PREDICTION INDONESIA COMPOSITE INDEX USING INTEGRATION DECOMPOSITION- NEURAL NETWORK ENSEMBLE DURING VUCA ERA by Imelda Saluza, Ensiwi Munarsih, Faradillah Faradillah, Leriza Desitama Anggraini

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Predicting IDH and 1p/19q molecular status of gliomas with multi-b values DWI by Shanshan Zhao, Peipei Wang, Eryuan Gao, Mengzhu Wang, Guang Yang, Shouhui Niu, Mengjiao Pan, Kai Zhao, Jingliang Cheng, Xiaoyue Ma

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Automated machine learning for predicting perioperative ischemia stroke in endovascularly treated ruptured intracranial aneurysm patients by Yuhang Peng, Ke Bi, Xiaolin Zhang, Ning Huang, Xiang Ji, Weifu Chen, Ying Ma, Yuan Cheng, Yongxiang Jiang, Jianhe Yue

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SWAT‐Based Hydrological Data Assimilation System (SWAT‐HDAS): Description and Case Application to River Basin‐Scale Hydrological Predictions by Ying Zhang, Jinliang Hou, Juan Gu, Chunlin Huang, Xin Li

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Teaching Writing In Law: A Model to Improve Student Learning by Annette Hansche

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Identification of specific risk factors and predictive analytics for cardio-cerebral arterial stenosis: a comparative study utilizing framingham risk stratification insights by Gege Zhang, Sijie Dong, Fanfan Feng, Weihao Kan, Taozhen Shi, Hongmei Ding, Ruiguo Dong

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Predicting Environmental Risks: Using Water Balances for Sustainable Power at Kusile Power Station (south Africa) by Amelia Meyer, Hendrik G. Brink

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Physical mathematical modelling of difficult elements of acrobatic rock-and-roll by V.M. Adashevskiy, S.S. Iermakov, Iu.Iu. Firsova

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The PRE‐registration podiatry: Accessible recruitment and employment (PREPARE) project: A commentary on the novel approach to the podiatry workforce crisis in Glasgow by Pamela Price, Paul Higgins, David Lambert, Diane M. Dickson, Ruth Barn

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Predicting biogas production in real scale anaerobic digester under dynamic conditions with machine learning approach by M. Erdem Isenkul, Sevgi Güneş-Durak, Yasemin Poyraz Kocak, İnci Pir, Mertol Tüfekci, Güler Türkoğlu Demirkol, Selçuk Sevgen, Aslı Seyhan Çığgın, Neşe Tüfekci

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Evaluating the potential of phenotypic age to enhance cardiovascular risk prediction over chronological age in the UK Biobank by Kristine J. S. Kwan, Shi-Shuai Xie, Hai-Lei Li, Xue-Guang Lin, Yi-Jie Lu, Bo Chen, Kai-Xin Ge, Shu-Ya Tang, Hui Zhang, Shuai Jiang, Jing-Dong Tang

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Estimation of Fuel Cell Power Demand on Commercial Vehicles Based on Improved Multiple Grey Prediction Method Considering Dynamic Time Window by Yuan Wang, Yingjia Li, Jianshan Lu, Hongbo Zhou

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Lateral Walking Gait Recognition and Hip Angle Prediction Using a Dual-Task Learning Framework by Mingxiang Luo, Meng Yin, Jinke Li, Ying Li, Worawarit Kobsiriphat, Hongliu Yu, Tiantian Xu, Xinyu Wu, Wujing Cao

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The landscape in telomere related gene prognostic signature for survival and medication treatment effectiveness prediction in hepatocellular carcinoma by Kai Zhou, Xingyu Liu, Mingda Wang, Jinjiang Duan, Xu Zhao, Hanjun Yin

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Predicting high-need high-cost pediatric hospitalized patients in China based on machine learning methods by Peng Zhang, Bifan Zhu, Xing Chen, Linan Wang

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Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome by Yaxuan He, Zekai Chen, Zhaohui Tang, Yuexiang Qin, Fang Wang

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Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale by Harsh Vazirani, Xiaofeng Wu, Anurag Srivastava, Debajyoti Dhar, Divyansh Pathak

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DeepDeconUQ estimates malignant cell fraction prediction intervals in bulk RNA-seq tissue. by Jiawei Huang, Yuxuan Du, Kevin R Kelly, Jinchi Lv, Yingying Fan, Jiang F Zhong, Fengzhu Sun

“…This study introduces DeepDeconUQ, a deep neural network model developed to estimate prediction intervals for malignant cell fractions based on bulk RNA-seq data. …”
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Transfer Learning and Neural Network-Based Approach on Structural MRI Data for Prediction and Classification of Alzheimer’s Disease by Farideh Momeni, Daryoush Shahbazi-Gahrouei, Tahereh Mahmoudi, Alireza Mehdizadeh

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Eliminating Effect of Moisture Content in Prediction of Lower Heating Value and Ash Content in Sugarcane Leaves Biomass by Kanvisit Maraphum, Kantisa Phoomwarin, Nirattisak Khongthon, Jetsada Posom

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