A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography

Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.

Rapid detection and risk assessment of soil contamination at lead smelting site based on machine learning

A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R^(2))values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

X-ray image distortion correction based on SVR

X-ray image has been widely used in many fields such as medical diagnosis,industrial inspection,and so on.Unfortunately,due to the physical characteristics of X-ray and imaging system,distortion of the projected image will happen,which restrict the application of X-ray image,especially in high accuracy fields.Distortion correction can be performed using algorithms that can be classified as global or local according to the method used,both having specific advantages and disadvantages.In this paper,a new global method based on support vector regression（SVR）machine for distortion correction is proposed.In order to test the presented method,a calibration phantom is specially designed for this purpose.A comparison of the proposed method with the traditional global distortion correction techniques is performed.The experimental results show that the proposed correction method performs better than the traditional global one.

Radiography Image Classification Using Deep Convolutional Neural Networks

Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.

Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic loss.But unlike smallpox,the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement inmedical aid and diagnostics.Data analytics,machine learning,and automation techniques can help in early diagnostics and supporting treatments of many reported patients.This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques.Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task.We used a publicly open CXR image dataset and implemented the detectionmodelwith task-specific pre-processing and near 80:20 split.This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597,which shall help better decision-making for various aspects of identification and treat the infection.

Multi-Label Chest X-Ray Classification via Deep Learning

In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specific needs within the industry. Deep learning in healthcare had become incredibly powerful for supporting clinics and in transforming patient care in general. Deep learning is increasingly being applied for the detection of clinically important features in the images beyond what can be perceived by the naked human eye. Chest X-ray images are one of the most common clinical method for diagnosing a number of diseases such as pneumonia, lung cancer and many other abnormalities like lesions and fractures. Proper diagnosis of a disease from X-ray images is often challenging task for even expert radiologists and there is a growing need for computerized support systems due to the large amount of information encoded in X-Ray images. The goal of this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. Along with the image features, we are also going to use non-image features available in the data such as X-ray view type, age, gender etc. The original study conducted Stanford ML Group is our base line. Original study focuses on predicting 5 diseases. Our aim is to improve upon previous work, expand prediction to 14 diseases and provide insight for future chest radiography research.

Chest Radiographs Based Pneumothorax Detection Using Federated Learning

Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse,causing air to enter the pleural cavity,the area close to the lungs and chest wall.The most persistent disease,as well as one that necessitates particular patient care and the privacy of their health records.The radiologists find it challenging to diagnose pneumothorax due to the variations in images.Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems.However,it is challenging to employ it in the medical field due to privacy issues and a lack of data.To address this issue,a federated learning framework based on an Xception neural network model is proposed in this research.The pneumothorax medical image dataset is obtained from the Kaggle repository.Data preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s performance.Min-max normalization technique is used to normalize the data,and the features are extracted from chest Xray images.Then dataset converts into two windows to make two clients for local model training.Xception neural network model is trained on the dataset individually and aggregates model updates from two clients on the server side.To decrease the over-fitting effect,every client analyses the results three times.Client 1 performed better in round 2 with a 79.0%accuracy,and client 2 performed better in round 2 with a 77.0%accuracy.The experimental result shows the effectiveness of the federated learning-based technique on a deep neural network,reaching a 79.28%accuracy while also providing privacy to the patient’s data.

A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

Coronavirus(COVID-19 or SARS-CoV-2)is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries.The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission.There is a signifi-cant increase in the number of patients infected,resulting in a lack of test resources and kits in most countries.To overcome this panicked state of affairs,researchers are looking forward to some effective solutions to overcome this situa-tion:one of the most common and effective methods is to examine the X-radiation(X-rays)and computed tomography(CT)images for detection of Covid-19.How-ever,this method burdens the radiologist to examine each report.Therefore,to reduce the burden on the radiologist,an effective,robust and reliable detection system has been developed,which may assist the radiologist and medical specia-list in effective detecting of COVID.We proposed a deep learning approach that uses readily available chest radio-graphs(chest X-rays)to diagnose COVID-19 cases.The proposed approach applied transfer learning to the Deep Convolutional Neural Network(DCNN)model,Inception-v4,for the automatic detection of COVID-19 infection from chest X-rays images.The dataset used in this study contains 1504 chest X-ray images,504 images of COVID-19 infection,and 1000 normal images obtained from publicly available medical repositories.The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.

