scholarly journals MRI-Based Radiomics for Differentiating Orbital Cavernous Hemangioma and Orbital Schwannoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Liang Chen ◽  
Ya Shen ◽  
Xiao Huang ◽  
Hua Li ◽  
Jian Li ◽  
...  
Keyword(s):  
Cavernous Hemangioma ◽  
Training Group ◽  
Superior Performance ◽  
Support Vector ◽  
Classification Models ◽  
Validation Data ◽  
Group 12 ◽  
Svm Model ◽  
Magnetic Resonance Imaging Mri ◽  
Selection Operator

Aim: The purpose of this work was to develop and evaluate magnetic resonance imaging (MRI)-based radiomics for differentiation of orbital cavernous hemangioma (OCH) and orbital schwannoma (OSC).Methods: Fifty-eight patients (40 OCH and 18 OSC, confirmed pathohistologically) screened out from 216 consecutive patients who presented between 2015 and 2020 were divided into a training group (28 OCH and 12 OSC) and a validation group (12 OCH and 6 OSC). Radiomics features were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI). T-tests, the least absolute shrinkage and selection operator (LASSO), and principal components analysis (PCA) were used to select features for use in the classification models. A logistic regression (LR) model, support vector machine (SVM) model, decision tree (DT) model, and random forest (RF) model were constructed to differentiate OCH from OSC. The models were evaluated according to their accuracy and the area under the receiver operator characteristic (ROC) curve (AUC).Results: Six features from T1WI, five features from T2WI, and eight features from combined T1WI and T2WI were finally selected for building the classification models. The models using T2WI features showed superior performance on the validation data than those using T1WI features, especially the LR model and SVM model, which showed accuracy of 93% (85–100%) and 92%, respectively, The SVM model showed high accuracy of 93% (91–96%) on the combined feature group with an AUC of 98% (97–99%). The DT and RF models did not perform as well as the SVM model.Conclusion: Radiomics analysis using an SVM model achieved an accuracy of 93% for distinguishing OCH and OSC, which may be helpful for clinical diagnosis.

scholarly journals A Pilot Study Using Accelerometers to Characterise the Licking Behaviour of Penned Cattle at a Mineral Block Supplement

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1153
Author(s):  
Gamaliel Simanungkalit ◽  
Jamie Barwick ◽  
Frances Cowley ◽  
Robin Dobos ◽  
Roger Hegarty
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Beef Cattle ◽  
Superior Performance ◽  
Support Vector ◽  
Controlled Environment ◽  
Nearest Neighbour ◽  
Classification Models ◽  
Licking Behaviour ◽  
Supplement Intake

Identifying the licking behaviour in beef cattle may provide a means to measure time spent licking for estimating individual block supplement intake. This study aimed to determine the effectiveness of tri-axial accelerometers deployed in a neck-collar and an ear-tag, to characterise the licking behaviour of beef cattle in individual pens. Four, 2-year-old Angus steers weighing 368 ± 9.3 kg (mean ± SD) were used in a 14-day study. Four machine learning (ML) algorithms (decision trees [DT], random forest [RF], support vector machine [SVM] and k-nearest neighbour [kNN]) were employed to develop behaviour classification models using three different ethograms: (1) licking vs. eating vs. standing vs. lying; (2) licking vs. eating vs. inactive; and (3) licking vs. non-licking. Activities were video-recorded from 1000 to 1600 h daily when access to supplement was provided. The RF algorithm exhibited a superior performance in all ethograms across the two deployment modes with an overall accuracy ranging from 88% to 98%. The neck-collar accelerometers had a better performance than the ear-tag accelerometers across all ethograms with sensitivity and positive predictive value (PPV) ranging from 95% to 99% and 91% to 96%, respectively. Overall, the tri-axial accelerometer was capable of identifying licking behaviour of beef cattle in a controlled environment. Further research is required to test the model under actual grazing conditions.

Human Activity Recognition Using The Human Skeleton Provided by Kinect

2021 ◽  
Vol 17 (2) ◽  
pp. 183-189
Author(s):  
Heba Salim ◽  
Musaab Alaziz ◽  
Turki Abdalla
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Human Body ◽  
Support Vector ◽  
Classification Models ◽  
Kinect Sensor ◽  
People Tracking ◽  
Human Skeleton ◽  
Average Accuracy ◽  
Svm Model

In this paper, a new method is proposed for people tracking using the human skeleton provided by the Kinect sensor, Our method is based on skeleton data, which includes the coordinate value of each joint in the human body. For data classification, the Support Vector Machine (SVM) and Random Forest techniques are used. To achieve this goal, 14 classes of movements are defined, using the Kinect Sensor to extract data containing 46 features and then using them to train the classification models. The system was tested on 12 subjects, each of whom performed 14 movements in each experiment. Experiment results show that the best average accuracy is 90.2 % for the SVM model and 99 % for the Random forest model. From the experiments, we concluded that the best distance between the Kinect sensor and the human body is one meter.

