Mapping and interpretability of aftershock hazards using hybrid machine learning algorithms by Bo Liu, Haijia Wen, Mingrui Di, Junhao Huang, Mingyong Liao, Jingyuan Yu, Yutao Xiang

“…By employing the stacking algorithm to optimize and combine XGBoost and LightGBM models, the proposed model significantly improves the prediction performance. …”
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The malaria secretome: from algorithms to essential function in blood stage infection. by Christiaan van Ooij, Pamela Tamez, Souvik Bhattacharjee, N Luisa Hiller, Travis Harrison, Konstantinos Liolios, Taco Kooij, Jai Ramesar, Bharath Balu, John Adams, Andrew P Waters, Chris J Janse, Kasturi Haldar

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Optimization based machine learning algorithms for software reliability growth models by Myeongguen Shin, Juwon Jung, Jihyun Lee, Insoo Ryu, Sanggun Park

“…However, many previous studies have relied on single optimization methods or deep learning approaches, which are prone to local optima and extrapolation issues, reducing prediction accuracy. To fill this gap, current study employs a broader range of optimization algorithms based on the Least Squares Method (LSM) and Maximum Likelihood Estimation (MLE) to approximate global optima. …”
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Machine Learning Models to Predict Google Stock Prices by Cosmina Elena Bucura, Paolo Giudici

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Automated Input Variable Selection for Analog Methods Using Genetic Algorithms by P. Horton, O. Martius, S. L. Grimm

“…Previous work showed the potential of genetic algorithms (GAs) to optimize most of the AM parameters. …”
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A hybrid BOA-SVR approach for predicting aerobic organic and nitrogen removal in a gas-liquid-solid circulating fluidized bed bioreactor by Shaikh Abdur Razzak, Nahid Sultana, S.M. Zakir Hossain, Muhammad Muhitur Rahman, Yue Yuan, Mohammad Mozahar Hossain, Jesse Zhu

“…This study introduces the hybrid of the Bayesian optimization algorithm and support vector regression (BOA-SVR) models to predict the removal of aerobic organic (total chemical oxygen demand, COD) and nitrogen compounds such as total Kjeldahl Nitrogen (TKN), ammonium nitrogen (NH4-N), and nitrate nitrogen (NO3-N) from municipal wastewater in a gas-liquid-solid circulating fluidized bed (GLSCFB) bioreactor. …”
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Optimizing Renewable Energy Integration Using IoT and Machine Learning Algorithms by Orken Mamyrbayev, Ainur Akhmediyarova, Dina Oralbekova, Janna Alimkulova, Zhibek Alibiyeva

“…Results showed significant improvements in forecasting accuracy, with the LSTM model achieving a 59.1% reduction in Mean Absolute Percentage Error compared to the persistence model. …”
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Logatom articulation index evaluation of speech enhanced by blind source separation and single-channel noise reduction by Szymon DRGAS, Jędrzej KOCIŃSKI, Aleksander P. SĘK

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Enhancing power transformer health assessment through dimensional reduction and ensemble approaches in Dissolved Gas Analysis by Abdelmoumene Hechifa, Saurabh Dutta, Abdelaziz Lakehal, Hazlee Azil Illias, Arnaud Nanfak, Chouaib Labiod

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An Exploratory Application of Machine Learning Algorithms in Estimating Net Salaries in Romania by Adriana Aiftincăi

“…The results demonstrate a high prediction accuracy (MAE: 59.47 lei, RMSE: 97.60 lei – Random Forest model), providing realistic values for future salary scenarios. …”
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Scattering-Based Machine Learning Algorithms for Momentum Estimation in Muon Tomography by Florian Bury, Maxime Lagrange

“…Several real-life requirements are considered, such as the inclusion of hit reconstruction efficiency and resolution and the need for a momentum resolution prediction that can improve reconstruction methods.…”
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Comparison of algorithms using deep reinforcement learning for optimization of hyperbolic metamaterials by Kenta Hamada, Hui-Hsin Hsiao, Wakana Kubo

