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Dr. G. G. Md. Nawaz Ali
Assistant Professor

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Research Keywords & Expertise

0 Broadcasting
0 Network Coding
0 Wireless Communication and Networks
0 Connected and Autonomous Vehicles
0 V2X communication

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Journal article
Published: 27 August 2021 in Healthcare
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There is a compelling and pressing need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February and late March 2021 shows that, despite the overall strength of positive sentiment and despite the increasing numbers of Americans being fully vaccinated, negative sentiment towards COVID-19 vaccines still persists among segments of people who are hesitant towards the vaccine. In this study, we perform sentiment analytics on vaccine tweets, monitor changes in public sentiment over time, contrast vaccination sentiment scores with actual vaccination data from the US CDC and the Household Pulse Survey (HPS), explore the influence of maturity of Twitter user-accounts and generate geographic mapping of tweet sentiments. We observe that fear sentiment remained unchanged in populous states, whereas trust sentiment declined slightly in these same states. Changes in sentiments were more notable among less populous states in the central sections of the US. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework, which was developed for COVID-19 sentiment analytics, to systematically posit implications for public policy processes with the aim of improving the positioning, messaging, and administration of vaccines. These insights are expected to contribute to policies that can expedite the vaccination program and move the nation closer to the cherished herd immunity goal.

ACS Style

G. G. Md. Nawaz Ali; Mokhlesur Rahman; Amjad Hossain; Shahinoor Rahman; Kamal Chandra Paul; Jean-Claude Thill; Jim Samuel. Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics. Healthcare 2021, 9, 1110 .

AMA Style

G. G. Md. Nawaz Ali, Mokhlesur Rahman, Amjad Hossain, Shahinoor Rahman, Kamal Chandra Paul, Jean-Claude Thill, Jim Samuel. Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics. Healthcare. 2021; 9 (9):1110.

Chicago/Turabian Style

G. G. Md. Nawaz Ali; Mokhlesur Rahman; Amjad Hossain; Shahinoor Rahman; Kamal Chandra Paul; Jean-Claude Thill; Jim Samuel. 2021. "Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics." Healthcare 9, no. 9: 1110.

Preprint
Published: 19 May 2021
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There exists a compelling need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February of 2021 and late March of 2021 shows that in spite of overall strength of positive sentiment, and increasing numbers of Americans being fully vaccinated, negative sentiment about COVID-19 vaccines still persists among sections of people who are hesitant towards the vaccine. In this study, we performed sentiment analytics on vaccine tweets, studied changes in public sentiment over time, conducted vaccination sentiment validation using actual vaccination data from the US CDC and Household Pulse Survey (HPS), explored influence of maturity of Twitter user-accounts and generated geographic mapping of sentiments by location of Twitter users. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework which was developed for COVID-19 sentiment analytics, to systematically analyze directions for public policy processes to potentially improve the administration of vaccines. Application of the PSS framework provides important time sensitive insights for state and federal government agencies and associated organizations to better implement public policy processes for healthcare management, communication, transparency, motivation and societal operational policies such as social distancing. These insights are expected to contribute to processes that can expedite the vaccination program and move closer to the cherished herd immunity goal.

ACS Style

G. G. Md. Nawaz Ali; Mokhlesur Rahman; Amjad Hossain; Shahinoor Rahman; Kamal Chandra Paul; Jean-Claude Thill; Jim Samuel. Public Perceptions about COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics. 2021, 1 .

AMA Style

G. G. Md. Nawaz Ali, Mokhlesur Rahman, Amjad Hossain, Shahinoor Rahman, Kamal Chandra Paul, Jean-Claude Thill, Jim Samuel. Public Perceptions about COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics. . 2021; ():1.

Chicago/Turabian Style

G. G. Md. Nawaz Ali; Mokhlesur Rahman; Amjad Hossain; Shahinoor Rahman; Kamal Chandra Paul; Jean-Claude Thill; Jim Samuel. 2021. "Public Perceptions about COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics." , no. : 1.

Review
Published: 11 May 2021 in IEEE Access
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The ongoing COVID-19 global pandemic is touching every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and non-pharmaceutical interventions of lockdown and confinement implemented citywide, regionally or nationally are affecting virus transmission, people’s travel patterns, and air quality. Many studies have been conducted to predict the diffusion of the COVID-19 disease, assess the impacts of the pandemic on human mobility and on air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This literature review aims to analyze the results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people’s socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 viral transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also explores the spatio-temporal aspects of lockdown and confinement measures on coronavirus diffusion, human mobility, and air quality. Additionally, we discuss policy implications, which will be helpful for policy makers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

ACS Style

Mokhlesur Rahman; Kamal Chandra Paul; Amjad Hossain; G. G. Md. Nawaz Ali; Shahinoor Rahman; Jean-Claude Thill. Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review. IEEE Access 2021, 9, 72420 -72450.

