A new sliding mode control strategy to improve active power management in a laboratory scale microgrid by Oscar Gonzales-Zurita, Jean-Michel Clairand, Guillermo Escrivá-Escrivá

“…This study proposes a robust control solution based on the second-order sliding mode control (SMC-2) algorithm to overcome the mentioned challenges. This algorithm employed a non-conventional sliding surface to improve the microgrid’s capacities for energy management. …”
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Artificial intelligence-optimized shield parameters for soft ground tunneling in urban environment: A case study of Bangkok MRT Blue Line by Sahatsawat Wainiphithapong, Chana Phutthananon, Sompote Youwai, Pitthaya Jamsawang, Phattarawan Malaisree, Ochok Duangsano, Pornkasem Jongpradist

“…This integrated framework, which combines the non-dominated sorting genetic algorithm (NSGA-II) with LSTM neural networks, is applied to MOO to identify the optimal SOPs, while accounting for their influence on S variation as a time-series over 11 timesteps, as considered in this study. …”
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Adaptive gradient scaling: integrating Adam and landscape modification for protein structure prediction by Vitalii Kapitan, Michael Choi

“…Despite their success, machine learning methods face fundamental limitations in optimizing complex high-dimensional energy landscapes, which motivates research into new methods to improve the robustness and performance of optimization algorithms. …”
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Alzheimer’s Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network by Raheleh Ghadami, Javad Rahebi

“…<b>Method:</b> This proposal methodology involves sourcing Alzheimer’s disease-related MRI images and extracting features using convolutional neural networks (CNNs) and the Gray Level Co-occurrence Matrix (GLCM). The Harris Hawks Optimization (HHO) algorithm is applied to select the most significant features. …”
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Research on dynamic prediction and optimization of high altitude photovoltaic power generation efficiency using GVSAO-CNN Model under 8-climate modes by Xiaoming Xiong, Heng Hu, Qiangfu Jia, Rongjian Zhang, Chongan Huang, Qingyuan Lu

“…The GVSAO algorithm is a sophisticated optimization technique that fine-tunes the hyperparameters of CNNs. …”
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Enhancing Compressive Strength Prediction in Recycled Aggregate Concrete through Robust Hybrid Machine Learning Approaches by Samuel Keown, Dylan O’Dwyer

“…This investigation explores the integration of LSSVR with two innovative optimizers, namely the Giant Trevally Optimizer (GTO) and the Dingo Optimization Algorithm (DOA). …”
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Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content by Xuguang Sun, Baoyuan Zhang, Menglei Dai, Cuijiao Jing, Kai Ma, Boyi Tang, Kejiang Li, Hongkai Dang, Limin Gu, Wenchao Zhen, Xiaohe Gu

“…The SPA algorithm also improved the inversion efficiency of LWC. …”
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Advanced removal of butylparaben from aqueous solutions using magnetic molybdenum disulfide nanocomposite modified with chitosan/beta-cyclodextrin and parametric evaluation through... by Saeed Hosseinpour, Alieh Rezagholizade-shirvan, Mohammad Golaki, Amir Mohammadi, Amir Sheikhmohammadi, Zahra Atafar

“…The predictive stability of PR emerges through these different dataset applications. The L-BFGS algorithm established the optimal control factors as pH = 6.64 and initial concentration = 1.00 mg/L and contact time = 60 min and adsorbent dosage = 0.8 g/L which dramatically improved the removal efficiency due to the collaborative properties of the nanocomposite. …”
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Intelligent design of high-performance fluids for thermal management: integrating response surface methodology, weighted Tchebycheff method, and strength Pareto evolutionary algori... by Mohamed Bechir Ben Hamida, Ali Basem, Neeraj Varshney, Loghman Mostafa

“…Abstract Optimizing nanofluid thermophysical properties (TPPs) is essential for advancing heat transfer applications; however, most studies focus on two-objective optimization, limiting their real-world applicability. …”
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Designing a new tomato leaf disease classification framework using ran-based adaptive fuzzy c-means with heuristic algorithm model by Kanti Rongali Divya, Rao Gottapu Sasibhushana, Aruna Singam

