Short-term prediction of regional energy consumption by metaheuristic optimized deep learning models by Ngoc-Quang Nguyen, Phuong-Thao-Nguyen Nguyen, Quynh-Chau Truong

“…Results showed that the proposed method outperformed conventional numerical input methods. The optimized model yielded a mean absolute percentage error improvement of 0.5% compared to the default models, indicating that JS is a promising method for achieving the optimal hyperparameters. …”
Get full text

A Fast and Accurate Numerical Approach for Pricing American-Style Power Options by Tsvetelin S. Zaevski, Hristo Sariev, Mladen Savov

“…As a consequence, we obtain the option price quickly and with relatively high accuracy—the error is at the third decimal position. We further provide a comprehensive analysis of the impact of the parameters on the options’ value, and discuss ordinary European and American capped options. …”
Get full text

High-Precision Modal Decomposition of Laser Beams Based on Globally Optimized SPGD Algorithm by Kyuhong Choi, Changsu Jun

“…Calculation of the modal weight and phase percentage errors, as well as simulations of far-field evolution images, confirmed the importance of finding the global minimum in improving the accuracy and real-time analysis of MD. …”
Get full text

Double weighted combat data quality evaluation method based on CVF optimized FAHP by Jianwei Wang, Chengsheng Pan, Qing Zhang

“…The double-weighted evaluation theory combines these two tiers of weights to produce the final assessment. Analysis of the experimental results indicates that the proposed method reduces the mean squared error to 5.35 when compared to results obtained using FAHP, interval intuitionistic fuzzy methods, and artificial neural networks, bringing it closer to actual standard values. …”
Get full text

Epidemiological insights into complication and outcomes in corneal refractive surgery population: findings from KNHANES 2010–2012 by Joon Yul Choi, Sun Young Ryu, Tae Keun Yoo

“…Abstract Purpose Epidemiological studies on corneal refractive surgery remain limited, particularly regarding complications such as dry eye disease and refractive error regression, which impact long-term visual outcomes and patient satisfaction. …”
Get full text

Multi-objective optimization of gold price forecasting using the pareto alpha-cut technique by Pullooru Bhavana

“…We employed three distinct models: the Autoregressive Distributed Lag (ARDL) model, a stochastic model, and the Autoregressive Integrated Moving Average (ARIMA) model, to capture the underlying dynamics of gold price fluctuations influenced by macroeconomic factors.The methodology incorporates the Pareto optimality approach combined with fuzzy logic to manage trade-offs among multiple performance metrics, specifically Root Mean Squared Error (RMSE), volatility, and R-squared. …”
Get full text

AIME-solve: Accessible interactive mathematical expressions solver for optimized learning and visual empowerment by Amjad Ali, Shah Khusro, Fakhre Alam, Inayat Khan, Shabbab Ali Algamdi

Get full text

Application of Deep Learning for Stock Prediction Within the Framework of Portfolio Optimization in Quantitative Trading by Xiaoyu Qin

“… This paper proposes a method for stock prediction and portfolio optimization as a part of quantitative trading based on a combination of Bi-RNN and a modified snake optimization algorithm (MSOA) to build optimal portfolios and outperform conventional models and benchmarks. …”
Get full text

Optimizing deep learning models to combat amyotrophic lateral sclerosis (ALS) disease progression by Haoshen Qin, Lal Hussain, Ziang Liu, Xu Yan, Fuad A. Awwad, Faisal Mehmood Butt, Umair Ahmad Salaria, Emad A.A. Ismail

“…Feature importance analysis with optimized XGBoost identified ZBTB2P1 as the most influential feature, followed by RNF181, with WASH9P being the least influential among the top eight. …”
Get full text

Optimization of SA-Gel Hydrogel Printing Parameters for Extrusion-Based 3D Bioprinting by Weihong Chai, Yalong An, Xingli Wang, Zhe Yang, Qinghua Wei

“…Comparative experiments showed the optimized parameters predicted by the model with a mean error of 5.15% for printing precision, which outperformed random sets. …”
Get full text

Application of an improved pelican optimization algorithm based on comprehensive strategy in PV parameter identification by Xu Yong, Sang Bicong, Zhang Yi

