Prediction of Power System Ramping Demand Using Meteorological Features by Kuan Lu, Song Gao, Jun Li, Kang Chen, Chunhao Yu

“…This study focuses on predicting uncertain ramping demand influenced by meteorological factors. …”
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Spatial distribution prediction of pore pressure based on Mamba model by Xingye Liu, Xingye Liu, Bing Liu, Wenyue Wu, Qian Wang, Yuwei Liu

“…The model is a structured state-space model designed to process complex time-series data, and improve efficiency through parallel scan algorithm, making it suitable for large-scale three-dimensional data prediction. …”
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Clinical prediction model for MODY type diabetes mellitus in children by D. N. Laptev, E. A. Sechko, E. M. Romanenkova, I. A. Eremina, O. B. Bezlepkina, V. A. Peterkova, N. G. Mokrysheva

“…Based on clinical data, a feedforward neural network (NN) was implemented - a multilayer perceptron.MATERIALS AND METHODS: Development of the most effective algorithm for predicting MODY in children based on available clinical indicators of 1710 patients with diabetes under the age of 18 years using a multilayer feedforward neural network.RESULTS: The sample consisted of 1710 children under the age of 18 years with T1DM (78%) and MODY (22%) diabetes. …”
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Development and validation of machine learning models predicting hospitalizations of hypertensive patients over 12 months by A. E. Andreychenko, A. D. Ermak, D. V. Gavrilov, R. E. Novitsky, O. M. Drapkina, A. V. Gusev

“…To develop models for predicting hospitalizations of hypertensive (HTN) over 12 months using machine learning algorithms and to validate them using real-world practice data.Material and methods. …”
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Predicting equilibrium scour depth around non-circular bridge piers with shallow foundations using hybrid explainable machine learning methods by Nasrin Eini, Saeid Janizadeh, Sayed M. Bateni, Changhyun Jun, Essam Heggy, Marek Kirs

“…This study combines two metaheuristic optimization techniques—Siberian tiger optimization (STO) and brown-bear optimization algorithms (BOA)—with artificial neural networks (ANNs) to enhance deq prediction accuracy for both round- and sharp-nosed piers using both field and laboratory data. …”
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Cardiometabolic index predicts cardiovascular events in aging population: a machine learning-based risk prediction framework from a large-scale longitudinal study by Yuanxi Luo, Yuanxi Luo, Zhiyang Yin, Xin Li, Xin Li, Chong Sheng, Ping Zhang, Dongjin Wang, Dongjin Wang, Yunxing Xue

“…Following baseline characteristic comparisons and CVD incidence rate calculations, we implemented multiple Cox regression models to assess CMI’s cardiovascular risk prediction capabilities. For nomogram construction, we utilized an ensemble machine learning framework, combining Boruta algorithm-based feature selection with Random Forest (RF) and XGBoost analyses to determine key predictive parameters.ResultsThroughout the median follow-up duration of 84 months, we documented 1,500 incident CVD cases, comprising 1,148 cardiac events and 488 cerebrovascular events. …”
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Review of Modular Multiplication Algorithms over Prime Fields for Public-Key Cryptosystems by Hai Huang, Jiwen Zheng, Zhengyu Chen, Shilei Zhao, Hongwei Wu, Bin Yu, Zhiwei Liu

“…Furthermore, the core concepts, implementation challenges, and research advancements of multiplication algorithms are systematically summarized. This paper also gives a brief overview of modular reduction algorithms for various types of moduli and discusses the implementation principles, application scenarios, and current research results. …”
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Enhanced air quality prediction using adaptive residual Bi-LSTM with pyramid dilation and optimal weighted feature selection by R. Sudha, Ajith Damodaran, Gunaselvi Manohar

Subjects: “…Air quality prediction…”
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Establishment of an Improved Elman Neural Network Model for Predicting the Corrosion Rate of 3C Steel in Marine Environment and Analysis of the Factors Affecting Model Accuracy by Wenbo Jin, Zhuo Chen, Wanying Liu, Qing Quan, Zongxiao Ren

“…Based on the experimental data of corrosion rates of 3C steel in different seawater environments, an improved Elman neural network model was established by using the whale optimization algorithm. The corrosion rates of 3C steel in different seawater environments were predicted, and the influences of the number of hidden layer nodes, the population sizes, and the number of iterations on the prediction results of the improved model were analyzed. …”
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Prediction of postpartum depression in women: development and validation of multiple machine learning models by Weijing Qi, Yongjian Wang, Yipeng Wang, Sha Huang, Cong Li, Haoyu Jin, Jinfan Zuo, Xuefei Cui, Ziqi Wei, Qing Guo, Jie Hu

“…Seven feature selection methods and six ML algorithms were employed to develop models, and their prediction performances were compared. …”
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AFCPOA-based optimal dispatch of hybrid PV-wind DGs for voltage stability and loss reduction in radial distribution network by Sunil Ankeshwarapu

“…Results show that AFCPOA achieved a 42.6% reduction in total losses compared to the base case and outperformed other algorithms by 9–18% in loss reduction, with an average voltage profile improvement of 5.3%. …”
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Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only by Yonina Ron, Tchelet Ron, Naomi Fridman, Anat Goldstein

“…Among them, 429 (11%) developed myopia. The models predicted myopia with up to 77% sensitivity and 92% specificity. …”
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Power losses reduction by optimal allocation of renewable distributed generation in distribution networks by Abdelkader Boukaroura, Mohammed Amroune

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Novel disctete grey Bernoulli seasonal model with a time powter term for predicting monthly carbon dioxide emissions in the United States by Jianming Jiang, Yandong Ban, Sheng Nong

“…This study proposes a more efficient discrete grey prediction model to describe the seasonalvariation trends of carbon dioxide emissions. …”
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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

Subjects: “…Gravity search optimization algorithm…”
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Predicting specific wear rate of laser powder bed fusion AlSi10Mg parts at elevated temperatures using machine learning regression algorithm: Unveiling of microstructural morphology analysis by Vijaykumar S. Jatti, R. Murali Krishnan, A. Saiyathibrahim, V. Preethi, Suganya Priyadharshini G, Abhinav Kumar, Shubham Sharma, Saiful Islam, Dražan Kozak, Jasmina Lozanovic

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Non-Invasive Techniques for Monitoring and Fault Detection in Internal Combustion Engines: A Systematic Review by Norah Nadia Sánchez Torres, Jorge Gomes Lima, Joylan Nunes Maciel, Mario Gazziro, Abel Cavalcante Lima Filho, Cicero Rocha Souto, Fabiano Salvadori, Oswaldo Hideo Ando Junior

“…Finally, concluding remarks point towards future research directions, emphasizing the need to develop the integration of AI algorithms with digital twins for internal combustion engines and identify gaps for further improvements in fault diagnosis and prediction techniques.…”
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A dimension reduction assisted credit scoring method for big data with categorical features by Tatjana Miljkovic, Pei Wang

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Ecological Momentary Assessment of emotional dysregulation and outbursts among youth with ADHD: a feasibility study of a biomarker-driven predictive algorithm in the special education pre-K and early childhood classroom settings by Ripudaman (Zeeba) Singh, Janav Panchal, Sami Ali, Beth Krone, Isaac J. Wert, Mark Owens, Mark Stein, Maulik V. Shah

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Transformer network for time series prediction via wavelet packet decomposition by Zhichao Wu, Aiye Shi, Yan Ping Tao

“…Although, conventional time series processing methods—such as multi-scale feature extraction or Transformer-based algorithms—produce superior prediction results, when dealing with data that contain morenoise and outliers, the prediction ability of such methods can suffer. …”
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