Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model.

Enhancement of security, personalization, and safety in advanced transportation systems depends on driver identification. In this context, this work suggests a new method to find drivers by means of a Random Forest model optimized using the osprey optimization algorithm (OOA) for feature selection and the salp swarm optimization (SSO) for hyperparameter tuning based on driving behavior. The proposed model achieves an accuracy of 92%, a precision of 91%, a recall of 93%, and an F1-score of 92%, significantly outperforming traditional machine learning models such as XGBoost, CatBoost, and Support Vector Machines.

Effectiveness of ChatGPT in facilitating learning for students with special educational needs: An empirical study in Saudi Arabia.

Study Purpose: This paper aims to explore the effectiveness of ChatGPT in facilitating learning for medical students with special educational needs (SEN) while acknowledging and addressing the challenges that SEN students may encounter in utilizing this technology.

Methods: This cross-sectional survey study assessed ChatGPT's efficacy in supporting medical students with SEN across three Saudi Arabian universities. Utilizing purposive and convenience sampling, a questionnaire was administered to 283 SEN students.

Energy-Efficient and Trust-Based Autonomous Underwater Vehicle Scheme for 6G-Enabled Internet of Underwater Things.

This paper introduces a novel energy-efficient lightweight, void hole avoidance, localization, and trust-based scheme, termed as Energy-Efficient and Trust-based Autonomous Underwater Vehicle (EETAUV) protocol designed for 6G-enabled underwater acoustic sensor networks (UASNs). The proposed scheme addresses key challenges in UASNs, such as energy consumption, network stability, and data security. It integrates a trust management framework that enhances communication security through node identification and verification mechanisms utilizing normal and phantom nodes.

Synthetic approaches for novel fused pyrimidine derivatives: Design, structural characterization, antiviral, antitumor, and molecular docking evaluation.

The goal of this work was to synthesize new compounds for anticancer evaluation as a trial to obtain new antitumor agents with higher activity and fewer side effects. Therefore, the precursor 2,2'-(1,4-phenylenebis (thiazole-4,2-diyl))bis (3-(dimethylamino)acrylonitrile) was used to synthesize various azolopyrimidine derivatives connected to the thiazole moiety. Compounds -, including pyrazolopyrimidine, triazolopyrimidine, and others, were produced by reacting enaminonitrile with different -nucleophiles.

Advance drought prediction through rainfall forecasting with hybrid deep learning model.

Drought is a natural disaster that can affect a larger area over time. Damage caused by the drought can only be reduced through its accurate prediction. In this context, we proposed a hybrid stacked model for rainfall prediction, which is crucial for effective drought forecasting and management.