Research on Measurement of Coal–Water Slurry Solid–Liquid Two-Phase Flow Based on a Coriolis Flow Meter and a Neural Network by Jie Liu, Lingfei Kong, Jiahao Ma, Xuemei Zhang, Chengjie Wang, Dongze Wu

“…The first correction results showed that the corrected error of the predictive model was 3.98%, a significant improvement compared to the 5.11% error measured by the X company’s meter. (2) Building on this, a second correction model was established through algorithm optimization, successfully reducing the corrected error of the predictive model to 1.01%. …”
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Modelos univariados de séries temporais para previsão das temperaturas médias mensais de Erechim, RS Univariate time series methods for forecasting the monthly mean air temperature... by Leonardo Chechi, Fábio M. Bayer

“…In the class of ARIMA models using criteria information, SARIMA type models that consider the seasonal characteristics of air temperature were selected, whereas for exponential smoothing models Holt-Winters additive algorithm were used. Smoothing constants are determined to minimize the mean square error between observed and predicted values. …”
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Detecting Freezing of Gait in Parkinson Disease Using Multiple Wearable Sensors Sets During Various Walking Tasks Relative to Medication Conditions (DetectFoG): Protocol for a Pros... by Sébastien Cordillet, Sophie Drapier, Frédérique Leh, Audeline Dumont, Florian Bidet, Isabelle Bonan, Karim Jamal

“…There is no consensus on the number and location of IMU, type of algorithm, and method of triggering and scoring the FoG episodes. …”
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G4 & the balanced metric family – a novel approach to solving binary classification problems in medical device validation & verification studies by Andrew Marra

“…A new metric called G4 is presented, which is the geometric mean of sensitivity, specificity, the positive predictive value, and the negative predictive value. …”
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Primary Care Physician Use of Elastic Scattering Spectroscopy on Skin Lesions Suggestive of Skin Cancer by Stephen P. Merry, Ivana T. Croghan, Kimberly A. Dukes, Brian C. McCormick, Gerard T. Considine, Michelle J. Duvall, Curtis T. Thompson, David J. Leffell

“…Objectives: To evaluate the performance of noninvasive, elastic scattering spectroscopy, algorithm-powered device (DermaSensor) to detect melanoma and basal and squamous cell cancers in the primary care setting. …”
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Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an international multicenter study by Bingtian Dong, Ruiling He, Shenghong Ju, Yuping Chen, Ivica Grgurevic, Jianzhong Ma, Ying Guo, Huizhen Fan, Qiang Yan, Chuan Liu, Huixiong Xu, Anita Madir, Kristian Podrug, Jia Wang, Linxue Qian, Zhengzi Geng, Shanghao Liu, Tao Ren, Guo Zhang, Kun Wang, Meiqin Su, Fei Chen, Sumei Ma, Liting Zhang, Zhaowei Tong, Yonghe Zhou, Xin Li, Fanbin He, Hui Huan, Wenjuan Wang, Yunxiao Liang, Juan Tang, Fang Ai, Tingyu Wang, Liyun Zheng, Zhongwei Zhao, Jiansong Ji, Wei Liu, Jiaojiao Xu, Bo Liu, Xuemei Wang, Yao Zhang, Qiong Yan, Hui Liu, Xiaomei Chen, Shuhua Zhang, Yihua Wang, Yang Liu, Li Yin, Yanni Liu, Yanqing Huang, Li Bian, Ping An, Xin Zhang, Shaoting Zhang, Jinhua Shao, Xiangman Zhang, Wei Rao, Chaoxue Zhang, Christoph Frank Dietrich, Won Kim, Xiaolong Qi

“…Shapley Additive exPlanations method was used to interpret the model predictions. Results We analyzed data from 502 patients with compensated cirrhosis who underwent EGD screening. …”
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Robust development of data-driven models for methane and hydrogen mixture solubility in brine by Kashif Saleem, Abhinav Kumar, K. D. V. Prasad, Ahmad Alkhayyat, T. Ramachandran, Protyay Dey, Navdeep Kaur, R. Sivaranjani, I. B. Sapaev, Mehrdad Mottaghi

“…The results indicate that Ensemble Learning and AdaBoost yield the highest accuracy algorithms in prediction capability as they tend to illustrate the lowest values of mean squared error and mean absolute relative error (%) and highest R-squared values. …”
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“VisualFields Easy”: an iPad Application as a Simple Tool for Detecting Visual Field Defects by Angelane S. Santos, MD, Evelyn S. Morabe, MD

“…Purpose/Objective: This study aims to determine the reliability of the “VisualFields Easy” application in detecting visual field loss among ophthalmology patients; and to determine the sensitivity, specificity, positive predictive and negative predictive values of this examination using the Humphrey Visual Field Analyzer as the gold standard. …”
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Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling. by Biche Osong, Eric Sribnick, Jonathan Groner, Rachel Stanley, Lauren Schulz, Bo Lu, Lawrence Cook, Henry Xiang

