Protection Analysis of a Traveling-Wave, Machine-Learning Protection Scheme for Distributions Systems With Variable Penetration of Solar PV by Miguel Jimenez-Aparicio, Trupal R. Patel, Matthew J. Reno, Javier Hernandez-Alvidrez

“…The TW, ML method’s protection scheme is built upon an efficient signal-processing stage, using the Discrete Wavelet Transform, and scaled-down Random Forest models that classify the fault location into several protection zones. …”
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Determination of high-confidence germline genetic variants in next-generation sequencing through machine learning models: an approach to reduce the burden of orthogonal confirmatio... by Muqing Yan, Qiandong Zeng, Zhenxi Zhang, Patricia Okamoto, Stanley Letovsky, Angela Kenyon, Natalia Leach, Jennifer Reiner

“…Further assessment using simulated false positive events as well as different combinations of quality features showed that model performance can be refined. …”
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The comparative study of machine learning agent models in flood forecasting for tidal river reaches by Ju Zhou, Liming Chen, Tengfei Hu, Hao Lu, Yong Shi, Liangang Chen

“…This study focuses on this region, constructing a one-dimensional hydrodynamic model based on Saint-Venant’s equations to simulate flood-tide evolution. Using the model’s results, three typical machine learning surrogate models—Long Short-Term Memory (LSTM), Random Forest (RF), and Support Vector Machine (SVM)—are developed to predict the water level at the key control section of Changsha Station. …”
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Periodic cosmic string formation and dynamics by Michael A. Fedderke, Junwu Huang, Nils Siemonsen

“…We demonstrate these periodic dynamics with numerical simulations in both 2 + 1 and 3 + 1 dimensions, in both Minkowski spacetime and in a radiation-dominated Friedmann-Lemaître-Robertson-Walker (FLRW) universe, and we explain some features of the evolution (semi-)analytically. …”
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Survival analysis using machine learning in transplantation: a practical introduction by Andrea Garcia-Lopez, Maritza Jiménez-Gómez, Andrea Gomez-Montero, Juan Camilo Gonzalez-Sierra, Santiago Cabas, Fernando Giron-Luque

“…The integration of machine learning techniques, particularly the Random Survival Forest (RSF) model, offers potential enhancements to predictive modeling and decision-making. …”
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Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning by Jiazheng Zhu, Xize Huang, Xiaoyu Liang, Meng Wang, Yu Zhang

“…This research provides a robust technical foundation for reducing the labor intensity of traditional prediction methods and offers valuable insights for forecasting airborne forest diseases.…”
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ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool by D. Di Santo, C. He, F. Chen, L. Giovannini

“…This tool leverages the strengths of multiple regression-based and probabilistic machine learning methods, including LASSO (see the list of abbreviations in Appendix B), support vector machine, classification and regression trees, random forest, extreme gradient boosting, Gaussian process regression, and Bayesian ridge regression. …”
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Automated Detection of Recent Mud Extrusions Using UAV Imagery and Deep Learning: A Comparative Analysis of Traditional and CNN-Based Approaches by M. Guastella, M. Guastella, A. Pisciotta, R. Martorana, A. D’Alessandro

“…Traditional machine learning algorithms, including Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost), were compared with deep learning architectures such as Convolutional Neural Networks (CNNs), both leveraging transfer learning and custom models. …”
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Comparative Analysis of Machine Learning Methods with Chaotic AdaBoost and Logistic Mapping for Real-Time Sensor Fusion in Autonomous Vehicles: Enhancing Speed and Acceleration Pre... by Mehmet Bilban, Onur İnan

“…CAB is evaluated alongside k-Nearest Neighbors (kNNs), Artificial Neural Networks (ANNs), standard AdaBoost (AB), Gradient Boosting (GBa), and Random Forest (RF) for speed and acceleration prediction using CARLA simulator data. …”
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Predicting the shield effectiveness of carbon fiber reinforced mortars utilizing metaheuristic algorithms by Mana Alyami, Irfan Ullah, Furqan Ahmad, Hisham Alabduljabbar

“…Conventional ML techniques like random forest (RF) and decision tree (DT) were also employed for comparison. …”
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Advancing soil mapping and management using geostatistics and integrated machine learning and remote sensing techniques: a synoptic review by Sunshine A. De Caires, Chaney St Martin, Melissa A. Atwell, Fuat Kaya, Glorious A. Wuddivira, Mark N. Wuddivira

