Deep learning based screening model for hip diseases on plain radiographs. by Jung-Wee Park, Seung Min Ryu, Hong-Seok Kim, Young-Kyun Lee, Jeong Joon Yoo

“…Four different models were designed-raw image for both training and test set, preprocessed image for training but raw image for the test set, preprocessed images for both sets, and change of backbone algorithm from DenseNet to EfficientNet. The deep learning models were compared in terms of accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-score, and area under the receiver operating characteristic curve (AUROC).…”
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Research on Measurement of Coal–Water Slurry Solid–Liquid Two-Phase Flow Based on a Coriolis Flow Meter and a Neural Network by Jie Liu, Lingfei Kong, Jiahao Ma, Xuemei Zhang, Chengjie Wang, Dongze Wu

“…The first correction results showed that the corrected error of the predictive model was 3.98%, a significant improvement compared to the 5.11% error measured by the X company’s meter. (2) Building on this, a second correction model was established through algorithm optimization, successfully reducing the corrected error of the predictive model to 1.01%. …”
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Detecting Freezing of Gait in Parkinson Disease Using Multiple Wearable Sensors Sets During Various Walking Tasks Relative to Medication Conditions (DetectFoG): Protocol for a Pros... by Sébastien Cordillet, Sophie Drapier, Frédérique Leh, Audeline Dumont, Florian Bidet, Isabelle Bonan, Karim Jamal

“…The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the most effective combination of wearable sensors for detecting FoG episodes will be studied. …”
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G4 & the balanced metric family – a novel approach to solving binary classification problems in medical device validation & verification studies by Andrew Marra

“…A new metric called G4 is presented, which is the geometric mean of sensitivity, specificity, the positive predictive value, and the negative predictive value. …”
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Primary Care Physician Use of Elastic Scattering Spectroscopy on Skin Lesions Suggestive of Skin Cancer by Stephen P. Merry, Ivana T. Croghan, Kimberly A. Dukes, Brian C. McCormick, Gerard T. Considine, Michelle J. Duvall, Curtis T. Thompson, David J. Leffell

“…Device specificity was 20.7%. The negative predictive value was 96.6%, and the positive predictive value was 16.6% (NNB 6). …”
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Towards a shape-performance integrated digital twin for lumbar spine analysis [version 2; peer review: 2 approved, 1 not approved] by Wei Sun, Xueguan Song, Xiwang He, Xiaonan Lai, Yiming Qiu, Liming Shu, Zhonghai Li

“…Methods A finite element model (FEM) of the lumbar spine was firstly developed using computed tomography (CT) and constrained by the body movement which calculated by the inverse kinematics algorithm. The Gaussian process regression was utilized to train the predicted results and create the digital twin of the lumbar spine in real-time. …”
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Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors. by Anna Cichonska, Balaguru Ravikumar, Elina Parri, Sanna Timonen, Tapio Pahikkala, Antti Airola, Krister Wennerberg, Juho Rousu, Tero Aittokallio

“…Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. …”
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THE APPROVAL OF COMPLEX TREATMENT EFFECTIVENESS OF GENERALIZED PERIODONTITIS FOR THE PATIENTS AFTER TRANSMITTED CORONAVIRUS DISEASE AND REMAIN ON REHABILITATION by T.I. Matviykiv, M.M. Rozhko

“…Examination of areas compromised by generalized periodontitis and abutment teeth based on the obtained periotestometric data of tooth mobility, indicates a significant reduction in inflammation and strengthening of the ligaments and is a highly informative diagnostic method. …”
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National-scale mapping topsoil organic carbon of cropland in China using multitemporal Sentinel-2 images by Jie Xue, Xianglin Zhang, Songchao Chen, Zhongxing Chen, Rui Lu, Feng Liu, Bas van Wesemael, Zhou Shi

“…Finally, bootstrapping random forest models were fitted using the covariates selected through forward recursive feature selection (FRFS), and the spatial prediction SOC map was created. The results indicated that the framework was suitable for mapping SOC in croplands of China, with the best model using remote sensing indices and environmental covariates selected through FRFS achieving an R2 of 0.62, an RMSE of 4.84 g kg−1, and an uncertainty depicted by a 90 % prediction interval range of 17.88 g kg−1. …”
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Integrating Social Determinants of Health in Machine Learning–Driven Decision Support for Diabetes Case Management: Protocol for a Sequential Mixed Methods Study by Seung-Yup Lee, Leslie W Hayes, Bunyamin Ozaydin, Steven Howard, Alison M Garretson, Heather M Bradley, Andrew M Land, Erin W DeLaney, Amy O Pritchett, Amanda L Furr, Ashleigh Allgood, Matthew C Wyatt, Allyson G Hall, Jane C Banaszak-Holl

