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Behnam Askarian
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA

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Journal article
Published: 12 March 2020 in Sustainability
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The aim of this study was twofold: (1) to assess the performance accuracy of support vector machine (SVM) models with different kernels to predict rock brittleness and (2) compare the inputs’ importance in different SVM models. To this end, the authors developed eight SVM models with different kernel types, i.e., the radial basis function (RBF), the linear (LIN), the sigmoid (SIG), and the polynomial (POL). Four of these models were developed using only the SVM method, while the four other models were hybridized with a feature selection (FS) technique. The performance of each model was assessed using five performance indices and a simple ranking system. The results of this study show that the SVM models developed using the RBF kernel achieved the highest ranking values among single and hybrid models. Concerning the importance of variables for predicting the brittleness index (BI), the Schmidt hammer rebound number (Rn) was identified as the most important variable by the three single-based models, developed by POL, SIG, and LIN kernels. However, the single SVM model developed by RBF identified density as the most important input variable. Concerning the hybrid SVM models, three models that were developed using the RBF, POL, and SIG kernels identified the point load strength index as the most important input, while the model developed using the LIN identified the Rn as the most important input. All four single-based SVM models identified the p-wave velocity (Vp) as the least important input. Concerning the least important factors for predicting the BI of the rock in hybrid-based models, Vp was identified as the least important factor by FS-SVM-POL, FS-SVM-SIG, and FS-SVM-LIN, while the FS-SVM-RBF identified Rn as the least important input.

ACS Style

Danial Jahed Armaghani; Panagiotis G. Asteris; Behnam Askarian; Mahdi Hasanipanah; Reza Tarinejad; Van Van Huynh. Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness. Sustainability 2020, 12, 2229 .

AMA Style

Danial Jahed Armaghani, Panagiotis G. Asteris, Behnam Askarian, Mahdi Hasanipanah, Reza Tarinejad, Van Van Huynh. Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness. Sustainability. 2020; 12 (6):2229.

Chicago/Turabian Style

Danial Jahed Armaghani; Panagiotis G. Asteris; Behnam Askarian; Mahdi Hasanipanah; Reza Tarinejad; Van Van Huynh. 2020. "Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness." Sustainability 12, no. 6: 2229.

Journal article
Published: 02 March 2020 in Applied Sciences
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Despite the vast usage of machine learning techniques to solve engineering problems, a very limited number of studies on the rock brittleness index (BI) have used these techniques to analyze issues in this field. The present study developed five well-known machine learning techniques and compared their performance to predict the brittleness index of the rock samples. The comparison of the models’ performance was conducted through a ranking system. These techniques included Chi-square automatic interaction detector (CHAID), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), and artificial neural network (ANN). This study used a dataset from a water transfer tunneling project in Malaysia. Results of simple rock index tests i.e., Schmidt hammer, p-wave velocity, point load, and density were considered as model inputs. The results of this study indicated that while the RF model had the best performance for training (ranking = 25), the ANN outperformed other models for testing (ranking = 22). However, the KNN model achieved the highest cumulative ranking, which was 37. The KNN model showed desirable stability for both training and testing. However, the results of validation stage indicated that RF model with coefficient of determination (R2) of 0.971 provides higher performance capacity for prediction of the rock BI compared to KNN model with R2 of 0.807 and ANN model with R2 of 0.860. The results of this study suggest a practical use of the machine learning models in solving problems related to rock mechanics specially rock brittleness index.

ACS Style

Deliang Sun; Mahshid Lonbani; Behnam Askarian; Danial Jahed Armaghani; Reza Tarinejad; Binh Thai Pham; Van Van Huynh. Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index. Applied Sciences 2020, 10, 1691 .

AMA Style

Deliang Sun, Mahshid Lonbani, Behnam Askarian, Danial Jahed Armaghani, Reza Tarinejad, Binh Thai Pham, Van Van Huynh. Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index. Applied Sciences. 2020; 10 (5):1691.

