A digital twin-enabled fog-edge-assisted IoAT framework for Oryza Sativa disease identification and classification by Goluguri N.V. Rajareddy, Kaushik Mishra, Satish Kumar Satti, Gurpreet Singh Chhabra, Kshira Sagar Sahoo, Amir H. Gandomi

“…To boost the model's predictive accuracy, the Chaotic Honey Badger Algorithm (CHBA) is employed to optimize the CNN hyperparameters, resulting in an impressive average accuracy of 93.5 %. …”
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Preliminary Research on Intelligent Baking Room Dehydration and Drying Technology for Rice Sterile Seeds by Man Luo

“…An adaptive integral sliding mode control algorithm based on Smith prediction was proposed for intelligent baking room temperature. …”
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Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model by Dengke Xu, Ruiqin Tian, Ying Lu

“…This study introduces a partial functional linear spatial autoregressive model which can explore the relationship between a scalar spatially dependent response variable and predictive variables containing both multiple scalar covariates and a functional covariate. …”
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Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning by Jiaze Wu, Hao Liang, Changsong Ding, Xindi Huang, Jianhua Huang, Qinghua Peng

“…We have established a new algorithm with high accuracy to predict BP classification from PPG via matching of CWT type and segment length, which is a promising solution for rapid prediction of BP classification from real-time processing of PPG signal on a wearable device.…”
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Ability of verbal autopsy data to detect deaths due to uncontrolled hyperglycaemia: testing existing methods and development and validation of a novel weighted score by Justine I Davies, David Beran, Miles D Witham, Alisha N Wade, Amelia Crampin, Sarah Blackstock, Graham D Ogle

“…Our WS produced a receiver operator curve with area under the curve of 0.952 (95% CI 0.920 to 0.985). However, positive predictive value (PPV) was below 50% when the WS was applied to the development set and the score was dominated by the necessity for a premortem diagnosis of diabetes. …”
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Computed Tomography‐Based Habitat Analysis for Prognostic Stratification in Colorectal Liver Metastases by Chaoqun Zhou, Hao Xin, Lihua Qian, Yong Zhang, Jing Wang, Junpeng Luo

“…Compared with CRS and TBS, the habitat model demonstrated superior predictive accuracy, particularly for DFS and liver‐specific DFS, with higher time‐dependent AUC values and improved model calibration (lower IBS). …”
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An integrated machine learning framework for developing and validating a prognostic risk model of gastric cancer based on endoplasmic reticulum stress-associated genes by Gang Wei, Yan Wang, Ru Liu, Lei Liu

“…This risk model proved to have a good predictive performance for estimating the overall survival of these patients. …”
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Large language models for accurate disease detection in electronic health records: the examples of crystal arthropathies by Kim Lauper, Denis Mongin, Nils Bürgisser, Samia Mehouachi, Clement P. Buclin, Delphine S. Courvoisier, Etienne Chalot

“…The framework was further validated on 600 paragraphs assessing ‘Calcium Pyrophosphate Deposition Disease (CPPD)’.Results The LLM-based algorithm outperformed the regex method, achieving a 92.7% (88.7%–95.4%) positive predictive value, a 96.6% (94.6%–97.8%) negative predictive value and an accuracy of 95.4% (93.6%–96.7%) for gout. …”
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What are the implications of using individual and combined sources of routinely collected data to identify and characterise incident site-specific cancers? a concordance and valida... by Rachael Williams, Helen Strongman, Krishnan Bhaskaran

“…We calculated positive predictive values and sensitivities of each definition, compared with a gold standard algorithm that used information from all linked data sets to identify first cancers. …”
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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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Microsatellite instability in endometrial cancer: the role of diagnostic methods and their clinical application. Expert consensus by A. S. Tyulyandina, E. A. Ulrikh, L. A. Kolomiets, S. E. Krasilnikov, A. G. Kedrova, A. A. Rumyantsev, G. A. Raskin, A. I. Nesterova, M. V. Volkonskiy, O. N. Churuksaeva, A. Yu. Goryainova, V. V. Zhavoronkova, V. N. Dmitriev, S. T. Nazranova, A. V. Shkradyuk, K. S. Volkova, A. I. Arutyunova, S. N. Kunitskaya, L. V. Stepura, T. G. Zolotoreva, E. B. Shakhnovich, E. V. Ponomareva, M. A. Strokova, A. S. Danilova, E. S. Martynova

“…This review details current concepts of MSI diagnostics and discusses its predictive value in patients with endometrial cancer. …”
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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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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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