Outliers and anomalies in training and testing datasets for AI-powered morphometry—evidence from CT scans of the spleen by Yuriy Vasilev, Yuriy Vasilev, Anastasia Pamova, Tatiana Bobrovskaya, Anton Vladzimirskyy, Anton Vladzimirskyy, Olga Omelyanskaya, Elena Astapenko, Artem Kruchinkin, Novik Vladimir, Kirill Arzamasov, Kirill Arzamasov

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Optimal Parameter Extraction of PEM Fuel Cell Using a Hybrid Weighted Mean of Vectors and Nelder-Mead Simplex Method by Rahul Khajuria, Mahipal Bukya, Ravita Lamba, Rajesh Kumar

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Renal parenchymal volume analysis: Clinical and research applications by Carlos Munoz‐Lopez, Kieran Lewis, Nityam Rathi, Eran Maina, Akira Kazama, Anne Wong, Angelica Bartholomew, Worapat Attawettayanon, Yunlin Ye, Zhiling Zhang, Wen Dong, Rebecca A. Campbell, Nicholas Heller, Erick Remer, Christopher Weight, Steven C. Campbell

“…This simple principle forms the basis for parenchymal volume analysis (PVA) with semiautomated software, which can be leveraged to predict SRF and new‐baseline glomerular filtration rate (NBGFR) following nephrectomy. …”
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Interpretable machine learning modeling of temperature rise in a medium voltage switchgear using multiphysics CFD analysis by Mahmood Matin, Amir Dehghanian, Amir Hossein Zeinaddini, Hossein Darijani

“…SHAP analysis identified the most significant variables affecting temperature prediction as current, air velocity, duct area, and switchgear conditions, in that order.…”
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Combining first principles and machine learning for rapid assessment response of WO3 based gas sensors by Ran Zhang, Guo Chen, Shasha Gao, Lu Chen, Yongchao Cheng, Xiuquan Gu, Yue Wang

“…The collected data was subsequently utilized to develop a correlation model linking the multi-physical parameters to gas sensitive performance using intelligent algorithms. The model’s performance was assessed through receiver operating characteristic (ROC) curves, confusion matrices, and other evaluation metrics, ultimately achieving a prediction accuracy of 90% for identifying key features influencing gas adsorption performance. …”
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Spatial and temporal characteristics of water conservation services and rapid response framework for water yield in key ecological zones of the Yiluo River basin by Junqiang Xu, Fan Wang, Chao Ren, Jianmin Bian, Tao Li, Zikai Ping

“…The artificial neural network-based prediction framework achieved high performance with Pearson correlation coefficients exceeding 0.90 across all datasets. …”
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Accurate Virtual Trial Assembly Method of Prefabricated Steel Components Using Terrestrial Laser Scanning by Yin Zhou, Daguang Han, Kaixin Hu, Guocheng Qin, Zhongfu Xiang, Chunli Ying, Lidu Zhao, Xingyi Hu

“…Experimental results show that the geometric prediction deviation of VTA is less than 1/1800 of the experimental bridge span, and the mean stress predicted via VTA is 90% of the measured mean stress. …”
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StratLearn-z: Improved photo-$z$ estimation from spectroscopic data subject to selection effects by Chiara Moretti, Maximilian Autenrieth, Riccardo Serra, Roberto Trotta, David A. van Dyk, Andrei Mesinger

“…We benchmark our results against the GPz algorithm, quantifying the performance of the two algorithms with a set of metrics. …”
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Integration of single‐cell and bulk RNA‐sequencing data reveals the prognostic potential of epithelial gene markers for prostate cancer by Zhuofan Mou, Lorna W. Harries

“…Current clinicopathological factors inadequately predict biochemical recurrence, a critical indicator guiding post‐treatment strategies following radical prostatectomy. …”
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ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool by D. Di Santo, C. He, F. Chen, L. Giovannini

“…These regression algorithms are used to construct computationally inexpensive surrogate models to effectively predict the impact of input parameter variations on model output, thereby significantly reducing the computational burden of running high-fidelity models for sensitivity analysis. …”
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Optimization of clustering parameters for single-cell RNA analysis using intrinsic goodness metrics by Nicolina Sciaraffa, Antonino Gagliano, Luigi Augugliaro, Claudia Coronnello, Claudia Coronnello