Characterization of the Convoluted 3D Internetallic Phases in a Recycled Al Alloy by Synchrotron X-ray Tomography and Machine Learning

Fe-rich intermetallic phases in recycled Al alloys often exhibit complex and 3D convoluted structures and morphologies.They are the common detrimental intermetallic phases to the mechanical properties of recycled Al alloys.In this study,we used synchrotron X-ray tomography to study the true 3D morphologies of the Ferich phases,Al_(2)Cu phases and casting defects in an ascast Al-5Cu-1.5Fe-1Si alloy.Machine learning-based image processing approach was used to recognize and segment the diff erent phases in the 3D tomography image stacks.In the studied condition,theβ-Al_(9)Fe_(2)Si_(2)andω-Al_(7)Cu_(2)Fe are found to be the main Fe-rich intermetallic phases.Theβ-Al_(9)Fe_(2)Si_(2)phases exhibit a spatially connected 3D network structure and morphology which in turn control the 3D spatial distribution of the Al_(2)Cu phases and the shrinkage cavities.The Al_(3)Fe phases formed at the early stage of solidification aff ect to a large extent the structure and morphology of the subsequently formed Fe-rich intermetallic phases.The machine learning method has been demonstrated as a powerful tool for processing big datasets in multidimensional imaging-based materials characterization work.

Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data

High-performance all-solid-state lithium-ion batteries require observation,control,and optimization of the electrode structure.X-ray computational tomography(CT)is an effective nondestructive method for observing the electrode structure in three dimensions.However,the limited availability of synchrotron radiation CT,which offers high-resolution imaging with a high signal-to-noise ratio,makes it difficult to conduct experiments and restricts the use of X-ray CT in battery development.Conversely,laboratory CT systems are widely available,but they use X-rays emitted from a metal target,resulting in lower image quality and resolution compared with synchrotron radiation CT.This study explores a method for achieving comparable resolution in laboratory CT images of all-solid-state batteries to that of synchrotron radiation CT.Our method involves using the synchrotron radiation CT images as training data for machine learning super-resolution.The results demonstrate that,by employing an appropriate machine learning algorithm and activation function,along with a sufficiently deep network,the image quality of laboratory CT becomes equivalent to that of synchrotron radiation CT.

Preparation of CePO_4-coated zirconia ceramics and their mechanical behavior

Tetragonal ZrO2-3 mol% Y2O3 （3Y-TZP） coated with CePO4 was synthesized by a co-precipitation method and the effects of CePO4 content and sintering temperature on its mechanical properties were investigated. The microstructure and phase composition of the products were characterized using scanning and transmission electron microscopy as well as X-ray diffraction, respectively. The machinability index of CePO4-coated zirconia was calculated to be 1.05 when the CePO4 content is 25 wt.%. The sample hardness, bending strength and fracture toughness are 7.08 GPa, 457.85 MPa and 9.75 MPa m1/2, respectively, when the sintering temperature is 1400°C. The results show that as-prepared CePO4-coated 3Y-TZP ceramics are highly suitable biomaterials for dental applications and are expected to be used in a com-puter-aided design and computer-aided manufacturing （CAD/CAM） system to make dental crowns or bridge prostheses in a one-step sinter-ing process.

Multi-modality measurement and comprehensive analysis of hepatocellular carcinoma using synchrotron-based microscopy and spectroscopy

The visualization and data mining of tumor multidimensional information may play a major role in the analysis of the growth,metastasis,and microenvironmental changes of tumors while challenging traditional imaging and data processing techniques.In this study,a general trans-scale and multi-modality measurement method was developed for the quantitative diagnosis of hepatocellular carcinoma(HCC)using a combination of propagation-based phase-contrast computed tomography(PPCT),scanning transmission soft X-ray microscopy(STXM),and Fourier transform infrared micro-spectroscopy(FTIR).Our experimental results reveal the trans-scale micro-morpho-logical HCC pathology and facilitate quantitative data analysis and comprehensive assessment.These results include some visualization features of PPCT-based tissue microenvironments,STXM-based cellular fine structures,and FTIR-based bio-macromolecular spectral characteris-tics during HCC tumor differentiation and proliferation.The proposed method provides multidimensional feature data support for constructing a high-accuracy machine learning algorithm based on a gray-level histogram,gray-gradient co-occurrence matrix,gray-level co-occurrence matrix,and back-propagation neural network model.Multi-dimensional information analysis and diagnosis revealed the morphological pathways of HCC pathological evolution and we explored the relationships between HCC-related feature changes in inflammatory microenviron-ments,cellular metabolism,and the stretching vibration peaks of biomolecules of lipids,proteins,and nucleic acids.Therefore,the proposed methodology has strong potential for the visualization of complex tumors and assessing the risks of tumor differentiation and metastasis.

