Real-Time jamming detection using windowing and hybrid machine learning models for pre-saturation alerts by J. Sormayli, M. Darvishi, K. Zarrinnegar, M. R. Mosavi

“…Abstract This paper proposes a new deep learning and machine learning model for detecting deception and suppression jamming in Ublox-M8T receivers operating under GNSS interference. …”
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Machine learning approach for prediction of safe mud window based on geochemical drilling log data by Hongchen Cai, Yunliang Yu, Yingchun Liu, Xiangwei Gao

“…Traditional geomechanical methods for SMW determination face challenges in handling complex, nonlinear relationships within drilling datasets.PurposeThis study aims to develop robust machine learning (ML) models to predict two key SMW parameters—Mud Pressure below shear failure (MWsf) and tensile failure (MWtf)—using geochemical drilling log data from Middle Eastern carbonate reservoirs.MethodsHybrid ML models combining Least Squares Support Vector Machine (LSSVM) and Multilayer Perceptron (MLP) with optimization algorithms (Gray Wolf Optimization, GWO; Grasshopper Optimization Algorithm, GOA) were trained on 2,820 data points from three wells. …”
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Machine Learning for Chinese Corporate Fraud Prediction: Segmented Models Based on Optimal Training Windows by Chang Chuan Goh, Yue Yang, Anthony Bellotti, Xiuping Hua

“…Using the best machine learning model and optimal training window, we build general model and segmented models to compare fraud types and industries based on their respective predictive performance via four evaluation metrics and top features using SHAP. …”
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Classifying Food Items During an Eating Occasion: A Machine Learning Approach with Slope Dynamics for Windowed Kinetic Data by Ileana Baldi, Corrado Lanera, Mohammad Junayed Bhuyan, Paola Berchialla, Luca Vedovelli, Dario Gregori

“…Machine learning tools are necessary to deal with the complexity of signals gathered by the devices, and research is ongoing to improve accuracy further and work on large-scale and real-time implementation and testing.…”
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Research on cultivating students’ creative thinking ability in art design teaching based on machine learning by Ke Xue

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From reactive to proactive: Machine learning models for continuous positive airway pressure adjustments using heart rate variability and oximetry-related parameters by Chih-Fan Kuo, Yi-Chih Lin, Ze-Yu Chen, Jiunn-Horng Kang, Cheng-Chen Chang, Zhihe Chen, Arnab Majumdar, Yen-Ling Chen, Yi-Chun Kuan, Kang-Yun Lee, Po-Hao Feng, Kuan-Yuan Chen, Hsin-Chien Lee, Wun-Hao Cheng, Wen-Te Liu, Cheng-Yu Tsai

“…Therefore, this study developed machine learning models using leading indicators, such as heart rate variability (HRV) and oximetry-related metrics, to proactively predict optimal adjustment timings. …”
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Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing by Wenchao Zhu, Yingzi Lin

“…Plan 1 utilized a grid search methodology with a 10-fold cross-validation framework to optimize time windows (1–5 s) and machine learning hyperparameters for pain classification tasks. …”
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Carbon additives to improve polymer performance in energy applications using machine learning by Juan Chen, Khidhair Jasim Mohammed, Elimam Ali, Riadh Marzouki

“…To guide composite optimization, a hybrid Machine Learning (ML) framework combining Random Forest Regression (RFR) and Support Vector Regression (SVR) was developed. …”
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Forecasting acute childhood malnutrition in Kenya using machine learning and diverse sets of indicators. by Girmaw Abebe Tadesse, Laura Ferguson, Caleb Robinson, Shiphrah Kuria, Herbert Wanyonyi, Samuel Murage, Samuel Mburu, Rahul Dodhia, Juan M Lavista Ferres, Bistra Dilkina

“…<h4>Results</h4>We found that machine learning based models consistently outperform the Window Average baselines on forecasting sub-county malnutrition rates in Kenya. …”
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Forecasting formation density from well logging data based on machine learning model by Xiankang Cheng, Haoyu Zhang, Haoyu Zhang

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Machine learning enhanced formation pressure prediction using integrated well logging and mud logging by Jiwen Liang, Ming Luo, Wentuo Li, Bo Sun, Chuanliang Yan, Zhongying Han, Yuanfang Cheng

