Industry 5.0 is increasingly understood as a human-centric, resilient, and sustainable extension of digital industrial transformation, yet empirical evidence on its implementation at firm level remains limited. Existing studies have relied largely on conceptual discussion or expert-based prioritisation techniques, while the structural relationships among implementation…

We study the effects of batch normalization and spatial dropout on the performance of VGG-11, Network in Network, and GoogLeNet architectures. We incorporate these regularization techniques into each model and evaluate their impact on classification accuracy and average loss. Experimental results show that spatial dropout…

Greenwashing refers to deceptive green marketing strategies used by companies and organizations to enhance their competitive edge and generate higher profits by appealing to environmentally conscious consumers. This practice arises when the actions of these entities do not align with the claims they make regarding…

Currently, there are many JavaScript libraries on the market for calendar and event management, but most are quite simple and lack flexibility in terms of the elements we can place on the calendar. Most offer a single event type and do not always allow data…

Predictive maintenance (PdM) has become a strategic factor in modern manufacturing systems. While deep learning (DL) architectures such as Long Short-Term Memory (LSTM) models have demonstrated strong performance on large-scale industrial datasets, their effectiveness in PdM under limited data conditions remains unclear, creating an important…

The article is a comparative essay on the traditional deep learning structures, i.e., Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Gated Recurrent Unit (BiGRU) as opposed to a transformer-based model ( DistilBERT + Logistic Regression ) regarding the automated detection of…