AP-GRIP evaluation framework for data-driven train delay prediction models: systematic literature review by Tiong Kah Yong, Zhenliang Ma, Carl-William Palmqvist

“…Abstract The surging demand for Intelligent Transportation Systems (ITS) to deliver advanced train-related Information for dispatchers and passengers has spurred the development of advanced train delay prediction models. Despite considerable efforts devoted to developing methodologies that can be used to model train operation conditions and produce anticipated train delays, the evaluation strategies for train delay prediction models remain under-researched, particularly evident when accuracy is always found to be the only determinant in model selection. …”
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Artificial Intelligence Models May Aid in Predicting Lymph Node Metastasis in Patients with T1 Colorectal Cancer by Ji Eun Baek, Hahn Yi, Seung Wook Hong, Subin Song, Ji Young Lee, Sung Wook Hwang, Sang Hyoung Park, Dong-Hoon Yang, Byong Duk Ye, Seung-Jae Myung, Suk-Kyun Yang, Namkug Kim, Jeong-Sik Byeon

“…We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. …”
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Prediction of the Spatial Distribution of Petrophysical Properties of Sediment Formations Using Multidimensional Splines by V. V. Lapkovsky, V. A. Kontorovich, K. I. Kanakova, S. E. Ponomareva, B. V. Lunev

“…The results can be computed for individual wells as for inter-well space, allowing for the creation of geological cross-sections of predicted properties and 3D models of their distribution. …”
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Application of machine learning for predicting the incubation period of water droplet erosion in metals by Khaled AlHammad, Mamoun Medraj, Moussa Tembely

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Development of Comprehensive Predictive Models for Evaluating Böhme Abrasion Value (BAV) of Dimension Stones Using Non-Destructive Testing Methods by Ekin Köken

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Assessing dengue forecasting methods: a comparative study of statistical models and machine learning techniques in Rio de Janeiro, Brazil by Xiang Chen, Paula Moraga

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Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study by Shuyu Wen, Chao Zhang, Junwei Zhang, Ying Zhou, Yin Xu, Minghui Xie, Jinchi Zhang, Zhu Zeng, Long Wu, Weihua Qiao, Xingjian Hu, Xingjian Hu, Nianguo Dong, Nianguo Dong

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Explainable machine learning-driven models for predicting Parkinson’s disease and its prognosis: obesity patterns associations and models development using NHANES 1999–2018 data by Jiaxin Fan, Shuai Cao, Hang Peng, Yuanjie Zhi, Shuqin Zhan, Rui Li

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Prediction of barite scale formation and inhibition in hydrocarbon reservoirs using AI modeling: Focus on different optimization algorithms by Ouafa Belkacem, Ahmed Rezrazi, Kamel Aizi, Lokmane Abdelouahed, Maamar Laidi, Abdelhafid Touil, Leila Cherifi, Salah Hanini

“…The ELM model emerged as the most precise (RMSE = 0.0005, R² = 0.999), closely followed by the SVR-PSO model (RMSE = 0.0008, R² = 0.999). …”
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Construction of a model for predicting sensory attributes of cosmetic creams using instrumental parameters based on machine learning by He Jingru, Qian Xuedan, Huang Hu, Lin Bao, Zhang Jun, Zhang Chunxiao, Chen Yuyan

“…The results showed that K-Nearest Neighbors, AdaBoost, and LightGBM were the algorithms with the best performance for most sensory attributes, and the overall model achieved over 95% prediction accuracy for 80% of the sensory dimensions, demonstrating strong reproducibility and accuracy in the verification test, with predicted sensory scores closely aligning with actual values. …”
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Research on Freight Locomotive Automatic Driving Based on Predictive Control by LI Cheng, ZHANG Zhengfang, SHEN Ziyang

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Model to Predict Hyperbilirubinemia in Healthy Term and Near-Term Newborns with Exclusive Breast Feeding by Hsin-Chung Huang, Hwai-I Yang, Yu-Hsun Chang, Rui-Jane Chang, Mei-Huei Chen, Chien-Yi Chen, Hung-Chieh Chou, Wu-Shiun Hsieh, Po-Nien Tsao

“…We provide a good model to predict the risk of subsequent significant hyperbilirubinemia.…”
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Models for analyzing and forecasting share prices on the stock exchange by Р. Пізнак, Т. Ліхоузова

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Models, systems, networks in economics, engineering, nature and society by M.I. Krevskiy, A.S. Bozhday

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Establishing a long-term predictive model for aggressive behavior in schizophrenia: a 21-year longitudinal study in rural China by Hui Jin, Hui Jin, Cong Wang, Cong Wang, Yi-Yue Yang, Yi-Yue Yang, Lie Zhou, Lie Zhou, Yun Xiao, Yun Xiao, Yang Wen, Yang Wen, Jawad Ahmad, Jawad Ahmad, Yun-Fei Mu, Yun-Fei Mu, Jia Cai, Ming Li, Wei Luo, Xiao-Fei Zhou, Jian-Jun Luo, Bo Liu, Eric Yu-Hai Chen, Mao-Sheng Ran, Mao-Sheng Ran

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Construction of a predictive model for concurrent infection in liver failure patients based on prognostic nutritional index and inflammatory cytokine analysis by Hong Yang, Bin Zhang, Chun Yu, Xiao Zhu

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Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study by Yubing Wang, Chao Qu, Jiange Zeng, Yumin Jiang, Ruitao Sun, Changlei Li, Jian Li, Chengzhi Xing, Bin Tan, Kui Liu, Qing Liu, Dianpeng Zhao, Jingyu Cao, Weiyu Hu

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AI-based prediction of traffic crash severity for improving road safety and transportation efficiency by Ayman Mohamed Mostafa, Bader Aldughayfiq, Mayada Tarek, Alaa S. Alaerjan, Hisham Allahem, Murtada K. Elbashir, Mohamed Ezz, Eslam Hamouda

Subjects: “…Traffic crash prediction…”
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PolyLLM: polypharmacy side effect prediction via LLM-based SMILES encodings by Sadra Hakim, Alioune Ngom

“…The drug pair representation is then fed into two separate models including a Multilayer Perceptron (MLP) and a Graph Neural Network (GNN) to predict the side effects. …”
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Real-time ocean wave prediction in time domain with autoregression and echo state networks by Karoline Holand, Henrik Kalisch

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