Ensemble Learning Framework for Anomaly Detection in Autonomous Driving Systems.

The inherent limitations of individual AI models underscore the need for robust anomaly detection techniques for securing autonomous driving systems. To address these limitations, we propose a comprehensive ensemble learning framework specifically designed for anomaly detection in autonomous driving systems. We comprehensively assess the effectiveness of ensemble learning models for detecting anomalies in autonomous vehicle datasets, focusing primarily on the VeReMi and Sensor datasets.

Deep learning-based decision support system for cervical cancer identification in liquid-based cytology pap smears.

BackgroundCervical cancer is the fourth most common cause of women cancer deaths worldwide. The primary etiology of cervical cancer is the persistent infection of specific high-risk strains of the human papillomavirus. Liquid-based cytology is the established method for early detection of cervical cancer.

Preparation of dual mode spectroscopic system for testing: Analysis of sample holder and investigation of intensity noise.

Absorption spectroscopy is combined with the principle of multiple wavelengths to develop a biomedical sensing mechanism, laid by two Fibre Bragg Gratings. It is essential to incorporate a sample holder in the setup in which the substances can be tested, necessitating its complete investigation without and with the holder, in both directions. The average losses of the fibre junctions are 0.

Optimizing Open Radio Access Network Systems with LLAMA V2 for Enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and Massive Machine-Type Communications: A Framework for Efficient Network Slicing and Real-Time Resource Allocation.

This study presents an advanced framework integrating LLAMA_V2, a large language model, into Open Radio Access Network (O-RAN) systems. The focus is on efficient network slicing for various services. Sensors in IoT devices generate continuous data streams, enabling resource allocation through O-RAN's dynamic slicing and LLAMA_V2's optimization.

A Novel Hybrid XAI Solution for Autonomous Vehicles: Real-Time Interpretability Through LIME-SHAP Integration.

The rapid advancement in self-driving and autonomous vehicles (AVs) integrated with artificial intelligence (AI) technology demands not only precision but also output transparency. In this paper, we propose a novel hybrid explainable AI (XAI) framework that combines local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP). Our framework combines the precision and globality of SHAP and low computational requirements of LIME, creating a balanced approach for onboard deployment with enhanced transparency.