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4th International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2266549
Statistical Modeling in Machine Learning: Concepts and Applications ; : 37-53, 2022.
Article in English | Scopus | ID: covidwho-2270945
Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University ; 57(5):562-573, 2022.
Article in English | Scopus | ID: covidwho-2206245
19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022 ; : 47-52, 2022.
Article in English | Scopus | ID: covidwho-2192066
SN Comput Sci ; 4(1): 91, 2023.
Article in English | MEDLINE | ID: covidwho-2158268


In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.

Journal of Financial Reporting and Accounting ; 2022.
Article in English | Web of Science | ID: covidwho-2097566
Studies in Computational Intelligence ; 1007:85-97, 2022.
Article in English | Scopus | ID: covidwho-1767461
Sensors (Basel) ; 21(19)2021 Sep 27.
Article in English | MEDLINE | ID: covidwho-1468448


Early and self-identification of locomotive degradation facilitates us with awareness and motivation to prevent further deterioration. We propose the usage of nine squat and four one-leg standing exercise features as input parameters to Machine Learning (ML) classifiers in order to perform lower limb skill assessment. The significance of this approach is that it does not demand manpower and infrastructure, unlike traditional methods. We base the output layer of the classifiers on the Short Test Battery Locomotive Syndrome (STBLS) test used to detect Locomotive Syndrome (LS) approved by the Japanese Orthopedic Association (JOA). We obtained three assessment scores by using this test, namely sit-stand, 2-stride, and Geriatric Locomotive Function Scale (GLFS-25). We tested two ML methods, namely an Artificial Neural Network (ANN) comprised of two hidden layers with six nodes per layer configured with Rectified-Linear-Unit (ReLU) activation function and a Random Forest (RF) regressor with number of estimators varied from 5 to 100. We could predict the stand-up and 2-stride scores of the STBLS test with correlation of 0.59 and 0.76 between the real and predicted data, respectively, by using the ANN. The best accuracies (R-squared values) obtained through the RF regressor were 0.86, 0.79, and 0.73 for stand-up, 2-stride, and GLFS-25 scores, respectively.

Locomotion , Machine Learning , Feasibility Studies , Lower Extremity , Risk Assessment