Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm by Xiang Tang, Jianxiao Ma, Shun Zhou, Tianci Shan

“…Hence, in this paper, a combined algorithm based on the MRA model, beetle antennae search (BAS) algorithm, and BP neural network is proposed for demand prediction. …”
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Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm by Xuehui Fan, Ruixue Ye, Yan Gao, Kaiwen Xue, Zeyu Zhang, Jing Xu, Jingpu Zhao, Jun Feng, Yulong Wang

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Coordinative Two-Stage Superheated Steam Temperature Linkage Control Based on Multivariable Generalized Predictive Control Algorithm by Sheng TONG, Le LIU, Pengfei WANG

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An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease by Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Majad Mansoor, Filippo Sanfilippo

“…Accurate and early prediction of heart disease remains a significant challenge due to the complexity of symptoms and the variability of contributing factors. …”
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Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor by Iman Setiawan, Mohammad Dahlan Th. Musa, Dini Aprilia Afriza, Siti Nur Hafidah

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Prediction of Anthocyanin Content in Purple-Leaf Lettuce Based on Spectral Features and Optimized Extreme Learning Machine Algorithm by Chunhui Liu, Haiye Yu, Yucheng Liu, Lei Zhang, Dawei Li, Junhe Zhang, Xiaokai Li, Yuanyuan Sui

“…The results indicated the following: (1) For the feature band selection methods, the UVE-CARS-SNV-DBO-ELM model achieved an R_m² of 0.8623, an RMSE_m of 0.0098, an R_v² of 0.8617, and an RMSE_v of 0.0095, resulting in an RPD of 2.7192, further demonstrating that UVE-CARS enhances feature band extraction based on UVE and indicating a strong model performance. (2) For the vegetation index, VI3 showed a better predictive accuracy than VI2. The VI3-WOA-ELM model achieved an R_m² of 0.8348, an RMSE_m of 0.0109 mg/g, an R_v² of 0.812, an RMSE_v of 0.011 mg/g, and an RPD of 2.3323, demonstrating good performance. (3) For the optimization algorithms, the DBO, SABO, and WOA all performed well in optimizing the ELM model. …”
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Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility by Na Li, Huaishi Wu

“…Therefore, this paper takes the star-rated hotels in the six districts of Tianjin as the research subject and proposes a few-shot hotel location prediction method based on meta-learning algorithms and transportation accessibility. …”
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Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach by Rasool Jader, Sadegh Aminifar

“…Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. …”
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RUL Prediction of Rolling Bearings Based on Fruit Fly Optimization Algorithm Optimized CNN-LSTM Neural Network by Jiaping Shen, Haiting Zhou, Muda Jin, Zhongping Jin, Qiang Wang, Yanchun Mu, Zhiming Hong

“…To address this challenge, this paper proposes a data-driven artificial neural network method, namely the CNN-LSTM bearing remaining life prediction model based on the fruit fly optimization algorithm (FOA). …”
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Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio by Lulut Alfaris, Anas Noor Firdaus, Ukta Indra Nyuswantoro, Ruben Cornelius Siagian, Aldi Cahya Muhammad, Rohana Hassan, Rodulfo T. Aunzo, Jr., Reza Ariefka

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A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia by Xu Y, Zu Y, Zhang Y, Liang Z, Xu X, Yan J

“…Yanhong Xu,1,* Yizheng Zu,2,* Ying Zhang,1,* Zewei Liang,1 Xia Xu,1,3– 5 Jianying Yan1,3– 5 1College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, Fuzhou, Fujian, People’s Republic of China; 2The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu, People’s Republic of China; 3Fujian Clinical Research Center for Maternal-Fetal Medicine, Fuzhou, Fujian, People’s Republic of China; 4Laboratory of Maternal-Fetal Medicine, Fujian Maternity and Child Health Hospital, Fuzhou, Fujian, People’s Republic of China; 5National Key Obstetric Clinical Specialty Construction Institution of China, Fuzhou, Fujian, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xia Xu, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, No. 18 Daoshan Road, Gulou District, Fuzhou, Fujian, People’s Republic of China, Email xuxia0623@fjmu.edu.cn Jianying Yan, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, No. 18 Daoshan Road, Gulou District, Fuzhou, Fujian, People’s Republic of China, Email yanjy2019@fjmu.edu.cnPurpose: To analyze the risk factors for preterm birth in patients with early-onset preeclampsia (EOPE) based on multi-algorithm machine learning and to construct a predictive model to explore the predictive value of the model.Methods: A retrospective analysis was conducted on 442 EOPE patients from a single tertiary center, divided into preterm birth (< 37 weeks, n=358) and term-born (≥ 37 weeks, n=84) groups. …”
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Integrating Hyperspectral, Thermal, and Ground Data with Machine Learning Algorithms Enhances the Prediction of Grapevine Yield and Berry Composition by Shaikh Yassir Yousouf Jewan, Deepak Gautam, Debbie Sparkes, Ajit Singh, Lawal Billa, Alessia Cogato, Erik Murchie, Vinay Pagay

“…The use of multimodal data and machine learning (ML) algorithms could overcome these challenges. Our study aimed to assess the potential of multimodal data (hyperspectral vegetation indices (VIs), thermal indices, and canopy state variables) and ML algorithms to predict grapevine yield components and berry composition parameters. …”
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A study on method for bearing residual life prediction based on optimized Bray-Curtis dissimilarity and PSO algorithms by LI Quanfu, JIA Chen, SONG Dongli, XU Xiao

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End‐To‐End Deep Learning Temperature Prediction Algorithms of a Phase Change Materials From Experimental Photos by Mohammad Hassan Ranjbar, Kobra Gharali, Artie Ng

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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

“…This study introduces a novel and highly effective approach for predicting barite scale formation and inhibition by leveraging advanced artificial intelligence (AI) techniques. …”
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Fatigue and stamina prediction of athletic person on track using thermal facial biomarkers and optimized machine learning algorithm by P. K. Santhosh, B. Kaarthick

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Presenting a Prediction Model for CEO Compensation Sensitivity using Meta-heuristic Algorithms (Genetics and Particle Swarm) by Saeed Khaljastani, Habib Piri, Reza Sotoudeh

“…Given these points, the aim of this research is to provide a model for predicting the sensitivity of CEO compensation using meta-heuristic algorithms, specifically genetic algorithms and particle swarm optimization. …”
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A Hybrid Internet of Behavior Algorithm for Predicting IoT Data of Plant Growing using LSTM and NB Models by Khansaa Yaseen Ahmad, Omar Muayad Abdullah

“…The researches that compare the accuracy between classical statistical prediction procedures and deep learning algorithms represent an important and modern field. …”
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A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm by G. Ramkumar, Satyajeet Sahoo, T. M. Amirthalakshmi, S. Ramesh, R. Thandaiah Prabu, Kasipandian Kasirajan, Antony V. Samrot, A. Ranjith

“…The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. …”
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Pipeline corrosion rate prediction model using BP neural network based on improved sparrow search algorithm by Shuhui XIAO, Chuanjia DU, Chengjun WANG

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