Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause

Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the proposed technology may be feasible for processing SXI-simulated observation images.

Simulation of the SMILE Soft X-ray Imager response to a southward interplanetary magnetic field turning

The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)Soft X-ray Imager(SXI)will shine a spotlight on magnetopause dynamics during magnetic reconnection.We simulate an event with a southward interplanetary magnetic field turning and produce SXI count maps with a 5-minute integration time.By making assumptions about the magnetopause shape,we find the magnetopause standoff distance from the count maps and compare it with the one obtained directly from the magnetohydrodynamic(MHD)simulation.The root mean square deviations between the reconstructed and MHD standoff distances do not exceed 0.2 RE(Earth radius)and the maximal difference equals 0.24 RE during the 25-minute interval around the southward turning.

SMILE soft X-ray Imager flight model CCD370 pre-flight device characterisation

Throughout the SMILE mission the satellite will be bombarded by radiation which gradually damages the focal plane devices and degrades their performance.In order to understand the changes of the CCD370s within the soft X-ray Imager,an initial characterisation of the devices has been carried out to give a baseline performance level.Three CCDs have been characterised,the two flight devices and the flight spa re.This has been carried out at the Open University in a bespo ke cleanroom measure ment facility.The results show that there is a cluster of bright pixels in the flight spa re which increases in size with tempe rature.However at the nominal ope rating tempe rature(-120℃) it is within the procure ment specifications.Overall,the devices meet the specifications when ope rating at -120℃ in 6 × 6 binned frame transfer science mode.The se rial charge transfer inefficiency degrades with temperature in full frame mode.However any charge losses are recovered when binning/frame transfer is implemented.

Automated Algorithms for Detecting and Classifying X-Ray Images of Spine Fractures

This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spinefractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picturesegmentation, feature reduction, and image classification. Two important elements are investigated to reducethe classification time: Using feature reduction software and leveraging the capabilities of sophisticated digitalprocessing hardware. The researchers use different algorithms for picture enhancement, including theWiener andKalman filters, and they look into two background correction techniques. The article presents a technique forextracting textural features and evaluates three picture segmentation algorithms and three fractured spine detectionalgorithms using transformdomain, PowerDensity Spectrum(PDS), andHigher-Order Statistics (HOS) for featureextraction.With an emphasis on reducing digital processing time, this all-encompassing method helps to create asimplified system for classifying fractured spine fractures. A feature reduction program code has been built toimprove the processing speed for picture classification. Overall, the proposed approach shows great potential forsignificantly reducing classification time in clinical settings where time is critical. In comparison to other transformdomains, the texture features’ discrete cosine transform (DCT) yielded an exceptional classification rate, and theprocess of extracting features from the transform domain took less time. More capable hardware can also result inquicker execution times for the feature extraction algorithms.

The Soft X-ray Imager(SXI)on the SMILE Mission

The Soft X-ray Imager(SXI)is part of the scientific payload of the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.SMILE is a joint science mission between the European Space Agency(ESA)and the Chinese Academy of Sciences(CAS)and is due for launch in 2025.SXI is a compact X-ray telescope with a wide field-of-view(FOV)capable of encompassing large portions of Earth’s magnetosphere from the vantage point of the SMILE orbit.SXI is sensitive to the soft X-rays produced by the Solar Wind Charge eXchange(SWCX)process produced when heavy ions of solar wind origin interact with neutral particles in Earth’s exosphere.SWCX provides a mechanism for boundary detection within the magnetosphere,such as the position of Earth’s magnetopause,because the solar wind heavy ions have a very low density in regions of closed magnetic field lines.The sensitivity of the SXI is such that it can potentially track movements of the magnetopause on timescales of a few minutes and the orbit of SMILE will enable such movements to be tracked for segments lasting many hours.SXI is led by the University of Leicester in the United Kingdom(UK)with collaborating organisations on hardware,software and science support within the UK,Europe,China and the United States.

