Forecasting Tunnel-Induced Ground Settlement: A Hybrid Deep Learning Approach and Traditional Statistical Techniques With Sensor Data by Syed Mujtaba Hussaine, Linlong Mu, Yimin Lu, Syed Sajid Hussain

“…Additionally, the statistical Autoregressive Integrated Moving Average (ARIMA)/Seasonal ARIMA (SARIMA) models were enhanced through seasonality removal, automated model selection using the auto_arima algorithm, and parameter fine-tuning via grid search to improve their predictive accuracy. …”
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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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Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scope by Bozhou Li, Dajiang Ju, Xingwang Li, Yan Liu, Hongru He, Hao Wang

“…With the acceleration of industrialization and the increase in the complexity of equipment, it is particularly important to accurately predict and effectively maintain equipment failures. …”
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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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Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma by Duo Wang, Duo Wang, Duo Wang, Jihao Tu, Jihao Tu, Jianfeng Liu, Jianfeng Liu, Yuting Piao, Yuting Piao, Yiming Zhao, Yiming Zhao, Ying Xiong, Ying Xiong, Jianing Wang, Jianing Wang, Xiaotian Zheng, Xiaotian Zheng, Bin Liu, Bin Liu

“…Building up a GPR-TME classifier, low GPR combined with high TME exhibited the most favorable prognosis and immunotherapeutic efficacy, which was further performed for immune infiltration, functional enrichment, somatic mutation, immunotherapy response prediction, and scRNA-seq analyses.ConclusionsOur study constructed a GPRS that can serve as a promising tool for diagnosis and prognosis prediction, targeted prevention, and personalized medicine in STS.…”
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Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer by Haojie Dai, Zijie Yu, You Zhao, Ke Jiang, Zhenyu Hang, Xin Huang, Hongxiang Ma, Li Wang, Zihao Li, Ming Wu, Jun Fan, Weiping Luo, Chao Qin, Weiwen Zhou, Jun Nie

“…Conclusion The model we developed was a powerful predictive tool for BLCA prognosis and revealed the impact of mitotic catastrophe heterogeneity on BLCA in multiple dimensions, which then guided clinical decision-making. …”
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Comparison of Random Survival Forest Based‐Overall Survival With Deep Learning and Cox Proportional Hazard Models in HER‐2‐Positive HR‐Negative Breast Cancer by Wenqi Cai, Yan Qi, Linhui Zheng, Huachao Wu, Chunqian Yang, Runze Zhang, Chaoyan Wu, Haijun Yu

“…Methods and Results This study analyzed 8,119 HER2‐positive HR‐negative breast cancer patients from the SEER database, randomly allocated to training/validation/test cohorts (7:1:2 ratio). Predictive models were developed using five feature sets and three algorithms (Cox PH, RSF, DeepSurv), with feature selection optimized via Concordance index (C‐index). …”
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Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models by Qing Zhang, Qi Li, Zhilong Yu, Ruibo Yang, Emmanuel Eric Pazo, Yue Huang, Hui Liu, Chen Zhang, Salissou Moutari, Shaozhen Zhao

“…Regression and classification models were developed using gradient boosting, random forest, and CatBoost algorithms. Regression models predicted vault height as a continuous variable, while classification models categorized vault heights into binary and multi-class tasks. …”
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The 3-level Wells score combined with D-dimer can accurately diagnose acute pulmonary embolism in hospitalized patients with acute exacerbation of COPD: A multicentre cohort study by Xiaojing Jiao, Yixiao zhang, Tuguang Kuang, Juanni Gong, Yadong Yuan, Guohua Zhen, Jifeng Li, Suqiao Yang, Jianguo He, Yuanhua Yang

“…We aim to investigate the predictive accuracy of two clinical scoring systems combined with D-dimer for APE in patients with AECOPD. …”
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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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Digital Land Suitability Assessment for Irrigated Cultivation of Some Agricultural Crops Using Machine Learning Approaches (Case Study: Qazvin-Abyek) by F. Jannati, F. Sarmadian

“…The utilization of modern mapping techniques such as digital soil mapping and machine learning algorithms can significantly improve the accuracy of land suitability assessment and crop performance prediction. …”
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Target repositioning using multi-layer networks and machine learning: The case of prostate cancer by Milan Picard, Marie-Pier Scott-Boyer, Antoine Bodein, Mickaël Leclercq, Julien Prunier, Olivier Périn, Arnaud Droit

“…Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.…”
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Facies-Controlled Modeling for Permeability of Tight Gas Reservoir Based on Hydrodynamic and Geophysics Characteristics by Peng Yu

“…Due to poor physical properties and strong heterogeneity of Daniudi tight gasfield (China), traditional methods are not ideal for predicting reservoir permeability. Based on geoscience data mining algorithms and modeling techniques, this parameter is predicted and characterized from a new perspective. …”
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DAFDM: A Discerning Deep Learning Model for Active Fire Detection Based on Landsat-8 Imagery by Xu Gao, Wenzhong Shi, Min Zhang, Lukang Wang

“…The proposed method outperforms other AF detection algorithms, achieving IoU and F1-score of 87.28% and 93.21%, respectively. …”
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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

“…It also describes a new diagnostic algorithm for the detection of dMMR and MSI.…”
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Multi-tier cooperative caching in fog radio access network by Yanxiang JIANG, Chengyu XIA

“…Aiming at the problem of reducing the load of the backward link in the edge buffer and fog wireless access network technology,a multi-tier cooperative caching scheme in F-RAN was proposed to further reduce the backhaul traffic load.In particular,by considering the network topology,content popularity prediction and link capacity,the optimization problem was decomposed into knapsack subproblems in multi-tiers,and effective greedy algorithms were proposed to solve the corresponding subproblems.Simulation results show that the proposed multi-tier cooperative caching scheme can effectively reduce the backhaul traffic and achieve relatively high cache hit rate.…”
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