scholarly journals Real-Time Prediction of Rate of Penetration in S-Shape Well Profile Using Artificial Intelligence Models

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3506 ◽  
Author(s):  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Support Vector ◽  
Empirical Correlations ◽  
Validation Data ◽  
Inference System ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Svm Model ◽  
Data Points

Rate of penetration (ROP) is defined as the amount of removed rock per unit area per unit time. It is affected by several factors which are inseparable. Current established models for determining the ROP include the basic mathematical and physics equations, as well as the use of empirical correlations. Given the complexity of the drilling process, the use of artificial intelligence (AI) has been a game changer because most of the unknown parameters can now be accounted for entirely at the modeling process. The objective of this paper is to evaluate the ability of the optimized adaptive neuro-fuzzy inference system (ANFIS), functional neural networks (FN), random forests (RF), and support vector machine (SVM) models to predict the ROP in real time from the drilling parameters in the S-shape well profile, for the first time, based on the drilling parameters of weight on bit (WOB), drillstring rotation (DSR), torque (T), pumping rate (GPM), and standpipe pressure (SPP). Data from two wells were used for training and testing (Well A and Well B with 4012 and 1717 data points, respectively), and one well for validation (Well C) with 2500 data points. Well A and Well B data were combined in the training-testing phase and were randomly divided into a 70:30 ratio for training/testing. The results showed that the ANFIS, FN, and RF models could effectively predict the ROP from the drilling parameters in the S-shape well profile, while the accuracy of the SVM model was very low. The ANFIS, FN, and RF models predicted the ROP for the training data with average absolute percentage errors (AAPEs) of 9.50%, 13.44%, and 3.25%, respectively. For the testing data, the ANFIS, FN, and RF models predicted the ROP with AAPEs of 9.57%, 11.20%, and 8.37%, respectively. The ANFIS, FN, and RF models overperformed the available empirical correlations for ROP prediction. The ANFIS model estimated the ROP for the validation data with an AAPE of 9.06%, whereas the FN model predicted the ROP with an AAPE of 10.48%, and the RF model predicted the ROP with an AAPE of 10.43%. The SVM model predicted the ROP for the validation data with a very high AAPE of 30.05% and all empirical correlations predicted the ROP with AAPEs greater than 25%.

scholarly journals The impact of feature extraction and selection for the classification of gait patterns between ACL deficient and intact knees based on different classification models

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wei Zeng ◽  
Shiek Abdullah Ismail ◽  
Evangelos Pappas
Keyword(s):  
Feature Extraction ◽  
Superior Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Models ◽  
Acl Deficiency ◽  
Feature Extraction And Selection ◽  
Dynamical Features ◽  
The Impact ◽  
Acl Deficient

AbstractThe anterior cruciate ligament (ACL) plays an important role in stabilizing translation and rotation of the tibia relative to the femur. Individuals with ACL deficiency usually demonstrate alterations in gait characteristics. Evidence indicates that walking speed, alterations in kinetics and kinematics on the ACL deficient limb, and inter-limb asymmetries between deficient and intact knees may contribute to poor long-term outcomes following ACL deficiency. They corrode function of the knee joint and put it at higher risk of degeneration. For the purpose of developing an automatic and highly accurate system for detection of ACL deficiency, this study investigated the classification capability of different dynamical features extracted from gait kinematic and kinetic signals when evaluating their impact on different classification models. A general feature extraction framework was proposed and various dynamical features, such as recurrence rate, determinism and entropy from the recurrence quantification analysis, fuzzy entropy, Teager-Kaiser energy feature and statistical analysis, were included. Different classification models, including support vector machine (SVM), K-nearest neighbor (KNN), naive Bayes (NB) classifier, decision tree (DT) classifier and ensemble learning based Adaboost (ELA) classifier, derived for discriminant analysis of multiple dynamical gait features were evaluated for a comparative study. The effectiveness of this strategy was verified using a dataset of knee, hip and ankle kinematic and kinetic waveforms from 43 patients with unilateral ACL deficiency. When evaluated with 2-fold, 10-fold and leave-one-out cross-validation styles, the highest classification accuracy for discriminating between groups of ACL deficient and contralateral ACL intact knees was reported to be 91.22 $$\%$$ % , 95.12$$\%$$ % and 96.34$$\%$$ % , respectively,by using the SVM classifier and the optimal feature set. For other four classifiers, KNN achieved the accuracy of 78.05$$\%$$ % , 85.37$$\%$$ % and 87.80$$\%$$ % , respectively. NB achieved the accuracy of 57.56$$\%$$ % , 60.98$$\%$$ % and 61.22$$\%$$ % , respectively. DT achieved the accuracy of 77.56$$\%$$ % , 80.49$$\%$$ % and 83.66$$\%$$ % , respectively. ELA achieved the accuracy of 73.66$$\%$$ % , 78.05$$\%$$ % and 79.27$$\%$$ % , respectively. Compared with other state-of-the-art methods, the results demonstrate superior performance and support the validity of the proposed method.

