The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to st...The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.展开更多
A device,that is used for biomedical operation or safety-critical applications like point-of-care health asssment,massive parallel DNA analysis,automated drug discovery,air-quality monitoring and food-safety testing,m...A device,that is used for biomedical operation or safety-critical applications like point-of-care health asssment,massive parallel DNA analysis,automated drug discovery,air-quality monitoring and food-safety testing,must have the attributes like relia bility,dependability and correctness.As the biochips are used for these purposes;therefore,these devices must be fault free all the time.Naturally before usi ng these chips,they must be well tested.We are proposing a novel technique that can detect mutiple fults,locate the fault positions within the biochip,as well as calculate the traversal time if the biochip is fault free.The proposed technique also highlights a new idea how to select the appropriate base node or pseudo source(start electrode).The main idea of the proposed technique is to form multiple loops with the neighboring electrode arrays and then test each loop by traversing test droplet to check whether there is any fault.If a fault is detected then the propoed technique also locates it by backtracking the test droplet.In case,no fault is detected,the biochip is fault free then the proposed technique also calculates the time to traverse the chip.The result suggests that the proposed technique is eficient and shows significant improvement to ca lculate fault-free biochip traversal time over existing method.展开更多
文摘The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.
文摘A device,that is used for biomedical operation or safety-critical applications like point-of-care health asssment,massive parallel DNA analysis,automated drug discovery,air-quality monitoring and food-safety testing,must have the attributes like relia bility,dependability and correctness.As the biochips are used for these purposes;therefore,these devices must be fault free all the time.Naturally before usi ng these chips,they must be well tested.We are proposing a novel technique that can detect mutiple fults,locate the fault positions within the biochip,as well as calculate the traversal time if the biochip is fault free.The proposed technique also highlights a new idea how to select the appropriate base node or pseudo source(start electrode).The main idea of the proposed technique is to form multiple loops with the neighboring electrode arrays and then test each loop by traversing test droplet to check whether there is any fault.If a fault is detected then the propoed technique also locates it by backtracking the test droplet.In case,no fault is detected,the biochip is fault free then the proposed technique also calculates the time to traverse the chip.The result suggests that the proposed technique is eficient and shows significant improvement to ca lculate fault-free biochip traversal time over existing method.
