The intelligent fault identification method based on multi-source information fusion and deep learning by Dashu Guo, Xiaoshuang Yang, Peng Peng, Lei Zhu, Handong He

“…By training samples and applying fusion algorithms, the spectral, topographic, geomorphic, and structural features are integrated to enhance the morphological features information of faults. …”
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Advanced strategies for the efficient optimization and control of industrial compressed air systems by D. Salomone-González

“…Real-time data transmission, powered by big data algorithms, enables continuous analysis to optimize the overall performance of the compressor plant. …”
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EFFICIENCY OF NEW IMMOBILIZED PROBIOTIC AS A PART OF TREATMENT OF PULMONARY TUBERCULOSIS PATIENTS by I. V. Belovа, I. V. Solovievа, A. G. Tochilinа, S. F. Bаrbolinа, A. V. Pаvlunin, A. S. Shprykov

“…The offered algorithm proved to be effective for prevention of disorders in microorganism community, enhancement of adherence to treatment, reduction of frequency, severity and duration of side effects to anti-tuberculosis drugs.…”
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Neuro-fuzzy techniques for modeling process parameters in pulsed electrochemical machining of steel by Irvin Uriel Nopalera Angeles, Everardo Efrén Granda Gutiérrez, Roberto Alejo Eleuterio, René Arnulfo García Hernández, Ángel Hernández Castañeda, María Guadalupe Pineda Arizmendi

“…In this way, a root mean square error in the prediction of the material removal rate of 0.394 and a coefficient of determination  of 0.845 were obtained. …”
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Refined Assessment Method of Offshore Wind Resources Based on Interpolation Method by Wenchuan Meng, Zaimin Yang, Zhi Rao, Shuang Li, Xin Lin, Jingkang Peng, Yuwei Cao, Yingquan Chen

“…To enhance the prediction accuracy of offshore wind speed, this study employs an interpolation algorithm to improve spatial resolution based on the ERA5 reanalysis dataset. …”
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Multi-Objective Optimization of the Forming Process Parameters of Disc Forgings Based on Grey Correlation Analysis and the Response Surface Method by Shizhong Wei, Yuna Liang, Hao Li, Guizhong Xie, Feng Mao, Ji Zhang

“…Then, a quadratic polynomial prediction model based on the RSM was constructed, and its accuracy was ensured through model validation. …”
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Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem by Peng Zhao, Zhengyang Dong, Jianfeng Zhang, Yi Zhang, Mingyi Cao, Zhou Zhu, Hongwei Zhou, Jianzhong Fu

“…A particle swarm optimization (PSO) algorithm contributes to the tuning of the hyperparameters of the SVC model in order to minimize the error between the SVC prediction results and the experimental results. …”
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An Immune-Related Prognostic Classifier Is Associated with Diffuse Large B Cell Lymphoma Microenvironment by Xiao-Jie Liang, Rui-ying Fu, He-nan Wang, Jing Yang, Na Yao, Xin-di Liu, Liang Wang

“…The multi-IRG classifier showed powerful predictive ability. Patients with a high-risk score had poor survival. …”
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<i>Clostridioides difficile</i> Infections in Children: What Is the Optimal Laboratory Diagnostic Method? by Mohammed Suleiman, Patrick Tang, Omar Imam, Princess Morales, Diyna Altrmanini, Jill C. Roberts, Andrés Pérez-López

“…The results of these tests as standalone methods or in four different testing algorithms were compared to a composite reference method on the basis of turnaround time, ease of use, cost, and performance characteristics including specificity, sensitivity, negative predictive value, and positive predictive value. …”
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Efficient diagnosis of diabetes mellitus using an improved ensemble method by Blessing Oluwatobi Olorunfemi, Adewale Opeoluwa Ogunde, Ahmad Almogren, Abidemi Emmanuel Adeniyi, Sunday Adeola Ajagbe, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Asif Mehmood, Habib Hamam

“…The second phase employed the same algorithms alongside sequential ensemble methods—XG Boost, AdaBoostM1, and Gradient Boosting—using an average voting algorithm for binary classification. …”
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STING is significantly increased in high-grade glioma with high risk of recurrence by Meishi Zhong, Manmei Long, Chenjie Han, Saiyan Ji, Qingyuan Yang

