Gravity Predictions in Data-Missing Areas Using Machine Learning Methods by Yubin Liu, Yi Zhang, Qipei Pang, Sulan Liu, Shaobo Li, Xuguo Shi, Shaofeng Bian, Yunlong Wu

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Machine Learning Methods for Predicting Cardiovascular Diseases: A Comparative Analysis by Aiym B. Temirbayeva, Arshyn Altybay

“…Logistic Regression and Random Forest were identified as the most reliable models for predicting heart disease in this dataset. Future work will explore hyperparameter tuning and ensemble methods to further enhance model performance, providing valuable insights for early diagnosis and treatment of cardiovascular diseases.…”
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Accurate Prediction of Drug Activity by Computational Methods: Importance of Thermal Capacity by Luigi Leonardo Palese

“…Here, a simple computational method for determining the change in heat capacity upon the binding of small ligands has been evaluated. …”
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Investigation of commonly used assessment methods for predicting fall risk in the elderly by Aziz DENGİZ, Ahmet AYTEPE, Bayram SIRRI, Mehmet EFE

“…Aim: This study aimed to evaluate the effectiveness of four different commonly used assessment methods Berg Balance Scale (BBS), Timed Up and Go Test (TUG), Modified Falls Efficacy Scale (MFES), and Morse Fall Scale (MFS) in predicting fall risk in elderly. …”
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The Influence of Non-Landslide Sample Selection Methods on Landslide Susceptibility Prediction by Yu Fu, Zhihao Fan, Xiangzhi Li, Pengyu Wang, Xiaoyue Sun, Yu Ren, Wengeng Cao

“…Additionally, the EIV method identified smaller, more concentrated high-susceptibility zones, covering 87.37% of historical landslide points, compared to the larger, less precise zones predicted by other methods. …”
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Advanced Machine Learning Methods for the Prediction of the Optical Parameters of Tellurite Glasses by Fahimeh Ahmadi, Mohsen Hajihassani, Tryfon Sivenas, Stefanos Papanikolaou, Panagiotis G. Asteris

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Steam-Jet Evaluation for Predicting Leakage Behavior and Interpretation of Experimental Verification by Dae Kyung Choi, Won Man Park, Woo-Shik Kim, Dong-Jin Euh, Tae-Soon Kwon, Choengryul Choi

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Performance Evaluation of Long Short-Term Memory for Chili Price Prediction by Fata Nabil Fikri, Nurochman Nurochman

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On the estimation of inverse-probability-of-censoring weights for the evaluation of survival prediction error. by Thomas Prince, Andrea Bommert, Jörg Rahnenführer, Matthias Schmid

“…Inverse probability weighting (IPW) is a popular method for making inferences regarding unobserved or unobservable data of a target population based on observed data. …”
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Evaluating the three-level approach of the U-smile method for imbalanced binary classification. by Barbara Więckowska, Katarzyna B Kubiak, Przemysław Guzik

“…The U-smile method outperformed traditional evaluation measures across most of the imbalance range. …”
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Plaseval: a framework for comparing and evaluating plasmid detection tools by Aniket Mane, Haley Sanderson, Aaron P. White, Rahat Zaheer, Robert Beiko, Cédric Chauve

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Battery Life Evaluation Method Based on Temporal Convolution Network by SUN Yushu, AN Juan, HUANG Cunqiang, ZHANG Shunzhen, DANG Yanyang, PEI Wei, TANG Xisheng

“…In contrast, RUL will irreversibly decrease as the service time increases, which can provide a more reliable criterion for evaluation. To predict RUL, the same five key features (cycle time, average voltage, voltage sample entropy, temperature sample entropy, current) along with capacity are used. …”
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Multispectral imaging as a predictive tool for freshness of whole Atlantic cod: Compared with sensory, chemical and microbiological analysis by Andrea Rakel Sigurðardóttir, Hildur Inga Sveinsdóttir, Nette Schultz, Hafsteinn Einarsson, María Gudjónsdóttir

“…These models were trained to predict sensory evaluations performed by trained panelists using the Quality Index Method (QIM) as well as chemical- and microbiological analyses, total viable counts (TVC) and total volatile base nitrogen (TVB-N). …”
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Recent advances in AI-based toxicity prediction for drug discovery by Hyundo Lee, Jisan Kim, Ji-Woon Kim, Yoonji Lee, Yoonji Lee

“…Today, AI models are capable of predicting wide range of toxicity endpoints, such as hepatotoxicity, cardiotoxicity, nephrotoxicity, neurotoxicity, and genotoxicity, based on diverse molecular representations ranging from traditional descriptors to graph-based methods. …”
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Agent‐based versus correlative models of species distributions: Evaluation of predictive performance with real and simulated data by Jukka Sirén, Panu Somervuo, Otso Ovaskainen

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A novel anthropometric method to accurately evaluate tissue deformation by Chongyang Ye, Xiaolu Li, Haiyan Song, Yu Shi, Ruixin Liang, Jun Zhang, Ka Po Lee, Zhaolong Chen, Beibei Zhou, Raymond Kai-Yu Tong, Kit-Lun Yick, Sun-Pui Ng, Joanne Yip

“…The mechanical properties of five leggings samples are tested by using the Instron 4,411 and KES-FB1 systems to determine the uniaxial tension and pure shear.ResultsThe predicted results are then compared with the experimental results, which shows that they are in good agreement, with deviations within 1.15 mm for the static condition and 2.36 mm for the dynamic condition, thus validating the proposed novel method.DiscussionThis anthropometric approach is an invaluable tool for evaluating tissue deformation patterns, thus providing key insights for sportswear designers to optimize garment performance and design.…”
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The MAR method versus the visual estimation method in predicting external blood loss: a randomized controlled study by Betul Akbuga Ozel, Gurkan Ozel, Elmas Burcu Mamak Ekinci, Ahmet Eren Demirtola, Gizem Sahin, Gokcen Aksoy, Ilke Yanikyurek, Mehmet Ufuk Karaaslan

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An intelligent prediction method for rock core integrity based on deep learning by Zhaoxia Hu, Hua Mei, Lei Yu

“…The relative error between the prediction results and the manual statistical results of the fissure rate is only 4.38%, and the prediction accuracy for the degree of rock core integrity is 93.8%, indicating that the proposed method in this paper is able to accomplish the intelligent evaluation task of rock core integrity with high precision.…”
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MBLSTM is a contextual interaction refined method for time series prediction by Weihan Qiu, Fangjie Zhu, Tianyong Hao, Maojie Wang, Runyue Huang

“…Besides, more features of time series data are learned utilizing contrastive learning and a data augmentation scheme based on Kernel Density Estimation is designed to identify more accurate features from time series data. The method is evaluated on a real clinical dataset including 1053 records of patient with Gouty arthritis from the Guangdong Provincial Traditional Chinese Medicine Hospital by predicting the subsequent status of patients. …”
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Comparative Analysis of Transfer Function Method with Advanced Flood Prediction Techniques by Jafar Chabokpour

“…In this paper, the evaluation of the performance of five flood prediction models in the Simineh-Rood River, Lake Urmia basin, Iran, is discussed in detail. …”
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