Car accidents remain a leading cause of unintentional fatalities, with many incidents stemming from driver behaviors that impact vehicle control, such as steering, braking, accelerating, and gear shifting. Activities like searching for items, using mobile devices, or listening to the radio can distract drivers visually, audibly, and physically, posing significant risks to road safety. While various methods have been developed to detect such distractions, their effectiveness often falls short in real-world applications. This paper introduces a novel approach that combines machine learning (ML) and deep learning (DL) techniques to identify both safe and risky driving behaviors. Six ML classifiers were evaluated on real-world data to distinguish between driving behaviors such as aggressive, fatigued, and normal driving, with the Random Forest classifier demonstrating superior performance. Additionally, a specialized deep-learning baseline model was developed using ResNet50 and EfficientNetB6 to classify driving-related images into distinct categories. The hybrid model integrates ML for analyzing tabular data and DL for image recognition, achieving a classification accuracy of 99.3% on the UAH-Drive dataset. Deep learning experiments further revealed that the Base Model outperformed other models, achieving accuracies of 99.32% on the UAH-Drive dataset and 99.87% on the SFD3 dataset. This research presents a robust hybrid ML-DL framework for detecting abnormal driving behaviors, addressing shortcomings of existing techniques in real-world conditions, and offering valuable insights for improving road safety and reducing accidents.

COVID-19 is a severe respiratory contagious disease that has now spread all over the world. COVID-19 has terribly impacted public health, daily lives and the global economy. Although some developed countries have advanced well in detecting and bearing this coronavirus, most developing countries are having difficulty in detecting COVID-19 cases for the mass population. In many countries, there is a scarcity of COVID-19 testing kits and other resources due to the increasing rate of COVID-19 infections. Therefore, this deficit of testing resources and the increasing figure of daily cases encouraged us to improve a deep learning model to aid clinicians, radiologists and provide timely assistance to patients. In this article, an efficient deep learning-based model to detect COVID-19 cases that utilizes a chest X-ray images dataset has been proposed and investigated. The proposed model is developed based on ResNet50V2 architecture. The base architecture of ResNet50V2 is concatenated with six extra layers to make the model more robust and efficient. Finally, a Grad-CAM-based discriminative localization is used to readily interpret the detection of radiological images. Two datasets were gathered from different sources that are publicly available with class labels: normal, confirmed COVID-19, bacterial pneumonia and viral pneumonia cases. Our proposed model obtained a comprehensive accuracy of 99.51% for four-class cases (COVID-19/normal/bacterial pneumonia/viral pneumonia) on Dataset-2, 96.52% for the cases with three classes (normal/ COVID-19/bacterial pneumonia) and 99.13% for the cases with two classes (COVID-19/normal) on Dataset-1. The accuracy level of the proposed model might motivate radiologists to rapidly detect and diagnose COVID-19 cases.