A systematic review on sleep stage classification and sleep disorder detection using artificial intelligence by Tayab Uddin Wara, Ababil Hossain Fahad, Adri Shankar Das, Md Mehedi Hasan Shawon

“…At the same time, Long Short-Term Memory, Ensemble Learning, Support Vector Machine, and Random Forest accounted for 15 %, 12 %, 7 %, and 6 % of usage, respectively. …”
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State Identification of Charging Module Based on SN‐EMD‐SSEE and DBO‐HKELM by Bingyu Li, Xianhai Pang, Xuhao Du, Ziwen Cai

“…In modeling, DBO‐HKELM identification model is constructed by improving ELM (Extreme Learning Machine) and optimizing parameters based on DBO 60 state characteristics values and 24 states including normal state, which are, respectively, used as the input and output of the identification model. …”
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Integration of Deep Learning Neural Networks and Feature-Extracted Approach for Estimating Future Regional Precipitation by Shiu-Shin Lin, Kai-Yang Zhu, He-Yang Huang

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Energy consumption forecasting and thermal insulator selection with random forest regression by Mohammed Fellah, Salma Ouhaibi, Naoual Belouaggadia, Khalifa Mansouri

“…Moreover, predicting this energy consumption is complex due to the diversity of parameters to be considered as well as the different existing climate zones.In this context, this work proposes a machine learning-based model to predict an energy consumption index in the case of using thermal insulators in various climate zones around the world. …”
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Chemometric and meta-heuristic algorithms to find optimal wavelengths and predict ‘Red Delicious’ apples traits using Vis-NIR by Mahsa S. Razavi, Vali Rasouli Sharabiani, Mohammad Tahmasebi, Silvia Grassi, Mariusz Szymanek

“…This approach can contribute to the development of portable quality assessment devices to estimate quality parameters including pH, TA, ascorbic acid, firmness, soluble solids content (SSC), and anthocyanins using machine learning.Good performances were obtained for all the tested regressors (PLSR, PCR, MLR, SVM-R, and ANN) in terms of R2, RMSE, and RPD values in cross-validation, validation, and test phases. …”
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Development and application of a model for the automatic evaluation and classification of onions (Allium cepa L.) using a Deep Neural Network (DNN) by Piotr Rybacki, Przemysław Przygodziński, Przemysław Łukasz Kowalczewski, Zuzanna Sawinska, Ireneusz Kowalik, Andrzej Osuch, Ewa Osuch

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Modeling the unconfined compressive strength of lateritic soil treated with FGD gypsum as a partial cement replacement by Chidananda M Linganagoudar, Shiva Kumar G, M S Ujwal, G Rohith, A Vinay, Poornachandra Pandit

“…This integrated experimental–computational approach not only validates the feasibility of using FGD gypsum in sustainable soil stabilization but also demonstrates the effectiveness of machine learning in predicting key geotechnical parameters, reducing reliance on extensive laboratory testing and promoting data-driven pavement design.…”
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Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation by Junsong Jin, Fangtao Chai, Jinchuan Long, Chang Gao, Shaolei Wang, Pan Zeng, Xuefeng Tang, Pan Gong, Mao Zhang, Lei Deng, Xinyun Wang

“…The deformation characteristics, flow behavior and microstructure/texture evolution mechanisms of pre-deformed AZ31 alloy were systematically investigated under varying process parameters. A genetic algorithm-optimized artificial neural network (GA-ANN) constitutive model was developed using machine learning methods, and hot processing maps were established based on this model. …”
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Increasing Ecosystem Fluxes Observed from Eddy Covariance and Solar-Induced Fluorescence Data by Jiao Zheng, Hao Zhou, Xu Yue, Xichuan Liu, Zhuge Xia, Jun Wang, Jingfeng Xiao, Xing Li, Fangmin Zhang

“…We developed six global GPP and ET products at 0.05° spatial and 8-day temporal resolution, using two machine learning models and three SIF products, which integrate vegetation physiological parameters with data-driven approaches. …”
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The colours of the ocean: using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas, the Gulf of Mexico and the UK by Solomon White, Tiago Silva, Laurent O. Amoudry, Evangelos Spyrakos, Adrien Martin, Adrien Martin, Encarni Medina-Lopez

