A novel coordinative spatio-temporal operation strategy for EV battery swapping-charging system based on mobile battery energy storage by Zhijian Liu, Duanyu Li, Jing Dai, Pengcheng Li, Wenguo Yan

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Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin by Pei Zhang, Qiong Chen, Jiahui Lao, Juan Shi, Jia Cao, Xiao Li, Xin Huang

“…Univariate analyses and the least absolute shrinkage and selection operator algorithm were used to screen risk factors and construct the model. …”
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State of Health Estimation for Lithium-Ion Batteries Using an Explainable XGBoost Model with Parameter Optimization by Zhenghao Xiao, Bo Jiang, Jiangong Zhu, Xuezhe Wei, Haifeng Dai

“…Additionally, the Tree SHapley Additive exPlanation (TreeSHAP) technique is employed to analyze the explainability of the estimation model and reveal the influence of different features on SOH evaluation. Experiments involving two types of batteries under various aging conditions are conducted to obtain battery cycling aging data for model training and validation. …”
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Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application by Xiaolei Zhou, Xingyue Wang, Ruifeng Guo

“…Then three mainstream machine learning models are compared for SHAP analysis to obtain the significance results of relevant features. Finally, the IPSO algorithm is combined with SHAP analysis to dynamically adjust the training features to optimize the performance of the CNN model. …”
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An Improved Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System for Predicting the Energy Consumption of University Residence by Stephen Oladipo, Yanxia Sun, Oluwatobi Adeleke

“…A comparative analysis is conducted between the MPSO, the original PSO, and six other hybrid models using a dataset division of 70% for training and 30% for testing. Performance evaluation was carried out using three well-known performance benchmarks: root mean square error (RMSE), mean absolute deviation (MAD), and coefficient of variation (RCoV). …”
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Study of machine learning techniques for outcome assessment of leptospirosis patients by Andreia Ferreira da Silva, Karla Figueiredo, Igor W. S. Falcão, Fernando A. R. Costa, Marcos César da Rocha Seruffo, Carla Cristina Guimarães de Moraes

“…In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. …”
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Prediction of Auditory Performance in Cochlear Implants Using Machine Learning Methods: A Systematic Review by Beyza Demirtaş Yılmaz

“…Study design, machine learning algorithms, and audiological measurements were evaluated in the data analysis. …”
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Hybrid Optimized Feature Selection and Deep Learning Method for Emotion Recognition That Uses EEG Data by asmaa Bashar Hmaza, Rajaa K. Hasoun

“…The process begins with collecting and preprocessing EEG information to use the data for training and testing the proposed system. Optimization, machine learning, and deep learning algorithms are applied in this study. …”
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Forecasting Insurance Company Commitments with Long Short-Term Memory Models by Negar Tehraniyazdi, Reza Vaezi, Saeed Setayeshi, Iman Raeesi Vanani

“…The model is trained using historical data from Karafarin Insurance Company covering the years 2017 to 2021. …”
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Elastic Optimization for Stragglers in Edge Federated Learning by Khadija Sultana, Khandakar Ahmed, Bruce Gu, Hua Wang

“…We customize a benchmark algorithm, FedAvg, to obtain a new elastic optimization algorithm (FedEN) which is applied in local training of edge devices. …”
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Research on short-term power load forecasting based on deep reinforcement learning with multiple intelligences by Tianyun Luo, Dunlin Zhu, Jinming Liu, Sheng Yang, Jinglong He, Yuan Fu

“…In this paper, we analyze the multi-intelligence application architecture in power load forecasting, and analyze the function of each intelligent unit applied to short-term power load forecasting; based on clarifying the interaction relationship of each intelligent unit in short-term power load forecasting, we model short-term power load forecasting as a distributed and partially observable Markov decision-making process, which is suitable for multi-intelligence deep reinforcement learning; based on the MATD3 algorithm, a centralized training-distributed execution framework is used to train multiple intelligences within the model to achieve short-term power load forecasting. …”
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Dual-Mode Visual System for Brain–Computer Interfaces: Integrating SSVEP and P300 Responses by Ekgari Kasawala, Surej Mouli

