PAD-SA: a method for predicting the turnover of scientific researchers based on ADASYN-Stacking algorithm by Tianyi Zhang, Jing Yang, Ru Liu, Qiyuan Feng

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Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

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Machine learning approaches for mapping and predicting landslide-prone areas in São Sebastião (Southeast Brazil) by Enner Alcântara, Cheila Flávia Baião, Yasmim Carvalho Guimarães, José Roberto Mantovani, José Antonio Marengo

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The efficacy of artificial intelligence in predicting mortality rate and cardiogenic shock in acute coronary syndrome patients by Lies Dina Liastuti, Syafarrani Arma Bahirah, Haifa Mayang Lestari, Yosilia Nursakina

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Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learning by Po-Ting Pan, Yamine Bouzembrak, Miguel Quemada, Bedir Tekinerdogan

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Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones by Ying Yao, Xiaohua Zhao, Chang Liu, Jian Rong, Yunlong Zhang, Zhenning Dong, Yuelong Su

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Interpretable Prediction of a Decentralized Smart Grid Based on Machine Learning and Explainable Artificial Intelligence by Ahmet Cifci

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Intelligence Types Predict Different Domains of Emotional Creativity Through Self-Assessed Creativity and Intelligence by Tatiana V. Kornilova, Maria A. Chumakova, Lydia B. Maksarova

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A Robust Framework for Bamboo Forest AGB Estimation by Integrating Geostatistical Prediction and Ensemble Learning by Lianjin Fu, Qingtai Shu, Cuifen Xia, Zeyu Li, Hailing He, Zhengying Li, Shaoyang Ma, Chaoguan Qin, Rong Wei, Qin Xiang, Xiao Zhang, Yiran Zhang, Huashi Cai

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Nomogram to predict periprosthetic joint infection after total hip arthroplasty using laboratory tests by Junzhe Lang, Zetao Dong, Boyuan Shi, Dongdong Wang, Jiandong Yuan, Lei Chen, Jianqing Gao, Anan Sun, Jiyue Huang, Zhiqiang Xue

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Out-of-distribution reject option method for dataset shift problem in early disease onset prediction by Taisei Tosaki, Eiichiro Uchino, Ryosuke Kojima, Yohei Mineharu, Yuji Okamoto, Mikio Arita, Nobuyuki Miyai, Yoshinori Tamada, Tatsuya Mikami, Koichi Murashita, Shigeyuki Nakaji, Yasushi Okuno

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Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound Analysis by Xuming Kang, Zhijun Tan, Yanfang Zhao, Lin Yao, Xiaofeng Sheng, Yingying Guo

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Maize and soybean yield prediction using machine learning methods: a systematic literature review by Ramandeep Kumar Sharma, Jasleen Kaur, Gary Feng, Yanbo Huang, Chandan Kumar, Yi Wang, Sandhir Sharma, Johnie Jenkins, Jagmandeep Dhillon

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Machine Learning‐Based Mobile Application for Predicting Posterior Canal Benign Paroxysmal Positional Vertigo by Emre Soylemez, Sait Demir, Kasım Ozacar

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Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction. by Kang Xu, Bin Pan, MingXin Zhang, Xuan Zhang, XiaoYu Hou, JingXian Yu, ZhiZhu Lu, Xiao Zeng, QingQing Jia

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Remaining useful life prediction method of centrifugal pump rolling bearings based on digital twins by ShengWen Zhou, Li Zhang, Xiaoming Yang, Ruiping Luo, BaiGang Du, Wenhui Zeng

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Incorporating subjective survival information in mortality and change in health status predictions: A Bayesian approach by Apostolos Papachristos, Dimitrios Fouskakis

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Classification prediction of load losses in power stations using machine learning multilayer stack ensemble by Bathandekile M. Boshoma, Oluwole S. Akinola, Peter Olukanmi, Peter Olukanmi

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Leveraging machine learning and molecular docking techniques to predict novel melanocortin-4 receptor agonists by Stavroula Giakoumopoulou, Paris Christodoulou, Minos-Timotheos Matsoukas, Vasilis Panagiotopoulos, Dionisis Cavouras, Eftichia Kritsi

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GPCR-A17 MAAP: mapping modulators, agonists, and antagonists to predict the next bioactive target by Ana B. Caniceiro, Ana M. B. Amorim, Nícia Rosário-Ferreira, Irina S. Moreira

“…To address the growing need for effective treatments, the GPCR-A17 Modulator, Agonist, Antagonist Predictor (MAAP) was introduced as an advanced ensemble machine learning model that combines XGBoost, Random Forest, and LightGBM to predict the functional roles of agonists, antagonists, and modulators in GPCR-A17 interactions. …”
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