Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses by Masashi Hayakawa, Shinji Hirooka, Koichiro Michimoto, Stelios M. Potirakis, Yasuhide Hobara

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Fully automatic reconstruction of prostate high-dose-rate brachytherapy interstitial needles using two-phase deep learning-based segmentation and object tracking algorithms by Mohammad Mahdi Moradi, Zahra Siavashpour, Soheib Takhtardeshir, Eman Showkatian, Ramin Jaberi, Reza Ghaderi, Bahram Mofid, Farzad Taghizadeh-Hesary

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Quantifying the spatial patterns of retinal ganglion cell loss and progression in optic neuropathy by applying a deep learning variational autoencoder approach to optical coherence tomography by Jui-Kai Wang, Jui-Kai Wang, Brett A. Johnson, Zhi Chen, Zhi Chen, Honghai Zhang, Honghai Zhang, David Szanto, Brian Woods, Brian Woods, Michael Wall, Young H. Kwon, Young H. Kwon, Edward F. Linton, Edward F. Linton, Andrew Pouw, Mark J. Kupersmith, Mark J. Kupersmith, Mark J. Kupersmith, Mona K. Garvin, Mona K. Garvin, Mona K. Garvin, Mona K. Garvin, Randy H. Kardon, Randy H. Kardon

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Using machine learning to forecast peak health care service demand in real-time during the 2022-23 winter season: A pilot in England, UK. by Roger A Morbey, Dan Todkill, Phil Moura, Liam Tollinton, Andre Charlett, Conall Watson, Alex J Elliot

“…We utilised machine learning techniques to train and select models that would best forecast the timing and intensity of peaks up to 28 days ahead. …”
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Retracted: Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis by Journal of Food Quality

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Penerapan Hidden Markov Model (HMM) dan Mel-Frequency Cesptral Coefficients (MFCC) pada E-Learning Bahasa Madura untuk Anak Usia Dini by Ubaidi Ubaidi, Nindian Puspa Dewi

“…There hasn’t been any interesting learning media to learn Bahasa Madura so far. In fact, a fun learning activity is needed to help children to enhance their ability in pronouncing animals’ names, numbers, fruits and things in Bahasa Madura. …”
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Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditions by Seda Günaydın, Necati Çetin, Cevdet Sağlam, Kamil Sacilik, Ahmad Jahanbakhshi

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Transition Guide Dissemination to Foster Patient–Care Team Conversations: A Childhood Arthritis Rheumatology Research Alliance Transition Learning Collaborative Pilot Implementation Study by Joyce C. Chang, Gabrielle Alonzi, Cora Sears, Nicole Bitencourt, Alex Hernandez, Rosemary Peterson, Risa Alperin, Rebecca S. Overbury, Mariel dela Paz, Lindsay N. Waqar‐Cowles, Patience H. White, Kristine Carandang, Erica F. Lawson, for the CARRA Transition Workgroup

“…Objective Uptake of evidence‐informed health care transition processes among pediatric rheumatologists is low despite poor outcomes of transition from pediatric to adult care. We piloted a learning collaborative model to implement transition guides. …”
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Harnessing Machine Learning and Nomogram Models to Aid in Predicting Progression-Free Survival for Gastric Cancer Patients Post-Gastrectomy with Deficient Mismatch Repair(dMMR) by Yifan Li, JinFeng Ma, Wenhua Cheng

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Corrigendum for “Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort study” by Fajuan Rui, Jie Li

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Ionospheric Response to the 24–27 February 2023 Solar Flare and Geomagnetic Storms Over the European Region Using a Machine Learning–Based Tomographic Technique by Ting Li, Dunyong Zheng, Changyong He, Fei Ye, Pengfei Yuan, Yibin Yao, Mengguang Liao, Jian Xie

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Machine learning identification of a novel vasculogenic mimicry-related signature and FOXM1’s role in promoting vasculogenic mimicry in clear cell renal cell carcinoma by Chao Xu, Sujing Zhang, Jingwei Lv, Yilong Cao, Yao Chen, Hao Sun, Shengtao Dai, Bowei Zhang, Meng Zhu, Yuepeng Liu, Junfei Gu

Subjects: “…Clear cell renal cell carcinoma;Vasculogenic mimicry;Machine learning;FOXM1;Tumor microenvironment;Prognosis;Therapeutic targets…”
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Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors by Linan Yin, Ruibao Liu, Wei Li, Shijie Li, Xunbo Hou

Subjects: “…Deep learning model…”
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Predicting Factors Affecting Survival Rate in Patients Undergoing On‐Pump Coronary Artery Bypass Graft Surgery Using Machine Learning Methods: A Systematic Review by Alireza Jafarkhani, Behzad Imani, Soheila Saeedi, Amir Shams

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Integrating Technology Acceptance Model With UTAUT to Increase the Explanatory Power of the Effect of HCI on Students’ Intention to Use E-Learning System and Perceive Success by Fareed Al-Sayid, Gokhan Kirkil

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PENGARUH MODUL AJAR IPA BERBASIS PROBLEM BASED LEARNING TERHADAP KEMAMPUAN PEMECAHAN MASALAH SISWA KELAS IX SMP NEGERI 4 MANDREHE UTARA by Lusia Zebua, Agnes Renostini Harefa, Desman Telaumbanua, Toroziduhu Waruwu

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MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort study by Yiru Wang, Fuli Chen, Zhechen Ouyang, Siyi He, Xinling Qin, Xian Liang, Weimei Huang, Rensheng Wang, Kai Hu

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Index-based livestock insurance schemes to manage climate risks in Ethiopia: determinants of farmer’s willingness to pay and lessons learned from Dasenech district, South Omo by Tagesse Melketo, Degefa Tolossa, Meskerem Abi, Sisay Bedeke, Tenaw Fentaw

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Globalization vs. Glocalization: Learn Lessons from Two Global Crises, Such as the Russia–Ukraine Conflict and the COVID-19 Pandemic, for the Agro-Food and Agro-Industrial Sector by Tomas Gabriel Bas

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UPAYA MENINGKATKAN MOTIVASI BELAJAR DAN KEMAMPUAN PEMECAHAN MASALAH SISWA MELALUI MODEL PROBLEM BASED LEARNING BERBANTU ALAT PERAGA KONSEP GERAK LURUS by Budi Santoso, Desy Hanisa Putri, Rosane Medriati

“…Student learning motivation before participating in the learning process was in the low category with the average height of 71.73 and after the learning process the average score of students' learning motivation increased to 77.32 which is in the high category. …”
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