“…Classification accuracy was evaluated based on correct task intention recognition. …”
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Prediction of the thermophysical properties of Ag-reduced graphene oxide-water/ethylene-glycol hybrid nanofluids using different machine learning methods by Huaguang Li, Ali B.M. Ali, Rasha Abed Hussein, Narinderjit Singh Sawaran Singh, Barno Abdullaeva, Zubair Ahmad, Soheil Salahshour, Mohammadreza Baghoolizadeh, Mostafa Pirmoradian

“…Evaluating the performance of algorithms is based on the evaluation indices of Correlation coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Standard Deviation (STD). …”
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Machine learning assisted estimation of total solids content of drilling fluids by B.T. Gunel, Y.D. Pak, A.Ö. Herekeli, S. Gül, B. Kulga, E. Artun

“…The relationships among various rheological parameters were analyzed using statistical methods and machine learning algorithms. Several machine learning algorithms of diverse classes, namely linear (linear regression, ridge regression, and ElasticNet regression), kernel-based (support vector machine) and ensemble tree-based (gradient boosting, XGBoost, and random forests) algorithms, were trained and tuned to estimate solids content from other readily available drilling fluid properties. …”
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Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion by Jinyin Pan, Shidong Wang, Huachao Li, Zhanliang Yuan, Binbin Yuan, Jinyan Peng, Yuanyuan Liu

“…This model has shown promising performance in terms of qualitative visual effects and quantitative evaluation metrics.…”
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Detection of Wild Mushrooms Using Machine Learning and Computer Vision by Christos Chaschatzis, Chrysoula Karaiskou, Chryssanthi Iakovidou, Panagiotis Radoglou-Grammatikis, Stamatia Bibi, Sotirios K. Goudos, Panagiotis G. Sarigiannidis

“…The proposed approach utilises unmanned aerial vehicles (UAVs) equipped with multispectral imaging and the YOLOv5 object detection algorithm. A custom dataset, the wild mushroom detection dataset (WOES), comprising 907 annotated aerial and ground images, was developed to support model training and evaluation. …”
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Dataset of polarimetric images of mechanically generated water surface waves coupled with surface elevation records by wave gauges linear arrayScienceDB by Noam Ginio, Michael Lindenbaum, Barak Fishbain, Dan Liberzon

“…To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2]. The developed method training and evaluation was based on in-house made supervised dataset. …”
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Application of a Hybrid Model for Data Analysis in Hydroponic Systems by Kuanysh Bakirov, Jamalbek Tussupov, Akhmet Tussupov, Ibraheem Shayea, Aruzhan Shoman

“…The model continuously identifies the deviations in environmental parameters and recommends corrective actions to stabilize the growth conditions. Experimental evaluation demonstrated superior predictive performance by using XGBoost, achieving an accuracy and F1-score of 97.88%, ROC-AUC of 99.99%, and computational efficiency (training completed in 2.3 s), outperforming RandomForest and GradientBoosting algorithms. …”
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Comparing 2D and 3D Feature Extraction Methods for Lung Adenocarcinoma Prediction Using CT Scans: A Cross-Cohort Study by Margarida Gouveia, Tânia Mendes, Eduardo M. Rodrigues, Hélder P. Oliveira, Tania Pereira

“…Both 2D and 3D CT data were initially explored, with the Lung-PET-CT-Dx dataset being employed for training and the NSCLC-Radiomics and NSCLC-Radiogenomics datasets used for external evaluation. …”
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Assessment of the effectiveness of measures to preserve, strengthen, and restore mental health and psychological well-being of specialists of EMERCOM of Russia by Julia S. Shoigu, Anastasia А. Tarasova

“…Maintaining mental health and psychological well-being of the specialists of EMERCOM of Russia requires, along with implementation of measures for the rapid restoration of their working capacity, also the development of their self-regulation skills by means of training with the use of special psychophysiological equipment. …”
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