Data enhanced iterative few-sample learning algorithm-based inverse design of 2D programmable chiral metamaterials by Zhao Zeyu, You Jie, Zhang Jun, Du Shiyin, Tao Zilong, Tang Yuhua, Jiang Tian

“…In this way, the DEIFS algorithm replaces the time-consuming iterative optimization process with a faster and simpler approach that achieves accurate inverse design with dataset whose amount is at least one to two orders of magnitude less than most previous deep learning methods, reducing the dependence on simulated spectra. …”
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An Enhanced Distribution System Performance with Optimization Techniques for Location of Electrical Vehicle Charging Stations by Sainadh Singh Kshatri, Venkata Anjani Kumar G, Chilakapati Lenin Babu, Palepu Suresh Babu

“…The proposed methodology leverages the Grey Wolf Optimization (GWO) metaheuristic algorithm, enthused by the grey wolves hunting, to identify the most strategic locations for EVCSs. …”
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Adaptive Optimization and Dynamic Representation Method for Asynchronous Data Based on Regional Correlation Degree by Sichao Tang, Yuchen Zhao, Hengyi Lv, Ming Sun, Yang Feng, Zeshu Zhang

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Research on the optimization model of anti-breast cancer candidate drugs based on machine learning by Zhou Dong, Hong Chen, Yuchen Yang, Hairong Hao

“…Additionally, a multi-model fusion strategy and Particle Swarm Optimization (PSO) algorithm were employed to optimize both biological activity and ADMET properties, thereby improving the prediction of Caco-2, CYP3A4, hERG, HOB, and MN properties. …”
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Leveraging a disulfidptosis-based signature to characterize heterogeneity and optimize treatment in multiple myeloma by Bingxin Zhang, Dong Zheng, Shuxia Zhu, Xinyi Zhang, Quanqiang Wang, Zhili Lin, Ziwei Zheng, Shujuan Zhou, Zixing Chen, Sisi Zheng, Enqing Lan, Luning Cui, Hansen Ying, Yu Zhang, Xuanru Lin, Qiang Zhuang, Honglan Qian, Xudong Hu, Yan Zhuang, Qianying Zhang, Zhouxiang Jin, Songfu Jiang, Yongyong Ma, Yongyong Ma, Yongyong Ma

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Optimizing electric vehicle energy consumption prediction through machine learning and ensemble approaches by Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adeeb Noor, Sufyan Ali Memon

“…The K-Nearest Neighbors (KNN) algorithm is employed as the base model, with hyperparameter optimization performed using GridSearchCV, RandomizedSearchCV, Optuna, and Particle Swarm Optimization (PSO). …”
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Enhancing Quality Control of Packaging Product: A Six Sigma and Data Mining Approach by Resty Ayu Ramadhani, Rina Fitriana, Anik Nur Habyba, Yun-Chia Liang

“… Six Sigma is of paramount importance to organizations as it provides a structured and data-driven approach, fostering continuous improvement, minimizing defects, and optimizing processes to meet and exceed customer expectations. …”
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Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations by Panagiotis Michailidis, Iakovos Michailidis, Elias Kosmatopoulos

“…The current review systematically examines RL-based control strategies applied in BEMS frameworks integrating RES technologies between 2015 and 2025, classifying them by algorithmic approach and evaluating the role of multi-agent and hybrid methods in improving real-time adaptability and occupant comfort. …”
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Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network by Zhiqiang Liu, Wenbo Zhu, Hongzhou Zhang, Shengjin Wang, Lu Fang, Weijun Hong, Hua Shao, Guopeng Wang

“…In this article, we propose a novel evaluation method with True Positive Identification Rate in dynamic and M:N mode and create a novel evaluation model of system reliability with the improved Fuzzy Dynamic Bayesian Network. Subsequently, we infer to solve the fuzzy reliability state probabilities of the six systems with Netica and get two most important factors with the improved fuzzy C-means algorithm. …”
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The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…It indicates that most algorithms can achieve good detection results when the targets are sparse, and the lightweight models may have more advantages with considering the demand of computing resources. …”
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DGCA3QM: DESIGN OF A DUAL GENETIC ALGORITHM BASED AUTOREGRESSION MODEL FOR CORRELATIVE PREDICTION OF AIR QUALITY METRICS by Harna M. Bodele, G. M. Asutkar, Kiran G. Asutkar

