Regression-Based Networked Virtual Buoy Model for Offshore Wave Height Prediction by Eleonora M. Tronci, Matteo Vitale, Therese Patrosio, Thomas Søndergaard, Babak Moaveni, Usman Khan

“…While numerical wave models and machine learning techniques have been explored for wave height prediction, most approaches rely heavily on historical data from the same buoy, limiting their applicability when the target sensor is offline. …”
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Regional Tropospheric Delay Prediction Model Based on LSTM-Enhanced Encoder Network by Yuanfang Peng, Chenglin Cai, Zexian Li, Kaihui Lv, Xue Zhang, Yihao Cai

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Predicting Energy Consumption in Building Heating Systems Using Model Identification Methods by Qu Minglu, Du Shanghe, Zhang Xinlin, Yu Zhen, Li Huai

“…Using historical building data and simulation data of the heating system in TRNSYS, load prediction and equipment energy consumption models were established using the developed model identification method. …”
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Between Domestic and Commonly Known Bankruptcy Prediction Models, How Differ It Can Be? by Muhamad Fiqri Aripin

“…As such the needs of research and improvement of Bankruptcy Prediction Models as risk assessment tool is a must. …”
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Study on Prediction of Sluice Settlement Based on Improved GM (1,1) Model by LI Xuede, ZHOU Mingming, ZHAO Jinlei, LIU Yang, JIANG Wenzhi, WANG Guizhi

“…In the settlement monitoring,the amount of initial monitoring data is little with great changes.It can effectively enhance the correlation of the data series and improve the accuracy of the model through pre-processing the data and modeling with GM prediction model.In view of the single value of weight coefficient in the traditional GM prediction model,this paper selects the optimal weight coefficient for the establishment of the GM prediction model by introducing superimposed weight coefficients and changing the initial value.The results show that the improved GM prediction model with high accuracy can accurately predict the actual measured value.…”
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Efficient productivity prediction model based on edge data compression in smart farms by Peng Jin, Wenshuang Du, Wenquan Jin

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Development and validation of machine learning models for predicting blastocyst yield in IVF cycles by Wen-jie Huo, Fei Peng, Song Quan, Xiao-cong Wang

“…We then stratified predictions and actual yields into three categories (0, 1–2, and ≥ 3 blastocysts) to evaluate the model’s discriminative performance. …”
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Hybridized Deep Learning Model for Perfobond Rib Shear Strength Connector Prediction by Jamal Abdulrazzaq Khalaf, Abeer A. Majeed, Mohammed Suleman Aldlemy, Zainab Hasan Ali, Ahmed W. Al Zand, S. Adarsh, Aissa Bouaissi, Mohammed Majeed Hameed, Zaher Mundher Yaseen

“…Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network (DLNN) for shear strength prediction of PRSC. …”
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Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information by Wei He, Haoxuan Li, Tao Wang, Nan Wang

Subjects: “…trajectory prediction…”
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Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests by E. J. Gustafson, D. R. Bronson, M. A. Windmuller-Campione, R. A. Slesak, D. M. Donner

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A method for predicting postpartum depression via an ensemble neural network model by Yangyang Lin, Dongqin Zhou

“…In the future, plans include collecting more disease datasets, using the proposed model to predict these diseases, and constructing an online disease prediction platform to embed the proposed model, which will help with real-time disease prediction.…”
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A New Ground-Motion Prediction Model for Shallow Crustal Earthquakes in Türkiye by Ulubey Çeken, Fadime Sertçelik, Abdullah İçen

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iPRISM: Intelligent Predicting Response to Cancer Immunotherapy through Systematic Modeling by Yinchun Su, Siyuan Li, Qian Wang, Bingyue Pan, Jiyin Lai, Guangyou Wang, Junwei Han, Qingfei Kong

“…Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Herein, we present iPRISM (Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling), which is a novel network‐based model that integrates multiomics data to predict immunotherapy outcomes. …”
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Predicting global educational inequality with a hierarchical belief rule base model by Aosen Gong, Wei He, Gaixia Ge, Cuiping Yang, Shaohua Li

“…The Belief Rule Base (BRB) is used as an interpretable model that incorporates expert knowledge, making it suitable for these predictions. …”
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Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks by Emmanuel Oluwatosin Rabiu, Damilare Oluwole Akande, Zachaeus Kayode Adeyemo, Isaac Akinwale Akanbi, Oluwole Oladele Obanisola

“…However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. …”
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Prediction of the volume of shallow landslides due to rainfall using data-driven models by J. Tuganishuri, C.-Y. Yune, G. Kim, S. W. Lee, M. D. Adhikari, S.-G. Yum

“…The objectives of this research are to construct a model using advanced data-driven algorithms (i.e., ordinary least squares or linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), deep neural network (DNN), <span class="inline-formula"><i>k</i></span>-nearest-neighbor (KNN), and ridge regression (RR) algorithms) for the prediction of the volume of landslides due to rainfall, considering geological, geomorphological, and environmental conditions. …”
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A high‐resolution model of the grapevine leaf morphospace predicts synthetic leaves by Daniel H. Chitwood, Efrain Torres‐Lomas, Ebi S. Hadi, Wolfgang L. G. Peterson, Mirjam F. Fischer, Sydney E. Rogers, Chuan He, Michael G. F. Acierno, Shintaro Azumaya, Seth Wayne Benjamin, Devendra Prasad Chalise, Ellice E. Chess, Alex J. Engelsma, Qiuyi Fu, Jirapa Jaikham, Bridget M. Knight, Nikita S. Kodjak, Adazsofia Lengyel, Brenda L. Muñoz, Justin T. Patterson, Sundara I. Rincon, Francis L. Schumann, Yujie Shi, Charlie C. Smith, Mallory K. St. Clair, Carly S. Sweeney, Patrick Whitaker, James Wu, Luis Diaz‐Garcia

“…Summary Grapevine leaves are a model morphometric system. Sampling over 10,000 leaves using dozens of landmarks, the genetic, developmental, and environmental basis of leaf shape has been studied and a morphospace for the genus Vitis predicted. …”
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Auditing the fairness of the US COVID-19 forecast hub's case prediction models. by Saad Mohammad Abrar, Naman Awasthi, Daniel Smolyak, Nekabari Sigalo, Vanessa Frias Martinez

“…In this paper, we carry out a comprehensive fairness analysis of the Forecast Hub model predictions and we show statistically significant diverse predictive performance across social determinants, with minority racial and ethnic groups as well as less urbanized areas often associated with higher prediction errors. …”
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Employment of a Radial Basis Function Model for Predicting the Heating Load of Construction by Yuxuan Dai

“…The fact that this research is directed toward the goal of finding energy efficiency and cost-effectiveness, and generally toward the objective of improving the sustainability of the environment in building operation, speaks to the very central role that accurate HL prediction will play. These validations also prove that the RBPV model is the most outstanding regarding real-world applicability and accuracy. …”
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A model based LSTM and graph convolutional network for stock trend prediction by Xiangdong Ran, Zhiguang Shan, Yukang Fan, Lei Gao

“…It is widely believed that modeling these dependencies can improve the accuracy of stock trend prediction and enable investors to earn more stable profits. …”
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