scholarly journals Whole blood transcriptome biomarkers of unruptured intracranial aneurysm

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241838
Author(s):  
Kerry E. Poppenberg ◽  
Lu Li ◽  
Muhammad Waqas ◽  
Nikhil Paliwal ◽  
Kaiyu Jiang ◽  
...  
Keyword(s):  
Intracranial Aneurysm ◽  
Roc Curve ◽  
Whole Blood ◽  
Support Vector ◽  
Training Cohort ◽  
Svm Model ◽  
Selection Operator ◽  
Pathway Analyses ◽  
Subarachnoid Hemorrhages ◽  
Blood Transcriptome

Background The rupture of an intracranial aneurysm (IA) causes devastating subarachnoid hemorrhages, yet most IAs remain undiscovered until they rupture. Recently, we found an IA RNA expression signature of circulating neutrophils, and used transcriptome data to build predictive models for unruptured IAs. In this study, we evaluate the feasibility of using whole blood transcriptomes to predict the presence of unruptured IAs. Methods We subjected RNA from peripheral whole blood of 67 patients (34 with unruptured IA, 33 without IA) to next-generation RNA sequencing. Model genes were identified using the least absolute shrinkage and selection operator (LASSO) in a random training cohort (n = 47). These genes were used to train a Gaussian Support Vector Machine (gSVM) model to distinguish patients with IA. The model was applied to an independent testing cohort (n = 20) to evaluate performance by receiver operating characteristic (ROC) curve. Gene ontology and pathway analyses investigated the underlying biology of the model genes. Results We identified 18 genes that could distinguish IA patients in a training cohort with 85% accuracy. This SVM model also had 85% accuracy in the testing cohort, with an area under the ROC curve of 0.91. Bioinformatics reflected activation and recruitment of leukocytes, activation of macrophages, and inflammatory response, suggesting that the biomarker captures important processes in IA pathogenesis. Conclusions Circulating whole blood transcriptomes can detect the presence of unruptured IAs. Pending additional testing in larger cohorts, this could serve as a foundation to develop a simple blood-based test to facilitate screening and early detection of IAs.

Rain Classification for Autonomous Vehicle Navigation : A Support Vector Machine Approach

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 74-80
Author(s):  
Abdul Haleem Habeeb Mohamed ◽  
Muhammad Aizzat Zakaria ◽  
Mohd Azraai Mohd Razman ◽  
Anwar P. P. Abdul Majeed ◽  
Mohamed Heerwan Peeie
Keyword(s):  
Autonomous Vehicle ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Controlled Environment ◽  
Great Success ◽  
Classification Models ◽  
Svm Model ◽  
The Time Domain ◽  
Autonomous Vehicle Navigation ◽  
Rain Rates

The advancement of LIDAR technology used in the autonomous vehicle (AV) system has made it increasingly popular. Despite that, the ability of the sensor to adjust to human behaviour in sensing and perceiving different environments is still unsolved as it significantly impacting the performance of LIDAR, causing the effect of missing points and false positives detection. The immerging of machine learning algorithms that have greatly impacted solving uncertainties and LIDAR's reliability in making judgments has proven a great success. This paper aims to classify different rain rates conditions in a controlled environment with real rain using a LIDAR. Then, the feature extraction using the time-domain method was employed to generate more features with a variation of SVM models in developing classification models. The preliminary observation shows that the Poly-SVM model can achieve a test classification accuracy of 97%. Noting that, the proposed method has the potential to evaluate weather classification.

scholarly journals Support Vector Machine Model Predicts Dose for Organs at Risk in High-Dose Rate Brachytherapy of Cervical Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Ping Zhou ◽  
Xiaojie Li ◽  
Hao Zhou ◽  
Xiao Fu ◽  
Bo Liu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Small Intestine ◽  
Sigmoid Colon ◽  
Mean Squared Error ◽  
Organs At Risk ◽  
Training Group ◽  
Support Vector ◽  
Validation Group ◽  
Squared Error ◽  
Svm Model

