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    Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes by Richard Lupat, Rashindrie Perera, Sherene Loi, Jason Li

    Published 2023-01-01
    “…Here, we propose a novel deep-learning-based algorithm, Moanna, that is trained to integrate multi-omics data for predicting breast cancer subtypes. …”
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    Prediction of Sound Insulation of Sandwich Partition Panels by Means of Artificial Neural Networks by Naveen GARG, Siddharth DHRUW, Laghu GANDHI

    Published 2017-11-01
    “…The paper presents the application of Artificial Neural Networks (ANN) in predicting sound insulation through multi-layered sandwich gypsum partition panels. …”
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    An improved multi-step prediction control algorithm for urban rail hybrid energy storage system by YANG Fengping, ZHOU Mingzhi, CHENG Quan, ZHANG Yin

    Published 2022-05-01
    “…An improved multi-step prediction control algorithm for urban rail hybrid energy storage system was proposed in this paper. …”
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    Predictive channel scheduling algorithm between macro base station and micro base station group by Yinghai XIE, Ruohe YAO, Bin WU

    Published 2019-11-01
    “…A novel predictive channel scheduling algorithm was proposed for non-real-time traffic transmission between macro-base stations and micro-base stations in 5G ultra-cellular networks.First,based on the stochastic stationary process characteristics of wireless channels between stationary communication agents,a discrete channel state probability space was established for the scheduling process from the perspective of classical probability theory,and the event domain was segmented.Then,the efficient scheduling of multi-user,multi-non-real-time services was realized by probability numerical calculation of each event domain.The theoretical analysis and simulation results show that the algorithm has low computational complexity.Compared with other classical scheduling algorithms,the new algorithm can optimize traffic transmission in a longer time dimension,approximate the maximum signal-to-noise ratio algorithm in throughput performance,and increase system throughput by about 14% under heavy load.At the same time,the new algorithm is accurate.Quantitative computation achieves a self-adaption match between the expected traffic rate and the actual scheduling rate.…”
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    Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm by Jianhui He, Xinan Yue, Huijun Le, Zhipeng Ren, Weixing Wan

    Published 2020-03-01
    “…Abstract In this work, we evaluated the quasi‐realistic ionosphere forecasting capability by an ensemble Kalman filter (EnKF) ionosphere and thermosphere data assimilation algorithm. The National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model is used as the background model in the system. …”
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    A Comprehensive Review of AI Algorithms for Performance Prediction, Optimization, and Process Control in Desalination Systems by Mahmoud Ibnouf, Hadi Jaber, Hadil Abukhalifeh, Mohammed Ghazal, Mohamad Ramadan, Mohammad Alkhedher

    Published 2025-01-01
    “…This comprehensive review examines the various AI algorithms employed in desalination literature. In addition, it reviews their various applications which include performance prediction models. …”
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    Estimated ultimate recovery prediction of shale gas wells based on stacked integrated learning algorithm by Min Pang, Zheyuan Zhang, Zhaoming Zhou, Wendi Zhou, Qiong Li

    Published 2025-06-01
    “…Still, single algorithms are susceptible to outliers or feature selection in the data, leading to unstable predictions. …”
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