Hybrid ANN-PSO Based MPPT Optimization for Enhanced Solar Panel Efficiency by Muhammad ilham hasby Hamzah, Happy Aprillia, Andhika Giyantara

“…In this research, a hybrid approach that combines Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) algorithms is used to optimize the Maximum Power Point Tracking (MPPT) system for solar panels. …”
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Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography by Laihu Peng, Hao Fan, Yubao Qi, Jianqiang Li

“…Compared with the traditional method of determining the optimal solid ink density, the current printing equipment used to determine the optimal solid ink density will be faster at improving industrial production efficiency and product quality. …”
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Optimizing Solid Oxide Fuel Cell Performance Using Advanced Meta-Heuristic Algorithms by Siva Ram Rajeyyagari, Srinivas Nowduri

“…Our approach utilizes a Radial Basis Function (RBF) neural network trained with experimental data encompassing five input parameters: oxygen concentration, operating temperature, instrumentation, electrolyte thickness, and electrical current, with the goal of optimizing the single output parameter of power. …”
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State of Charge Estimation on Lithium-Ion Batteries Using Particle Swarm Optimization Method by Muhammad Ridho Dewanto, Riza Hadi Saputra, Kharis Sugiarto, Agung Adi Saputra

“…This research aims to develop a method that can provide accurate SoC estimates for Li-ion batteries using the Particle Swarm Optimization (PSO) method. In this research, a 12V 8.4 Ah Lithium-Ion battery was used as a test subject, utilizing a voltage sensor, ACS712 sensor, and LM35 temperature sensor to measure key parameters such as voltage, current, and temperature. …”
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Utilization of Machine-Learning-Based model Hybridized with Meta-Heuristic Frameworks for estimation of Unconfined Compressive Strength by She Wang, Qi Zhang

“…After a great deal of effort and growing investment in time, the proper adoption of machine learning methods, especially the radial basis function (RBF), opens a route to promising alternatives against empirical methods for better real-time prediction of UCS. The current study considers the RBF-based machine learning model, whose parameters have been optimized using two enhanced metaheuristic frameworks: Improved Arithmetic Optimization Algorithm (IAOA) and Flying Foxes Optimization (FFO). …”
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Wind energy resource assessment based on joint wolf pack intelligent optimization algorithm. by Jiayuan Wang

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Research on Oil Well Production Prediction Based on GRU-KAN Model Optimized by PSO by Bo Qiu, Jian Zhang, Yun Yang, Guangyuan Qin, Zhongyi Zhou, Cunrui Ying

“…To overcome the shortcomings in the current study of oil well production prediction, we propose a hybrid model (GRU-KAN) with the gated recurrent unit (GRU) and Kolmogorov–Arnold network (KAN). …”
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Prediction of Gasification Process via Random Forest Regression Model Optimized with Meta-Heuristic Algorithms by Eunsung Oh

“…The results demonstrate the superior predictive performance of the RFSO model, achieving a remarkable R2 value of 99.7% in training for both Hydrogen (H_2) and Nitrogen (N_2). The optimized models, RFEO and RFSO, consistently outperform the baseline RFR model in forecasting accuracy metrics such as MSE and RMSE, emphasizing their reliability and effectiveness in predicting gasification outcomes.…”
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Optimization of four-diode equivalent circuit models for solar cells: Analytical formulation and performance enhancement by Martin Calasan, Snezana Vujosevic, Mohammed Alruwaili, Moustafa Ahmed Ibrahim

“…This work presents two novel Four-Diode Model (FDM) equivalent circuits—PEC1 and PEC2—that offer closed-form current–voltage (I–V) characteristics derived analytically via the Lambert W function. …”
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A multi-objective optimization-based ensemble neural network wind speed prediction model by Haoyuan Ma, Chang Liu, Ziyuan Qiao, Yuan Liang, Hongqing Wang

“…Additionally, there is a lack of effective ensemble models capable of integrating the strengths of various neural networks, and current hyperparameter optimization strategies often fail to simultaneously address the issues of overfitting and underfitting. …”
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Machine learning and thermodynamic modeling for optimizing hydrogen production via algae-biomass co-gasification by Thanadol Tuntiwongwat, Takashi Yukawa, Thongchai Rohitatisha Srinophakun, Kanit Manatura, Somboon Sukpancharoen, Seyedali Mirjalili

