimization of eUnaG2 resulted in five times brighter fluorescence and 10 °C increased thermostability than UnaG. The optimized DnbALFA has 10-fold the level of expression of the starting protein. Following this, different plasmids were developed to create a complex platform allowing a broad range of protein expression organizations and labeling strategies. Our platform showed up to five times better separation between nonexpressing and expressing cells compared with traditional pCTcon2 and c-myc labeling, allowing for fewer rounds of selection and achieving higher binding affinities. Testing 16 different proteins, the enhanced system showed consistently stronger expression signals over c-myc labeling. In addition to gains in simplicity, speed, and cost-effectiveness, new applications were introduced to monitor protein surface exposure and protein retention in the secretion pathway that enabled successful protein engineering of hard-to-express proteins. As an example, we show how we optimized the WD40 domain of the ATG16L1 protein for yeast surface and soluble bacterial expression, starting from a nonexpressing protein. As a second example, we show how using the here-presented enhanced yeast display method we rapidly selected high-affinity binders toward two protein targets, demonstrating the simplicity of generating new protein–protein interactions. While the methodological changes are incremental, it results in a qualitative enhancement in the applicability of yeast display for many applications.

An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

At present, the research on fault analysis based on text data focuses on fault diagnosis and classification, but it rarely suggests how to use that information to troubleshoot faults reported in unmanned aerial vehicles (UAVs). Selecting the exact troubleshooting procedure to address faults reported by UAVs generally requires experienced technicians with professional equipment. To improve the efficiency of UAV troubleshooting, this paper proposed a troubleshooting mode selection method based on SIF-SVM (Serial information fusion and support vector machine) using the text feature data from fault description records. First, Word2Vec was used in text data feature extraction. Second, in order to increase the amount of information in the modeling data, we used the information fusion method. SVM was then used to construct the classification model for troubleshooting mode selection. Finally, the effectiveness of the proposed model was verified by using the fault record data of a new fixed-wing UAV.