IntroductionThis study aimed to establish a support vector machine (SVM) model to predict the dose for organs at risk (OARs) in intracavitary brachytherapy planning for cervical cancer with tandem and ovoid treatments.MethodsFifty patients with loco-regionally advanced cervical cancer treated with 200 CT-based tandem and ovoid brachytherapy plans were included. The brachytherapy plans were randomly divided into the training (N = 160) and verification groups (N = 40). The bladder, rectum, sigmoid colon, and small intestine were divided into sub-OARs. The SVM model was established using MATLAB software based on the sub-OAR volume to predict the bladder, rectum, sigmoid colon, and small intestine D2cm3. Model performance was quantified by mean squared error (MSE) and δ (δ=|D2cm3/Dprescription(actual)−D2cm3/Dprescription(predicted)|). The goodness of fit of the model was quantified by the coefficient of determination (R2). The accuracy and validity of the SVM model were verified using the validation group.ResultsThe D2cm3 value of the bladder, rectum, sigmoid colon, and small intestine correlated with the volume of the corresponding sub-OARs in the training group. The mean squared error (MSE) in the SVM model training group was <0.05; the R2 of each OAR was >0.9. There was no significant difference between the D2cm3 -predicted and actual values in the validation group (all P > 0.05): bladder δ = 0.024 ± 0.022, rectum δ = 0.026 ± 0.014, sigmoid colon δ = 0.035 ± 0.023, and small intestine δ = 0.032 ± 0.025.ConclusionThe SVM model established in this study can effectively predict the D2cm3 for the bladder, rectum, sigmoid colon, and small intestine in cervical cancer brachytherapy.

scholarly journals A Unique Six Sigma Based Segmentation Technique for Brain Tumor Detection and Classification using Hybrid CNN-SVM Model

2019 ◽  
Vol 8 (2) ◽  
pp. 35-40
Keyword(s):  
Brain Tumor ◽  
Six Sigma ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Rate ◽  
Segmentation Technique ◽  
Svm Model ◽  
Tumor Region ◽  
Magnetic Resonance Imaging Mri ◽  
Feature Mining

An intelligent organizing scheme to detect and classify normal, abnormal MRI brain sequences has been illustrated here. At present, handling of brain tumors disease and decision is based on radiological appearance and its symptoms. Magnetic-Resonance-Imaging (MRI) is a powerful substantial precise instrument for functional conclusion of brain tumorous. In existing study, broad range of methods is used for brain cancer detection and classification. Under this methods viz., image pre-processing, enhancement, segmentation, feature mining and resulting classification is efficiently conducted. Furthermore, when various machine learning algorithms like: Six Sigma, Convolutional Neural Network (CNN), Support Vector Machine (SVM), are employed to detect and extract the tumor region and classify numerous sequence of imageries, it is witnessed from our results that this Hybrid CNN-SVM model gives maximum classification accuracy rate of 99.33% compared to previous models. The foremost aim of this research is to get an effective result for detecting type of brain tumor using six sigma based segmentation technique, and to achieve efficient classification rate, using hybrid CNN-SVM model.

scholarly journals Five-Class Classification of Cervical Pap Smear Images: A Study of CNN-Error-Correcting SVM Models

2021 ◽  
Vol 27 (4) ◽  
pp. 298-306
Author(s):  
Audrey K. C. Huong ◽  
Kim Gaik Tay ◽  
Xavier T. I. Ngu
Keyword(s):  
Image Classification ◽  
Pap Smear ◽  
False Negative ◽  
Area Under The Curve ◽  
Superior Performance ◽  
Support Vector ◽  
Training Time ◽  
Binary Coding ◽  
Svm Model

Objectives: Different complex strategies of fusing handcrafted descriptors and features from convolutional neural network (CNN) models have been studied, mainly for two-class Papanicolaou (Pap) smear image classification. This paper explores a simplified system using combined binary coding for a five-class version of this problem.Methods: This system extracted features from transfer learning of AlexNet, VGG19, and ResNet50 networks before reducing this problem into multiple binary sub-problems using error-correcting coding. The learners were trained using the support vector machine (SVM) method. The outputs of these classifiers were combined and compared to the true class codes for the final prediction.Results: Despite the superior performance of VGG19-SVM, with mean ± standard deviation accuracy and sensitivity of 80.68% ± 2.00% and 80.86% ± 0.45%, respectively, this model required a long training time. There were also false-negative cases using both the VGGNet-SVM and ResNet-SVM models. AlexNet-SVM was more efficient in terms of running speed and prediction consistency. Our findings also showed good diagnostic ability, with an area under the curve of approximately 0.95. Further investigation also showed good agreement between our research outcomes and that of the state-of-the-art methods, with specificity ranging from 93% to 100%.Conclusions: We believe that the AlexNet-SVM model can be conveniently applied for clinical use. Further research could include the implementation of an optimization algorithm for hyperparameter tuning, as well as an appropriate selection of experimental design to improve the efficiency of Pap smear image classification.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

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