A New Comprehensive Well Logging Data Method for Evaluating Fracture Reservoir Productivity and Its Application by SHEN Qinyu, LI Shengqing, CUI Yunjiang, SU Yuanda, WANG Peichun, TANG Xiaoming

“…For both scenarios, the model is able to fit the productivity of the wells and predict the production of new wells. The results of this work provide a new method for evaluating productivity of fracture reservoirs by integrating data from multidisciplinary well logging technologies.…”
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Critical evaluation of feature importance assessment in FFNN-based models for predicting Kamlet-Taft parameters by Yoshiyasu Takefuji

“…Mohan et al. developed a feed-forward neural network (FFNN) model to predict Kamlet-Taft parameters using quantum chemically derived features, achieving notable predictive accuracy. …”
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Comprehensive evaluation of machine learning models for predicting the cognitive status of Alzheimer's disease subjects and susceptible by Lucien Gnegne Meteumba, Vaghawan Prasad Ojha, Shantia Yarahmadian

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Evaluation of physiological severity scores for predicting COVID-19 disease progression: a retrospective study by Sujaree Poopipatpab, Ratchaya Weerayutwattana, Pruchwilai Nuchpramool, Piyarat Phairatwet, Tospon Lertwattanachai, Konlawij Trongtrakul

“…Therefore, this study aimed to evaluate the performance of the severity scores upon admission in predicting the progression of COVID-19 patients to a severe condition within 14 days after hospitalization. …”
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Establishment and evaluation cuproptosis-related gene signature for predicting the prognosis and immunotherapy response of hepatocellular carcinoma by Shuo Wang, Xinzi Xue, Hongyan Bai, Junwen Qi, Sujuan Fei, Bei Miao

“…Immune checkpoint, drug sensitivity, and IPS were used to evaluate immunotherapy response. The model’s predictive ability was further validated with the ICGC database and IMvigor210 cohort. …”
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Development and Evaluation of a Hypertension Prediction Model for Community-Based Screening of Sleep-Disordered Breathing by Feng T, Shan G, Hu Y, He H, Pei G, Zhou R, Ou Q

“…This study aimed to develop a hypertension prediction model tailored for primary care physicians, utilizing simple, readily available predictors derived from type IV sleep monitoring devices.Patients and Methods: Participants were recruited from communities in Guangdong Province, China, between April and May 2021. …”
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Evaluation and Comparison of the PREVENT and Pooled Cohort Equations for 10‐Year Atherosclerotic Cardiovascular Risk Prediction by Hui Zhou, Yiyi Zhang, Matt M. Zhou, Soon Kyu Choi, Kristi Reynolds, Teresa N. Harrison, Brandon K. Bellows, Andrew E. Moran, Lisandro D. Colantonio, Norrina B. Allen, Monika M. Safford, Jaejin An

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Construction and evaluation of nomogram for risk prediction of cognitive impairment in chronic obstructive pulmonary disease comorbidity by JiaFeng Luo, Wen Yang, Yang Liu, HongLian Ji, XinRan Li, Jing Bai, TieJun Liu, WeiBin Chen, Li Xiao, GuoXin Mo, JingShan Bai, CongHui Liu, WenQiang Li, AiShuang Fu, YanLei Ge

“…The aim of this study was to construct a nomogram prediction model for the risk of comorbid cognitive impairment in COPD patients and to evaluate its clinical application. …”
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Prediction of the thermophysical properties of Ag-reduced graphene oxide-water/ethylene-glycol hybrid nanofluids using different machine learning methods by Huaguang Li, Ali B.M. Ali, Rasha Abed Hussein, Narinderjit Singh Sawaran Singh, Barno Abdullaeva, Zubair Ahmad, Soheil Salahshour, Mohammadreza Baghoolizadeh, Mostafa Pirmoradian