“…By analyzing the absorption spectra obtained from the three algorithms with limited number of datasets, we assessed the prediction accuracy of each algorithm. …”
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Forecasting: Analyze Online and Offline Learning Mode with Machine Learning Algorithms by Farida Ardiani, Rodhiyah Mardhiyyah, Izaaz Azaam Syahalam, Nasmah Nur Amiroh

“…The result of this study is the prediction of a more effective learning mode used, as decision support carried out by the forecasting method, comparing the Naïve Bayes and Decision Tree algorithm in getting the best accuracy value, by analyzing the learning mode offline to online.…”
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Neural Network VS Genetic and Particle Swarm Optimization Algorithms in Bankruptcy by Alireza Azarberahman

“…The evidence reveals the effectiveness of the metaheuristic algorithms compared to linear ones in predicting bankruptcy. …”
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PAPR optimization based on SLM and PTS algorithms in NC-OFDM systems by Jie ZHOU, Bernardo Esono Esono Mikue, Xueying WANG, Huiting ZHOU, Hong LUO

“…Based on the non-continuous orthogonal frequency division multiplexing (NC-OFDM) model, a fusion optimization technology based on selected mapping (SLM) algorithm and partial transmit sequence (PTS) algorithm was proposed, and a system model of fusion technology was designed.Through simulation comparison with other literature methods, it was verified that the SLM-PTS fusion technology had excellent peak to average power ratio (PAPR) reduction ability, but the algorithm implementation complexity was too high.Therefore, a complementary SLM-Clipping fusion solution was proposed, and the deep learning method PAPRnet model was construted.The simulation results verif that prove the effectiveness of the method, the algorithm has an excellent PAPR suppressed effect on the NC-OFDM system, and greatly improves the computational efficiency.…”
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PAPR optimization based on SLM and PTS algorithms in NC-OFDM systems by Jie ZHOU, Bernardo Esono Esono Mikue, Xueying WANG, Huiting ZHOU, Hong LUO

“…Based on the non-continuous orthogonal frequency division multiplexing (NC-OFDM) model, a fusion optimization technology based on selected mapping (SLM) algorithm and partial transmit sequence (PTS) algorithm was proposed, and a system model of fusion technology was designed.Through simulation comparison with other literature methods, it was verified that the SLM-PTS fusion technology had excellent peak to average power ratio (PAPR) reduction ability, but the algorithm implementation complexity was too high.Therefore, a complementary SLM-Clipping fusion solution was proposed, and the deep learning method PAPRnet model was construted.The simulation results verif that prove the effectiveness of the method, the algorithm has an excellent PAPR suppressed effect on the NC-OFDM system, and greatly improves the computational efficiency.…”
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Validation of Material Algorithms for Femur Remodelling Using Medical Image Data by Shitong Luo, Xingquan Shen, Xin Bai, Jing Bai, Jianning Han, Yu Shang

“…Moreover, the simulated L-T ratios (the ratio of longitude modulus to transverse modulus) by the orthotropic algorithm were close to the reported results. This study suggests a role for “error-driven” algorithm in bone material prediction in abnormal mechanical environment and holds promise for optimizing implant design as well as developing countermeasures against bone loss due to weightlessness. …”
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Advancing Hyperspectral Image Classification with Deep Features Learning and Evolutionary Algorithms by M. B. Amri, M. B. Amri, D. Yedjour, M. E. A. Larabi, F. Berrichi

“…In addition, the experiments conducted on simulated benchmarks validate the superior performance of the proposed approach compared to the baseline ML model in terms of prediction accuracy and F1-score.…”
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Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms by Silviu Gresoi, Grigore Stamatescu, Ioana Făgărășan

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Allocation algorithms for multicore partitioned mixed-criticality real-time systems by Luis Ortiz, Ana Guasque, Patricia Balbastre, José Simó

“…This is achieved through the implementation of a Mixed-Integer Linear Programming (MILP) algorithm. The second phase involves the allocation of tasks to cores, employing both, an additional MILP algorithm and a modified worst fit decrease utilisation approach. …”
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