Estimating the subsolar magnetopause position from soft X-ray images using a low-pass image filter

The Lunar Environment heliospheric X-ray Imager(LEXI)and Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)missions will image the Earth’s dayside magneto pause and cusps in soft X-rays after their respective launches in the near future,to specify glo bal magnetic reconnection modes for varying solar wind conditions.To suppo rt the success of these scientific missions,it is critical to develop techniques that extract the magnetopause locations from the observed soft X-ray images.In this research,we introduce a new geometric equation that calculates the subsolar magnetopause position(RS)from a satellite position,the look direction of the instrument,and the angle at which the X-ray emission is maximized.Two assumptions are used in this method:(1)The look direction where soft X-ray emissions are maximized lies tangent to the magnetopause,and(2)the magnetopause surface near the subsolar point is almost spherical and thus RSis nea rly equal to the radius of the magneto pause curvature.We create synthetic soft X-ray images by using the Open Geospace General Circulation Model(OpenGGCM)global magnetohydrodynamic model,the galactic background,the instrument point spread function,and Poisson noise.We then apply the fast Fourier transform and Gaussian low-pass filte rs to the synthetic images to re move noise and obtain accurate look angles for the soft X-ray pea ks.From the filte red images,we calculate RS and its accuracy for different LEXI locations,look directions,and solar wind densities by using the OpenGGCM subsolar magnetopause location as ground truth.Our method estimates RS with an accuracy of<0.3 RE when the solar wind density exceeds>10 cm-3.The accuracy improves for greater solar wind densities and during southward interplanetary magnetic fields.The method ca ptures the magnetopause motion during southwa rd interplaneta ry magnetic field turnings.Consequently,the technique will enable quantitative analysis of the magnetopause motion and help reveal the dayside reconnection modes for dynamic solar wind conditions.This technique will suppo rt the LEXI and SMILE missions in achieving their scientific o bjectives.

A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

An illness known as pneumonia causes inflammation in the lungs.Since there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven challenging.To improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been devised.To date,several methods have been employed to identify pneumonia.The Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology.However,these methods are complex,inefficient,and imprecise to analyze a big number of datasets.In this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is proposed.The proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and accuracy.The Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)approach.Relevant features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of Pneumonia.Experimental results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification tasks.The proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

Organic X-Ray Image Sensors Using a Medium Bandgap Polymer Donor with Low Dark Current

The development of portable X-ray detectors is necessary for diagnosing fractures in unconscious patients in emergency situations.However,this is quite challenging because of the heavy weight of the scintillator and silicon photodetectors.The weight and thickness of X-ray detectors can be reduced by replacing the silicon layer with an organic photodetectors.This study presents a novel bithienopyrroledione-based polymer donor that exhibits excellent photodetection properties even in a thick photoactive layer(~700 nm),owing to the symmetric backbone and highly soluble molecular structure of bithienopyrroledione.The ability of bithienopyrroledione-based polymer donor to strongly suppress the dark current density(Jd~10−10 A cm^(−2))at a negative bias(−2.0 V)while maintaining high responsivity(R=0.29 A W−1)even at a thickness of 700 nm results in a maximum shot-noise-limited specific detectivity of D_(sh)^(*)=2.18×10^(13)Jones in the organic photodetectors.Printed organic photodetectors are developed by slot-die coating for use in X-ray detectors,which exhibit D_(sh)^(*)=2.73×10^(12)Jones with clear rising(0.26 s)and falling(0.29 s)response times upon X-ray irradiation.Detection reliability is also proven by linear response of the X-ray detector,and the X-ray detection limit is 3 mA.