“…This procedure has enabled the effective prediction of clustering accuracy through the utilization of intrinsic metrics. …”
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Structural and population-based evaluations of TBC1D1 p.Arg125Trp. by Tom G Richardson, Elaine C Thomas, Richard B Sessions, Debbie A Lawlor, Jeremy M Tavaré, Ian N M Day

“…We investigated these findings in the Avon Longitudinal Study of Parents and Children (ALSPAC), a large European birth cohort of mothers and offspring, and by generating a predicted model of the structure of this domain. Structural prediction involved the use of three separate algorithms; Robetta, HHpred/MODELLER and I-TASSER. …”
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Short-Term Electricity Price Forecasting Using the Empirical Mode Decomposed Hilbert-LSTM and Wavelet-LSTM Models by Kunal Shejul, R. Harikrishnan, Amit Kukker

“…The proposed techniques show better performance in terms of rank correlation, mean square error, and root mean square error compared to the existing algorithms of LSTM and CNN-LSTM. The prediction results achieved with wavelet-LSTM and Hilbert-LSTM (1-month dataset of 8 years) are rank correlation 0.9746 and 0.9749, MSE 0.2962 and 0.1363, and RMSE 0.5443 and 0.3692, respectively. …”
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Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods by Asmaa Ameen, Ibrahim Eldesouky Fattoh, Tarek Abd El-Hafeez, Kareem Ahmed

“…This work compares and reports the classification, machine learning, and deep learning algorithms that predict cardiovascular illnesses. For this study, articles from 2012 to 2023 were considered; after filtering, 82 articles were chosen for primary research. …”
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Leveraging machine learning techniques to analyze nutritional content in processed foods by K. A. Muthukumar, Soumya Gupta, Doli Saikia

“…The findings reveal that the SVR model is particularly effective in predicting nutrient retention, outperforming the RF model. …”
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Climatic variables determining <i>Rhododendron</i> sister taxa distributions and distributional overlaps in the Himalayas by Madan Krishna Suwal, Ole Reidar Vetaas

“…We used Generalized Linear Modelling to select variables, and modelled the distribution of each species using Random Forest algorithms, predicting their potential distribution in current and future climates. …”
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Machine Learning and Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring by Sehat Ullah, Sangeen Khan, David Vanecek, Inam Ur Rehman

“…The system was also tested in the clinical setting to collect patient data and the best-performing algorithm (KNN) was used for status prediction, obtaining 98% accuracy.…”
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Identification and optimization of relevant factors for chronic kidney disease in abdominal obesity patients by machine learning methods: insights from NHANES 2005–2018 by Xiangling Deng, Lifei Ma, Pin Li, Mengyang He, Ruyue Jin, Yuandong Tao, Hualin Cao, Hengyu Gao, Wenquan Zhou, Kuan Lu, Xiaoye Chen, Wenchao Li, Huixia Zhou

“…Furthermore, an optimal predictive model was developed for CKD using ten machine learning algorithms and enhanced model interpretability with the Shapley Additive Explanations (SHAP) method. …”
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Diagnostics and Prognostics of Boilers in Power Plant Based on Data-Driven and Machine Learning by Achmad Widodo, Toni Prahasto, Mochamad Soleh, Herry Nugraha

“…The proposed method utilizes machine learning techniques through support vector machine (SVM) and random forest algorithm (RFA) for anomaly detection and similarity-based method of dynamic time warping (DTW) for RUL prediction. …”
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COMBINED ANALYSIS OF LINKAGE AND WHOLE EXOME SEQUENCING REVEALS CIC AS A CANDIDATE GENE FOR ISOLATED DYSTONIA by Barış Salman, Emrah Yücesan, Bedia Samancı, Başar Bilgiç, Haşmet Ayhan Hanağası, İbrahim Hakan Gürvit, Uğur Özbek, Sibel Aylin Uğur İşeri

“…An in-house pipeline compiled for WES analysis along with in-depth in silico prediction algorithms were used to assess the associated data produced in this study. …”
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