“…This study presents a methodology for extracting SST and SSS using machine learning algorithms trained with in-situ and multispectral satellite data. …”
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Gut and respiratory microbiota landscapes in IgA nephropathy: a cross-sectional study by Xiaoli Yuan, Jianbo Qing, Wenqiang Zhi, Feng Wu, Yan Yan, Yafeng Li

“…Spearman correlation analysis was employed to link key bacteria with clinical parameters.Results We observed a reduced microbial diversity in IgAN patients compared to healthy controls. …”
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Crushing Force Prediction Method of Controlled-Release Fertilizer Based on Particle Phenotype by Linlin Sun, Xiubo Chen, Zixu Chen, Linlong Jing, Jinxing Wang, Xinpeng Cao, Shenghui Fu, Yuanmao Jiang, Hongjian Zhang

“…A principal component analysis was applied for data reduction, and the optimal parameters for the support vector machine (SVM) were selected using particle swarm optimization (PSO) combined with k-fold cross-validation. …”
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AI-driven wastewater management through comparative analysis of feature selection techniques and predictive models by Faruk Dikmen, Ahmet Demir, Bestami Özkaya, Muhammad Owais Raza, Jawad Rasheed, Tunc Asuroglu, Shtwai Alsubai

“…This study evaluates the performance of machine learning models in predicting key wastewater effluent parameters Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total Suspended Solids (TSS), Total Effluent Nitrogen and Total Effluent Phosphorus. …”
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Prediction of Pollutant Emissions from a Low-Speed Marine Engine Based on Harris Hawks Optimization and Lightgbm by Yue Chen, Yulong Shen, Miaomiao Wen, Cunfeng Wei, Junjie Liang, Yuanqiang Li, Ying Sun

“…With the rapid development of data science, machine learning has been widely applied to research on pollutant emission prediction in internal combustion engines due to its excellent responsiveness and generalization ability. …”
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Multi-Feature Fusion for Estimating Above-Ground Biomass of Potato by UAV Remote Sensing by Guolan Xian, Jiangang Liu, Yongxin Lin, Shuang Li, Chunsong Bian

“…The spectral, textural, and structural features extracted by UAV multispectral and RGB imaging, coupled with agricultural meteorological parameters, were integrated to estimate the AGB in potato during the whole growth period. …”
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Heart disease prediction using autoencoder and DenseNet architecture by Norah Saleh Alghamdi, Mohammed Zakariah, Achyut Shankar, Wattana Viriyasitavat

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Prediction of peripheral blood lymphocyte subpopulations after renal transplantation by Bo Peng, Xuyu Xiang, Han Tian, Kaiqiang Xu, Quan Zhuang, Junhui Li, Pengpeng Zhang, Yi Zhu, Min Yang, Jia Liu, Yujun Zhao, Ke Cheng, Yingzi Ming

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Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms? by Khabat Khosravi, Aitazaz A. Farooque, Sayed M. Bateni, Changhyun Jun, Dorsa Mohammadi, Zahra Kalantari, James R. Cooper

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Evaluating multitemporal vegetation indices from Zhuhai-1 hyperspectral images for detecting a rapidly spreading invasive species - Spartina alterniflora by Yuanhui Zhu, Soe W. Myint, Jingjing Cao, Kai Liu, Mei Zeng, Chenxi Diao

“…This study examined multitemporal VIs from nine months using hyperspectral images and common machine learning methods (i.e., K-nearest neighbor, support vector machine, random forest) to compare a variety of VIs' performance in identifying SA invasion in the Guangxi Zhuang Autonomous Region. …”
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Improving the accuracy of remotely sensed TSS and turbidity using quality enhanced water reflectance by a statistical resampling technique by Kunwar Abhishek Singh, Dongryeol Ryu, Meenakshi Arora, Manoj Kumar Tiwari, Bhabagrahi Sahoo

“…The statistical resampling approach based on GMM was applied to Sentinel-2 (S2) imagery to produce input to Machine Learning (ML) algorithms to retrieve the TSS and turbidity for target river sections. …”
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