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Showing 661 - 680 results of 31,887 for search '(predictive OR reduction) processing', query time: 0.26s Refine Results
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    AI powered blockchain framework for predictive temperature control in smart homes using wireless sensor networks and time shifted analysis by Cong Feng, Ahmed Kateb Jumaah Al-Nussairi, Mustafa Habeeb Chyad, Narinderjit Singh Sawaran Singh, Jianyong Yu, Amirfarhad Farhadi

    Published 2025-05-01
    “…The framework integrates machine learning (ML) algorithms for predictive temperature management, blockchain technology for secure data handling, and edge computing for real-time data processing, resulting in a highly efficient and secure system. …”
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    Measured Rare Voltage Sags and Clusters of Sags: Prediction Models Driven by the Intermittence Indices by G. M. Casolino, M. de Santis, L. Di Stasio, C. Noce, P. Varilone, P. Verde

    Published 2024-01-01
    “…The clusters of sags represent a stochastic process due to their time dependence; the rare satisfy the requirements for a Poisson distribution process. …”
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    BSFL biodiesel production and cost reduction: A review by Lingfeng He, Abdullah Amru Indera Luthfi, Noorashikin Md Saleh, Zahira Yaakob, Nor Sakinah Mohd Said, Peer Mohamed, Xiaoling Li

    Published 2025-06-01
    “…This manuscript provides an overview of black soldier fly larva processing and biodiesel, its production, conventional handling, and current practices. …”
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    Study on the fabrication of active CaO from steel slag of the converter and its application in CO2 adsorption process by Cheng J., Mao R., Wang F., Yao H.-W., Yang Z.-B.

    Published 2025-01-01
    “…An acetic acid leaching process was used to extract calcium components from converter steel slag, followed by evaporation and crystallization to obtain high-purity CaO material. …”
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  20. 680

    Machine-learning-driven prediction of flow curves and development of processing maps for hot-deformed Ni–Cu–Co–Ti–Ta alloy by Reliance Jain, Sandeep Jain, Sheetal Kumar Dewangan, M.R. Rahul, Sumanta Samal, Eunhyo Song, Younggeon Lee, Yongho Jeon, Krishanu Biswas, Gandham Phanikumar, Byungmin Ahn

    Published 2025-05-01
    “…To reduce experimental efforts and enhance prediction accuracy, five machine learning (ML) models random Forest (RF), XGBoost (XGB), decision tree (DT), K-Nearest neighbor (KNN), and gradient boosting (GB) were applied to predict the flow stress–strain response and construct processing maps. …”
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