An improved biometric stress monitoring solution for working employees using heart rate variability data and Capsule Network model.

Biometric stress monitoring has become a critical area of research in understanding and managing health problems resulting from stress. One of the fields that emerged in this area is biometric stress monitoring, which provides continuous or real-time information about different anxiety levels among people by analyzing physiological signals and behavioral data. In this paper, we propose a new approach based on the CapsNets model for continuously monitoring psychophysiological stress.

A deep learning-based approach for predicting COVID-19 diagnosis.

This paper focuses on forecasting the total count of confirmed COVID-19 cases in Saudi Arabia through a range of methodologies, including ARIMA, mathematical modeling, and deep learning network (DQN) techniques. Its primary aim is to anticipate the verified COVID-19 cases in Saudi Arabia, aiding in decision-making for life-saving interventions by enhancing awareness of COVID-19 infection. Mathematical modeling and ARIMA are employed for their efficacy in forecasting, while DQN approaches, particularly through comparative analysis, are utilized for prediction.

Correction: Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction.

[This corrects the article DOI: 10.1371/journal.pone.

Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction.

Cervical cancer remains a leading cause of female mortality, particularly in developing regions, underscoring the critical need for early detection and intervention guided by skilled medical professionals. While Pap smear images serve as valuable diagnostic tools, many available datasets for automated cervical cancer detection contain missing data, posing challenges for machine learning models' efficacy. To address these hurdles, this study presents an automated system adept at managing missing information using ADASYN characteristics, resulting in exceptional accuracy.

Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model.

Esophageal cancer (EC) is a commonly occurring malignant tumor that significantly affects human health. Earlier recognition and classification of EC or premalignant lesions can result in highly effective targeted intervention. Accurate detection and classification of distinct stages of EC provide effective precision therapy planning and improve the 5-year survival rate.