Detection of Corona virus Disease Using a Novel Machine Learning Approach

Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. The results showed that in the both of considered methods, the models including discharges of one, two and three days ago had higher accuracy in verification step and the accuracy of models decreased with increasing discharge values. Comparing the performance of GP and SVM methods indicated that, however the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than SVM method with R=0.976 and RMSE=1.80 (m3/s), but the SVM is easier than GP method. Thus, the SVM method can be used as an alternative method in forecasting daily river discharge.

Accuracy of data and information on various types of land use and land cover (LULC) is crucial in watershed management planning. The development of remote sensing technology in predicting LULC has been widely used, such as the Support Vector Machine (SVM) and Random Trees (RT) methods. Both methods are part of the model of machine learning and belong to the category of supervised learning. This method can predict, study, and recognize data patterns from the results of training data. The aim of this research is the use of SVM and RT methods for the identification of LULC. Based on the SVM method analysis results, the forest area is 211.61 km2, coffee plantations are 32.92 km2, shrubs are 16.85 km2, built-up/bare land is 3.40 km2, the water body is 3.17 km2, and cloud/shadow is 6.79 km2. While the RT method obtained a forest area of 215.51 km2, coffee plantations covering an area of 29.32 km2, shrubs are covering an area of 16.32 km2, built-up/bare land surrounding an area of 7.22 km2, water body covering an area of 2.62 km2, and cloud/shadow covering an area of 3.75 km2. Overall, the percentage accuracy of the SVM method of 87%, and the RT of 86%. The success rate of modeling SVM (AUC = 0.73) and RT (AUC = 0.75) based on the results of data validation using the area under the receiver operating characteristic curve (AUC) was considered fair good.

The specifications of the forests in Kalimantan are part of the vegetation type which is characteristic of dry lowland forests that are rich in biodiversity. The Kalimantan forest area is known as an area that has the highest vascular biodiversity in the world. Until 2014, the rate of forest loss as a source of biodiversity was always high every year. The National Polar-Orbiting Suomi Partnership (Suomi NPP) is a new generation of weather satellite imagery from NASA which currently continues to develop application algorithms for environmental monitoring. The support vector machine (SVM) method is a one-class learning machine method with artificial neural network methods that can recognize patterns from input or examples given and also include supervised learning. This study aims to analyze the effect of each parameter on the SVM method and the combination that can produce the highest accuracy, and analyze the ability of the SVM method for making hotspot maps based on the number of forest fire areas in East Kalimantan. The results of the study showed that the Support Vector Machine (SVM) method on the spectral data produced a total of 85%.

Sirekap is a mobile application that was built to help the public monitor and oversee the development of the 2024 elections held in Indonesia. The research aims to apply the Support Vector Machine algorithm in analyzing sentiment about the use of the Sirekap application in 2024. The Support Vector Machine method is used to classify user sentiment into classes, namely positive, negative, and very negative. The amount of data used is 15,000 data sourced from Sirekap application reviews on Google PlayStore, with more detailed research stages including data collection, data preprocessing, data labeling, visualization, word weighting, and testing and analysis. The results show that the Support Vector Machine algorithm provides an accuracy of 88% for the Sirekap 2024 application. These results are expected to help developers to develop further the Sirekap 2024 application in improving the quality of the application and providing better user comfort Based on the results of the sentiment analysis of Sirekap 2024 application users on the Google Play store using the Support Vector Machine (SVM) method, an accuracy rate of 88% was obtained in classifying the sentiment of reviews into positive, negative, and very negative. This shows that the Support Vector Machine method is quite accurate for sentiment analysis of Indonesian text data. Overall, most reviews are very negative with a percentage reaching 76.9%, followed by negative reviews at 12.6%, and the least are positive reviews at 11%.

In this paper, we develop a low-cost portable spectrometer based on Support Vector Machines (SVM) method. The purpose of the spectrometer development is for halal food research project. The portable spectrometer uses various light-emitting diodes (LEDs), light sensor and Arduino microcontroller. The initial research of the spectrometer is testing various oil, such as palm oil, olive oil, sesame oil, soybean oil, and lard. The waveform analysis of each oil can be distinguished by spectroscopy method. But, by using Support Vector Machines (SVM) method the various oil can be distinguished and classified better. The data measurement, classification, and graphical interface result show on the interface of Raspberry Pi based on Python 2.7. Thus, the spectrometer can be used for lard detection.

