stdClass Object
(
    [id] => 15100
    [paper_index] => 202502-01-020155
    [title] => DEEP LEARNING AND BIG DATA IN MANUFACTURING: APPLICATIONS, CHALLENGES AND THE ROLE IN THE FOURTH INDUSTRIAL REVOLUTION
    [description] => 
    [author] => Dr. Rosemary Chika Nweze, Dr Inyang Unoh Nkanu, Dr Ogbonnaya Sunday Ogah, Ukamaka Victoria Maduahonwu, Dr Nneka MaryAnn Okafor
    [googlescholar] => 
    [doi] => https://doi.org/10.36713/epra20155
    [year] => 2025
    [month] => February
    [volume] => 11
    [issue] => 2
    [file] => fm/jpanel/upload/2025/February/202502-01-020155.pdf
    [abstract] => The advent of the fourth industrial revolution (industry 4.0) has sparked a transformation in manufacturing through the integration of deep learning (DL) and big data technologies. These innovations have enhanced the ability of manufacturers to process and analyze vast amount of data, providing insights that improve decision-making, efficiency, and overall productivity. Deep learning, a subset of artificial intelligence (AI), offers advanced capabilities in data pattern recognition and predictive modeling, while big data facilitates the management of large and complex datasets from various sources. However, literature on the integration of deep learning, big data and industry 4.0 is still limited in the manufacturing context. This paper provides a detailed overview of big data, deep learning and industry 4.0 in manufacturing. It explores the applications of deep learning and big data in manufacturing, highlighting their role in optimizing production processes, predictive maintenance, quality control, and supply chain management. Furthermore, the paper addresses the key challenges and solutions associated with the integration of these technologies, such as data privacy, security, computational complexity, and the need for skilled labor. 
    [keywords] => Deep learning, Industry 4.0, Supply chain management, Big data, Predictive maintenance, Artificial intelligence, ERP systems.
    [doj] => 2025-02-19
    [hit] => 
    [status] => 
    [award_status] => P
    [orderr] => 51
    [journal_id] => 1
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Multidisciplinary Research (IJMR)
    [short_code] => IJMR
    [eissn] =>  2455-3662 (Online)
    [pissn] => - --
    [home_page_wrapper] => images/products_image/11.IJMR.png
)
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