“…Emphasis was placed on hybrid approaches that fuse geostatistics with ML algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), along with the enrichment of spatial models using RS data. …”
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Evaluation and Optimization of Traditional Mountain Village Spatial Environment Performance Using Genetic and XGBoost Algorithms in the Early Design Stage—A Case Study in the Cold... by Zhixin Xu, Xiaoming Li, Bo Sun, Yueming Wen, Peipei Tang

“…After comparing machine learning models like RandomForest and XGBoost, the highest-performing XGBoost model was selected for further training. …”
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PROACTIVE MITIGATION OF DDoS IoT-RELATED ATTACK USING MACHINE LEARNING AND SOFTWARE DEFINED NETWORKING TECHNIQUES by Emmanuel J. Ebong, Samuel N. John, Dominic S. Nyitamen, Samuel F. Kolawole

“…The WAN is emulated, and includes a single RYU SDN controller, three routers, three OpenFlow switches with three simulated IoT devices connected to each switch, to form the 3 LANs topology. …”
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Accurate multi-category student performance forecasting at early stages of online education using neural networks by Naveed Ur Rehman Junejo, Muhammad Wasim Nawaz, Qingsheng Huang, Xiaoqing Dong, Chang Wang, Gengzhong Zheng

“…Specially, students’ VLE interactions are aggregated by total clicks to represent daily engagement and assess online activity. Comparative simulations indicate that the proposed model significantly outperforms existing baseline models including artificial neural network long short-term memory (ANN-LSTM), random forest (RF) ‘gini’, RF ‘entropy’ and deep feed forward neural network (DFFNN) in terms of accuracy, precision, recall, and F1-score. …”
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The Responses of Vegetation Production and Evapotranspiration to Inter-Annual Summer Drought in Northeast Asia Dryland Regions (NADRs) by Wenping Kang, Sinkyu Kang, Shulin Liu, Tao Wang

“…The diverse response of GPP and ET to drought depending on biomes, grassland, barren/sparse vegetation and shrub showed a positive response to summer drought, while cropland and forest showed a negative response to summer drought. …”
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Multi-Trait Phenotypic Analysis and Biomass Estimation of Lettuce Cultivars Based on SFM-MVS by Tiezhu Li, Yixue Zhang, Lian Hu, Yiqiu Zhao, Zongyao Cai, Tingting Yu, Xiaodong Zhang

“…On this basis, four biomass prediction models were developed using Adaptive Boosting (AdaBoost), Support Vector Regression (SVR), Gradient Boosting Decision Tree (GBDT), and Random Forest Regression (RFR). The results indicated that the RFR model based on the projected convex hull area, point cloud convex hull surface area, and projected convex hull perimeter performed the best, with an <i>R</i>² of 0.90, an RMSE of 2.63 g, and an RMSEn of 9.53%, indicating that the RFR was able to accurately simulate lettuce biomass. …”
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Accurate and Efficient Fluid Flow Regime Classification Using Localized Texture Descriptors and Machine Learning by Manimaran Renganathan, Palani Thanaraj Krishnan, C. Christopher Columbus, T. Sunil Kumar

“…These features are then classified using various machine learning models, namely Random Forest (RF), Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). …”
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Echoes From the Void: Detecting DNS Tunneling With Blackhole Features in Encrypted Scenarios With High Accuracy by Wafa S. Alorainy

“…A five-fold cross-validation with a random forest classifier achieved 99.9% classification accuracy, a 99.9% F1 score, and 100% precision and recall. …”
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Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform by S Ramana Kumar Joga, Chidurala SaiPrakash, Srikanth Velpula, Alivarani Mohapatra, Theophilus A. T. Kambo Jr.

“…To evaluate the effectiveness of the proposed method, extensive simulations and experiments are conducted using real‐world PV array data. …”
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Anomaly Detection Dataset for Industrial Control Systems by Alireza Dehlaghi-Ghadim, Mahshid Helali Moghadam, Ali Balador, Hans Hansson

“…Finally, we implement several ML models, including the decision tree, random forest, and artificial neural network to detect anomalies and attacks, demonstrating that our dataset can be used effectively for training intrusion detection ML models.…”
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