AMA Style

Mokhlesur Rahman, Kamal Chandra Paul, Amjad Hossain, G. G. Md. Nawaz Ali, Shahinoor Rahman, Jean-Claude Thill. Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review. IEEE Access. 2021; 9 (99):72420-72450.

Chicago/Turabian Style

Mokhlesur Rahman; Kamal Chandra Paul; Amjad Hossain; G. G. Md. Nawaz Ali; Shahinoor Rahman; Jean-Claude Thill. 2021. "Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review." IEEE Access 9, no. 99: 72420-72450.

Journal article
Published: 26 March 2021 in IEEE Transactions on Intelligent Transportation Systems
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This paper presents and evaluates a unified cooperative perception framework that employs vehicle-to-vehicle (V2V) connectivity. At the core of the framework is a decentralized data association and fusion process that is scalable with respect to participation variances. The evaluation considers the effects of the communication losses in the ad-hoc V2V network and the random vehicle motions in traffic by adopting existing models along with a simplified algorithm for individual vehicle's on-board sensor field of view. Furthermore, a multi-target perception metric is adopted to evaluate both the errors in the estimation of the motion states of vehicles in the surrounding traffic and the cardinality of the fused estimates at each participating node/vehicle. The extensive analysis results demonstrate that the proposed approach minimizes the perception metric for a much larger percentage of the participating vehicles than a baseline approach, even at modest participation rates, and that there are diminishing returns in these benefits. The computational and data traffic trade-offs are also analyzed.

ACS Style

DoHyun Daniel Yoon; Beshah Ayalew; G. G. Md. Nawaz Ali. Performance of Decentralized Cooperative Perception in V2V Connected Traffic. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -14.

AMA Style

DoHyun Daniel Yoon, Beshah Ayalew, G. G. Md. Nawaz Ali. Performance of Decentralized Cooperative Perception in V2V Connected Traffic. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-14.

Chicago/Turabian Style

DoHyun Daniel Yoon; Beshah Ayalew; G. G. Md. Nawaz Ali. 2021. "Performance of Decentralized Cooperative Perception in V2V Connected Traffic." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-14.

Journal article
Published: 06 February 2021 in Heliyon
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Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel patterns due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 50 States of the US and Washington D.C, the capital city of the US. State-wide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of family members and high income are less interested in reopening the economy. The accuracy of the model is reasonable (i.e., the model can correctly classify 56.18% of the sentiments). The Pearson chi-squared test indicates that this model has high goodness-of-fit. This study provides clear insights for public and corporate policymakers on potential areas to allocate resources, and directional guidance on potential policy options they can undertake to improve socioeconomic conditions, to mitigate the impact of pandemic in the current situation, and in the future as well.

ACS Style

Mokhlesur Rahman; G.G.Md. Nawaz Ali; Xue Jun Li; Jim Samuel; Kamal Chandra Paul; Peter H.J. Chong; Michael Yakubov. Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data. Heliyon 2021, 7, e06200 -e06200.

AMA Style

Mokhlesur Rahman, G.G.Md. Nawaz Ali, Xue Jun Li, Jim Samuel, Kamal Chandra Paul, Peter H.J. Chong, Michael Yakubov. Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data. Heliyon. 2021; 7 (2):e06200-e06200.

Chicago/Turabian Style

Mokhlesur Rahman; G.G.Md. Nawaz Ali; Xue Jun Li; Jim Samuel; Kamal Chandra Paul; Peter H.J. Chong; Michael Yakubov. 2021. "Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data." Heliyon 7, no. 2: e06200-e06200.