A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

Many respiratory infections around the world have been caused by coronaviruses.COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate.There is a high need for computer-assisted diagnostics(CAD)in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems.Machine learning(ML)has been used to examine chest X-ray frames.In this paper,a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes,a pneumonia patient,a COVID-19 patient,or a normal person.First,three different pre-trainedConvolutionalNeuralNetwork(CNN)models(resnet18,resnet25,densenet201)are employed for deep feature extraction.Second,each feature vector is passed through the binary Butterfly optimization algorithm(bBOA)to reduce the redundant features and extract the most representative ones,and enhance the performance of the CNN models.These selective features are then passed to an improved Extreme learning machine(ELM)using a BOA to classify the chest X-ray images.The proposed paradigm achieves a 99.48%accuracy in detecting covid-19 cases.

A survey of machine learning techniques for detecting and diagnosing COVID-19 from imaging

Background: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT- PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images.Methods: A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality research. We selected papers based on our inclusion criteria.Results: In this survey, we reviewed 98 articles that fulfilled our inclusion criteria. We have surveyed a complete pipeline of chest imaging analysis techniques related to COVID-19, including data collection, pre-processing, feature extraction, classification, and visualization. We have considered CT scans and X-rays as both are widely used to describe the latest developments in medical imaging to detect COVID-19.Conclusions: This survey provides researchers with valuable insights into different machine learning techniques and their performance in the detection and diagnosis of COVID-19 from chest imaging. At the end, the challenges and limitations in detecting COVID-19 using machine learning techniques and the future direction of research are discussed.

COVIDX: Computer-aided diagnosis of COVID-19 and its severity prediction with raw digital chest X-ray scans

Background: Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the globe.Objective: In the absence or inadequate provision of therapeutic treatments of COVID-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X-ray (CXR) image can effortlessly be used as a substitute modality to diagnose the COVID-19.Method: In this study, we present an automatic COVID-19 diagnostic and severity prediction system (COVIDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COVID-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs pneumonia, and COVID-19 severity) using different conventional supervised classification algorithms.Results: We evaluated COVIDX through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDX showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this purpose.Conclusions: Our proposed method (COVIDX), with vivid performance in COVID-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COVID-19 infected patients.Availability: We made COVIDX easily accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/covidx, respectively.

A Universal Blown Film Apparatus for in Situ X-ray Measurements

A setup of blown film machine combined with in situ synchrotron radiation X-ray diffraction measurements and infrared temperature testing is reported to study the structure evolution of polymers during film blowing. Two homemade auto-lifters are constructed and placed under the blown machine at each end of the beamline platform which move up and down with a speed of 0.05 mm/s bearing the 200 kg weight machine. Therefore, structure development and temperature changes as a function of position on the film bubble can be obtained. The blown film machine is customized to be conveniently installed with precise servo motors and can adjust the processing parameters in a wide range. Meanwhile, the air ring has been redesigned in order to track the structure information of the film bubble immediately after the melt being extruded out from the die exit. Polyethylene（PE） is selected as a model system to verify the feasibility of the apparatus and the in situ experimental techniques. Combining structure information provided by the WAXD and SAXS and the actual temperature obtained from the infrared probe, a full roadmap of structure development during film blowing is constructed and it is helpful to explore the molecular mechanism of structure evolution behind the film blowing processing, which is expected to lead to a better understanding of the physics in polymer processing.

