PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model by Minh Hieu Nguyen, Phi Le Nguyen, Kien Nguyen, Van An Le, Thanh-Hung Nguyen, Yusheng Ji

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Predicting Social Media Popularity With Large Language Models: Transforming Metadata Into Semantic-Enriched and Contextualized Text by Tianjian Chen, Jiang Huang, Xuetong Wu, Changcheng Shao

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Predicting habitat suitability of Illicium griffithii under climate change scenarios using an ensemble modeling approach by Anubhav Bhuyan, Amal Bawri, Bhrigu Prasad Saikia, Shilpa Baidya, Suhasini Hazarika, Bijay Thakur, Vivek Chetry, Bidya Sagar Deka, Pangkhi Bharali, Amit Prakash, Kuladip Sarma, Ashalata Devi

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Online Prediction Method for Power System Frequency Response Analysis Based on Swarm Intelligence Fusion Model by Lin Xu, Li Li, Meiying Wang, Xiangxu Wang, Yicong Li, Weidong Li, Kuanjiu Zhou

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Performance prediction of radio frequency based negative ion source using fusion neural network model by Yu Gu, Chundong Hu, Yang Li, Yuwen Yang, Yahong Xie, Qinglong Cui, Yuanzhe Zhao

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A Water Quality Prediction Model Based on Long Short-Term Memory Networks and Optimization Algorithms by Aihua Yu, Qingjie Xiao

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Modeling and Predicting Uncertainty in Tidal Turbine Power Output: A Data-Driven Time-Series Approach by Niousha Talebpour, Mohammad Ilbeigi

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Prediction of grain size in TB18 titanium alloy forgings by re-crystallization and grain growth modelling by Wei Xiang, Qineng Li, Feng Zhang, Wuhua Yuan

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Retentive Time Series: A Scalable Machine Learning Model for Traffic Prediction in Elastic Optical Networks by Faranak Khosravi, Mehdi Shadaram

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Forming limit prediction of advanced high-strength steels (AHSS) using an enhanced ductile damage model by Nguyen H. Hao

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Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer by Dawei Jiang, Zixi Chen, Zixi Chen, Hongli Du

“…Experimental membrane permeability testing is costly, and precise in silico prediction tools are scarce. In this study, we developed CPMP (https://github.com/panda1103/CPMP), a cyclic peptide membrane permeability prediction model based on the Molecular Attention Transformer (MAT) frame. …”
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Advancing early warning mechanisms: an augmented intelligence-driven model for predicting multidimensional vulnerability levels in Afghanistan by Damla Aras, Timothy Westlake

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Drone Attitude and Position Prediction via Stacked Hybrid Deep Learning Model for Massive MIMO Applications by Abdullah Al-Ahmadi

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A Novel Encoder–Decoder Model for Short-Term Multistep Wind Power Prediction by Qiushi Wang, Dekuan Wang, Yifan Li

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A Dynamic Prediction Method for Rolling Bearings Residual Life via Multi-Stage Exponential Model by Xueyan Ye, Suhua Qiao, Chen Sun, Yinjun Wang

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A Fuzzy Neural Network-Based Intelligent Prediction Model for Useful Lifespan of Lithium-Ion Batteries by Yaowei Xu, Zhenxing Hao, Wei Li, Angang Cao

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USING ARTIFICIAL NEURAL NETWORKS FOR PREDICTION MODELING IN THE PATIENTS WITH CONCURRENT HIV INFECTION AND TUBERCULOSIS IN THE PENITENTIARY SYSTEM by D. S. Ponomarev, S. A. Sterlikov, S. B. Ponomarev, E. L. Аveryanova

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Correction: Development and validation of a cardiovascular risk prediction model for Sri Lankans using machine learning. by Chamila Mettananda, Isuru Sanjeewa, Tinul Benthota Arachchi, Avishka Wijesooriya, Chiranjaya Chandrasena, Tolani Weerasinghe, Maheeka Solangaarachchige, Achila Ranasinghe, Isuru Elpitiya, Rashmi Sammandapperuma, Sujeewani Kurukulasooriya, Udaya Ranawaka, Arunasalam Pathmeswaran, Anuradhini Kasturiratne, Norihiro Kato, Rajitha Wickramasinghe, Prasanna Haddela, Janaka de Silva

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Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data by Yihang Zhou, Ruige Kong, Zhengsen Xu, Linlin Xu, Sibo Cheng

“…However, the selection of deep learning methods for wildfire prediction remains uncertain due to the lack of comparative analysis in a quantitative and explainable manner, crucial for improving prevention measures and refining models. …”
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Modeling for the Prediction and Evaluation of the Crimp Percentage of Plain Woven Fabric Based on Yarn Count and Thread Density by Kura Alemayehu Beyene, Chirato Godana Korra

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