“…Furthermore, this optimization method yields the smallest mean square error across all types of solar cells, demonstrating the superiority of the proposed algorithm.…”
Get full text

Technology and Method Optimization for Foot–Ground Contact Force Detection in Wheel-Legged Robots by Chao Huang, Meng Hong, Yaodong Wang, Hui Chai, Zhuo Hu, Zheng Xiao, Sijia Guan, Min Guo

“…Combined with global sensitivity analysis (GSA), the optimal placement of PVDF sensors is determined and experimentally validated. …”
Get full text

Inverse Gravimetric Problem Solving via Prolate Ellipsoidal Parameterization and Particle Swarm Optimization by Ruben Escudero González, Zulima Fernández Muñiz, Antonio Bernardo Sánchez, Juan Luis Fernández Martínez

“…The subsurface is modeled as a set of prolate ellipsoids whose parameters are optimized to minimize the misfit between observed and predicted anomalies. …”
Get full text

Parameter Optimization and Experimental Study on Alfalfa Stem Flattening Process Based on DEM–MBD by Zhikai Yang, Keping Zhang, Jinlong Yang, Yaping Yao

“…According to the optimal combination of parameters to carry out field tests, the average flattening rate of stem and stem crushing rate were 95.71% and 1.73%, respectively, which showed small relative error with the predicted value and met the requirements of alfalfa steam flattening and modulation operation. …”
Get full text

Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete by Muhammad Izhar Shah, Shazim Ali Memon, Muhammad Sohaib Khan Niazi, Muhammad Nasir Amin, Fahid Aslam, Muhammad Faisal Javed

“…The performance of the developed models was then evaluated by statistical criteria and error assessment tests. The result shows that the performance of MEP with PSO algorithm significantly enhanced its accuracy. …”
Get full text

Design Optimization and Performance Evaluation of an Automated Pelleted Feed Trough for Sheep Feeding Management by Xinyu Gao, Chuanzhong Xuan, Jianxin Zhao, Yanhua Ma, Tao Zhang, Suhui Liu

“…The automatic feeding trough showed a feeding error of 0.3% with PLC-HMI. This study’s optimization of the automatic feeding trough offers a strong foundation and guidance for efficient, accurate pellet feed distribution.…”
Get full text

Dynamic sealing simulation and performance optimization of conical rubber core in rotary blowout preventer by Lianglin Guo, Zhiqiang Huang, Hao Huang, Hao Zhang, Fubin Xin, Junjie Ji, Chengyu Xia, Hengda Che

“…After optimization, the peak value of Mises stress was reduced by 3.64 MPa, the amplitude of Mises stress was reduced by 39%, and the prediction error was only 0.42 MPa, which verified the validity and accuracy of the response surface prediction model. …”
Get full text

Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Ibrahim Elbatal, Ehab M. Almetwally, Marwa M. Eid, El-Sayed M. El-Kenawy

“…Experimental results also demonstrate that the proposed NiOA-DPRNNs framework gets the highest value of R2 (0.9736), lowest error rates and fitness values than other existing models and optimization methods. …”
Get full text

Point Cloud Registration for Lava Tube Surface Reconstruction Using Curvature-Optimized Projection by Jiaqi Yao, Yanqiu Wang, Wen Li, Yuan Han, Hognxu Ai, Zhenchen Ji, Fu Zheng, Zhibin Sun

“…First, the normal vectors were estimated and normalized on an unstructured point cloud using Principal Component Analysis. Subsequently, the point cloud was projected using the Moving Least Squares method based on an anisotropic weighting function to obtain curvature-optimized point cloud data. …”
Get full text

ACGNet: An Alternating Conjugate Gradient Optimization-Based Neural Network for SAR Image Despeckling by Xin Mao, Ying Liu, Chenghao Qiu, Cong Lin

“…To address this issue, this article proposes a supervised collaborative denoising method with alternating optimization, which combines the alternating conjugate gradient method with an SAR despeckling network trained on paired noisy-clean simulated SAR data to progressively optimize image quality, thereby effectively reducing noise while preserving more details and texture information in the image. …”
Get full text