Integrated energy microgrid bi-level game scheduling optimization taking into account electricity-heat-hydrogen enriched compressed natural gas coupling and shared energy storage by Changgang Wang, Xiaoning Sun

“…Finally, the proposed model is solved using the Black-winged Kite algorithm in conjunction with the CPLEX solver.Case studies demonstrate the effectiveness of the proposed method, showing revenue increases of 9.15% for integrated energy microgrid operators and 8.29% for load aggregators, alongside a reduction in carbon emissions by 8.76%.…”
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Tire Pressure Monitoring System Using Feature Fusion and Family of Lazy Classifiers by Arpit Pandey, Sridharan Naveen Venkatesh, Prabhakaranpillai Sreelatha Anoop, B. R. Manju, Vaithiyanathan Sugumaran

“…Empirically, the research achieved 97.92% accuracy using the local weighted learning (LWL) algorithm, demonstrating the effectiveness of combined statistical, histogram, and ARMA features in enhancing TPMS predictive capabilities.…”
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A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks by Nan Li, Jiabin Chen, Yan Yuan, Xiaochun Tian, Yongqiang Han, Mingzhe Xia

“…Thus, with the combined strategy, we can reduce the positioning error and shorten the prediction time. We implement the proposed system on a mobile phone and the positioning results show that our algorithm can provide a higher localization accuracy and significantly improves the prediction speed.…”
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System biology-based assessment of the molecular mechanism of epigallocatechin gallate in Parkinson’s disease: via network pharmacology, in-silico evaluation & in-vitro studies by Hannah Lalengzuali Fanai, Jagdish Chand, Sheikh F. Ahmad, Sabry M. Attia, Talha Bin Emran

“…The targets SIRT3, FOXO1, and PPARGC1A were predicted to have the highest binding energies via dual algorithm-based molecular docking studies. …”
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Energy optimization control of extended-range hybrid combine harvesters based on quasi-cycle power demand estimation by Shuofeng Weng, Chaochun Yuan, Youguo He, Jie Shen, Lizhang Xu, Zhihao Zhu, Qiuye Yu, Xiaowei Yang

“…Key innovations include quasi-periodic load segmentation, component-response-based feed rate prediction, and GA-driven multi-objective optimization. …”
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Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) Koehne by Yashun Wang, Huirong Chen, Jianting Gong, Yang Cui, Huiqin Zou, Yonghong Yan

“…Discrimination and prediction models based on color features combined with multiple machine learning algorithms were established using 10-fold cross-validation and external test set validation. …”
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Data-Driven and Mechanistic Soil Modeling for Precision Fertilization Management in Cotton by Miltiadis Iatrou, Panagiotis Tziachris, Fotis Bilias, Panagiotis Kekelis, Christos Pavlakis, Aphrodite Theofilidou, Ioannis Papadopoulos, Georgios Strouthopoulos, Georgios Giannopoulos, Dimitrios Arampatzis, Evangelos Vergos, Christos Karydas, Dimitris Beslemes, Vassilis Aschonitis

“…By comparing the Mean Absolute Error (MAE) between predicted and observed cotton yield values across three ML algorithms, i.e., Random Forest (RF), XGBoost, and LightGBM, the RF model achieved the lowest error (422.6 kg/ha), outperforming XGBoost (446 kg/ha) and LightGBM (449 kg/ha). …”
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Aerodynamic Optimization of Morphing Airfoil by PCA and Optimization-Guided Data Augmentation by Ao Guo, Jing Wang, Miao Zhang, Han Wang

“…A Multi-Island Genetic Algorithm (MIGA) efficiently explores the reduced design space, while iterative retraining of the surrogate model enhances prediction accuracy, particularly in high-performance regions. …”
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Grassland management and phenology affect trait retrieval accuracy from remote sensing observations by Maksim Iakunin, Franziska Taubert, Reimund Goss, Severin Sasso, Hannes Feilhauer, Daniel Doktor

“…This study combines radiative transfer model (RTM) and machine learning algorithms to assess the efficacy of the model inversion in predicting plant functional traits under different grassland management regimes. …”
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Adaptively Receding Galerkin Optimal Control for a Nonlinear Boiler-Turbine Unit by Gang Zhao, Zhi-gang Su, Jun Zhan, Hongxia Zhu, Ming Zhao