“…Integrated analysis showed that ATRX is positively correlated with STING in high-grade glioma (HGG) with wild-type ATRX and IDH1. Then, a relapse predictive risk-scoring model was established using the least absolute shrinkage and selection operator regression algorithms. …”
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Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach by Tianshu Chen, Yuhan Yang, Zhizhong Huang, Feng Pan, Zhendi Xiao, Kunxue Gong, Wenguang Huang, Liu Xu, Xueqin Liu, Caiyun Fang

“…Results We identified 10 critical genes (PTGIS, TIMP3, SRPX, SNCA, HIC1, BAK1, STXBP2, TRIB3, RTKN2, E2F1) and constructed a prognostic model with superior predictive performance. The StepCox[forward] + plsRcox algorithm combination demonstrated excellent predictive accuracy (AUC > 0.8). …”
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Structural insights into SOD1: from in silico and molecular dynamics to experimental analyses of ALS-associated E49K and R115G mutants by Seyed Mahdi Hosseini Faradonbeh, Bagher Seyedalipour, Nasrin Keivan Behjou, Kimiya Rezaei, Payam Baziyar, Saman Hosseinkhani

“…Our analysis was performed through multiple algorithms on the structural characterization of the hSOD1 protein using computational predictions, molecular dynamics (MD) simulations, and experimental studies to understand the effects of amino acid substitutions. …”
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Preparedness of European pediatric oncologists to integrate AI in the clinical routine by Alberto E. Tozzi, Diana Ferro, Ileana Croci, Francesco Fabozzi, Angela Mastronuzzi

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A manganese metabolism-related gene signature stratifies prognosis and immunotherapy efficacy in kidney cancer by Yang Liu, Hao Ye, Ruoxuan Zhang, Xiaolong Liu, Ranlu Liu

“…We constructed a clinical nomogram incorporating the MMCG risk score and other clinical parameters, which demonstrated highly accurate predictive capabilities. Immune infiltration analysis and immune therapy response predictions indicated that patients in Cluster I and the high-risk group showed low responses to immune therapy. …”
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Identification of a novel ferroptosis-induced immunogenic cell death related signature based on a machine learning framework in colorectal cancer by Feng Zhu, Xin Liu, Huiyuan Li, Jianfeng Li, Hongzhang Liu, Yusheng Wang

“…The RSF algorithm was chosen for further analysis due to its strong predictive performance, making it a robust tool for our study. …”
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Modeling residue formation from crude oil oxidation using tree-based machine learning approaches by Mohammad-Reza Mohammadi, Seyyed-Mohammad-Mehdi Hosseini, Behnam Amiri-Ramsheh, Saptarshi Kar, Ali Abedi, Abdolhossein Hemmati-Sarapardeh, Ahmad Mohaddespour

“…Four advanced tree-based machine learning algorithms comprising gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost) were utilized to develop accurate predictive models. …”
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Identification of intraoperative hypoxemia and hypoproteinemia as prognostic indicators in anastomotic leakage post-radical gastrectomy: an 8-year multicenter study utilizing machi... by Yuan Liu, Songyun Zhao, Xingchen Shang, Wei Shen, Wenyi Du, Ning Zhou

“…To ensure model robustness, we applied k-fold cross-validation for internal validation of the four models and subsequently validated them using independent datasets.ResultsIn contrast to the other machine learning models employed in this study, the XGBoost algorithm exhibited superior predictive performance in identifying mortality risk factors for patients with AL across one, three, and five-year intervals. …”
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Forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditions by Carlos Alejandro Perez Garcia, Dafni Despoina Avgoustaki, Enrica Santolini, Daniele Torreggiani, Thomas Bartzanas, Patrizia Tassinari, Marco Bovo

“…This research develops and validates a prediction model based on NeuralProphet algorithm to assess the vapor pressure deficit in a vertical farming facility. …”
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Impact of foam-mat drying conditions of “Gấc” aril on drying rate and bioactive compounds: Optimization by novel statistical approaches by Nguyen Minh Thuy, Vo Quoc Tien, Tran Ngoc Giau, Hong Van Hao, Vo Quang Minh, Ngo Van Tai

“…ANN model of 3–10–3 showed more accuracy and faster prediction capacity than RSM model did. ANN-GA model predicted the optimal conditions to be 13.31 % EA, 0.26 % xanthan gum and drying temperature of 73.1 °C, with the drying rate of 1.89 g-water/g-dry matter/min, β-carotene content of 395.88 μg/g, TPC of 1.68 mgGAE/g. …”
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