Article
Published: 18 November 2020 in Mobile Networks and Applications
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This paper describes a new comparison-based model for fault diagnosis in wireless ad hoc networks. Fault diagnosis is crucial for ensuring the dependability of systems. Wireless ad hoc networks are highly prone to faults as consequence of their dynamical conditions. The comparison approach is a practical diagnosis model that has been used to develop self-diagnosis systems in wired and wireless networks. This approach can detect and diagnose hard and soft faults in systems. The traditional fault diagnostic models were designed for static networks. Thus, they cannot provide complete and correct fault diagnosis in mobile wireless networks. In this paper, we introduce a time-free self-diagnosis model that respects the design requirements of mobile wireless networks. That is, it adapts to the topology’s changes, it imposes no known bounds on time delays, and it requires limited network information. Further, we develop a fault diagnosis protocol that can correctly diagnose faulty nodes undergoing static and dynamic faults in mobile ad-hoc networks (MANETs). Both an analytical model and a simulation study have been used to prove and evaluate the efficiency of our protocol under various scenarios. Furthermore, the performance of our protocol is compared with related protocols. The results show that our proposed protocol is efficient in terms of communication and time complexity.

ACS Style

Hazim Jarrah; G. G. Md. Nawaz Ali; Arun Kumar; Peter H. J. Chong; Nurul I. Sarkar; Jairo Gutierrez. The Time-Free Comparison Model for Fault Diagnosis in Wireless Ad Hoc Networks. Mobile Networks and Applications 2020, 1 -14.

AMA Style

Hazim Jarrah, G. G. Md. Nawaz Ali, Arun Kumar, Peter H. J. Chong, Nurul I. Sarkar, Jairo Gutierrez. The Time-Free Comparison Model for Fault Diagnosis in Wireless Ad Hoc Networks. Mobile Networks and Applications. 2020; ():1-14.

Chicago/Turabian Style

Hazim Jarrah; G. G. Md. Nawaz Ali; Arun Kumar; Peter H. J. Chong; Nurul I. Sarkar; Jairo Gutierrez. 2020. "The Time-Free Comparison Model for Fault Diagnosis in Wireless Ad Hoc Networks." Mobile Networks and Applications , no. : 1-14.

Journal article
Published: 04 September 2020 in IEEE Transactions on Intelligent Transportation Systems
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Vehicular networks, an enabling technology for Intelligent Transportation System (ITS), smart cities, and autonomous driving, can deliver numerous on-board data services, e.g., road-safety, easy navigation, traffic efficiency, comfort driving, infotainment, etc. Providing satisfactory Quality of Service (QoS) in vehicular networks, however, is a challenging task due to a number of limiting factors such as erroneous and congested wireless channels (due to high mobility or uncoordinated channel-access), increasingly fragmented and congested spectrum, hardware imperfections, and anticipated growth of vehicular communication devices. Therefore, it will be critical to allocate and utilize the available wireless network resources in an ultra-efficient manner. In this paper, we present a comprehensive survey on resource allocation schemes for the two dominant vehicular network technologies, e.g. Dedicated Short Range Communications (DSRC) and cellular based vehicular networks. We discuss the challenges and opportunities for resource allocations in modern vehicular networks and outline a number of promising future research directions.

ACS Style

Noor- A- Rahim; Zilong Liu; Haeyoung Lee; G. G. Md. Nawaz Ali; Dirk Pesch; Pei Xiao. A Survey on Resource Allocation in Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -21.

AMA Style

Noor- A- Rahim, Zilong Liu, Haeyoung Lee, G. G. Md. Nawaz Ali, Dirk Pesch, Pei Xiao. A Survey on Resource Allocation in Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-21.

Chicago/Turabian Style

Noor- A- Rahim; Zilong Liu; Haeyoung Lee; G. G. Md. Nawaz Ali; Dirk Pesch; Pei Xiao. 2020. "A Survey on Resource Allocation in Vehicular Networks." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-21.

Journal article
Published: 03 August 2020 in IEEE Access
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The Coronavirus pandemic has created complex challenges and adverse circumstances. This research identifies public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential public sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research focuses on emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, timealigned to the COVID-19 reopening debate, to identify dominant sentiment trends associated with the push to reopen the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of textual data analytics, including textual data visualization and statistical validation, that tweets data from American Twitter users shows more positive sentiment support, than negative, for reopening the US economy. This research develops a novel sentiment polarity based public sentiment scenarios (PSS) framework, which will remain useful for future crises analysis, well beyond COVID-19. With additional validation, this research stream could present valuable time sensitive opportunities for state governments, the federal government, corporations and societal leaders to guide local and regional communities, and the nation into a successful new normal future.

ACS Style

Jim Samuel; Mokhlesur Rahman; G. G. Md. Nawaz Ali; Yana Samuel; Alexander Pelaez; Peter Han Joo Chong; Michael Yakubov. Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics. IEEE Access 2020, 8, 142173 -142190.

