fine-tuning

Advanced text-based transformer architecture for malicious social bots detection

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

Models and means of clothing elements patterns classification using machine learning

The task of pattern classification remains relevant in the fields of trends, style, fashion, personalization, manufacturing, and design. Research aimed at the design and development of models and means of classification of patterns of clothing elements using machine learning is highlighted. The study addresses a pertinent issue in computer vision, namely: increasing the efficiency of classification of patterns of clothing elements. The research was conducted with a proprietary dataset comprising 600 images.