“…Pore pressure has a medium to low correlation with the rotation per minute. Based on machine learning algorithms and a large amount of known data, a machine learning formation pressure model with integrated well logging and mud logging data (IWM) was established. …”
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Classifying metro drivers’ cognitive distractions during manual operations using machine learning and random forest-recursive feature elimination by Haiyue Liu, Yue Zhou, Chaozhe Jiang

“…The HR-HRV features are extracted by 30-s and 60-s time-windows in driving phase, and 25-s time-windows in parking phase, respectively. …”
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Using machine learning techniques for early prediction of tracheal intubation in patients with septic shock: a multi-center study in South Korea by Ji Han Heo, Taegyun Kim, Tae Gun Shin, Gil Joon Suh, Woon Yong Kwon, Hayoung Kim, Heesu Park, Heejun Kim, Sol Han, the Korean Shock Society

“…However, the criteria for tracheal intubation are subjective, based on physician experience, or require serial evaluations over relatively long intervals to make accurate predictions, which might not be feasible in the ED. We used supervised learning approaches and features routinely available during the initial stages of evaluation and resuscitation to stratify the risks of tracheal intubation within a 24-hour time window. …”
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On-Demand Gait-Synchronous Electrical Cueing in Parkinson&#x0027;s Disease Using Machine Learning and Edge Computing: A Pilot Study by Ardit Dvorani, Constantin Wiesener, Christina Salchow-Hommen, Magdalena Jochner, Lotta Spieker, Matej Skrobot, Hanno Voigt, Andrea Kuhn, Nikolaus Wenger, Thomas Schauer

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Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction by Jingye Zhang, Ke Sun, Xiaoming Han, Ning Mao

“…Taking the southern part of China’s North–South Seismic Belt (20° N~30° N, 96° E~106° E), where strong earthquakes frequently occur, as an example, we used the sliding time window method to calculate 11 seismicity indicators from the earthquake catalog data as the characteristic parameters of the training model, and compared six machine learning models, including the random forest (RF) and long short-term memory (LSTM) models, to select the best-performing LSTM model for predicting the maximum magnitude of an earthquake in the study area in the coming year. …”
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Prediction of Monthly Temperature Over China Based on a Machine Learning Method by Ping Mei, Zixin Yin, Haoyu Wang, Changzheng Liu, Yaoming Liao, Qiang Zhang, Liping Yin

“…The core idea of dynamic modeling is that the machine learning model is trained using a sliding time window, so that the relationship between predictors and predictands is optimized for a specific and recent period rather than for the entire time span. …”
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Fatigue State Evaluation of Urban Railway Transit Drivers Using Psychological, Biological, and Physical Response Signals by Hao Wu, Yubo Jiao, Chaozhe Jiang, Tong Wang, Jiangbo Yu

“…Moreover, we utilized heart rate signals, electrodermal activity, and eye movements from wearable devices and cameras to build a driver fatigue detection model with machine-learning methods. …”
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Predicting main behaviors of beef bulls from accelerometer data: A machine learning framework by Vinicius A. Camargo, Edmond A. Pajor, Sayeh Bayat, Jennifer M. Pearson

“…Traditional methods to monitor free-range cattle, such as breeding beef bulls, are time-consuming. However, most current remote monitoring technologies operate at high sampling rates, making their use on bulls impractical due to their high battery consumption. …”
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Mountain flood forecasting in small watershed based on loop multi-step machine learning regression model by Songsong Wang, Bo Peng, Ouguan Xu, Yuntao Zhang, Jun Wang

“…The traditional hydrodynamic and manual forecasting methods have high error rates for hourly forecasting. In order to improve the accuracy and real-time of water level forecasting in small watershed, we extract effective disaster-causing information, integrate multi-dimensional disaster-causing factors (such as hydrology, meteorology, geography, etc.), use a short-term prediction window and loop multi-step input method to improve the Machine Learning (ML) regression models’ accuracy, which can reduce the ML model’s process error. …”
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Assessing the Severity of ODT and Factors Determinants of Late Arrival in Young Patients with Acute Ischemic Stroke by Zhu L, Li Y, Zhao Q, Li C, Wu Z, Jiang Y

“…Enhanced public education, particularly regarding stroke symptoms and the use of emergency services, is crucial for reducing pre-hospital delays and improving patient outcomes.Keywords: acute ischemic stroke, onset-to-door time, young adults, machine learning, emergency medical services…”
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