детекція

Emerging as a crucial tool for companies in many industries, emotion recognition offers a greater understanding of consumer views of goods and services, hence strengthening client connections, improving service delivery, and guiding emotionally intelligent marketing strategies.  Particularly with the use of transfer learning techniques, end-to-end image-based emotion categorization has gained popularity in the last several years.  In tackling such challenging tasks, deep learning models have shown great efficacy.  Though reading emotions from visual data presents diffic

The increasing prevalence of automated social media accounts, or Social Media Bots (SMBs), presents significant challenges in maintaining authentic online discourse and preventing disinformation campaigns on social platforms.  This research introduces a novel multiclass classification framework for detecting and categorizing SMBs, leveraging fine-tuned transformer-based models.  In this study, we conducted a comprehensive comparative analysis of various transformer variants, including BERT, DistilBERT, RoBERTa, DeBERTa, XLNet, and ALBERT, to evaluate their efficacy in r

In this work, we address the partial observation–detection problem for finite-dimensional dynamical linear systems that may not be fully observable or detectable.  We introduce the concepts of `observation–detection' and `partial observation–detection,' which involve reconstructing either the entirety or a portion of the system state and the source reacting on the system, even when the system is not fully observable or detectable.  We provide characterizations of `observable–detectable systems' and `observable–detectable spaces.'  The reconstruction of the state and sou