Tuberculosis Diagnosis and Visualization with a Large Vietnamese X-Ray Image Dataset

Tuberculosis is a dangerous disease to human life,and we need a lot of attempts to stop and reverse it.Significantly,in theCOVID-19 pandemic,access to medical services for tuberculosis has become very difficult.The late detection of tuberculosis could lead to danger to patient health,even death.Vietnamis one of the countries heavily affected by the COVID-19 pandemic,andmany residential areas as well as hospitals have to be isolated for a long time.Reality demands a fast and effective tuberculosis diagnosis solution to deal with the difficulty of accessingmedical services,such as an automatic tuberculosis diagnosis system.In our study,aiming to build that system,we were interested in the tuberculosis diagnosis problem from the chest X-ray images of Vietnamese patients.The chest X-ray image is an important data type to diagnose tuberculosis,and it has also received a lot of attention from deep learning researchers.This paper proposed a novel method for tuberculosis diagnosis and visualization using the deeplearning approach with a large Vietnamese X-ray image dataset.In detail,we designed our custom convolutional neural network for the X-ray image classification task and then analyzed the predicted result to provide visualization as a heat-map.To prove the performance of our network model,we conducted several experiments to compare it to another study and also to evaluate it with the dataset of this research.To support the implementation,we built a specific annotation system for tuberculosis under the requirements of radiologists in the Vietnam National Lung Hospital.A large experiment dataset was also from this hospital,and most of this data was for training the convolutional neural network model.The experiment results were evaluated regarding sensitivity,specificity,and accuracy.We achieved high scores with a training accuracy score of 0.99,and the testing specificity and sensitivity scores were over 0.9.Based on the X-ray image classification result,we visualize prediction results as heat-maps and also analyze them in comparison with annotated symptoms of radiologists.

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

Transfer Learning Approach to Classify the X-Ray Image that Corresponds to Corona Disease Using ResNet50 Pre-Trained by ChexNet

The COVID-19 pandemic has had a widespread negative impact globally. It shares symptoms with other respiratory illnesses such as pneumonia and influenza, making rapid and accurate diagnosis essential to treat individuals and halt further transmission. X-ray imaging of the lungs is one of the most reliable diagnostic tools. Utilizing deep learning, we can train models to recognize the signs of infection, thus aiding in the identification of COVID-19 cases. For our project, we developed a deep learning model utilizing the ResNet50 architecture, pre-trained with ImageNet and CheXNet datasets. We tackled the challenge of an imbalanced dataset, the CoronaHack Chest X-Ray dataset provided by Kaggle, through both binary and multi-class classification approaches. Additionally, we evaluated the performance impact of using Focal loss versus Cross-entropy loss in our model.

COVID-19 Detection from Chest X-Ray Images Using Convolutional Neural Network Approach

COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus, first identified in 2019. The primary mode of transmission is through respiratory droplets when an infected person coughs or sneezes. Symptoms can range from mild to severe, and timely diagnosis is crucial for effective treatment. Chest X-Ray imaging is one diagnostic tool used for COVID-19, and a Convolutional Neural Network (CNN) is a popular technique for image classification. In this study, we proposed a CNN-based approach for detecting COVID-19 in chest X-Ray images. The model was trained on a dataset containing both COVID-19 positive and negative cases and evaluated on a separate test dataset to measure its accuracy. Our results indicated that the CNN approach could accurately detect COVID-19 in chest X-Ray images, with an overall accuracy of 97%. This approach could potentially serve as an early diagnostic tool to reduce the spread of the virus.

A Deep Learning Approach for Detecting Covid-19 Using the Chest X-Ray Images

Real-time detection of Covid-19 has definitely been the most widely-used world-wide classification problem since the start of the pandemic from 2020 until now.In the meantime,airspace opacities spreads related to lung have been of the most challenging problems in this area.A common approach to do on that score has been using chest X-ray images to better diagnose positive Covid-19 cases.Similar to most other classification problems,machine learning-based approaches have been the first/most-used candidates in this application.Many schemes based on machine/deep learning have been proposed in recent years though increasing the performance and accuracy of the system has still remained an open issue.In this paper,we develop a novel deep learning architecture to better classify the Covid-19 X-ray images.To do so,we first propose a novel multi-habitat migration artificial bee colony(MHMABC)algorithm to improve the exploitation/exploration of artificial bee colony(ABC)algorithm.After that,we optimally train the fully connected by using the proposed MHMABC algorithm to obtain better accuracy and convergence rate while reducing the execution cost.Our experiment results on Covid-19 X-ray image dataset show that the proposed deep architecture has a great performance in different important optimization parameters.Furthermore,it will be shown that the MHMABC algorithm outperforms the state-of-the-art algorithms by evaluating its performance using some wellknown benchmark datasets.