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

A new kind of surface material is found and defined in the Balmer–Kapteyn (B-K) cryptomare region, Mare-like cryptomare deposits (MCD), representing highland debris mixed by mare deposits with a certain fraction. This postulates the presence of surface materials in the cryptomare regions. In this study, to objectively verify the existence of the MCD in the cryptomare regions, based on the Chang’E-2 microwave radiometer (MRM) data, the support vector machine (SVM) method was adopted, where the K-means algorithm was used to optimize the training samples and the random forest algorithm was used to select the proper band features. Finally, the extracted MCD is identified with the datasets including Lunar Reconnaissance Orbiter Wide Angle Camera, Diviner, and Clementine UV–VIS. The main findings are as follows: (1) Compared to the range outlined via the TB counter, the range of the MCD is objectively extracted using the SVM method in the B-K cryptomare region, which is reasonably indicated by the FeO abundance, TiO2 abundance, and rock abundance distributions. (2) The MCDs were extracted in the Dewar, Lomonosov–Fleming (L-F), and Schiller–Schickard (S-S) regions, indicating that the MCDs are widely distributed in the cryptomaria. (3) The presence of MCDs is concentrated in a limited region, accounting for 64.9%, 52.3%, 76.4%, and 64%, respectively, in the range of Dewar, L-F, S-S, and B-K regions identified using the optical data. The occurrence of the MCD gives a new understanding of the surface evolution in the cryptomare regions.

COVID-19 (Coronavirus Disease 2019) is a severe global pandemic infection induced by a new coronavirus also known as SARS Coronavirus (SARS-CoV). It is a dynamic infection that affects different people in different ways; thus, a delay in the precise detection and treatment leads to the spread of the infection leading to more deaths. Automatic intelligent diagnostic models are thus needed to help detect such severe diseases at an early stage, thereby saving lives. For this, researchers developed a machine learning (ML) methodology for screening and COVID-19 classification using SARS-CoV2 CT scan images along with in-depth analysis of various features that is fascinating due to their better performance than conventional CT scan image classification for COVID-19. In addition, CT scan image feature extraction was measured using in-depth analysis with global and local feature descriptors. Principal component analysis (PCA) was employed to determine the optimal subset for COVID-19 recognition and classification. We compared and checked the best-in-class ML strategies on SARS-CoV2 CT scan images. The accuracy of training data was 98.78% and the accuracy of test data was 93.96% for a proposed model tested on the SARS-CoV2 CT scan images dataset. The findings of the experiment indicate a major improvement in COVID-19 classification, which aids in better COVID-19 investigation and disease prevention.

Beras Miskin (Raskin) Program is a program of social protection, as supporters of other programs such as nutrition improvement, healthy increase, education and productivity improvement of Poor Households. According to Badan Pusat Statistika, there were 14 criteria to determine a household is classified as poor households. Based on these criteria it will be classified of recipient households and non-recipient households of Beras Miskin (Raskin) Program by Support Vector Machine (SVM) method using LibSVM. The concept of classification by SVM is search for the best hyperplane which serves as a separator of two classes of data in the input space. Kernel function is used to convert the data into a higher dimensional space to allow a separation. LibSVM is a package program created by Chih-Chung Chang and Chih-Jen Lin from Department of Computer Science at National Taiwan University. The method used by LibSVM to obtain global solution of duality lagrange problem is decomposition method. To determine the best parameters of kernel function, used k-vold cross validation method and grid search algorithm. In this classification by SVM method using LibSVM, obtain the best accuracy value as 83,1933%, which is the kernel function Radial Basis Function (RBF). Keywords : Beras Miskin (Raskin) Program, Classification, Support Vector Machine (SVM), LibSVM, Kernel Function

We have developed a support vector machine (SVM) method that relies on core-measured data as well as gamma-ray, deep resistivity, sonic, and density wireline well-log data in identifying thermally mature total organic carbon (TOC)-rich layers at depth intervals with missing geochemical data in unconventional resource plays. We first test the SVM method using the Duvernay Shale Formation data. The SVM method successfully classifies the TOC data set into TOC-rich and TOC-poor classes and the [Formula: see text] data set into thermally mature and thermally immature classes when the optimal features are selected. To further test the SVM approach, we generate depth-separated training and test data sets from a well in the Duvernay Shale Formation and successfully use the approach to identify thermally mature TOC-rich intervals. We also examine the successful cross basin application of the SVM approach in predicting TOC using data from the Barnett and Duvernay Shale Formations as the training and test data sets, respectively.

There are several studies in the medical field that classify data to diagnose and analyze decisions. To predict breast cancer, this study compares two methods, the Support Vector Machine method and the Neural Network method based on Particle Swarm Optimization (PSO) which is intended to determine the highest accuracy value in the Coimbra dataset data. To implement the Support Vector Machine and Neural Network method based on PSO, RapidMiner software is used. Then the application results are compared using Confusion Matrix and ROC Curve. Based on the accuracy of the two models, it is known that the PSO-based Neural Network model has a higher accuracy value of 84.55% than the results of the PSO-based Vector Support Machine with an accuracy value of 80.08%. The calculation results, the accuracy of the AUC performance obtained by the results of the study are, the two methods are PSO-based Neural Network with AUC value of 0.885 and PSO-based Support Vector Machine with a value of 0.819 included in the category of Good Classification.

