Hybrid TCN-transformer model for predicting sustainable food supply and ensuring resilience by Ibrahim Alrashdi, Rasha M. Abd El-Aziz, Ahmed I. Taloba, Mohammed Farsi

“…Hybrid design enables faster training, increased interpretability, and better prediction accuracy than current methods. Results from experiments have revealed that the suggested model surpasses the performance of the stand-alone TCN, ARIMA, LSTM, and GRU models in terms of accuracy of predictions, efficiency of computations, and adaptability. …”
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A correlation for predicting the abrasive water jet cutting depth for natural stones by Irfan Engin

“…The relationships between the rock properties or operating parameters and the cutting depth were evaluated using multiple linear and nonlinear regression analyses, and estimation models were developed. Some of the models included only rock properties under fixed operating conditions, and others included both rock properties and operating parameters to predict cutting depth. …”
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Excavating trajectory planning of electric shovel based on dynamic excavating volume prediction by Juan Wu, Rui Liu, Baoguo Lin

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Does machine learning outperform logistic regression in predicting individual tree mortality? by Aitor Vázquez-Veloso, Astor Toraño Caicoya, Felipe Bravo, Peter Biber, Enno Uhl, Hans Pretzsch

“…However, innovative classification algorithms can go deep into data to find patterns that can model or even explain their relationship. We use Logistic binomial Regression as the reference algorithm for predicting individual tree mortality. …”
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Near‐Resonant Generation of Internal Tide Superharmonics: Comparing Theoretical Predictions With a Global Ocean Model by L. E. Baker, A. Doak, D. Bi

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Seismic Shear Strength Prediction of Reinforced Concrete Shear Walls by Stacking Multiple Machine Learning Models by Siming Tian, Xiangyong Ni, Yang Wang

“…Finally, in order to improve the prediction accuracy and reliability of the ML methods, the individually trained models were integrated into a stacking model using the stacking method, and the stacking model’s prediction performance was assessed. …”
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Numerical assessments of scour depth predictions downstream of box culverts under various flow and blockage conditions by Kaywan Othman Ahmed, Mohammad Reza Kavianpour, Ata Amini, Younes Aminpour

“…The objective of this study is to investigate the performance of numerical models in predicting the culvert scour’s downstream profile, its maximum depth, and location, prediction was carried out by using Flow-3D software with the Renormalized Group (RNG) turbulence model and comparing these metrics with actual observed data. …”
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ENHANCING DISEASE DETECTION PREDICTION ACCURACY OF GRAPE LEAVES USING VGG16 MODEL AND INCEPTION V3 MODEL by Deepshikha Yadav, Archana Balyan, Suman Mann, Aayush Ranga

“…Our comparative analysis not only delves into the models' predictive capabilities but also considers computational efficiency and resource requirements, providing valuable insights for agricultural practitioners and researchers engaged in precision farming and disease management. …”
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Evaluation and Prediction of Comprehensive Efficiency of Wind Power System in China Based on Two-Stage EBM Model and FNN Model by Fang-Rong Ren, Hui-Lin Liu, Xiao-Yan Liu

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Soil Moisture Content Prediction Using Gradient Boosting Regressor (GBR) Model: Soil-Specific Modeling with Five Depths by Tarek Alahmad, Miklós Neményi, Anikó Nyéki

“…These results highlight the necessity for soil-specific modeling to enhance SMC prediction accuracy, optimize irrigation systems, and support water resources management approaches aligning with SDG6 objectives.…”
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Spatial modeling for predicting and identifying levels of climate-related disease hazards in Iraq using (GIS) Kirkuk as a model by Abdlwahd Jamel Alqdori Areej

“…In this study, a multinomial model was proposed to predict PM2.5 concentrations, and the relationship between PM2.5, PM10, and atmospheric parameters was studied. …”
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Prediction of air quality perception in aircraft cabin based on psychophysical model and artificial neural network (ANN)-based model by Yihui Yin, Lei Zhao, Ruoyu You, Jingjing Pei, Hanyu Li, Junzhou He, Yuexia Sun, Xudong Yang, Qingyan Chen

“…Limited by the two basic assumptions that VOC interaction was non-existent and that the odor intensity was only related to VOC, the accuracy of OI calculated by the existing model was about 0.4. In order to improve the accuracy of evaluation, a new data-driven model for human perception (CAQ and OI) prediction based on a knowledge-based BP neural network was proposed, and its prediction accuracy (R2: 0.81–0.87) and generalization (R2: 0.76–0.93) were verified. …”
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Vocational High School (SMK) Students Accounting Competence Prediction Model by Using Astin I-E-O Model by Defi Sri Harwati, Heri Yanto

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Detection of Cardiovascular Diseases Using Predictive Models Based on Deep Learning Techniques: A Hybrid Neutrosophic AHP-TOPSIS Approach for Model Selection by Julio Barzola-Monteses, Rosangela Caicedo-Quiroz, Franklin Parrales-Bravo, Cristhian Medina-Suarez, Wendy Yanez-Pazmino, David Zabala-Blanco, Maikel Y. Leyva-Vazquez

“…An approach based on deep learning is applied to improve the capacity for early prediction and reduce its incidence. In this work, three different models were proposed and compared: deep neural networks (DNN), convolutional neural networks (CNN), and multilayer perceptron (MLP). …”
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Optimizing electric vehicle energy consumption prediction through machine learning and ensemble approaches by Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adeeb Noor, Sufyan Ali Memon

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Don’t go chasing waterfalls: Multiple factor prediction of injuries in a performance context by Melanie I. Stuckey, Dean Kriellaars

“…This study analyzed demographic, sleep, fatigue, body composition, symmetry/proportionality, and psychological data from a cohort of circus arts students to predict injury presence and duration using regression models. …”
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Impacts of Uncontrolled Operator Splitting Methods on Parameter Identification, Prediction Uncertainty, and Subsurface Flux Representation in Conceptual Hydrological Models by Befekadu Taddesse Woldegiorgis, Helen Baulch, Howard Wheater, Jill Crossman, Martyn Clark, Tricia Stadnyk, Ajay Bajracharya

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Heavy metal adsorption efficiency prediction using biochar properties: a comparative analysis for ensemble machine learning models by Zaher Mundher Yaseen, Farah Loui Alhalimi

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Evaluation and validation of extensive growth and growth boundary models for mesophilic and psychrotolerant Bacillus cereus in dairy products (Part 2) by Maryam Maktabdar, Rannvá Høgnadóttir Houmann, Nanna Hulbæk Scheel, Karoline Broskov Skytthe, Ellen Wemmenhove, Elissavet Gkogka, Paw Dalgaard

Subjects: “…predictive modelling…”
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MILLING FORCE MODELING BASED ON EQUIVALENT TURNING FORCE AND OBLIQUE CUTTING FORCE MODEL (MT) by WU MingZhou, ZHANG GuangPeng, LIU MoHan, GOU JianFeng, ZHANG Peng

Subjects: “…Equivalent model…”
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