Construction and Validation of a Geometry-based Mathematical Model for the Hard X-Ray Imager

Quantitative and analytical analysis of the modulation process of the collimator is a great challenge,and is also of great value to the design and development of Fourier transform imaging telescopes.The Hard X-ray Imager(HXI),as one of the three payloads onboard the Advanced Space-based Solar Observatory(ASO-S) mission,adopts modulating Fourier-Transformation imaging technique and will be used to explore the mechanism of energy release and transmission in solar flare activities.As an important step to reconstruct the images of solar flares,accurate modulation functions of HXI are needed.In this paper,a mathematical model is developed to analyze the modulation function under a simplified condition first.Then its behavior under six degrees of freedom is calculated after adding the rotation matrix and translation change to the model.In addition,unparalleled light and extended sources are also considered so that our model can be used to analyze the X-ray beam experiment.Next,applied to the practical HXI conditions,the model has been confirmed not only by Geant4 simulations but also by some verification experiments.Furthermore,how this model helps to improve the image reconstruction process after the launch of ASO-S is also presented.

COVID-19 Classification from X-Ray Images:An Approach to Implement Federated Learning on Decentralized Dataset

The COVID-19 pandemic has devastated our daily lives,leaving horrific repercussions in its aftermath.Due to its rapid spread,it was quite difficult for medical personnel to diagnose it in such a big quantity.Patients who test positive for Covid-19 are diagnosed via a nasal PCR test.In comparison,polymerase chain reaction(PCR)findings take a few hours to a few days.The PCR test is expensive,although the government may bear expenses in certain places.Furthermore,subsets of the population resist invasive testing like swabs.Therefore,chest X-rays or Computerized Vomography(CT)scans are preferred in most cases,and more importantly,they are non-invasive,inexpensive,and provide a faster response time.Recent advances in Artificial Intelligence(AI),in combination with state-of-the-art methods,have allowed for the diagnosis of COVID-19 using chest x-rays.This article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning scheme.In order to build a progressive global COVID-19 classification model,two edge devices are employed to train the model on their respective localized dataset,and a 3-layered custom Convolutional Neural Network(CNN)model is used in the process of training the model,which can be deployed from the server.These two edge devices then communicate their learned parameter and weight to the server,where it aggregates and updates the globalmodel.The proposed model is trained using an image dataset that can be found on Kaggle.There are more than 13,000 X-ray images in Kaggle Database collection,from that collection 9000 images of Normal and COVID-19 positive images are used.Each edge node possesses a different number of images;edge node 1 has 3200 images,while edge node 2 has 5800.There is no association between the datasets of the various nodes that are included in the network.By doing it in this manner,each of the nodes will have access to a separate image collection that has no correlation with each other.The diagnosis of COVID-19 has become considerably more efficient with the installation of the suggested algorithm and dataset,and the findings that we have obtained are quite encouraging.

