ATTRN: Acoustic Information Encoder and Temperature Field Reconstruction Decoder Network for Boiler Temperature Field Reconstruction by Kunyu Wu, Keqi Ni, Liwei Chen, Hengyuan Xu, Junqiao Wang, Jingyi Zhou, Xinzhi Zhou

“…Traditional temperature field reconstruction algorithms, while effective, often suffer from accumulated errors, difficulty in solving ill-posed problems, low accuracy, and poor generalization. …”
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Bounds on the initial macroporosity of sea ice pressure ridges by Sönke Maus

“…The proposed macroporosity prediction can be useful for modeling the consolidation and property evolution of sea ice ridges and rubble fields and for improving thickness redistribution algorithms in sea ice models.…”
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Drosophila Eye Gene Regulatory Network Inference Using BioGRNsemble: An Ensemble-of-Ensembles Machine Learning Approach by Abdul Jawad Mohammed, Amal Khalifa

“…Our validation analysis using the TFLink online database yielded 3703 verified predicted gene links, out of 534,843 predictions. Conclusion: Although the BioGRNsemble approach presents a promising method for inferring smaller, focused regulatory networks, it encounters challenges related to algorithm sensitivity, prediction bias, validation difficulties, and the potential exclusion of broader regulatory interactions. …”
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Multisource Heterogeneous Sensor Processing Meets Distribution Networks: Brief Review and Potential Directions by Junliang Wang, Ying Zhang

“…Through a brief analysis of existing technologies and algorithms, this article reviews the technological development of MSH data analysis in DNs. …”
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Research on flight detection and data compliance analysis methods for ice meteorological by DING Junliang

“…Domestic icing meteorological prediction algorithms are calibrated and verified, and safety control strategies and risk degradation measures for natural icing flight test are explored. …”
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Two Symbol Expansion Methods and Their Application to Reversible Data Hiding by Hyoung Joong Kim, Changhee Kang, Sang-Ug Kang

“…The performance of the proposed simple method is comparable to or better than that of more advanced methods that rely on computationally complex prediction techniques.…”
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Inertial Subrange Optimization in Eddy Dissipation Rate Estimation and Aircraft-Dependent Bumpiness Estimation by Zhenxing Gao, Qilin Zhang, Kai Qi

“…In current vertical wind-based eddy dissipation rate (EDR) estimation algorithms based on flight data, the inertial subrange is determined empirically. …”
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Simulating urban growth through case-based reasoning by X. Ye, W. W. Yu, W. H. Yu, L. N. Lv

“…The results show that the UGSCBR model has strong flexibility and simplicity, and it provides an effective prediction method for urban growth.…”
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High-Frequency Cryptocurrency Price Forecasting Using Machine Learning Models: A Comparative Study by Fátima Rodrigues, Miguel Machado

“…We compare various machine learning models, including recurrent neural networks (RNNs), time series analysis (ARIMA), and conventional regression algorithms, using minute-step Bitcoin price data over a 30-day period to predict prices 60 min ahead. …”
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Dynamic analysis of malicious behavior propagation based on feature selection in software network by Huajian Xue, Huajian Xue, Yali Wang, Qiguang Tang

“…The method is divided into three stages: First, variable-length N-gram algorithms are utilized to extract subsequences of varying lengths from the sample APl call sequences as continuous dynamic features. …”
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Enabling disaggregation of Asian American subgroups: a dataset of Wikidata names for disparity estimation by Qiwei Lin, Derek Ouyang, Cameron Guage, Isabel O. Gallegos, Jacob Goldin, Daniel E. Ho

“…We show that these data, when combined with public geography-race distributions to predict subgroup membership, outperform existing deterministic name lists in key prediction settings, and enable critical Asian disparity assessments.…”
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FORECASTING STOCK MARKET LIQUIDITY WITH MACHINE LEARNING: AN EMPIRICAL EVALUATION IN THE GERMAN MARKET by Bogdan Ionut ANGHEL

“…The study benchmarks four machine-learning algorithms— Random Forest, XGBoost, CatBoost and Long Short-Term Memory (LSTM) networks—for forecasting stock market liquidity in Germany’s DAX equity market. …”
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Identification of Papua Cenderawasih Batik Motifs using Local Binary Pattern and K-Nearest Neighbor by Dian Dwi Ariani, Sitti Zuhriyah, Eva Yulia Puspaningrum, Mahabintang Pallawabonang

“…Future research could improve model accuracy by utilizing larger data sets and classification algorithms to make the models more accurate.…”
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Radiomics in glioma: emerging trends and challenges by Zihan Wang, Lei Wang, Yinyan Wang

“…Furthermore, radiomics analysis helps understand the glioma immune microenvironment by predicting immune‐related biomarkers and characterizing immune responses within tumors. …”
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A comparative artificial neural networks for Schwarzschild black hole (SBH) radius by Khalil Ur Rehman, Wasfi Shatanawi, Weam G. Alharbi

“…Two various training functions namely Levenberg-Marquardt training algorithm (LMTA) and Scaled Conjugate Gradient training algorithms (SCGTA) are used. …”
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Effects of neighborhood streetscape on the single-family housing price: Focusing on nonlinear and interaction effects using interpretable machine learning. by Jaewon Han, Ayoung Woo, Sugie Lee

“…Previous studies using the conventional Hedonic Price Model to predict existing housing prices may have limitations in addressing the relationship between house prices and streetscapes as visually perceived at the human eye level, due to the constraints of streetscape estimations. …”
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Travel Time Variability and Spatio-Temporal Analysis of Urban Streets Using Global Positioning System: A Review by Zainab Ahmed Alkaissi, Ruba Yousif Hussain

“…Travel time estimation algorithms based on speed data loop detectors induced insignificant differences when the study route was a relatively short and slow transition from free state to congestion state. …”
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The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach by Abed Mutemi, Fernando Bacao

“…Under a variety of hyperparameter configurations, we test and compare the outcomes of eight different machine learning classification algorithms, seven individual classifiers, and a stacked ensemble. …”
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A review of machine learning approaches for the discovery of thermoelectric materials by Övgü C. Yelgel, Celal Yelgel

“…Recent progress in computational infrastructure, along with the advent of data-centric approaches such as machine learning (ML), has transformed the landscape of thermoelectric research. ML algorithms, trained on comprehensive datasets including experimental measurements, crystallographic data, and density functional theory (DFT) results can predict key TE metrics, such as the figure of merit (ZT), with remarkable speed and accuracy. …”
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Deployment and Operation of Battery Swapping Stations for Electric Two-Wheelers Based on Machine Learning by Yu Feng, Xiaochun Lu

“…Then, on a 3000 m grid scale, a prediction model of BSS quantity with random forest, support vector regression, and gradient-boosting decision tree algorithm was built. …”
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