“…Data were split into two sets, where 80% developed a decision tree, and 20% tested predictive performance. We employed a conditional inference tree algorithm with bootstrap (B = 100) and grid search options to grow the decision tree and measure discrimination ability using the area under the curve (AUC) and calibration plots.…”
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Developing and validating a drug recommendation system based on tumor microenvironment and drug fingerprint by Yan Wang, Xiaoye Jin, Rui Qiu, Bo Ma, Sheng Zhang, Xuyang Song, Jinxi He

“…This study aimed to develop a personalized drug recommendation model leveraging genomic profiles to optimize therapeutic outcomes.MethodsA content-based filtering algorithm was implemented to predict drug sensitivity. …”
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Transformation of Management Technologies in the Digital Economy by P. A. Levchaev, B. Khezazna

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Intelligent Integration Method of AI English Teaching Resource Information under Multi-Agent Collaboration by Minjuan Liu

“…Moreover, this article analyzes the principles and properties of the three algorithms, ALO, GWO, and MFO, and integrates them into the D2D power allocation problem in the two communication modes. …”
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Young and Inverse Young Inequalities on Euclidean Jordan Algebra by Chien-Hao Huang

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Hybrid Series of Carbon‐Vacancy Electrodes for Multi Chemical Vapors Diagnosis Using a Residual Multi‐Task Model by Tianci Liu, Yun Ji Hwang, Lu Zhang, Jongwoo Hong, Teajong Hwang, Seong Chan Jun

“…Artificial intelligence (AI) integrated gas sensor systems effectively enable multi‐gas detection using specialized algorithms. Nevertheless, these algorithms are prone to overfitting owing to their high model complexity; this study proposes a sensor array that engineers carbon vacancies in graphene oxide via metal ion doping and high‐temperature reduction, enabling high‐sensitivity, simultaneous detection of various gases at low temperatures (20 °C). …”
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Revolutionizing total hip arthroplasty: The role of artificial intelligence and machine learning by Umile Giuseppe Longo, Sergio De Salvatore, Alice Piccolomini, Nathan Samuel Ullman, Giuseppe Salvatore, Margaux D'Hooghe, Maristella Saccomanno, Kristian Samuelsson, Rocco Papalia, Ayoosh Pareek

“…Abstract Purpose There has been substantial growth in the literature describing the effectiveness of artificial intelligence (AI) and machine learning (ML) applications in total hip arthroplasty (THA); these models have shown the potential to predict post‐operative outcomes using algorithmic analysis of acquired data and can ultimately optimize clinical decision‐making while reducing time, cost and complexity. …”
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Prevalence of Rocahepevirus ratti (rat hepatitis E virus) in humans and rats in China by Xiuji Cui, Jianwen Situ, Ting Tang, Zhiyu Li, Dongzhui Chen, Stanley Siu-Fung Ho, Hiu-Laam Chung, Tsz-Chung Wong, Yonghao Liang, Chaowen Deng, Yongxian Su, Huijun Cai, Simon Kam-Fai Lo, Shiyao Huang, Sheng Zeng, Liyuan Zhang, Yunchun Chen, Shusheng Wu, Estie Hon-Kiu Shun, Nicholas Foo-Siong Chew, James Yiu-Hung Tsoi, Kelvin Hon-Yin Lo, Fanfan Xing, Vincent Chi-Chung Cheng, Kwok-Yung Yuen, Feifei Yin, Jasper Fuk-Woo Chan, Siddharth Sridhar

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The PLSR-ML fusion strategy for high-accuracy leaf potassium inversion in karst region of Southwest China by Zhihao Song, Zhihao Song, Wen He, Yuefeng Yao, Ling Yu, Jinjun Huang, Yong Xu, Haoyu Wang

“…Validation coefficient of determination (R²) values reached 0.89, 0.94, and 0.96, respectively—representing improvements of 206%, 147%, and 108% over standalone algorithms. This performance gain was attributed to rigorous overfitting control: PLSR’s dimensionality reduction synergized with ensemble machine learning (RF, XGBoost, MLP) to eliminate redundant spectral features while retaining predictive signals. …”
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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

“…Three microalgae species (Chlorella vulgaris, Nannochloropsis oculata, Fucus serratus) were co-gasified with biomass feedstocks (Fir Pellet (FP), Palm Empty Fruit Bunch (PEFB), Pellet Pine Wood (PPW)) using Aspen Plus® simulation based on Gibbs free energy minimization. Six ML algorithms (XGB, RF, SVR, KNN, ANN, DT) with Shapley additive explanations (SHAP) analysis predicted H2 yield and syngas lower heating value (LHV) from 3609 data points across 24 input parameters. …”
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Exploring the possibilities of MADDPG for UAV swarm control by simulating in Pac-Man environment by Artem Novikov, Sergiy Yakovlev, Ivan Gushchin

“…Traditional Rule-Based Pursuit and Prediction Algorithms inspired by the behaviors of Blinky and Pinky ghosts from the classic Pac-Man game are included as benchmarks to assess the impact of learning-based methods. …”
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Characterizing low femoral neck BMD in Qatar Biobank participants using machine learning models by Nedhal Al-Husaini, Rozaimi Razali, Amal Al-Haidose, Mohammed Al-Hamdani, Atiyeh M. Abdallah

“…Here we applied machine learning (ML) algorithms to predict low femoral neck BMD using standard demographic and laboratory parameters. …”
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