Chicago/Turabian Style

Deliang Sun; Mahshid Lonbani; Behnam Askarian; Danial Jahed Armaghani; Reza Tarinejad; Binh Thai Pham; Van Van Huynh. 2020. "Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index." Applied Sciences 10, no. 5: 1691.

Journal article
Published: 27 July 2019 in Sensors
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In this paper, we propose a novel strep throat detection method using a smartphone with an add-on gadget. Our smartphone-based strep throat detection method is based on the use of camera and flashlight embedded in a smartphone. The proposed algorithm acquires throat image using a smartphone with a gadget, processes the acquired images using color transformation and color correction algorithms, and finally classifies streptococcal pharyngitis (or strep) throat from healthy throat using machine learning techniques. Our developed gadget was designed to minimize the reflection of light entering the camera sensor. The scope of this paper is confined to binary classification between strep and healthy throats. Specifically, we adopted k-fold validation technique for classification, which finds the best decision boundary from training and validation sets and applies the acquired best decision boundary to the test sets. Experimental results show that our proposed detection method detects strep throats with 93.75% accuracy, 88% specificity, and 87.5% sensitivity on average.

ACS Style

Behnam Askarian; Seung-Chul Yoo; Jo Woon Chong. Novel Image Processing Method for Detecting Strep Throat (Streptococcal Pharyngitis) Using Smartphone. Sensors 2019, 19, 3307 .

AMA Style

Behnam Askarian, Seung-Chul Yoo, Jo Woon Chong. Novel Image Processing Method for Detecting Strep Throat (Streptococcal Pharyngitis) Using Smartphone. Sensors. 2019; 19 (15):3307.

Chicago/Turabian Style

Behnam Askarian; Seung-Chul Yoo; Jo Woon Chong. 2019. "Novel Image Processing Method for Detecting Strep Throat (Streptococcal Pharyngitis) Using Smartphone." Sensors 19, no. 15: 3307.

Journal article
Published: 26 June 2019 in Sensors
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Photoplethysmography (PPG) is a commonly used in determining heart rate and oxygen saturation (SpO2). However, PPG measurements and its accuracy are heavily affected by the measurement procedure and environmental factors such as light, temperature, and medium. In this paper, we analyzed the effects of different mediums (water vs. air) and temperature on the PPG signal quality and heart rate estimation. To evaluate the accuracy, we compared our measurement output with a gold-standard PPG device (NeXus-10 MKII). The experimental results show that the average PPG signal amplitude values of the underwater environment decreased considerably (22% decrease) compared to PPG signals of dry environments, and the heart rate measurement deviated 7% (5 beats per minute on average. The experimental results also show that the signal to noise ratio (SNR) and signal amplitude decrease as temperature decreases. Paired t-test which compares amplitude and heart rate values between the underwater and dry environments was performed and the test results show statistically significant differences for both amplitude and heart rate values (p < 0.05). Moreover, experimental results indicate that decreasing the temperature from 45 °C to 5 °C or changing the medium from air to water decreases PPG signal quality, (e.g., PPG signal amplitude decreases from 0.560 to 0.112). The heart rate is estimated within 5.06 bpm deviation at 18 °C in underwater environment, while estimation accuracy decreases as temperature goes down.

ACS Style

Behnam Askarian; Kwanghee Jung; Jo Woon Chong. Monitoring of Heart Rate from Photoplethysmographic Signals Using a Samsung Galaxy Note8 in Underwater Environments. Sensors 2019, 19, 2846 .

AMA Style

Behnam Askarian, Kwanghee Jung, Jo Woon Chong. Monitoring of Heart Rate from Photoplethysmographic Signals Using a Samsung Galaxy Note8 in Underwater Environments. Sensors. 2019; 19 (13):2846.

Chicago/Turabian Style

Behnam Askarian; Kwanghee Jung; Jo Woon Chong. 2019. "Monitoring of Heart Rate from Photoplethysmographic Signals Using a Samsung Galaxy Note8 in Underwater Environments." Sensors 19, no. 13: 2846.