Effects of Matching on Evaluation of Accuracy, Fairness, and Fairness Impossibility in AI-ML Systems by Phillip Honenberger, Omolade Ola, William Mapp, Pilhwa Lee

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Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai by V. A. Nevzorova, T. A. Brodskaya, K. I. Shakhgeldyan, B. I. Geltser, V. V. Kosterin, L. G. Priseko

“…To develop and perform comparative assessment of the accuracy of models for predicting 5-year mortality risks according to the Epidemiology of Cardiovascular Diseases and their Risk Factors in Regions of Russian Federation (ESSE-RF) study in Primorsky Krai.Material and methods. …”
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Evaluating and implementing temporal, spatial, and spatio-temporal methods for outbreak detection in a local syndromic surveillance system. by Robert W Mathes, Ramona Lall, Alison Levin-Rector, Jessica Sell, Marc Paladini, Kevin J Konty, Don Olson, Don Weiss

“…The algorithms were compared on several metrics, including sensitivity, specificity, positive predictive value, coherence, and timeliness. We also evaluated each method's implementation, programming time, run time, and the ease of use. …”
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Evaluation of the clinical value of CCTA as the preferred screening method in patients with chronic coronary syndrome by Huan Luo, Wei Zhu, Rui-Juan Fan, Li-Xiong Duan, Rui Jing

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Evaluation of the relationships between physico-chemical parameters and the abundance of Vibrio spp. in blue crabs (Callinectes sapidus) and seawater from the Maryland Coastal Bays by Jasmine Smalls, John Jacobs, Howard Townsend, Paulinus Chigbu, Salina Parveen

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Construction of a deep learning-based predictive model to evaluate the influence of mechanical stretching stimuli on MMP-2 gene expression levels in fibroblasts by Ruozu Xiao, Haowei Zhou, Zhen Shi, Rong Huang, Yuheng Zhang, Jing Li

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Evaluating and predicting exclusive breastfeeding behaviour based on an energy vitality model during women’s lactation: a longitudinal study by Yibo Gu, Yuxin Xiang, Xingtong Chen, Liuhua Wang, Chunjian Shan, Minghui Ji

“…Methods A total of 570 women were recruited for this study at a baby-friendly hospital in Nanjing, China. …”
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Application of Supervised Learning Methods and Information Gain Methods in the Determination of Asbestos–Cement Roofs’ Deterioration State by Manuel Saba, David Valdelamar Martínez, Leydy K. Torres Gil, Gabriel E. Chanchí Golondrino, Manuel A. Ospina Alarcón

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Rapid prediction of beef colour evolution and myoglobin forms using near-infrared spectroscopy (NIRS) by Wenyang Jia, Nigel Scollan, Anastasios Koidis

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Short-Term Power Prediction Method for Photovoltaic Power Plants Based on ECA-MTGNN Integration by HUANG Congzhi, LIU Yantong

“…Finally, the prediction performance of the proposed method was evaluated using the prediction accuracy assessment criteria specific to PV power plants in the northern China region and the general evaluation metrics for time series prediction. …”
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Robust Controllability Network Method on Temporal Network Using Temporal Link Prediction and Network Embedding by Yan Dou, WanLin Liu, Peyman Arebi

“…The effectiveness of the proposed method against various network attacks has been evaluated and compared with other conventional methods. …”
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Bearing Lifespan Reliability Prediction Method Based on Multiscale Feature Extraction and Dual Attention Mechanism by Xudong Luo, Minghui Wang

“…However, due to the high nonlinearity and complexity of mechanical systems, traditional methods failed to meet the requirements of medium- and long-term prediction tasks. …”
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Carbon emission prediction method of steel plants based on long short-term memory network by Fengyun LI, Zehui DOU, Peng LI, Wei GUO

“…As the second largest carbon emitter in China, iron and steel enterprises have great potential for carbon emission reduction.In order to facilitate the supervision and control of carbon emissions by relevant departments, carbon emission prediction research is carried out.Taking a steelmaking plant as the research object, firstly, the carbon dioxide emissions in the steelmaking process were analyzed, and 10 energy substances that caused carbon emissions were determined.The basic energy data of the steelmaking plant from 2001 to 2023 were collected, and the carbon emissions were calculated from the basic energy data according to the carbon emission accounting method.Secondly, based on the long short-term memory network to predict the carbon emissions in the next 7 years, the training error and test error were close to 0.01, and the actual error was 1 323 307.46 tons of carbon dioxide.Then, the Mann-Kendall trend test was used to evaluate the overall carbon emission trend of the steelmaking plant.Finally, some reasonable suggestions were put forward for steelmaking plants in order to actively respond to the goal of low-carbon environmental protection.…”
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A New Method to Predict Shock-Type Coal-Gas Outburst Disaster and Its Application by Pengfei Lyu, Tan Li, Xuehua Chen

“…The mechanism of coal-gas outbursts is complex, and the prediction methods are immature at present. This article was based on previous research results. …”
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Hybrid regression method to predict forest variables from Earth observation data in boreal forests by Eelis Halme, Matti Mõttus

“…The prediction performance was evaluated using three independent test areas, two from Finland and one from Sweden. …”
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Random Displacement Method-based Model for Predicting the Distribution of Net Sediment Deposition in Vegetated Channels by Chuan LI, Sichen SUN, Yuqi SHAN, Yonghao LIU, Chao LIU

“…In conclusion, the proposed random displacement method-based numerical model accurately predicts the distribution of sediment deposition inside vegetated regions in open channels, provided the upstream sediment supply is not limited.…”
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A prediction method for anti-cancer drug combinations synergy based on graph attention network by QIN Weiqi;BAO Xin;CHEN Xiao;QIU Jianlong;WANG Donglin

“…It then employs a graph attention network(GAT) and multilayer perceptron(MLP) to extract features from both drug and cell line data, fusing these multi-source features to predict combination synergy. Evaluated on a public dataset, MFGSynergy outperforms Deep DDS, DeepSynergy, and six machine learning methods, achieving receiver operating characteri-stic area under the curve(ROC AUC), area under the precision-recall curve(PR AUC), accuracy(ACC), precision(PREC), true positive rate(TPR), and F1scores of 0.94, 0.94, 0.86, 0.87, 0.86, and 0.86, respectively, in five-fold cross-validation. …”
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Cropland Suitability Prediction Method Based on Biophysical Variables from Copernicus Data and Machine Learning by Dorijan Radočaj, Mateo Gašparović, Mladen Jurišić

“…The goal of this study was to propose and validate a method for predicting cropland suitability based on biophysical variables and machine learning according to an FAO land suitability standard using soybean (<i>Glycine max</i> L.) as a representative crop, aiming to provide an alternative to geographic information system (GIS)-based multicriteria analysis. …”
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Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones by Ying Yao, Xiaohua Zhao, Chang Liu, Jian Rong, Yunlong Zhang, Zhenning Dong, Yuelong Su

“…The results show that the average speed, average speed except for idle (ASEI), average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, and cruising time percentage are important indicators for fuel consumption evaluation. All three models could predict fuel consumption accurately, with an absolute relative error less than 10%. …”
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An Improved Phase Space Reconstruction Method-Based Hybrid Model for Chaotic Traffic Flow Prediction by Yue Hou, Da Li, Di Zhang, Zhiyuan Deng

“…Meanwhile, the five error evaluation indexes of the proposed PDRGA-CDBN-LSTM hybrid model are lower than those of the baseline model, providing a new modeling idea for chaotic traffic flow prediction.…”
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