The classification of fashion cloth images is an important and challenging task in the field of computer vision. In recent years, deep learning (DL) techniques, especially Convolutional Neural ...

Cyberbullying is a pervasive issue across all forms of media, affecting various demographics and platforms indiscriminately. From social media networks to online forums and comment sections on news ...

In this paper, we present the different neural network models for multi-label classification of microblogging data. The proposed models are based on convolutional neural network (CNN) architectures, ...

Artificial Intelligence has greatly influenced healthcare, most particularly in medical imaging. This paper represents a review in large form that classifies fetal ultrasound images with the use of ...

News text classification is crucial for efficient information acquisition and dissemination. While deep learning models, such as BERT and BiGRU, excel in accuracy for text classification, their high ...

Test-time adaptation approaches have recently emerged as a practical solution for handling domain shift without access to the source domain data. In this paper, we propose and explore a new ...

The paper presents the result of comparative analysis of the main approaches to multi-class classification, synthesis of their mathematical models based on the considered algorithms is carried out.

Traditional brain tumor diagnosis and classification are time-consuming and heavily reliant on radiologist expertise. The ever-growing patient population generates vast data, rendering existing ...

Network Traffic Classifier is a robust, production-ready solution for automated classification of network traffic into seven distinct application categories. Leveraging Python, Flask, and scikit-learn ...

Boosting is a simple and effective procedure that combines several weak learners with the aim of generating a strong classifier. Multi-class boosting has been only recently studied in the context of ...

Heart arrhythmia detection is critical for diagnosing and managing cardiovascular diseases. Traditional methods for real-time ECG signal analysis, such as featurebased approaches and template matching ...

Addressing the complex challenges inherent in the automated analysis of spine X-rays, our research introduces DeepSpine, a deep learning model designed for multi-class classification of diverse spine ...