Fault detection and diagnosis has an effective role for the safe operation and long life of systems. Condition monitoring is an appropriate way of the maintenance techniques which is applicable in the fault diagnosis of rotating machinery faults. We considered the Support Vector Machine (SVM) method for classifying the condition of centrifugal pump into two types of faults through six features: flow, temperature, suction pressure, discharge pressure, velocity, and vibration. The SVM method is based on statistical learning theory (SLT) and powerful for the problem with small sampling, nonlinear and high dimension. (L.V. Ganyun et al 2005). The SVM classifying is implemented with 4 kernel functions and the results of them are compared. We use an Artificial Neural Network (ANN) as the second classifying method to have comparison among the performance of two methods. After applying the two methods to our data set we make the data set noisy and again we try our SVMs and ANN to compare their robustness in noisy conditions and the results obtained from two methods confirmed the superiority of SVM with some specific kernel functions.

Wheat stripe rust has a severe impact on wheat yield and quality. An effective prediction method is necessary for food security. In this study, we extract the optimal vegetation indices (VIs) sensitive to stripe rust at different time-periods, and develop a wheat stripe rust prediction model with satellite images to realize the multi-temporal prediction. First, VIs related to stripe rust stress are extracted as candidate features for disease prediction from time series Sentinel-2 images. Then, the optimal VI combinations are selected using sequential forward selection (SFS). Finally, the occurrence of wheat stripe rust in different time-periods is predicted using the support vector machine (SVM) method. The results of the features selected demonstrate that, before the jointing period, the optimal VIs are related to the biomass, pigment, and moisture of wheat. After the jointing period, the red-edge VIs related to the crop health status play important roles. The overall accuracy and Kappa coefficient of the prediction model, which is based on SVM, is generally higher than those of the k-nearest neighbor (KNN) and back-propagation neural network (BPNN) methods. The SVM method is more suitable for time series predictions of wheat stripe rust. The model obtained accuracy based on the optimal VI combinations and the SVM increased over time; the highest accuracy was 86.2%. These results indicate that the prediction model can provide guidance and suggestions for early disease prevention of the study site, and the method combines time series Sentinel-2 images and the SVM, which can be used to predict wheat stripe rust.

One of the social media with 24.85 million active users is Twitter. Information published on Twitter can be in the form of user opinions on an object, such as a product or service. Therefore, the company utilizes Twitter as a medium to disseminate information. This makes the company use Twitter as a medium to disseminate information. An example is an Internet Service Provider (ISP) company such as Indihome. Through Twitter, users can discuss their complaints and satisfaction with Indihome services. A method is needed, namely sentiment analysis to understand whether the textual data includes neutral opinion, negative opinion or positive opinion. So, the authors use the Support Vector Machine (SVM) method in sentiment analysis of Indihome service user opinions on Twitter, with the aim of getting a sentiment classification model using SVM and to find out how much accuracy is produced by the SVM method applied to sentiment analysis and to find out how satisfied Indihome service users are based on Twitter. After testing with the SVM method the results are 91% accuracy. Precision 51% Recall 75% and F1-Score 59%.

Gas production during the electrochemical treatment of waste liquids has potential safety risks. Herein, hydrogen (H2) production in such treatment was taken as the control target. Electrode combinations with relatively low H2 production were selected for electrochemical treatment during a fracturing flowback fluid experiment, in which mesh-like titanium–based ordinary ruthenium–iridium–palladium-coated electrode was the anode and plate–like titanium–based ordinary ruthenium–iridium–palladium-coated electrode was the cathode. The effects of electrolysis time (t), electrolytic current (I), electrode spacing (D), and other factors on H2 production in the electrolytic process were investigated. On the basis of the experimental data, a model was established using the support vector machine (SVM) method. Firstly, the three parameters of the radial basis function kernel of the model were simultaneously optimized using the quantum–particle swarm optimization algorithm as follows: penalty parameter = 256, kernel parameter = 0.0097039, and loss parameter = 0.014928. Then, an SVM regression model was established according to the three optimal parameters. The correlation coefficient was 0.98291 (r = 0.9914), and the mean–square error was 1.1883. The regression model was used to predict the technological conditions (t, I, and D) for the maximum/minimum H2 production in the electrochemical treatment of fracturing flowback fluid. The values were t = 50 min, I = 1.5 A, and D = 2.0 cm for the maximum H2 production, and t = 30 min, I = 0.5 A, and D = 6.0 cm for the minimum H2 production. Under these process conditions, the predicted maximum/minimum H2 production volumes were 145.04 and 20.47 mL, respectively.