The development of a C5.0 machine learning model in a limited data set to predict early mortality in patients with ARDS undergoing an initial session of prone positioning by David M. Hannon, Jaffar David Abbas Syed, Bairbre McNicholas, Michael Madden, John G. Laffey

“…A C5.0 classifier algorithm with adaptive boosting was trained on data gathered before, during, and after initial proning. …”
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A Fused Multi-Channel Prediction Model of Pressure Injury for Adult Hospitalized Patients—The “EADB” Model by Eba’a Dasan Barghouthi, Amani Yousef Owda, Majdi Owda, Mohammad Asia

“…A first-hand dataset was collected retrospectively between March/2022 and August/2023 from the electronic medical records of three hospitals in Palestine. Results: The total number of patients was 49,500. …”
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Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea by Sooyeon Yi, G. Mathias Kondolf, Samuel Sandoval Solis, Larry Dale

“…Results indicate that XGBoost outperforms other models in all three groups during both training and testing phases. …”
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Intelligent Priority-Aware Spectrum Access in 5G Vehicular IoT: A Reinforcement Learning Approach by Adeel Iqbal, Tahir Khurshaid, Yazdan Ahmad Qadri

“…This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. …”
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Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design by Paul Adunola, Luis Felipe V. Ferrão, Juliana Benevenuto, Camila F. Azevedo, Patricio R. Munoz

“…To this end, we used a historical blueberry (Vaccinium corymbosun L.) breeding dataset containing more than 3000 individuals, genotyped using probe‐based target sequencing and phenotyped for three fruit quality traits over several years. Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data‐driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long‐term implication, we carried out a simulation study and emphasized that data‐driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. …”
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Development and validation of a machine learning-based prediction model for hepatorenal syndrome in liver cirrhosis patients using MIMIC-IV and eICU databases by Fengwei Yao, Ji Luo, Qian Zhou, Luhua Wang, Zhijun He

“…Core risk factors were determined from the intersection of the three methods. A predictive model was constructed using multivariable logistic regression and visualized via a nomogram. …”
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Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning by Mustapha Mouzai, Mohamed Amine Riahla, Amor Keziou, Hacène Fouchal

“…Then, we design tree-based machine learning algorithms, namely decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost), for the purpose of classifying signal types and to recognize spoofing attacks. …”
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Iraqi Stock Market Prediction Using Artificial Neural Network and Long Short-Term Memory by Sama Hayder Abdulhussein AlHakeem, Nashaat Jasim Al-Anber, Hayfaa Abdulzahra Atee, Mahmod Muhamad Amrir

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Research on the optimization method of inventory management of important spare parts of intercity railway. by Dongyan Wang, Ying Sun, Liang Yu, Kun Shen, Junbo Li, Xia Wu

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A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease by K. M. Tawsik Jawad, Anusha Verma, Fathi Amsaad, Lamia Ashraf

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Stacked ensemble model for NBA game outcome prediction analysis by Guangsen He, Hyun Soo Choi

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Advanced analytics to improve energy efficiency of steel industry - A systematic review on ladle logistics by Akhil Raja Keshetti, Victor S.P. Ruela, Hao Chen, Marcos R. Machado

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Embedding Ethics Into Artificial Intelligence: Understanding What Can Be Done, What Can't, and What Is Done by Clayton Peterson

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Spatially Explicit Model for Assessing the Impacts of Groundwater Protection Measures in the Vicinity of the Hranice Abyss by Jozef Sedláček, Hana Vavrouchová, Kryštof Chytrý, Ondřej Ulrich, Petra Oppeltová, Milan Geršl, Kristýna Kohoutková, Radim Klepárník, Petr Kučera, Vítězslav Vlček, Jana Šimečková, Eva Žallmannová

“…The model employs a multi-criteria decision analysis, integrated with hydrological modeling and a high-resolution random forest-based prediction algorithm, to downscale land surface temperature (LST) in order to obtain high-resolution 1 × 1 m spatial results. …”
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Nursing Educators’ Perspectives on the Integration of Artificial Intelligence Into Academic Settings by Moustaq Karim Khan Rony MSS, MPH, RN, Sumon Ahmad MPH, RN, Sabren Mukta Tanha MPH, RN, Dipak Chandra Das MSS, Mosammat Ruma Akter MSN, RN, Mst. Amena Khatun MPH, RN, Most. Hasina Begum MSN, RN, Md Ibrahim Khalil MSS, RN, Umme Rabeya Peu MSS, RN, Mst. Rina Parvin BSN, RN, Daifallah M Alrazeeni PhD, Fazila Akter MSc, RN

“…However, barriers such as insufficient training, infrastructural challenges, and ethical concerns related to data privacy, algorithmic bias, and AI-driven decision making were highlighted. …”
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Risk factors and an interpretability tool of in-hospital mortality in critically ill patients with acute myocardial infarction by Rui Yang, Tao Huang, Renqi Yao, Di Wang, Yang Hu, Longbing Ren, Shaojie Li, Yali Zhao, Zhijun Dai

“…The tool can help clinical decision-making.…”
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MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort... by Yiru Wang, Fuli Chen, Zhechen Ouyang, Siyi He, Xinling Qin, Xian Liang, Weimei Huang, Rensheng Wang, Kai Hu

“…The random forest algorithm was employed to identify the most valuable features. …”
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Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019 by Jenberu Mekurianew Kelkay, Deje Sendek Anteneh, Henok Dessie Wubneh, Abraham Dessie Gessesse, Gebeyehu Fassil Gebeyehu, Kalkidan Kassahun Aweke, Mikiyas Birhanu Ejigu, Mathias Amare Sendeku, Kirubel Adrissie Barkneh, Hasset Girma Demissie, Wubshet D. Negash, Birku Getie Mihret

“…"The most significant features that contribute to the accuracy of the top-performing models, notably the Random Forest should be highlighted because they outperformed the other model in the analysis.In general, ensemble learning algorithms can accurately predict short birth interval status, making them potentially useful as decision-support tools for the pertinent stakeholders.…”
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Reinforcement learning energy management control strategy of electric tractor based on condition identification by Liqiao Li, Jiangchun Chen, Jing Nie, Zongyu Gao

“…The historical driving data are used to construct the driving conditions of ET and obtain the Markov power state transfer probability matrix(MPSTPM) under different CI; Second, to minimize the energy consumption of lithium-titanate battery and supercapacitor hybrid power system(HPS), the power allocation strategy for ET under different CI is obtained by a Q-network RL algorithm; Finally, an learning vector quantization neural network(LVQNN) is used to identify the current ET driving CI through online and real-time, and the control system makes real-time power output decision through the current driving CI. …”
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Generative Local Interpretable Model-Agnostic Explanations by Mohammad Nagahisarchoghaei, Mirhossein Mousavi Karimi, Shahram Rahimi, Logan Cummins, Ghodsieh Ghanbari

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