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您的检索式：作者名="Syed Anas Ansar"
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| 1 | A Trailblazing Framework of Security Assessment for Traffic Data Management显示文摘Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior. | Abdulaziz Attaallah Khalil al-Sulbi Areej Alasiry Mehrez Marzougui Neha Yadav Syed Anas Ansar Pawan Kumar Chaurasia Alka Agrawal | 2023 | Intelligent Automation & Soft Computing2023，37，8： | 0 |
| 2 | Prediction of COVID-19 Pandemic Spread in Kingdom of Saudi Arabia显示文摘A significant increase in the number of coronavirus cases can easily be noticed in most of the countries around the world.Inspite of the consistent preventive initiatives being taken to contain the spread of this virus,the unabated increase in the cases is both alarming and intriguing.The role of mathematical models in predicting and estimating the spread of the virus,and identifying various preventive factors dependencies has been found important and effective in most of the previous pandemics like Severe Acute Respiratory Syndrome(SARS)2003.In this research work,authors have proposed the Susceptible-Infectected-Removed(SIR)model variation in order to forecast the pattern of coronavirus disease(COVID-19)spread for the upcoming eight weeks in perspective of Saudi Arabia.The study has been performed by using SIR model with a proposed simplification using average progression for further estimation ofβandγvalues for better curve fittings ratios.The predictive results of this study clearly show that under the current public health interventions,there will be an increase in the COVID-19 cases in Saudi Arabia in the next four weeks.Hence,a set of strong health primitives and precautionary measures are recommended in order to avoid and prevent the further spread of COVID-19 in Saudi Arabia. | Abdulaziz Attaallah Sabita Khatri Mohd Nadeem Syed Anas Ansar Abhishek Kumar Pandey Alka Agrawal | 2021 | Computer Systems Science & Engineering2021，37，6： | 0 |
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