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  1. 3021

    Application Deep Learning to Predict Crypto Currency Prices and their Relationship to Market Adequacy (Applied Research Bitcoin as an Example) by M. Kh. Abdalhammed, A. M. Ghazal, H. M. Ibrahim, A. Kh. Ahmed

    Published 2022-09-01
    “…The short-term data (365 observations) is processed using the LSTM model as one of the neural network models. Modeling is conducted by training a sample size of 67%, taking into account sharp fluctuations in the price of trade and a certain level of market efficiency. …”
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    A Computational–Cognitive Model of Audio-Visual Attention in Dynamic Environments by Hamideh Yazdani, Alireza Bosaghzadeh, Reza Ebrahimpour, Fadi Dornaika

    Published 2025-05-01
    “…While integrating auditory and visual information enhances prediction accuracy, many existing models rely solely on visual-temporal data. …”
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    Assessment and Prediction of Carbon Storage Based on Land Use/Land Cover Dynamics in the Gonghe Basin by Hong Jia, Siqi Yang, Lianyou Liu, Hang Li, Zeshi Li, Yixin Chen, Jifu Liu

    Published 2024-12-01
    “…Based on the land use data of the Gonghe Basin from 1990 to 2020, the InVEST model was applied to analyze the spatiotemporal changes in carbon storage, and the PLUS model was used to predict the changes in carbon storage under three different development scenarios in 2030. …”
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    Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

    Published 2025-03-01
    “…Broccoli shelf life prediction models were evaluated using three classification methods: RF, 1D-CNN, and 2D-CNN. …”
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    High-Performance stacking ensemble learning for thermoelectric figure-of-merit prediction by Yuelin Wang, Chengquan Zhong, Jingzi Zhang, Honghao Yao, Junjie Chen, Xi Lin

    Published 2025-01-01
    “…Here we present a machine learning (ML) approach, the stacking model, that significantly improves zT prediction accuracy for doped thermoelectric. …”
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