In this fast-moving world, accidents in four wheeled vehicles occur due to the break failure or because of the carelessness or the fatigue of the driver. The driver pattern of the driver plays a major role in providing road safety as well as in fuel consumption. The distraction of drivers is found by installing various sensors which is used for gathering real time data. The behaviour of drivers under stress condition and their behavioural patterns for early detection and avoidance of accidents are found using convolutional neural networks. Convolutional Neural Networks are efficient classifiers in handling image processing and computer vision problem. The input dataset is a collection of driving behaviour of 10 different drivers collected from Kaggle. The behaviour of drivers under 7 distracted situations like texting, talking through phone, playing music, drinking, eating, doing make up and talking to passenger are considered. The batch normalization is used at the right of the input layer in order to avoid skewing of data at a direction. It is shown, the convolutional neural networks at 4 epochs have shown 99% accuracy.
Background:
The global outbreak of COVID-19 incepted in Wuhan, China in the late 2019. It is still unclear
about the origin of the infection. Over time, it has migrated geographically to 150 countries in the world and World Health
Organization (WHO) has declared the infectious disease to be pandemic.
Objective:
Recently, COVID-19 has stepped into India by the travellers from other countries. The transmissibility and
epidemicity of COVID-19 in India is exponential. So, in-order to understand the above characteristics, specifically
COVID-19 status in India is analyzed. To analyze this into deeper, the state of Kerala is selected. The epidemiological
characteristics of patients in Kerala, South India and the possible transmission of COVID-19 from asymptomatic members
to other peers are shown using certain cases.
Methods:
The COVID-19 dataset is taken from Kaggle dataset. This dataset contains the details of the infected patients
from different states of India. Statistical analysis techniques where used to analyze the distribution of the affected cases in
a particular state.
Results:
The analysis shows that there is possibility of transmission of the infection even during incubation period. The
recent trend in the number of infected cases in India is discussed.
Conclusion:
The transmissibility of COVID-19 and its epidemicity in India is discussed. In specific, a case study on
COVID -19 cases in the state of Kerala relating the transmissibility is also summarized. Further, data related to patents on
corona virus is also discussed. From the analysis, it can be concluded that there is a possibility of COVID-19 transmission
even during incubation period. The preventive measures to overcome COVID-19 and methods to increase the immunity
are discussed.
The Internet of Healthcare Things is essential for enhancing people's protection, care, and health. Health-related criteria for patients can be remotely tracked and transferred to medical data centers via cloud storage, saving them the trip to the hospital. Additionally, the volume of data processed by Internet of Healthcare Things devices is growing exponentially. Due to the increased revelation of sensitive information, there are many unresolved issues about data security and privacy gathered through Internet of Healthcare Things devices. The requirement to apply machine learning algorithms to massive industrial data is developing as quickly as categorization methods themselves. We offer an Entangled Fuzzy Logic based Quantum Key Distribution for data protection to improve and assess the degree of security such breaches, data integrity, etc. in order to safely store and handle this information in the cloud. Results show that the proposed method enhancing the level of security to the cloud more than 99%, the works remarkably well.
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