“…So, the parameters of AFCM are tuned by utilizing the new improved algorithm named Dingo Optimization Algorithm (DOA) to improve the clustering accuracy. …”
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Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention by Wei Liu, Yu Cao

“…This study utilizes real Automatic Identification System (AIS) data and applies the PSO algorithm to optimize the model and determine the optimal parameters, using a sliding window method for input and output prediction. …”
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Heuristic based federated learning with adaptive hyperparameter tuning for households energy prediction by Liana Toderean, Mihai Daian, Tudor Cioara, Ionut Anghel, Vasilis Michalakopoulos, Efstathios Sarantinopoulos, Elissaios Sarmas

“…A genetic algorithm-based hyperparameter optimization method reduces the computational load on edge nodes by efficiently exploring different configurations and using only the most promising ones for edge nodes’ cross-validation. …”
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Machine Learning Approach and Model for Predicting Proton Stopping Power Ratio and Other Parameters Using Computed Tomography Images by Charles Ekene Chika

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Opportunities of machine learning algorithms for education by Olga Ovtšarenko

“…By predicting trends, identifying patterns, and optimizing resource allocation, machine learning can improve the efficiency of e-learning and provide students with tailored recommendations for acquiring relevant knowledge and skills. …”
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Monitoring of Glacier Area Changes in the Ili River Basin during 1992–2020 Based on Google Earth Engine by Qinqin Zhang, Zihui Zhang, Xiaofei Wang, Zhonglin Xu, Yao Wang

“…Utilizing the Landsat data series, we employed the random forest (RF) classification algorithm within the GEE platform to extract glacier areas, optimizing a multidimensional feature set using the Jeffries–Matusita (JM) distance method, and applied visual interpretation for data refinement. …”
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The open-closed mod-minimizer algorithm by Ragnar Groot Koerkamp, Daniel Liu, Giulio Ermanno Pibiri

“…Abstract Sampling algorithms that deterministically select a subset of $$k$$ k -mers are an important building block in bioinformatics applications. …”
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PERFORMANCE PREDICTION OF ROADHEADERS USING SUPPORT VECTOR MACHINE (SVM), FIREFLY ALGORITHM (FA) AND BAT ALGORITHM (BA) by Arash Ebrahimabadi, Alireza Afradi

“…Additionally, this study employed Firefly Algorithm (FA), Bat Algorithm (BA) and Support Vector Machine (SVM), which were assessed using coefficient of determination (R²), root mean square error (RMSE), mean squared error (MSE) and mean absolute error (MAE).The obtained results for Firefly Algorithm (FA) are found to be as R2 = 0.9104, RMSE = 0.0658, MSE= 0.0043 and MAE= 0.0039, for Bat Algorithm (BA) are found to be as R2 = 0.9421, RMSE = 0.0528, MSE= 0.0027 and MAE= 0.0024, and for Support Vector Machine (SVM) are found to be as R2 = 0.8795, RMSE = 0.0762, MSE= 0.0058 and MAE= 0.0052, respectively. …”
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Comparison of Doubling the Size of Image Algorithms by S. E. Vaganov, S. I. Khashin

“…However, these improvements are insignificant for complex algorithms (17-point interpolation, Lanczos a=3). …”
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Unobtrusive Sleep Posture Detection Using a Smart Bed Mattress with Optimally Distributed Triaxial Accelerometer Array and Parallel Convolutional Spatiotemporal Network by Zhuofu Liu, Gaohan Li, Chuanyi Wang, Vincenzo Cascioli, Peter W. McCarthy

“…For sleep posture classification, we employ an improved density peak clustering algorithm that incorporates the K-nearest neighbor mechanism. …”
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Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries by Sadiqa Jafari, Jisoo Kim, Wonil Choi, Yung-Cheol Byun

“…We utilized Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models, which were fine-tuned using hyperparameter optimization. The models were assessed using evaluation metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>. …”
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