Low Speed Longitudinal Control Algorithms for Automated Vehicles in Simulation and Real Platforms by Mauricio Marcano, José A. Matute, Ray Lattarulo, Enrique Martí, Joshué Pérez

“…In that sense, this paper presents a use case where three longitudinal low speed control techniques are designed, tuned, and validated using an in-house simulation framework and later applied in a real vehicle. Control algorithms include a classical PID, an adaptive network fuzzy inference system (ANFIS), and a Model Predictive Control (MPC). …”
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Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms by Zehra Koyuncu, Ömer Ekmekcioğlu

“…Random forest (RF), stochastic gradient boosting (SGB), and XGBoost algorithms were used. The best predictive performance was obtained with the XGBoost algorithm, followed by SGB and RF, respectively. …”
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Impact of Right-Hand Polarized Signals in GNSS-R Water Detection Algorithms by Jilun Peng, Estel Cardellach, Weiqiang Li, Serni Ribo, Antonio Rius

“…This analysis can offer a deeper understanding of RHCP data and yield predictive insights prior to the HydroGNSS launch. In this study, we initially analyzed coherence indicators in incoherently averaged dual-polarized signals, and subsequently, applied these indicators to a random forest classifier, similar to the HydroGNSS surface inundation algorithm. …”
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Deep Reinforcement Learning for Automated Insulin Delivery Systems: Algorithms, Applications, and Prospects by Xia Yu, Zi Yang, Xiaoyu Sun, Hao Liu, Hongru Li, Jingyi Lu, Jian Zhou, Ali Cinar

“…Advances in continuous glucose monitoring (CGM) technologies and wearable devices are enabling the enhancement of automated insulin delivery systems (AIDs) towards fully automated closed-loop systems, aiming to achieve secure, personalized, and optimal blood glucose concentration (BGC) management for individuals with diabetes. While model predictive control provides a flexible framework for developing AIDs control algorithms, models that capture inter- and intra-patient variability and perturbation uncertainty are needed for accurate and effective regulation of BGC. …”
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Anomaly detection using unsupervised machine learning algorithms: A simulation study by Edmund Fosu Agyemang

“…Through systematic analysis on a synthetically simulated dataset, the study assessed each algorithm’s predictive performance using accuracy, precision, recall, and F1 score specifically for outlier detection. …”
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Genetic Algorithms Applied to Optimize Neural Network Training in Reference Evapotranspiration Estimation by Eluã Ramos Coutinho, Jonni G.F. Madeira, Robson Mariano da Silva, Angel Ramon Sanchez Delgado, Alvaro L.G.A. Coutinho

“…This confirms that employing Genetic Algorithms (GA) to automate the training and optimization of the model is effective and enhances the neural network's capacity to predict ETo.…”
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Algorithms of Rickettsiae’s Detection and Laboratory Diagnosis of Rickettsioses of Spotted Fever Group in Russia by N. V. Rudakov, I. E. Samoylenko, L. V. Kumpan

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Swarm Intelligence Algorithms for Optimization Problems a Survey of Recent Advances and Applications by Mande Smita Samrat, M Srinivasulu, Anand Sruthi, K Anuradha, Tiwari Mohit, U Esakkiammal

“…Furthermore, moving past premature convergence provides more robust algorithms that can discover global optima. Moreover, the theoretical aspects of SI algorithms are still in their infancy and propose novel methods to improve predictability and reliability. …”
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Machine learning algorithms to detect patient–ventilator asynchrony: a feasibility study by Guillermo Gutierrez, Kendrew Wong, Arun Jose, Jeffrey Williams

“…The accuracy of these algorithms was evaluated based on their ability to correctly identify epochs, and their clinical reliability was assessed by comparing their predictions to those of clinicians with different levels of experience in asynchrony classification. …”
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Review of Fault Detection and Diagnosis Methods in Power Plants: Algorithms, Architectures, and Trends by Camelia Adela Maican, Cristina Floriana Pană, Daniela Maria Pătrașcu-Pană, Virginia Maria Rădulescu

“…A novel taxonomy of diagnostic configurations, mapping system types, sensor use, algorithmic strategy, and functional depth is proposed. …”
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Analysis of random factors of the self-education process by A. A. Solodov

“…The aim of the study is the statistical description of the random factors of the self-educationт process, namely that stage of the process of continuous education, in which there is no meaningful impact on the student’s educational organization and the development of algorithms for estimating these factors. …”
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Fault location and isolation technology for power grid automation based on intelligent algorithms by Qi Guo, Fuhe Wang, Suxia Cheng, Ke Wang, Yifan Zhang

“…Methodology The FLA algorithm uses a Support Vector Machine (SVM) classifier to predict fault locations based on key variables like voltage, current, frequency, line impedance, and meteorological conditions. …”
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Using Grayscale Photos to Introduce High School Statistics Teachers to Reasoning with Digital Image Data by Anna Fergusson, Maxine Pfannkuch

Subjects: “…Algorithmic modeling…”
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Lifetime Prediction of Power IGBT Module by WANG Yan-gang, Chamund Dinesh, LI Shi-ping, Jones Steve, DOU Ze-chun, XIN Lan-yuan, LIU Guo-you