“…Although biomass gasification presents a promising pathway, conventional single-feedstock approaches suffer from low H2 yields and process inefficiencies. Current biomass gasification research lacks integration of advanced algae co-feedstocks with intelligent process optimization. …”
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The Parameter-Optimized Recursive Sliding Variational Mode Decomposition Algorithm and Its Application in Sensor Signal Processing by Yunyi Liu, Wenjun He, Tao Pan, Shuxian Qin, Zhaokai Ruan, Xiangcheng Li

“…Second, a rate learning factor is introduced to automatically adjust the initial center frequency of the current window to reduce errors. Through simulation experiments with signals with different signal-to-noise ratios (SNR), it is found that as the SNR increases from 0 dB to 17 dB, the PO-RSVMD algorithm accelerates the iteration time by at least 53% compared to VMD and RSVMD; the number of iterations decreases by at least 57%; and the RMSE is reduced by 35% compared to the other two algorithms. …”
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Comparison of Markowitz Model and DCC-tCopula-LVaR for Portfolio Optimization in the Tehran Stock Exchange by Gholamreza Taghizadegan, Gholamreza Zomorodian, Mirfeiz Fallahshams, Rasoul Saadi

“…Objective: Considering that investing in the stock market is associated with risk, therefore, its measurement is one of the most important issues for investors. The focus of the current research is on the calculation of the value at risk of Dynamic Conditional Correlation with the Solvency Approach (DCC t-Cupola LVaR) based on the copula and also the minimization problems of the above model to choose the optimal portfolio. …”
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Enhancing shear strength predictions of UHPC beams through hybrid machine learning approaches by Sanjog Chhetri Sapkota, Ajad Shrestha, Moinul Haq, Satish Paudel, Waiching Tang, Hesam Kamyab, Daniele Rocchio

“…This study proposes hybrid ML models that integrate three nature inspired metaheuristic algorithms—Giant Armadillo Optimization (GOA), Spotted Hyena Optimization (SHO) and Leopard seal optimization (LSA)- Extreme Gradient Boosting (XGB) to predict the shear strength of UHPC beams. …”
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Snow depth estimation in Northeast China based on space-borne scatterometer data and ML model with optimal features by Wenfei Chen, Lingjia Gu, Xiaofeng Li, Xintong Fan

“…Furthermore, feature ablation experiments were conducted to obtain the optimal features. In comparison to the public SD product and the ground-based SD measurements, the experimental results using the RF model with optimal features demonstrate superior SD estimation performance, yielding a root mean square error (RMSE) of 3.91 cm, mean absolute error (MAE) of 2.27 cm, and an R2 of 0.80. …”
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Robust prediction of tool-tissue interaction force using ISSA-optimized BP neural networks in robotic surgery by Yong-Li Yan, Teng Ren, Li Ding, Tiansheng Sun, Shandeng Huang

“…A solution to predict clamp force accurately is needed to enhance surgical safety and efficiency. Methods The current proposal concerns a deep learning-based solution utilizing a backpropagation neural network (BPNN) optimized by improved sparrow search algorithm (ISSA) to predict clamp force on soft tissue. …”
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Optimization of a direct-detection UV wind lidar architecture for 3D wind reconstruction at high altitude by T. Boulant, T. Michel, M. Valla

“…We found that the optimum angle for an estimation at 100 <span class="inline-formula">m</span> is about 50°, resulting in an improvement of about 50 % compared to an angle of 15–30° typically used in current studies.</p>…”
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Determining parameters of surface defects in the base metal of pipelines using results of complex diagnostics by N.V. Krysko, S.V. Skrynnikov, N.A. Shchipakov, D.M. Kozlov, A.G. Kusyy

“…We discuss issues of determining surface operational defects parameters using results of complex diagnostics including ultrasonic, eddy current and visual non-destructive testing methods. …”
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Multiobjective Time-Space Grid Optimization of Lithium-Ion Electrochemical Model Based on Stability Analysis and Limit Region Decoupling Strategy by Libin Zhang, Minghang Zhang, Hongying Shan, Guan Xu, Jingsheng Dong, Xuemeng Bai

“…The effectiveness of the proposed method was validated by comparing simulation results under different scenarios. The model provides optimal time space grid configurations for various current profiles, including 1C-rate constant-current charging, pulse current (PC), hybrid current (HC), and urban dynamometer driving schedule (UDDS) current profiles. …”
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Optimizing data-driven arctic marine forecasting: a comparative analysis of MariNet, FourCastNet, and PhyDNet by Aleksei V. Buinyi, Aleksei V. Buinyi, Dias A. Irishev, Edvard E. Nikulin, Aleksandr A. Evdokimov, Polina G. Ilyushina, Natalia A. Sukhikh, Natalia A. Sukhikh

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