Identification and analysis of individuals who deviate from their genetically-predicted phenotype. by Gareth Hawkes, Loic Yengo, Sailaja Vedantam, Eirini Marouli, Robin N Beaumont, GIANT Consortium, Jessica Tyrrell, Michael N Weedon, Joel Hirschhorn, Timothy M Frayling, Andrew R Wood

“…Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions.…”
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Credit risk prediction with corruption perception index: machine learning approaches by Cuong Nguyen Thanh, Tam Phan Huy, Tuyet Pham Hong, An Bui Nguyen Quoc

“…This study examines the impact of corruption on credit risk in Southeast Asian commercial banks by using machine learning models to predict non-performing loans (NPLs) based on the Corruption Perception Index (CPI). …”
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Lesion-specific coronary artery calcium score to predict stent underexpansion by Wentao Yang, Ke Xu, Xi Fu, Weifeng Zhang, Ziyong Hao, Zhenchi Sang, Lisheng Jiang, Xingbiao Qiu, Shengxian Tu, Linghong Shen, Ben He

“…The optimal cutoff values of lesion-specific CAC score to predict stent underexpansion were >200 in both NCCT (sensitivity, 91.4%; specificity, 66.8%) and CCTA (sensitivity, 84.6%; specificity, 64.3%) cohort, which were associated with 24.94-fold increased risk of stent underexpansion in NCCT cohort and 13.56-fold increased risk of stent underexpansion in CCTA cohort.ConclusionsIn this study, we found that lesion-specific CAC scores in both NCCT and CCTA cohorts were significantly independently associated with an increased risk of stent underexpansion, and the cutoff value to predict stent underexpansion was >200.…”
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LSTM+MA: A Time-Series Model for Predicting Pavement IRI by Tianjie Zhang, Alex Smith, Huachun Zhai, Yang Lu

“…The accurate prediction of pavement performance is essential for transportation administration or management to appropriately allocate resources road maintenance and upkeep. …”
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The Importance of the Maxillary and Mandibular Incisors in Predicting the Canines and Premolars Crown Widths by Mohammed Rafid A. Al-Khannaq, Mohammed Nahidh, Dunia Ahmed Al-Dulaimy

“…A nonsignificant difference between actual and predicted mesiodistal crown widths was discovered. …”
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A deep ensemble learning framework for glioma segmentation and grading prediction by Liang Wen, Hui Sun, Guobiao Liang, Yue Yu

Subjects: “…Segmentation and grading prediction…”
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Leveraging transformer models to predict cognitive impairment: accuracy, efficiency, and interpretability by Kai Ma, Junzhi Zhang, Xinhang Huang, Mengyang Wang

Subjects: “…Neurodegenerative disease prediction…”
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Aquatic Toxicology Notes: Predicting the Fate and Effects of Aquatic and Ditchbank Herbicides by Chris Wilson

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Predicting Insemination Outcome in Holstein Dairy Cattle using Deep Learning by Mohammad Alishahi, Mahdi Ravakhah

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Fuel Cell Output Current Prediction with a Hybrid Intelligent System by José-Luis Casteleiro-Roca, Antonio Javier Barragán, Francisca Segura, José Luis Calvo-Rolle, José Manuel Andújar

“…This hybrid model uses artificial neural networks to predict the output current of the fuel cell in a very precise way. …”
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Stomach cancer epidemic in Chinese mainland: Current trends and future predictions by Wenxuan Zhu, Wanyue Dong, Yunning Liu, Ruhai Bai, Jinjiao Li

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Stock Price Change Rate Prediction by Utilizing Social Network Activities by Shangkun Deng, Takashi Mitsubuchi, Akito Sakurai

“…Predicting stock price change rates for providing valuable information to investors is a challenging task. …”
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Prediction of Stope Stability Using Variable Weight and Unascertained Measurement Technique by Qian Kang, Yunmin Wang, Shuwen Zhang, Chengzhi Pu, Chuxuan Zhang

“…A new model is established to analyze mining stope stability, using variable weight theory to calculate the index weight for each factor in different stopes and unascertained measure evaluation technique to predict the risk grade of stope stability. In this model, an evaluation index system by virtue of the 7 most important factors is established, including rock saturated uniaxial compressive strength, rock quality designation, rock joint and fissure, stope span, condition of pillar, groundwater seepage volume, and rate of supporting pit roof. …”
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Sustainable Selection of Machine Learning Algorithm for Gender-Bias Attenuated Prediction by Raik Orbay, Evelina Wikner

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Prediction of Soil Water Characteristic Curve Based on Soil Water Evaporation by Gaoliang Tao, Da Lei, Lisheng Liu, Yi Li, Xueliang Zhu

“…The proposed methods were validated using soil water evaporation tests of Hunan sand with six dry densities at three ambient temperatures, and the results showed that good prediction performances were achieved using these two methods.…”
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Osteoid Osteoma: Can Impedance Levels in Radiofrequency Thermocoagulation Predict Recurrence? by Matthew Ockendon, Jonathan J. Gregory, Gillian L. Cribb, W. Paul Cool, D. Charles Mangham, Radhesh Lalam

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Long-Term Predictions of COVID-19 in Some Countries by the SIRD Model by Lijun Pei, Mengyu Zhang

“…The predicted results suggest that the epidemic situation in some countries is very serious. …”
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Evaluation of the PANS method for the prediction of the fluid flow in a cyclone separator by Bitar, Zaher, Uystepruyst, David, Beaubert, François, Méresse, Damien, Morin, Céline

“…Even though classic models such as RSM and LES can effectively predict such type of flow, the quality of the results and the computation cost can be discussed. …”
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Potential for Vertical Heterogeneity Prediction in Reservoir Basing on Machine Learning Methods by Hongqing Song, Shuyi Du, Ruifei Wang, Jiulong Wang, Yuhe Wang, Chenji Wei, Qipeng Liu

“…This paper presents an alternative method to predict vertical heterogeneity of the reservoir utilizing various deep neural networks basing on dynamic production data. …”
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Predicting Low-Risk Prostate Cancer from Transperineal Saturation Biopsies by Pim J. van Leeuwen, Amila Siriwardana, Monique Roobol, Francis Ting, Daan Nieboer, James Thompson, Warick Delprado, Anne-Marie Haynes, Phillip Brenner, Phillip Stricker

“…., criteria 1–5, criterion 1 stringent (Gleason score 6 + ≤5 mm total max core length PC + ≤3 mm max per core length PC) up to criterion 5 less stringent (Gleason score 6-7 with ≤5% Gleason grade 4) was analysed to assess ability of each to predict insignificant disease in RP specimens (defined as Gleason score ≤6 and total tumour volume <2.5 mL, or Gleason score 7 with ≤5% grade 4 and total tumour volume <0.7 mL). …”
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