Machine Learning for Predicting Required Cross-Sectional Dimensions of Circular Concrete-Filled Steel Tubular Columns by Anton Chepurnenko, Samir Al-Zgul, Vasilina Tyurina

“…The first and second models are based on the CatBoost algorithm. They predict the column diameter at minimum and maximum wall thicknesses, respectively. …”
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Wavelet Decomposition-Based AVOA-DELM Model for Prediction of Monthly Runoff Time Series and Its Applications by ZHANG Yajie

Subjects: “…monthly runoff prediction…”
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Average Corrosion Rate Prediction Model for Buried Oil and Gas Pipelines Based on SSA-LightGBM by Weigang Fu, Haitao Wang, Kuankuan Zhang, Xia Wang, Kunlun Chen, Chunmei Sun, Zhengwei Wang, Liuyang Song, Niannian Wang

“…Corrosion represents a major cause of damage and leakage in oil and gas pipelines, making accurate corrosion rate prediction critical for operational safety. This study establishes an average corrosion rate prediction model using the Sparrow Search Algorithm-optimized Light Gradient Boosting Machine (SSA-LightGBM). …”
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A machine learning-based predictive model for predicting early neurological deterioration in lenticulostriate atheromatous disease-related infarction by Zhuangzhuang Jiang, Dongjuan Xu, Hongfei Li, Xiaolan Wu, Yuan Fang, Chen Lou

“…Background and aimThis study aimed to develop a predictive model for early neurological deterioration (END) in branch atheromatous disease (BAD) affecting the lenticulostriate artery (LSA) territory using machine learning. …”
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The Electricity Load Prediction Model for Residential Buildings: A Critical Review of Output Types, Prediction Methods and Driving Factors by Zhenjing Wu, Min Qi, Weiling Zhang, Xudong Zhang, Qiang Yang, Wenyuan Zhao, Bin Yang, Zhihan Lyu, Faming Wang, Zhichao Wang

“…Predictive model building methods were classified as classical, algorithms based on Machine Learning (ML) or Deep Learning (DL) and hybrid methods. …”
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Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study. by Michael King, Louise Marston, Igor Švab, Heidi-Ingrid Maaroos, Mirjam I Geerlings, Miguel Xavier, Vicente Benjamin, Francisco Torres-Gonzalez, Juan Angel Bellon-Saameno, Danica Rotar, Anu Aluoja, Sandra Saldivia, Bernardo Correa, Irwin Nazareth

“…<h4>Conclusions</h4>The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.…”
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From Tension to Triumph: Design and Implementation of an Innovative Algorithmic Metric for Quantifying Individual Performance in Women Volleyball’s Critical Moments by Carlos López-Serrano, María Zakynthinaki, Daniel Mon-López, Juan José Molina Martín

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Practical Application of Complementary Regulation Strategy of Run-of-River Small Hydropower and Distributed Photovoltaic Based on Multi-Scale Copula-MPC Algorithm by Xianpin Zhu, Weibo Li, Shuai Cao, Wei Xu

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i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus by Sakshi Gautam, Anamika Thakur, Manoj Kumar

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A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes by Hossein Moayedi, Atefeh Ahmadi Dehrashid, Binh Nguyen Le

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Comparative Study on Total Organic Carbon Content Logging Prediction Method Based on Machine Learning by TANG Shengshou, YANG Bin, JIN Jiulong, LIU Hongrui, DAI Xingyu, PU Jincheng

“…There are many influencing factors and difficulty in the prediction of total organic carbon content, so it is particularly important to explore the most suitable high-precision prediction method for the prediction of total organic carbon content in this area. …”
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Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP by Shuang Liu, Enxiang Qu, Chun LV, Xueyuan Zhang

“…Compared with the unoptimized BP neural network and the BP neural network optimized by the artificial bee colony algorithm (ABC) model, this model has higher prediction accuracy and is more suitable for predicting the blasting block size of open-pit coal mines, it provides a new method for predicting the fragmentation of blasting under the influence of multiple factors, filling the gap in related theoretical research, and has certain practical application value.…”
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microProtein Prediction Program (miP3): A Software for Predicting microProteins and Their Target Transcription Factors by Niek de Klein, Enrico Magnani, Michael Banf, Seung Yon Rhee

“…The algorithm is called miP prediction program (miP3), which is implemented in Python. …”
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A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVs by Yue Li, Yu Jiang, Jianhua Guo, Dong Xie

“…The widespread adoption of battery electric vehicles (BEVs) has highlighted the critical importance of precise energy consumption prediction models to address the problem of range anxiety among drivers. …”
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Multi-Condition Magnetic Core Loss Prediction and Magnetic Component Performance Optimization Based on Improved Deep Forest by Haotian Shi, Zipeng Jin

“…This paper proposes a core loss prediction model based on an improved deep forest algorithm and information entropy-enhanced genetic algorithm. …”
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Accurate Prediction of 327 Rice Variety Growth Period Based on Unmanned Aerial Vehicle Multispectral Remote Sensing by Zixuan Qiu, Hao Liu, Lu Wang, Shuaibo Shao, Can Chen, Zijia Liu, Song Liang, Cai Wang, Bing Cao

“…In this study, multispectral images of rice at various growth stages were captured using an unmanned aerial vehicle, and single-plant rice silhouettes were identified for 327 rice varieties by establishing a deep-learning algorithm. A growth stage prediction method was established for the 327 rice varieties based on the normalized vegetation index combined with cubic polynomial regression equations to simulate their growth changes, and it was first proposed that the growth stages of different rice varieties were inferred by analyzing the normalized difference vegetation index growth rate. …”
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Research on prediction of bottom hole flowing pressure for vertical coalbed methane wells based on improved SSA-BPNN by YU Yang, DONG Yintao, LI Yunbo, BAO Yu, ZHANG Lixia, SUN Hao

Subjects: “…|coalbed methane|sparrow search algorithm|neural network|bottom hole flowing pressure|prediction model…”
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Developing an electronic surprise question to predict end-of-life prognosis in a prospective cohort study of acute hospital admissions by Baldev Singh, Nisha Kumari-Dewat, Adam Ryder, Vijay Klaire, Hannah Jennens, Kamran Ahmed, Mona Sidhu, Ananth Viswanath, Emma Parry

“…Probability cut points triaged unknown prognosis into the GSFSQ equivalent ‘Yes’ or ‘No’ survival categories (> or < 1-year respectively), with subsidiary classification of ‘No’. Prediction was tested against prospective mortality. …”
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An Analytical Cost Function Design and Implementation for Predictive Control of Induction Machine Drives by Wei Wei, Liming Yan, Shun Tian, Xisheng Xu, Keke Sun

“…In finite control set-model predictive torque control (FCS-MPTC) of induction machine (IM), the optimal design of weighting factors for the cost function has always been a research difficulty in community of scholars. …”
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Improved gated recurrent unit-based osteosarcoma prediction on histology images: a meta-heuristic-oriented optimization concept by S. Prabakaran, S. Mary Praveena

Subjects: “…Osteosarcoma prediction…”
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