“…This signifies that machine learning methods can be successfully employed for the prediction and optimization of hybrid nanofluid characteristics.…”
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Prediction of Auditory Performance in Cochlear Implants Using Machine Learning Methods: A Systematic Review by Beyza Demirtaş Yılmaz

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Risk Factors of Severe Disease and Methods for Clinical Outcome Prediction in Patients with COVID-19 (Review) by S. V. Sokologorskiy, A. M. Ovechkin, I. V. Khapov, M. E. Politov, E. L. Bulanova

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Data Augmentation and Deep Learning Methods for Pressure Prediction in Ignition Process of Solid Rocket Motors by Huixin Yang, Pengcheng Yu, Yan Cui, Bixuan Lou, Xiang Li

“…By comparison, the AGN-CBiLSTM method has a higher prediction accuracy with a percentage error of 3.27% between the predicted and actual data. …”
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The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia by Ilija Doknić, Mirjana Mitrović, Zoran Bukumirić, Marijana Virijević, Nikola Pantić, Nikica Sabljić, Darko Antić, Živko Bojović

“…This thesis explores the potential of data science (DS) methods for predicting venous thrombosis in patients with acute myeloid leukemia. …”
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Using Deep Learning (CNN, RNN, LSTM, GRU) methods for the prediction of Protein Secondary Structure by Ezgi Çakmak, İhsan Hakan Selvi

“…Many emerging methods, including machine learning, as well as deep learning, have been used to predict the secondary structure of proteins and comprise a crucial part of Structural Bioinformatics. …”
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Assessing the performance of 28 pathogenicity prediction methods on rare single nucleotide variants in coding regions by Jee Yeon Heo, Ju Han Kim

“…Ten evaluation metrics were employed to comprehensively assess the predictive performance of each method. …”
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IMPLEMENTATION OF BACKPROPAGATION AND HYBRID ARIMA-NN METHODS IN PREDICTING ACCURACY LEVELS OF RAINFALL IN MAKASSAR CITY by Hisyam Ihsan, Irwan Irwan, Andi Illa Erviani Nensi

“…Using a gradient descent algorithm, backpropagation adjusts synaptic weights based on the error between the network's prediction and actual training data values. In this study, a comparison was made between the Backpropagation method and Hybrid ARIMA-NN in forecasting rainfall in Makassar City. …”
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Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing by Eslam Hussein Sayed, Amerah Alabrah, Kamel Hussein Rahouma, Muhammad Zohaib, Rasha M. Badry

“…The prediction of loan defaults is crucial for banks and financial institutions due to its impact on earnings, and it also plays a significant role in shaping credit scores. …”
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General models for predicting the liquid thermal conductivity of fatty acid esters based on smart methods by Chou-Yi Hsu, Ahmad Mohsin, Ramdevsinh Jhala, Nagaraj Patil, Debasish Shit, V.K. Bupesh Raja, Manoj Kumar Ojha, Abinash Mahapatro, Deepak Gupta, Fereydoon Ranjbar

“…A general and straightforward thermal conductivity correlation was also constructed employing the Gene Expression Programming (GEP) method. Evaluations based on statistical indices and visual techniques demonstrated that all of the developed models are capable of predicting thermal conductivity with a high degree of accuracy, because the majority of their predictions fell within a ± 2 % error band. …”
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Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods by Yihao Yu, Yuqi Yang, Qian Li, Jing Yuan, Yan Zha

“…With 50 medical characteristics easily obtained, six ML algorithms were used to develop prediction models. Several evaluation parameters were used to compare the predictive performance, including the area under the receiver-operating-characteristic curve (AUC) and precision-recall (P-R) curve. …”
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LEVERAGING MACHINE LEARNING METHODS IN PREDICTING AND ANALYZING THE ASSOCIATION BETWEEN DIETARY INFLAMMATORY INDEX AND ALOPECIA by Mohammed Sarwat M Salih, Hawal Lateef Fateh, Soran Abdulkarim Pasha, Hassan M Tawfiq

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