Application of Dual-Energy X-Ray Image Detection of Dangerous Goods Based on YOLOv7

X-ray security equipment is currently a more commonly used dangerous goods detection tool, due to the increasing security work tasks, the use of target detection technology to assist security personnel to carry out work has become an inevitable trend. With the development of deep learning, object detection technology is becoming more and more mature, and object detection framework based on convolutional neural networks has been widely used in industrial, medical and military fields. In order to improve the efficiency of security staff, reduce the risk of dangerous goods missed detection. Based on the data collected in X-ray security equipment, this paper uses a method of inserting dangerous goods into an empty package to balance all kinds of dangerous goods data and expand the data set. The high-low energy images are combined using the high-low energy feature fusion method. Finally, the dangerous goods target detection technology based on the YOLOv7 model is used for model training. After the introduction of the above method, the detection accuracy is improved by 6% compared with the direct use of the original data set for detection, and the speed is 93FPS, which can meet the requirements of the online security system, greatly improve the work efficiency of security personnel, and eliminate the security risks caused by missed detection.

Ultrafast two-dimensional x-ray imager with temporal fiducial pulses for laser-produced plasmas

It is challenging to make an ultrafast diagnosis of the temporal evolution of small and short-lived plasma in two dimensions. To overcome this difficulty, we have developed a well-timed diagnostic utilizing an x-ray streak camera equipped with a row of multi-pinhole arrays. By processing multiple sets of one-dimensional streaked image data acquired from various pinholes, we are capable of reconstructing high-resolution two-dimensional images with a temporal resolution of 38 ps and a spatial resolution of 18 μm. The temporal fiducial pulses accessed from external sources can advance the precise timing and accurately determine the arrival time of the laser. Moreover, it can correct the nonlinear sweeping speed of the streak camera. The effectiveness of this diagnostic has been successfully verified at the Shenguang-II laser facility,providing an indispensable tool for observing complex physical phenomena, such as the implosion process of laser-fusion experiments.

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs.Chest X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and portability.In radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer variability.Using lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is advantageous.The current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on optimization.The data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s edges.Then,the OSDL model is applied to classify the CXRs under different severity levels based on CXR data.The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work.OSDL model,applied in this study,was validated using the COVID-19 dataset.The experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

In computer vision,object recognition and image categorization have proven to be difficult challenges.They have,nevertheless,generated responses to a wide range of difficult issues from a variety of fields.Convolution Neural Networks(CNNs)have recently been identified as the most widely proposed deep learning(DL)algorithms in the literature.CNNs have unquestionably delivered cutting-edge achievements,particularly in the areas of image classification,speech recognition,and video processing.However,it has been noticed that the CNN-training assignment demands a large amount of data,which is in low supply,especially in the medical industry,and as a result,the training process takes longer.In this paper,we describe an attentionaware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties.AttentionModules provide attention-aware properties to the Attention Network.The attentionaware features of various modules alter as the layers become deeper.Using a bottom-up top-down feedforward structure,the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention module.In the present work,a deep neural network(DNN)is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray pictures.To produce attention-aware features,the suggested networkwas built by merging channel and spatial attentionmodules in DNN architecture.With this network,we worked on a publicly available Kaggle chest X-ray dataset.Extensive testing was carried out to validate the suggested model.In the experimental results,we attained an accuracy of 95.47%and an F-score of 0.92,indicating that the suggested model outperformed against the baseline models.

Knee Osteoarthritis Classification Using X-Ray Images Based on Optimal Deep Neural Network

X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost.However,the manual categorization of knee joint disorders is time-consuming,requires an expert person,and is costly.This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm.Two pre-trained deep learning models(Efficientnet-b0 and Densenet201)have been employed for the training and feature extraction.Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images.In the next step,fusion is performed using a canonical correlation approach and obtained a feature vector that has more information than the original feature vector.After that,an improved whale optimization algorithm is developed for dimensionality reduction.The selected features are finally passed to the machine learning algorithms such as Fine-Tuned support vector machine(SVM)and neural networks for classification purposes.The experiments of the proposed framework have been conducted on the publicly available dataset and obtained the maximum accuracy of 90.1%.Also,the system is explained using Explainable Artificial Intelligence(XAI)technique called occlusion,and results are compared with recent research.Based on the results compared with recent techniques,it is shown that the proposed method’s accuracy significantly improved.