“…With the help of a mathematical predictive model, optimal control law is then obtained based on a Galerkin optimization algorithm. …”
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Using random forests to forecast daily extreme sea level occurrences at the Baltic Coast by K. Bellinghausen, B. Hünicke, E. Zorita

“…<p>We have designed a machine learning method to predict the occurrence of daily extreme sea level at the Baltic Sea coast with lead times of a few days. …”
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Applying interpretable machine learning to assess intraspecific trait divergence under landscape‐scale population differentiation by Sambadi Majumder, Chase M. Mason

“…Methods Recursive feature elimination was applied to functional trait data from the HeliantHOME database, followed by the application of the Boruta algorithm to detect the traits that are most predictive of ecoregion. …”
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Traffic congestion forecasting using machine learning methods by Ramil R. Zagidullin, Almaz N. Khaybullin

“…To identify patterns in the data, additive time series decomposition, spectral analysis based on the fast Fourier transform, and autocorrelation analysis were applied. The predictive model was implemented using a two-stage approach: the classical ARIMA algorithm was used for baseline forecasting, while an LSTM architecture with two recurrent layers and regularization was trained on 24-hour sequences. …”
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An Adaptive Evolutionary Causal Dynamic Factor Model by Qian Wei, Heng-Guo Zhang

“…Results: The experimental results show that the AcNowcasting algorithm can extract common factors that reflect macroeconomic fluctuations better, and the prediction accuracy of the AcNowcasting algorithm is more accurate than that of traditional nowcasting models. …”
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Potential distribution of endemic lizards from Brazilian restingas: The present announcing the end by Hugo Andrade, Luisa Maria Diele‐Viegas Costa Silva, Carlos Frederico Duarte Rocha, Antônio Jorge Suzart Argôlo, Eduardo José dos Reis Dias

“…Here, we used an ensemble of three modeling algorithms (Bioclim, GLM, and SVM). In predicting the effects of climate change on their future distributions, we used intermediate and pessimistic socio‐economic pathway scenarios (SSP3 70 and SSP5 85, respectively) considering projections for 2081–2100. …”
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An Intelligent System for Medical Oxygen Consumption Management Using Oximetry and Barometry by Abbas Izadi, Hadi Ghasemifard, Mohamad Amin Bakhshali, Nadia Roudsarabi, Omid Sarrafzadeh

“…The system also utilizes an artificial neural network model to predict future oxygen consumption levels, enabling proactive supply chain management. …”
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Enhanced Conformer-Based Speech Recognition via Model Fusion and Adaptive Decoding with Dynamic Rescoring by Junhao Geng, Dongyao Jia, Zihao He, Nengkai Wu, Ziqi Li

“…Speech recognition is widely applied in fields like security, education, and healthcare. While its development drives global information infrastructure and AI strategies, current models still face challenges such as overfitting, local optima, and inefficiencies in decoding accuracy and computational cost. …”
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Assessing the Severity of ODT and Factors Determinants of Late Arrival in Young Patients with Acute Ischemic Stroke by Zhu L, Li Y, Zhao Q, Li C, Wu Z, Jiang Y

“…Enhanced public education, particularly regarding stroke symptoms and the use of emergency services, is crucial for reducing pre-hospital delays and improving patient outcomes.Keywords: acute ischemic stroke, onset-to-door time, young adults, machine learning, emergency medical services…”
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A Spatiotemporal Feature Extraction Technique Using Superlet-CNN Fusion for Improved Motor Imagery Classification by Neha Sharma, Manoj Sharma, Amit Singhal, Nuzhat Fatema, Vinay Kumar Jadoun, Hasmat Malik, Asyraf Afthanorhan

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Research on Mechanical Properties of Steel Tube Concrete Columns Reinforced with Steel–Basalt Hybrid Fibers Based on Experiment and Machine Learning by Bohao Zhang, Xiao Xu, Wenxiu Hao

“…On the basis of the experiments, a parametric expansion analysis of several structural parameters of the specimen was carried out by using ABAQUS finite element software, and a combined model NRBO-XGBoost, based on the Newton-Raphson optimization algorithm (NRBO), and the advanced machine learning model XGBoost was proposed for the prediction of the BSFCFST’s ultimate carrying capacity. …”
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