An online automatic sorting system for defective Ginseng Radix et Rhizoma Rubra using deep learning

Objective:To establish a deep-learning architecture based on faster region-based convolutional neural networks(Faster R-CNN)algorithm for detection and sorting of red ginseng(Ginseng Radix et Rhizoma Rubra)with internal defects automatically on an online X-ray machine vision system.Methods:A Faster R-CNN based classifier was trained with around 20000 samples with mean average precision value(mAP)of 0.95.A traditional image processing method based on feedforward neural network(FNN)obtained a bad performance with the accuracy,recall and specificity of 69.0%,68.0%,and70.0%,respectively.Therefore,the Faster R-CNN model was saved to evaluate the model performance on the defective red ginseng online sorting system.Results:An independent set of 2000 red ginsengs were used to validate the performance of the Faster RCNN based online sorting system in three parallel tests,achieving accuracy of 95.8%,95.2%and 96.2%,respectively.Conclusion:The overall results indicated that the proposed Faster R-CNN based classification model has great potential for non-destructive detection of red ginseng with internal defects.

Effect of signal-to-noise ratio on the automatic clustering of X-ray diffraction patterns from combinatorial libraries

Hierarchical clustering algorithm has been applied to identify the X-ray diffraction(XRD)patterns from a high-throughput characterization of the combinatorial materials chips.As data quality is usually correlated with acquisition time,it is important to study the hierarchical clustering performance as a function of data quality in order to optimize the efficiency of high-throughput experiments.This work investigated the effects of signal-to-noise ratio on the performance of hier-archical clustering using 29 distance metrics for the XRD patterns from Fe−Co−Ni ternary combinatorial materials chip.It is found that the clustering accuracies evaluated by the F1 score only fluctuate slightly with signal-to-noise ratio varying from 15.5 to 22.3(dB)under the experimental condition.This suggests that although it may take 40-50 s to collect a visually high-quality diffraction pattern,the measurement time could be significantly reduced to as low as 4 s without substantial loss in phase identification accuracy by hierarchical clustering.Among the 29 distance metrics,Pearsonχ^(2)shows the highest mean F1 score of 0.77 and lowest standard deviation of 0.008.It shows that the distance matrixes calculated by Pearsonχ^(2)are mainly controlled by the XRD peak shifting characteristics and visualized by the metric multidimensional data scaling.

Using pXRF and vis-NIR spectra for predicting properties of soils developed in loess

Visible near-infrared (vis-NIR) and portable X-ray fluorescence (pXRF) spectrometers have been increasingly utilized for predicting soil properties worldwide. However, only a few studies have focused on splitting the predictive models by horizons to evaluate prediction performance and systematically compare prediction performance for A, B, and combined A+B horizons. Therefore, we investigated the performance of pXRF and vis-NIR spectra, as individual or combined, for predicting the clay, silt, sand, total carbon (TC), and pH of soils developed in loess, and compared their prediction performance for A, B, and A+B horizons. Soil samples (176 in A horizon and 172 in B horizon) were taken from Mollisols and Alfisols in 136 pedons in Wisconsin, USA and analyzed for clay, silt, sand, pH, and TC. The pXRF and vis-NIR spectrometers were used to measure the pXRF and vis-NIR soil spectra. Data were separated into calibration (n = 244, 70%) and validation (n = 104, 30%) datasets. The Savitzky-Golay filter was applied to preprocess the pXRF and vis-NIR spectra, and the first 10 principal components (PCs) were selected through principal component analysis (PCA). Five types of predictor, i.e., PCs from vis-NIR spectra, pXRF of beams at 0–40 and 0–10 keV (XRF40 and XRF10, respectively) spectra, combined XRF40 and XRF10 (XRF40+XRF10) spectra, and combined XRF40, XRF10, and vis-NIR (XRF40+XRF10+vis-NIR) spectra, were compared for predicting soil properties using a machine learning algorithm (Cubist model). A multiple linear regression (MLR) model was applied to predict clay, silt, sand, pH, and TC using pXRF elements. The results suggested that pXRF spectra had better prediction performance for clay, silt, and sand, whereas vis-NIR spectra produced better TC and pH predictions. The best prediction performance for sand (R2= 0.97), silt (R2= 0.95), and clay (R2= 0.84) was achieved using vis-NIR+XRF40+XRF10 spectra in B horizon, whereas the best prediction performance for TC (R2= 0.93) and pH (R2= 0.79) was achieved using vis-NIR+XRF40+XRF10 spectra in A+B horizon. For all soil properties, the best MLR model had a lower prediction accuracy than the Cubist model. It was concluded that pXRF and vis-NIR spectra can be successfully applied for predicting clay, silt, sand, pH, and TC with high accuracy for soils developed in loess, and that spectral models should be developed for different horizons to achieve high prediction accuracy.