Integrating Genetic Algorithm and Geographically Weighted Approaches into Machine Learning Improves Soil pH Prediction in China by Wantao Zhang, Jingyi Ji, Binbin Li, Xiao Deng, Mingxiang Xu

“…This study integrates Geographic Weighted Regression (GWR) with three ML models (Random Forest, Cubist, and XGBoost) and designs and develops three geographically weighted machine learning models optimized by Genetic Algorithms to improve the prediction of soil pH values. …”
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Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction by Denisa Gándara, Hadis Anahideh, Matthew P. Ison, Lorenzo Picchiarini

“…Because predictive algorithms rely on historical data, they capture societal injustices, including racism. …”
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Intelligent classification and prediction of students’ mental health in online learning environments using boosting algorithm and LIWC features by Xiaomin Xu, Tianrong Zhang

“…The model extracts emotional and psychological features from online learning platforms using the LIWC dictionary and integrates multiple weak classifiers using the Boosting algorithm. The performance of the model is enhanced with the Antlion Optimization Algorithm. …”
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Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms by Shahnam Sedigh Maroufi, Maryam Soleimani Movahed, Azar Ejmalian, Maryam Sarkhosh, Ali Behmanesh

“…Results he RF algorithm showed high performance in both prediction approaches. …”
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Predicting the Compressive Strength of High-Performance Concrete utilizing Radial Basis Function Model integrating with Metaheuristic Algorithms by LiWei Hu

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Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine by Minjian Wang

“…In order to overcome the problem of low accuracy of traditional algorithms in prediction, this paper uses the improved gray wolf algorithm (IGWO) and support vector machine (SVM) for predictive analysis of college students' physical exercise behavior. …”
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Improving prediction accuracy of open shop scheduling problems using hybrid artificial neural network and genetic algorithm by Mohammad Reza Komari Alaei, Reza Rostamzadeh, Kadir Albayrak, Zenonas Turskis, Jonas Šaparauskas

“…Therefore, following a review of previous works, the goal of the present study is designing a mathematical model for open shop scheduling (OSS) problems using different machines aiming at minimizing the maximum time required to complete the works using an artificial neural network (ANN) and genetic algorithm (GA). The research data were driven from a Shoe company carried out between the years 2019 and 2020. …”
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Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality by Avisek Kundu, Seeboli Ghosh Kundu, Santosh Kumar Sahu, Nitesh Dhar Badgayan

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Advancing Knowledge on Machine Learning Algorithms for Predicting Childhood Vaccination Defaulters in Ghana: A Comparative Performance Analysis by Eliezer Ofori Odei-Lartey, Stephaney Gyaase, Dominic Asamoah, Thomas Gyan, Kwaku Poku Asante, Michael Asante

“…This study evaluated the utility of machine learning algorithms for predicting childhood vaccination defaulters in Ghana, addressing the limitations of traditional statistical methods when handling complex, high-dimensional health data. …”
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Artificial intelligence-based optimization and modeling of cadmium reduction via ultraviolet-assisted malathion/sulfite reaction mechanisms by Hossein Azarpira, Parsa Khakzad, Tayebeh Rasolevandi, Amir Sheikhmohammadi

Subjects: “…Cadmium reduction…”
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Enhanced LSTM-based AI model for accurate dissolved oxygen prediction in aquaculture systems by Ala Saleh Alluhaidan, Prabu P, Romana Aziz, Shakila Basheer

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Model order reduction of boiler system using nature-inspired metaheuristic optimization of PID controller by Anurag Singh, Shekhar Yadav, Nitesh Tiwari, Dinesh Kumar Nishad, Saifullah Khalid

“…This study proposes a dual-stage optimization framework that integrates balanced truncation-based model order reduction with nature-inspired metaheuristic algorithms for PID controller tuning to address these issues. …”
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Using Prediction Confidence Factors to Enhance Collaborative Filtering Recommendation Quality by Dionisis Margaris, Dimitris Spiliotopoulos, Kiriakos Sgardelis, Costas Vassilakis

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Prediction of Electric Vehicle Mileage According to Optimal Energy Consumption Criterion by Oleksii Chkalov, Roman Dropa

“…Within this context, a novel model-based predictive approach is introduced for estimating electric vehicle energy consumption. …”
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Sensor-validated simulations predict fracture healing outcomes in an ovine model by Alicia Feist, Carla Hetreau, Manuela Ernst, Peter Varga, Peter Schwarzenberg

“…Overall, this study demonstrated successful application of the healing algorithm with validation of the simulation results to healing outcomes. …”
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Comparative Study on Prediction Models for Crack Opening Degree in Concrete Dam by HUANG Song, WU Jie, FANG Zhanchao, CHU Huaping, WU Yan'gang, XUE Zilong, HE Linbo

“…However, there are some deficiencies in the predictive power of the former and the theoretical explanation of the latter. …”
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Photovoltaic power prediction system based on dual-layer decomposition strategy and a novel dynamic grouping multi-objective coati optimization algorithm by Xiaole Tang, Hao Lu, Yanting Kang, Wenjun Zhao

Subjects: “…Photovoltaic power prediction system…”
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Considering the Structure of Scientific and Cognitive Activity at Different Levels of Continuing Education by A. A. Frolov, Y. N. Frolova, I. A. Chernyayev

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Large-scale terminal access algorithm based on slot ALOHA and adaptive access class barring by Zhenyu ZHU, Xiaorong ZHU, Yan CAI, Hongbo ZHU

“…In order to solve the problem of high collision rate and low timeliness of large-scale terminals access in the Internet of things, a large-scale terminal access algorithm based on slot ALOHA and adaptive access class barring (ACB) was proposed.Firstly, the services were classified based on the data from each terminal by the volume of the services processed and the requirements for delay.For the services that were not time-sensitive and whose effective data portion was less than 1 000 bit, a slot-based ALOHA-based competitive access method was used.ACB-based random access was used for the services that were time-sensitive or whose data portion was greater than 1 000 bit.On this basis, a method was proposed for predicting the access application volume based on the quantitative estimation, and dynamically adjusting the ACB control parameters based on this predicted value.Simulation results show that compared with other existing access algorithms, the proposed algorithm reduces the collision rate and improves the system access success rate under the premise of ensuring the high priority service delay requirements.…”
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Impact of surrogate model accuracy on performance and model management strategy in surrogate-assisted evolutionary algorithms by Yuki Hanawa, Tomohiro Harada, Yukiya Miura

Subjects: “…Surrogate-assisted evolutionary algorithms…”
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