Intelligent design of Fe–Cr–Ni–Al/Ti multi-principal element alloys based on machine learning by Kang Xu, Zhengming Sun, Jian Tu, Wenwang Wu, Huihui Yang

“…Multi-principal element alloys (MPEAs), distinguished by their complex compositions and exceptional mechanical properties, pose significant challenges for conventional predictive approaches in mechanical property optimization. This study proposes an innovative intelligent optimization algorithm (OA) to refine feature selection in machine learning (ML) models, targeting the prediction of ultimate tensile strength (UTS) and fracture elongation (FE) in MPEAs. …”
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Machine-Learning-Algorithm-Assisted Portable Miniaturized NIR Spectrometer for Rapid Evaluation of Wheat Flour Processing Applicability by Yuling Wang, Chen Zhang, Xinhua Li, Longzhu Xing, Mengchao Lv, Hongju He, Leiqing Pan, Xingqi Ou

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Simulated Annealing-Based Hyperparameter Optimization of a Convolutional Neural Network for MRI Brain Tumor Classification by Sofia El Amoury, Youssef Smili, Youssef Fakhri

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Reliability Prediction for Computer Numerical Control Machine Servo Systems Based on an IPSO-Based RBF Neural Network by Zheng Jiang, GuangJian Wang, ZuGuang Huang, Ye He, RuiJuan Xue

“…A novel reliability prediction model based on radial basis function (RBF) neural network optimized by improved particle swarm optimization (IPSO) was proposed. …”
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A Review of Agent-Based Models for Energy Commodity Markets and Their Natural Integration with RL Models by Silvia Trimarchi, Fabio Casamatta, Laura Gamba, Francesco Grimaccia, Marco Lorenzo, Alessandro Niccolai

“…The main findings reveal that while agent-based models significantly enhance the understanding of energy market mechanisms, enabling better profit optimization and technical constraint coherence for traders, scaling these models to highly complex systems with a large number of agents remains a key limitation.…”
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Machine learning framework for oxytetracycline removal using nanostructured cupric oxide supported on magnetic chitosan alginate biocomposite by Hassan Rasoulzadeh, Hossein Azarpira, Mojtaba Pourakbar, Amir Sheikhmohammadi, Alieh Rezagholizade-shirvan

“…This research introduces an efficient method for removing oxytetracycline (OTC) from liquids using CuO-M-CAB nanoparticles. By optimizing key parameters such as pH and reaction time through machine learning models (Tikhonov Regularization and PSO), removal efficiency is significantly enhanced. …”
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Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China by Baoxin Zhao, Jingzhong Zhu, Youbiao Hu, Qimeng Liu, Yu Liu

“…The main purpose of this research is to apply the logistic regression (LR) model, the support vector machine (SVM) model based on radial basis function, the random forest (RF) model, and the coupled model of the whale optimization algorithm (WOA) and genetic algorithm (GA) with RF, to make landslide susceptibility mapping for the Ankang City of Shaanxi Province, China. …”
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Stress–Strain Prediction for Steam-Cured Steel Slag Fine Aggregate Concrete Based on Machine Learning Algorithms by Chuanshang Wang, Di Hu, Qiang Jin

“…The analysis results revealed that the RF model achieved optimal performance (R² = 1.00), whereas the SVR model underperformed overall. …”
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A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm by Yerui Fan, Chao Zhang, Yu Xue, Jianguo Wang, Fengshou Gu

“…It is based on a high-performance support vector machine (SVM) that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO). …”
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Low-cost fabrication and comparative evaluation of machine learning algorithms for flexible PDMS-based hexagonal patch antenna by Srivatsan Sarvesan, Mettu Goutham Reddy, S. S. Karthikeyan, Praveen K. Sekhar

“…To accelerate the design process and determine the most effective model for predicting optimal geometrical parameters that yield improved impedance matching at the target frequency, four supervised machine learning algorithms including Random Forest, XGBoost, CatBoost and LightGBM were evaluated and compared. …”
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Estimating Rainfall Erosivity in North Korea Using Automated Machine Learning: Insights into Regional Soil Erosion Risks by Jeongho Han, Seoro Lee

“…The GradientBoostingRegressor (GBR) model, optimized using the Tree-based Pipeline Optimization Tool (TPOT), was trained on South Korean data. …”
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Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine by Minjian Wang

“…A nonlinear decreasing convergence factor strategy and an inertia weight strategy are introduced to improve the gray wolf optimization algorithm, which is used to determine the SVM parameters for the purpose of improving the model accuracy. …”
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Adaptive continuous-discrete variables optimization for active learning with extremely sparse data in optical material design by Serang Jung, Eungkyu Lee

“…In the adaptive scheme, we examine the performance of three machine learning (ML) models—Gaussian process regression, factorization machines (FM), and field-aware FM—for a surrogate function, and ML model-specific optimization algorithms such as discrete particle swarm optimization, artificial bee colony optimization, and simulated annealing. …”
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Personalized Solutions for Foot Health: Machine Learning-Based Foot Condition Detection, Classification, and Recommendation of Customized Footwear by Sonaa Rajagopal, Muralikrishnan Mani, Shyam Venkatraman, R. Suganya

“…The proposed work is based on three large datasets: (1) Grayscale pressure sensor heat maps for foot posture, with high-resolution foot pressure maps that capture weight distribution and posture; (2) Clinically Validated Foot Condition Dataset, comprising foot conditions verified by physiotherapists and linked to real symptoms; and (3) Footwear Recommendation Dataset for Specific Foot Conditions, with expert-curated footwear suggestions tailored to various foot conditions for optimal support and comfort. The framework consists of three modules: a CNN-based VGG-16-GRU model which identifies gait posture based on pressure sensor heatmaps, an autoencoder-based random forest model which classifies foot diseases based on the detected gait posture, and an LSTM-ensembled XGBoost model which recommends features of suggested footwear. …”
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Navigating chemical design spaces for metal-ion batteries via machine-learning-guided phase-field simulations by Hamed Taghavian, Viktor Vanoppen, Erik Berg, Peter Broqvist, Jens Sjölund

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Dynamic climate graph network and adaptive climate action strategy for climate risk assessment and low-carbon policy responses by Fang Zhou, Yan Shi, Pengfei Zhao, Zhengzhao Gu, Ye Li

“…DCGN utilizes graph-based learning to model spatial dependencies and temporal feature extraction to analyze evolving climate patterns. …”
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A machine learning-based efficient anomaly detection system for enhanced security in compromised and maligned IoT Networks by Anita Punia, Manish Tiwari, Sourabh Singh Verma

“…The proposed approach combines Modified Whale Transfer and Sine-Cosine algorithms along with feature selection techniques such as ANOVA, RFE, and RFA to detect malicious communications accurately. We use HP Tuned Machine Learning Algorithms further for developing an anomaly detection model and optimization of them by implementing machine learning algorithms. …”
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Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning by Swetha Valavarasu, Yasaswini Sangu, Tanmaya Mahapatra

“…Both subsets underwent exploratory data analysis, preprocessing, machine learning model training, validation, and optimization. …”
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Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base Stations by Oluwole John Famoriji, Thokozani Shongwe

“…Based on experimental data, the estimation method is both feasible and effective; the machine learning model’s mean absolute percentage error is about 5.89%. …”
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Experimental Investigation on the Machining Behaviour, Surface Integrity and Tool Wear Analysis in Environment Friendly Milling of Inconel 825 by D. Nathan, T. Ramkumar, M. Selvakumar

“…Prediction of surface roughness by ANOVA linear model for MQL condition was found functionally adequate with R2 = 89.25% which fits with the experimental values. …”
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