“…The predicted values are further optimized via another bioinspired layer that assists in identification of high correlation value changes, thereby improving prediction performance under large data samples. …”
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Developing an Equitable Machine Learning–Based Music Intervention for Older Adults At Risk for Alzheimer Disease: Protocol for Algorithm Development and Validation by Chelsea S Brown, Luna Dziewietin, Virginia Partridge, Jennifer Rae Myers

“…The recommendation accuracy of the ML algorithm will be assessed using multiple performance metrics, including root-mean-square error and normalized discounted cumulative gain as well as the mean acceptability score with a goal of 85% user acceptability. …”
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Vibration Analysis and Optimization of Iron-Core Reactors Based on Fe-Based Soft Magnetic Composite Materials by Yangyang Ma, Wenle Song, Jie Gao, Yang Liu, Yilei Shang, Weimei Zhao, Fuyao Yang

“…The characteristic parameters of the improved model are identified using the particle swarm optimization–simulated annealing (PSO-SA) algorithm, with the identified root mean square error not exceeding 3.5, verifying the model’s accuracy. …”
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Action Recognition, Tracking, and Optimization Analysis of Training Process Based on SVR Model and Multimedia Technology by Xuejiao Zhong

“…Experimental results show the proposed algorithm has a significant performance improvement compared to before the improvement; at the same time, it is better than most current mainstream algorithms, which proves the feasibility and effectiveness of the algorithm. …”
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Research on Prediction and Optimization of Airport Express Passenger Flow Based on Fusion Intelligence Network Model by Jin He, Yinzhen Li, Yuhong Chao

“…The purpose of this paper is to optimize the accuracy of airport express passenger flow prediction so as to meet the need for the optimal allocation of traffic resources against the background of accelerated urbanization and the rapid development of airport express services. …”
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Interpretable prediction model for hand-foot-and-mouth disease incidence based on improved LSTM and XGBoost by Xiao LI, Shuyu HE, Yan PENG, Rongxin YANG, Lu TAO, Tingqi LOU, Wenqi HE

“…In order to address the issues of low accuracy and poor interpretability in existing HFMD incidence prediction models, in this paper, we propose an interpretable prediction model, namely, ARIMA–LSTM–XGBoost, which integrates multiple meteorological factors with Autoregressive integrated moving average model (ARIMA), Long short-term memory (LSTM), Extreme gradient boosting (XGBoost), Grey wolf optimizer (GWO), Genetic algorithm (GA) and Shapley additive explanations (SHAP). …”
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Bayesian Optimization-Based State-of-Charge Estimation with Temperature Drift Compensation for Lithium-Ion Batteries by Zhen-Rong Yuan, Ke-Feng Huang, Cai-Hua Xu, Jun-Chao Zou, Jun Yan

“…For this reason, this study proposes an algorithm focusing on Bayesian optimization-based adaptive extended Kalman filter (BO-AEKF) to enhance the numerical accuracy and stability of state-of-charge (SOC) estimation for lithium batteries under various operating conditions. …”
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Tuning of Pareto-optimal robust controllers for multivariable systems. Application on helicopter of two-degress-of-freedom by J. Carrillo Ahumada, G. Reynoso Meza, S. García Nieto, J. Sanchis, M.A. García Alvarado

“…The tuning of Pareto-optimal robust controllers was applied to improve the performance of a helicopter with two-degrees-of-freedom with a linear control algorithm. …”
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Hybrid Hunger Games Search optimization using a neural networks approach applied to UAVs by Nadia Samantha Zuñiga-Peña, Salatiel Garcia-Nava, Norberto Hernandez-Romero, Juan Carlos Seck-Touh-Mora

“…Optimization methods like population-based algorithms are valuable when applied to multidimensional and nonlinear problems. …”
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UV-Vis spectroscopy coupled with firefly algorithm-enhanced artificial neural networks for the determination of propranolol, rosuvastatin, and valsartan in ternary mixtures by Ahmed Serag, Maram H. Abduljabbar, Yusuf S. Althobaiti, Farooq M. Almutairi, Shaker T. Alsharif, Rami M. Alzhrani, Marwa F. Ahmed, Atiah H. Almalki

“…An experimental design of 25 samples was employed as a calibration set, and a central composite design of 20 samples was used as a validation set. The firefly algorithm (FA) was evaluated as a variable selection procedure to optimize the developed ANN models resulting in simpler models with improved predictive performance as evident by lower relative root mean square error of prediction (RRMSEP) values compared to the full spectrum ANN models. …”
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