As the number of vehicles increases, road accidents are on the rise every day. According to the World Health Organization (WHO) survey, 1.4 million people have died, and 50 million people have been injured worldwide every year. The key cause of death is the unavailability of medical care at the accident site or the high response time in the rescue operation. A cognitive agent-based collision detection smart accident alert and rescue system will help us to minimize delays in a rescue operation that could save many lives. With the growing popularity of smart cities, intelligent transportation systems (ITS) are drawing major interest in academia and business, and are considered as a means to improve road safety in smart cities. This article proposed an intelligent accident detection and rescue system which mimics the cognitive functions of the human mind using the Internet of Things (IoTs) and the Artificial Intelligence system (AI). An IoT kit is developed that detects the accident and collects all accident-related information, such as position, pressure, gravitational force, speed, etc., and sends it to the cloud. In the cloud, once the accident is detected, a deep learning (DL) model is used to validate the output of the IoT module and activate the rescue module. Once the accident is detected by the DL module, all the closest emergency services such as the hospital, police station, mechanics, etc., are notified. Ensemble transfer learning with dynamic weights is used to minimize the false detection rate. Due to the dataset’s unavailability, a personalized dataset is generated from the various videos available on the Internet. The proposed method is validated by a comparative analysis of ResNet and InceptionResnetV2. The experiment results show that InceptionResnetV2 provides a better performance compared to ResNet with training, validation, and a test accuracy of 98%, respectively. To measure the performance of the proposed approach in the real world, it is validated on the toy car.

The studies claim that COVID-19 has positive impacts on the environment because it minimizes air pollution, water pollution, and noise pollution due to lockdown. On the contrary, COVID-19 is harming the environment due to increased medical wastage. COVID-19 has been declared a pandemic by the World Health Organization (WHO). Due to the exponential growth of COVID-19 cases people using large quantities of medical accessories to shield themselves from coronavirus, a large amount of medical wastage is produced per day. This medical wastage is a major concern for the expert because this medical waste is not adequately handled. The early detection of the COVID patient is the only solution to control this coronavirus. Several COVID detection models have been proposed in the last few months. Most of the existing models have a high false-positive rate where COVID patients are classified as healthy. To address this problem, this paper explores the positive and negative environmental consequences of COVID-19 and suggests a novel method based on artificial intelligence (AI) to identify COVID-19 disease. A comparative analysis of different previously trained models such as Visual Geometry Group Network (VGGNet-19), Residual Network (ResNet50), and Inception ResNet V2 is presented in this paper. Experimental results show that Inception_ResNet_V2 is a better choice for COVID detection. It has a minimal false-positive rate and offers 99.26% and 94% higher training and test accuracy compared to VGGNet and ResNet, respectively.

The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.