Vision-language models (VLMs) have demonstrated significant potential in various domains, including natural language processing and computer vision. However, their application in medical imaging, particularly in X-ray diagnosis, remains underexplored. This review article examines the current state of VLMs in the context of X-ray diagnosis, focusing on their effectiveness, challenges, and potential for expandability. We discuss various models, including Knowledge-enhanced Auto Diagnosis (KAD) and BERTHop, and their performance on different datasets. Additionally, we explore the methods used for integrating domain-specific knowledge into these models and the implications for clinical practice. The review concludes with a discussion on future directions and the potential for VLMs to revolutionize medical imaging diagnostics.

Drowsy driving is a huge risk to road safety, resulting in serious accidents. Drowsiness detection in drivers can assist prevent accidents by providing timely alarms and actions. In this research, we look at how deep learning Convolutional Neural Networks (CNNs) can be used to identify tiredness. We discuss our technique, experimental results, and conclusions about the efficacy of deep learning CNNs in solving this essential road safety issue. In this work, we employed two convolutional neural networks, one is built from scratch and another one that is a pre-trained model (ResNet). Our findings show that the pre-trained outperformed the one built from scratch achieving an accuracy of 95.6%.

A new novel approach to control the autonomous mobile robot that moved along a collision free trajectory until it reaches its target is proposed in this study. The development of autonomous mobile robots requires sophisticated navigation systems that enable them to operate efficiently in dynamic and unstructured environments. This paper presents a novel mobile robot navigation system leveraging a neuro-fuzzy rule-based optimization technique. The proposed system integrates neuro-fuzzy logic with optimization algorithms to enhance the robot’s navigation capabilities, making it adept at obstacle avoidance, path planning, and adaptive learning. Experimental results demonstrate that the proposed system significantly improves navigation performance compared to traditional methods.

The field of artificial intelligence (AI) and machine learning (ML) has seen remarkable advances with the advent of neural networks. This paper presents a comprehensive comparison between Convolutional Neural Networks (CNNs) and traditional Backpropagation Neural Networks (BPNNs). By exploring the underlying mechanisms, performance metrics, and applications of both types of neural networks, we aim to highlight their respective strengths and limitations. The study provides a detailed examination of the architecture, training processes, and suitability for various tasks, supported by recent empirical research and theoretical analysis.