Breath Detection from a Microphone Using Machine Learning by Tomasz Sankowski, Piotr Sulewski, Jan Walczak, Aleksandra Bruska

“…VGGish Model for Feature Extraction and Classification with Random Forest: This method utilizes the VGGish model to extract sound feature vectors, followed by classification using a random forest classifier. 2. …”
Get full text

A Prediction Model of Structural Settlement Based on EMD-SVR-WNN by Xianglong Luo, Wenjuan Gan, Lixin Wang, Yonghong Chen, Xue Meng

“…EMD model is used to decompose the structure settlement monitoring data, and the settlement data can be effectively divided into relatively stable trend terms and residual components of random fluctuation by energy matrix. According to the different characteristics of random items and trend items, WNN and SVR methods are, respectively, used for prediction, and the final settlement prediction is obtained by integrating the prediction results. …”
Get full text

Modeling of autowave processes in active media with inhomogeneous properties by A. V. Gulaj, V. A. Gulaj, A. V. Dubovik

“…In this case, each cell of the model is assigned a random value of the specified coefficient, lying in a given interval from the minimum to the maximum value. …”
Get full text

Numerical Simulation of Dynamic Characteristics of Dam Concrete Based on Fuzzy Set by Fang Jianyin, Liu Ke, Dang Faning, Li Shutian

“…Then, based on the CT resolution unit, a concrete numerical calculation model of structural random is established, and the numerical simulation experiment of concrete under uniaxial dynamic load is carried out. …”
Get full text

Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model by Ngoc Hung- Chu, Hoai Nam- Nguyen, Van Du- Nguyen, Dang Binh- Nguyen

“…It leads to a rapid increase in cutting forces and strong random fluctuations. The discontinuous chip evacuation process makes the cutting force signal strongly nonlinear and random, making it difficult to predict accurately. …”
Get full text

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization by Mohammed Hasan Abdulameer, Siti Norul Huda Sheikh Abdullah, Zulaiha Ali Othman

“…However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. …”
Get full text

Prediction of GNSS Velocity Accuracies Using Machine Learning Algorithms for Active Fault Slip Rate Determination and Earthquake Hazard Assessment by Halil İbrahim Solak

“…ML models, including Support Vector Machine, Random Forest, K-Nearest Neighbors, and Multiple Linear Regression, were used to model the relationship between position and velocity accuracies. …”
Get full text

Nonlinear Stochastic Optimal Control Using Piezoelectric Stack Inertial Actuator by Q. F. Lü, X. F. Wang, K. Lu, R. H. Huan

“…An optimal control strategy for the random vibration reduction of nonlinear structures using piezoelectric stack inertial actuator is proposed. …”
Get full text

Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets by Durr e Nayab, Rehan Ullah Khan, Ali Mustafa Qamar

“…We conducted a comprehensive experiment to assess the efficacy of twelve base classifiers with the AdaBoost framework, namely, Bayes network, decision stump, ZeroR, decision tree, Naïve Bayes, J-48, voted perceptron, random forest, bagging, random tree, stacking, and AdaBoost itself. …”
Get full text

Study on the Stability of the Coal Seam Floor above a Confined Aquifer Using the Structural System Reliability Method by Haifeng Lu, Xiuyu Liang, Nan Shan, You-Kuan Zhang

“…A quantitative method of structural system reliability was proposed to study the influence of random rock mechanical parameters and loads on the stability of the coal seam floor above confined aquifers. …”
Get full text

Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques by Marium Mehmood, Nasser Alshammari, Saad Awadh Alanazi, Fahad Ahmad

“…The result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest MSE of 0.0618.…”
Get full text

The Prediction of Serum C-Reactive Protein Concentration Using Nonlinear Mixed-Effects Model by Suk Joo Bae, Gyu Ri Kim, Sun Geu Chae, Yeesuk Kim

“…The bi-exponential model with random effects is applied to predict temporal CRP concentrations in patients after hip surgery. …”
Get full text

Prediction of cardiovascular disease from factors associated with waist hip ratio by machine learning by Zeynep Kucukakcali, Ipek Balikci Cicek

“…Studies have shown that waist circumference (WC) and waist hip ratio (WHR) are better at identifying CVD than BMI. The study uses Random Forest (RF) machine learning to identify characteristics that affect WHR, an indication of CVD. …”
Get full text

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units by Thang Trung Nguyen, Nguyen Vu Quynh, Le Van Dai

“…The first is to be based on the radius between two solutions, the second is updated step size for each considered solution based on different new equations, and the third is to slightly modify a formula producing new solutions by using normally distributed random numbers and canceling uniform random numbers of conventional firefly algorithm (FA). …”
Get full text

Comparative use of different AI methods for the prediction of concrete compressive strength by Mouhamadou Amar

“…The most accurate model was found to be a gradient-boosted tree followed by deep learning and random forest. Forecasts were validated with high accuracy by comparing experimental results to numerical data.…”
Get full text

Lost circulation intensity characterization in drilling operations: Leveraging machine learning and well log data by Ahmad Azadivash

“…Random Forest, Extra Trees, and Hard Voting are the best-performing methods. …”
Get full text

Factors Influencing Brucellosis Preventive Behaviors among Marginalized Iranian Women: An Application of the Health Belief Model by Majid Barati, Rezvan Shayghan Zahed, Marjan Bakhtiari, Yadollah Fathi, Maryam Afshari, Zahra Taheri-Kharameh

“…Each woman in the selected comprehensive health services was then enrolled by the simple random sampling method. Data were gathered from a face-to-face interview via a questionnaire. …”
Get full text

The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues by Murat Işık, Mehmet Ali Yalçınkaya

“…In the regression analysis, seven models (Adaboost, Decision Tree, Gradient Boosting, K Nearest Neighbors, Random Forest, Ridge Regression, and Support Vector Machine) predicted players' market values. …”
Get full text

Complex Ecosystems Lose Stability When Resource Consumption Is Out of Niche by Yizhou Liu, Jiliang Hu, Hyunseok Lee, Jeff Gore

“…We pinpoint the onset of instability through random matrix analysis, finding that the critical discrepancy between growth and consumption depends on the ratio of the number of species to the number of resources. …”
Get full text

The Effects of Ovine Whey Powders on Durum Wheat-Based Doughs by Nicola Secchi, Costantino Fadda, Massimo Piccinini, Ivo Pinna, Antonio Piga, Pasquale Catzeddu, Simonetta Fois

“…Weak and strong semolina showed a different relative percentage of α-helix, random coil, and β-sheet structures. The longer mixing times for dough formation when using semolina with strong gluten led to an increase in α-helices and random coils, which caused a worse leavening performance than the weak-gluten semolina.…”
Get full text