Predicting the evolution of bacterial populations with an epistatic selection-mutation model by Raul Donangelo, Hugo Fort

“…It incorporates two recently noticed phenomena related to mutations: (i) the fact that the marginal improvement from a beneficial mutation declines with increasing fitness or diminishing returns epistasis and (ii) for some hypermutator variants, the mutation rate for the bacterial DNA undergoes a sudden increase by at least one order of magnitude. The model can simultaneously predict the experimental mean fitness trajectory, as well as other observables, such as the variance trajectory and the mean substitution trajectory, all through the 50,000 bacterial generations presently available.…”
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Comparison of Risk Assessment Models for Predicting Postpartum Venous Thromboembolism by Yonghui Xu, Sha Zhu, Ji He, XingSheng Xue, Fei Xiao

“…This study aimed to validate the accuracy of currently used risk assessment models (RAMs) for predicting postpartum VTE. …”
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Optimization of Markov chain modeling in predicting college student retention by Kien Nguyen

“…This study presents a rigorous algorithm, coupled with a prediction model, capable of selecting parameters that provide the most accurate results for term-to-term retention prediction using the Markov chain analysis. …”
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TBESO-BP: an improved regression model for predicting subclinical mastitis by Kexin Han, Yongqiang Dai, Huan Liu, Junjie Hu, Leilei Liu, Zhihui Wang, Liping Wei

“…In this study, an enhanced neural backpropagation (BP) network model for predicting somatic cell count is introduced. …”
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Predicting the generalization of computer aided detection (CADe) models for colonoscopy by Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg

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Predicting Building Energy Consumption with a New Grey Model by Yan Zhang, Huiping Wang, Yi Wang

“…Based on the existing grey prediction model, this paper proposes a new grey prediction model (the fractional discrete grey model, FDGM (1, 1, tα)), introduces the modeling mechanism and characteristics of the FDGM (1, 1, tα), and uses three groups of data to verify its effectiveness compared with that of other grey models. …”
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Macroeconomic models for predicting indirect impacts of disasters: A review by Tinger Zhu, Charalampos Avraam, Jack W. Baker

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Benchmarking machine learning models for predicting lithium ion migration by Artem D. Dembitskiy, Innokentiy S. Humonen, Roman A. Eremin, Dmitry A. Aksyonov, Stanislav S. Fedotov, Semen A. Budennyy

“…With LiTraj, we demonstrate that classical ML models and graph neural networks (GNNs) for structure-to-property prediction of percolation and migration barriers can distinguish between “fast” and “poor” ionic conductors. …”
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Explainable models for predicting crab weight based on genetic programming by Tao Shi, Lingcheng Meng, Limiao Deng, Juan Li

“…Thanks to the explicit ability of feature selection, GP can select more important features to improve the prediction performance. More importantly, the generated models can provide potential interpretability, which is particularly valuable for domain experts in fisheries management and ecological research.…”
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A Bayesian model for predicting monthly fire frequency in Kenya. by Levi Orero, Evans Otieno Omondi, Bernard Oguna Omolo

“…The Bayesian model also offers prediction intervals that closely align with actual predictions, indicating its flexibility in forecasting the frequency of monthly fires. …”
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PREDICTING THE SHELF LIFE OF SUNFLOWER MEAL USING KINETIC MODELS by T. Matveeva, V. Papchenko, P. Petik, N. Staroselska, V. Khareba, O. Khareba

“…The study proposes a method for predicting the oxidative stability of sunflower meal during long-term storage using the Arrhenius model, which describes the dependence of the reaction rate on temperature. …”
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Machine learning modeling for predicting adherence to physical activity guideline by Ju-Pil Choe, Seungbak Lee, Minsoo Kang

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Predicting avalanche danger in northern Norway using statistical models by K.-U. Eiselt, R. G. Graversen, R. G. Graversen

“…The binary-case RF model exhibits a much higher overall accuracy (76 %) than the four-level case RF model (57 %), which is due to the latter model often misclassifying ADL 1 as ADL 2 and ADL 4 as ADL 3. …”
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A novel hybrid model for predicting the bearing capacity of piles by Li Tao, Xinhua Xue

“…The main objective of this study is to propose a hybrid model coupling least squares support vector machine (LSSVM) with an improved particle swarm optimization (IPSO) algorithm for the prediction of bearing capacity of piles. …”
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Strategic model for predicting customer's intention to purchase apparel online by D. Malmarugan

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Comparative Study of Deep Learning Models for Predicting Stock Prices by Van-Thang Duong, Duc-Tuan-Anh Nguyen, Thi-Thu-Hang Pham, Van-Hau Nguyen, Van-Quoc Anh Le

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Predicting the Ultimate Bearing Capacity of Bolts with an Optimized Function Model by Bin Zheng, Jian Zhang, Tugen Feng, Maosen Cao

“…In this paper, several function models that are commonly used for predicting the ultimate bearing capacity of bolts are presented. …”
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Modeling and Predicting Human Actions in Soccer Using Tensor-SOM by Moeko Tominaga, Yasunori Takemura, Kazuo Ishii

“…This study proposes an action decision system based on self-organizing maps (SOM), a widely used unsupervised learning model, and evaluates its effectiveness in promoting cooperative play within human teams. …”
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A Fusion Model for Predicting the Vibration Trends of Hydropower Units by Dong Liu, Youchun Pi, Zhengyang Tang, Hongpeng Hua, Xiaopeng Wang

“…To enable timely monitoring of unit performance, it is critical to investigate the trends in vibration signals, to enhance the accuracy and reliability of vibration trend prediction models. This paper proposes a fusion model for the vibration signal trend prediction of hydropower units based on the waveform extension method empirical mode decomposition (W-EMD) and long short-term memory neural network (LSTMNN). …”
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Comparison of the effects of three time series models in predicting the trend of erythrocyte blood demand by QIU Yajuan, ZHANG Jianping, LUO Jia, LI Peilin, LUO Mengzhuo, LI Qiongying, LIU Ge, LEI Qing, LIAO Kai

Subjects: “…arima model…”
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