“…The paper presents a design for prediction validation and preimplementation assessment that uses a mixed methods approach to guide the implementation of the system. …”
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Two-Stage Distributionally Robust Optimal Scheduling for Integrated Energy Systems Considering Uncertainties in Renewable Generation and Loads by Keyong Hu, Qingqing Yang, Lei Lu, Yu Zhang, Shuifa Sun, Ben Wang

“…In modeling uncertainties, this article utilizes historical data on PV, WT, and loads, combined with the adjustability of decision variables, to generate a large set of initial scenarios through the Monte Carlo (MC) sampling algorithm. These scenarios are subsequently reduced using a combination of the K-means clustering algorithm and the Simultaneous Backward Reduction (SBR) technique to obtain representative scenarios. …”
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An Innovative Online Adaptive High-Efficiency Controller for Micro Gas Turbine: Design and Simulation Validation by Rui Yang, Yongbao Liu, Xing He, Zhimeng Liu

“…To evaluate the performance changes of the gas turbine, we applied deep learning techniques to enhance the extreme learning machine (ELM) algorithm, resulting in the development of a high-precision, high-real-time deep extreme learning machine (DL_ELM) prediction model. …”
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Abnormality detection and privacy protection strategies for power marketing inspection business of cyber–physical–social systems using big data and artificial intelligence by Li Kai, Mo Pingyan, Yang Yongjiao, Xie Hanyang, Shen Zhixiong

“…The proposed model incorporates a work order correlation matching algorithm, a fault interval detection algorithm, an electricity consumption prediction algorithm, and a business anomaly identification algorithm. …”
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Robust Tube-Based MPC with Piecewise Affine Control Laws by Meng Zhao, Xiaoming Tang

“…This paper presents a tube-based model predictive control (MPC) algorithm with piecewise affine control laws for discrete-time linear systems in the presence of bounded disturbances. …”
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Development of a host-signature-based machine learning model to diagnose bacterial and viral infections in febrile children by Fang Bai, Zelong Gong, Dong Cui, Xiaomei Zhang, Wenteng Hong, Yi Gao, Kai Lin, Weijie Chen, Lu Li, Juan Huang, Biying Zheng, Junfa Xu, Na Xiao

“…Subsequently, L1 regularization algorithms and variable significance analysis (multilayer perceptron) were used to simplify and rank the predictive features, and LCN2 (100.0%), IFI27 (84.4%), SLPI (63.2%), IFIT2 (44.6%) and PI3 (44.5%) were identified as the top predictors. …”
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Day-ahead photovoltaic power generation forecasting with the HWGC-WPD-LSTM hybrid model assisted by wavelet packet decomposition and improved similar day method by Ruxue Bai, Jinsong Li, Jinsong Liu, Yuetao Shi, Suoying He, Wei Wei

“…While deep learning algorithms have shown promise in energy applications, single algorithms often struggle with unstable predictions and limited generalizability for predicting photovoltaic (PV) output. …”
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Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses by Yuanying Zhao, Xingde Duan

“…We extend the partial posterior predictive p value for goodness-of-fit statistic to investigate the plausibility of the posited model. …”
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On Fuzzy Soft Expert Sets by Hilal Donmez, Serdar Enginoglu

“…Furthermore the results of this algorithm and Maji et al's algorithm without reduction are equivalent. …”
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Prevention of Cardiometabolic Syndrome in Children and Adolescents Using Machine Learning and Noninvasive Factors: The CASPIAN-V Study by Hamid Reza Marateb, Mahsa Mansourian, Amirhossein Koochekian, Mehdi Shirzadi, Shadi Zamani, Marjan Mansourian, Miquel Angel Mañanas, Roya Kelishadi

“…Cardiometabolic syndrome (CMS) is a growing concern in children and adolescents, marked by obesity, hypertension, insulin resistance, and dyslipidemia. This study aimed to predict CMS using machine learning based on data from the CASPIAN-V study, which involved 14,226 participants aged 7–18 years, with a CMS prevalence of 82.9%. …”
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Using Satellites to Monitor Soil Texture in Typical Black Soil Areas and Assess Its Impact on Crop Growth by Liren Gao, Yuhong Zhang, Deqiang Zang, Qian Yang, Huanjun Liu, Chong Luo

“…The results show that (1) the order of prediction accuracy differs for different soil texture types; April is determined to have the highest prediction accuracy for silt and sand, while May exhibits the greatest accuracy for clay. (2) Adding environmental variables can significantly improve the accuracy of soil texture predictions; the root mean square error (RMSE) has decreased to varying degrees (silt: 0.84; clay: 0.04; sand: 0.85). (3) Soybean growth has the strongest response to soil texture; clay grain is the key factor affecting crop growth in drought scenarios, and sand grain is the dominant factor influencing flooding. …”
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