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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Enhancing heart disease prediction accuracy by comparing classification models employing varied feature selection techniques by Balliu Lorena, Zanaj Blerina, Basha Gledis, Zanaj Elma, Meçe Elinda Kajo

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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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DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations by Yuyao Yan, Xinyi Chai, Jiajun Liu, Sijia Wang, Wenran Li, Tao Huang

“…Here we propose DeepMethyGene, an adaptive recursive convolutional neural network model based on ResNet that predicts gene expression using DNA methylation information. …”
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StatModPredict: A user-friendly R-Shiny interface for fitting and forecasting with statistical models. by Amanda Bleichrodt, Amelia Phan, Ruiyan Luo, Alexander Kirpich, Gerardo Chowell

“…<h4>Methods</h4>StatModPredict supports robust real-time forecasting and retrospective model analysis, including fitting, forecasting, evaluation, visualization, and comparison of results from four popular models. …”
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Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery by Yingcan Li, Yu Shen, Yezi Cai, Yulin zhang, Jiahui Gao, Lei Huang, Weinuo Si, Kai Zhou, Shan Gao, Qichao Luo

“…Abstract Identifying the active natural compounds remains a challenge for drug discovery, and new algorithms need to be developed to predict active ingredients from complex natural products. …”
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Construction of a clinically significant prostate cancer risk prediction model based on traditional diagnostic methods by Wen-Tong Ji, Yong-Kun Wang, Zhan-Yang Han, Si-Qi Wang, Yao Wang, Yao Wang

“…Subsequently, we developed an online calculator (https://jiwentong0.shinyapps.io/dynnomapp/) with six variables for biopsy optimization.ConclusionThis study incorporated the results of three traditional diagnostic methods to establish a cost-effective and highly accurate model for predicting csPCa before biopsy. …”
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MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer by Jie Lan, Heng Wang, Jing Huang, Weiyi Li, Min Ao, Wanfeng Zhang, Junhao Mu, Li Yang, Longke Ran

“…Furthermore, we embedded this model in a web application called MoLPre (https://molpre.cqmu.edu.cn/), a user‐friendly tool assisting in the metastasis prediction of cT1 solid lung cancer.…”
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A neural network based computational model to predict the output power of different types of photovoltaic cells. by WenBo Xiao, Gina Nazario, HuaMing Wu, HuaMing Zhang, Feng Cheng

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Design of a Machine Learning-Based Platform for Currency Market Prediction: A Fundamental Design Model by Kevin Gordillo-Orjuela, Paulo Alonso Gaona-García, Carlos Enrique Montenegro-Marín

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