“…The Weibull methodology for power cycling test data and some reported typical lifetime models were firstly discussed. Then, lifetime prediction procedures were presented including the conversion of mission profile to temperature profile, the temperature cycles counting by Rainflow algorithm, and lifetime calculating based on the fatigue linear accumulation damage theory and lifetime models. …”
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Genetic prediction of male pattern baldness. by Saskia P Hagenaars, W David Hill, Sarah E Harris, Stuart J Ritchie, Gail Davies, David C Liewald, Catharine R Gale, David J Porteous, Ian J Deary, Riccardo E Marioni

“…By splitting the cohort into a discovery sample of 40,000 and target sample of 12,000, we developed a prediction algorithm based entirely on common genetic variants that discriminated (AUC = 0.78, sensitivity = 0.74, specificity = 0.69, PPV = 59%, NPV = 82%) those with no hair loss from those with severe hair loss. …”
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Conformational ensembles for protein structure prediction by Jiaan Yang, Wen Xiang Cheng, Peng Zhang, Gang Wu, Si Tong Sheng, Junjie Yang, Suwen Zhao, Qiyue Hu, Wenxin Ji, Qiong Shi

“…The P53_HUMAN as a well-known protein and LEF1_HUMAN and Q8GT36_SPIOL as typical disordered proteins are token as the benchmark to evaluate the predicted outcomes. The results demonstrated an effective algorithm and biological meaningful process well to predict protein multiple conformation structures.…”
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Student knowledge tracking based multi-indicator exercise recommendation algorithm by Bin ZHUGE, Zhenghu YIN, Wenxue SI, Lei YAN, Ligang DONG, Xian JIANG

“…Personalized exercise recommendation was an important topic in the era of education informatization, the forgetting laws of students in the learning process were ignored by the traditional problem recommendation algorithm, which failed to fully tap the students’ knowledge mastery level and the common characteristics of similar students, insufficient, could not reasonably promote students’ learning of new knowledge or help students find and fill omissions.In view of the above defects, a multi-index exercise recommendation method based on student knowledge tracking was proposed, which was divided into two modules: preliminary screening and re-filtering of exercises, focusing on the novelty, difficulty and diversity of exercise recommendation.Firstly, a knowledge probability prediction (SF-KCCP) model combined with students’ forgetting law was constructed to ensure the novelty of the recommended exercises.Then, students’ knowledge and concept mastery level was accurately excavated based on the dynamic key-value knowledge tracking (DKVMN) model to ensure that exercises of appropriate difficulty were recommended.Finally, the user-based collaborative filtering (UserCF) algorithm was integrated into the re-filtering module, and the similarity between student groups was used to achieve the diversity of recommendation results.The proposed method is demonstrated by extensive experiments to achieve better performance than some existing baseline models.…”
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Student knowledge tracking based multi-indicator exercise recommendation algorithm by Bin ZHUGE, Zhenghu YIN, Wenxue SI, Lei YAN, Ligang DONG, Xian JIANG

“…Personalized exercise recommendation was an important topic in the era of education informatization, the forgetting laws of students in the learning process were ignored by the traditional problem recommendation algorithm, which failed to fully tap the students’ knowledge mastery level and the common characteristics of similar students, insufficient, could not reasonably promote students’ learning of new knowledge or help students find and fill omissions.In view of the above defects, a multi-index exercise recommendation method based on student knowledge tracking was proposed, which was divided into two modules: preliminary screening and re-filtering of exercises, focusing on the novelty, difficulty and diversity of exercise recommendation.Firstly, a knowledge probability prediction (SF-KCCP) model combined with students’ forgetting law was constructed to ensure the novelty of the recommended exercises.Then, students’ knowledge and concept mastery level was accurately excavated based on the dynamic key-value knowledge tracking (DKVMN) model to ensure that exercises of appropriate difficulty were recommended.Finally, the user-based collaborative filtering (UserCF) algorithm was integrated into the re-filtering module, and the similarity between student groups was used to achieve the diversity of recommendation results.The proposed method is demonstrated by extensive experiments to achieve better performance than some existing baseline models.…”
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Application of Metaheuristics for Optimizing Predictive Models in iHealth: A Case Study on Hypotension Prediction in Dialysis Patients by Felipe Cisternas-Caneo, María Santamera-Lastras, José Barrera-Garcia, Broderick Crawford, Ricardo Soto, Cristóbal Brante-Aguilera, Alberto Garcés-Jiménez, Diego Rodriguez-Puyol, José Manuel Gómez-Pulido

“…Particle Swarm Optimization, Grey Wolf Optimizer, Pendulum Search Algorithm, and Whale Optimization Algorithm were employed to reduce the feature space, removing approximately 45% of clinical and analytical variables while maintaining high recall for the minority class of patients experiencing hypotension. …”
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Research on Short-term Load Forecasting Algorithm Based on VMD and TCN by WANG Qing, CHEN Zhiru, LI Guimin, JING Zhen, ZHANG Zhi, WANG Pingxin, CUI Qi

“…The simulation results show that the forecasting effect of VMD-TCN is the best, MAPE and RMSE are 1. 65% and 15. 05kW, respectively, indicating that the algorithm can be used to achieve accurate short-term forecasting of the station load, so as to facilitate the dispatch management, optimization operation, energy saving and emission reduction of the station. …”
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