AMA Style

Jim Samuel, Mokhlesur Rahman, G. G. Md. Nawaz Ali, Yana Samuel, Alexander Pelaez, Peter Han Joo Chong, Michael Yakubov. Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics. IEEE Access. 2020; 8 (99):142173-142190.

Chicago/Turabian Style

Jim Samuel; Mokhlesur Rahman; G. G. Md. Nawaz Ali; Yana Samuel; Alexander Pelaez; Peter Han Joo Chong; Michael Yakubov. 2020. "Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics." IEEE Access 8, no. 99: 142173-142190.

Other
Published: 02 July 2020
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Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among the researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel pattern due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 51 states including Washington DC of the US. State-wide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of members and high income are less interested to reopen the economy. The accuracy of the model is good (i.e., the model can correctly classify 56.18% of the sentiments). The Pearson chi2 test indicates that overall this model has high goodness-of-fit. This study provides a clear indication to the policymakers where to allocate resources and what policy options they can undertake to improve the socioeconomic situations of the people and mitigate the impacts of pandemics in the current situation and as well as in the future.

ACS Style

Mokhlesur Rahman; G. G. Md. Nawaz Ali; Xue Jun Li; Kamal Chandra Paul; Peter H.J. Chong. Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment. 2020, 1 .

AMA Style

Mokhlesur Rahman, G. G. Md. Nawaz Ali, Xue Jun Li, Kamal Chandra Paul, Peter H.J. Chong. Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment. . 2020; ():1.

Chicago/Turabian Style

Mokhlesur Rahman; G. G. Md. Nawaz Ali; Xue Jun Li; Kamal Chandra Paul; Peter H.J. Chong. 2020. "Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment." , no. : 1.

Journal article
Published: 11 June 2020 in Information
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Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

ACS Style

Jim Samuel; G. Ali; Mokhlesur Rahman; Ek Esawi; Yana Samuel. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. Information 2020, 11, 314 .

AMA Style

Jim Samuel, G. Ali, Mokhlesur Rahman, Ek Esawi, Yana Samuel. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. Information. 2020; 11 (6):314.

Chicago/Turabian Style

Jim Samuel; G. Ali; Mokhlesur Rahman; Ek Esawi; Yana Samuel. 2020. "COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification." Information 11, no. 6: 314.

Other
Published: 03 June 2020
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Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

ACS Style

Jim Samuel; G. G. Md. Nawaz Ali; Mokhlesur Rahman; Ek Esawi; Yana Samuel. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. 2020, 1 .

AMA Style

Jim Samuel, G. G. Md. Nawaz Ali, Mokhlesur Rahman, Ek Esawi, Yana Samuel. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. . 2020; ():1.

Chicago/Turabian Style

Jim Samuel; G. G. Md. Nawaz Ali; Mokhlesur Rahman; Ek Esawi; Yana Samuel. 2020. "COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification." , no. : 1.

Preprint
Published: 20 May 2020
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The Coronavirus pandemic has created complex challenges and adverse circumstances. This research discovers public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research identifies emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to COVID-19, to identify dominant sentiment trends associated with the push to 'reopen' the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of exploratory textual analytics and textual data visualization, that Tweets data from American Twitter users shows more trust sentiment support, than fear, for reopening the US economy. With additional validation, this could present a valuable time sensitive opportunity for state governments, the federal government, corporations and societal leaders to guide the nation into a successful new normal future.

ACS Style

Jim Samuel; Mokhlesur Rahman; G. G. Md. Nawaz Ali; Yana Samuel; Alexander Pelaez. Feeling Like It Is Time to Reopen Now? COVID-19 New Normal Scenarios Based on Reopening Sentiment Analytics. 2020, 1 .

AMA Style

Jim Samuel, Mokhlesur Rahman, G. G. Md. Nawaz Ali, Yana Samuel, Alexander Pelaez. Feeling Like It Is Time to Reopen Now? COVID-19 New Normal Scenarios Based on Reopening Sentiment Analytics. . 2020; ():1.

Chicago/Turabian Style

Jim Samuel; Mokhlesur Rahman; G. G. Md. Nawaz Ali; Yana Samuel; Alexander Pelaez. 2020. "Feeling Like It Is Time to Reopen Now? COVID-19 New Normal Scenarios Based on Reopening Sentiment Analytics." , no. : 1.

Preprint
Published: 02 May 2020
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Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

ACS Style

Jim Samuel; G. G. Md. Nawaz Ali; Mokhlesur Rahman; Ek Esawi; Yana Samuel. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. 2020, 1 .

AMA Style

Jim Samuel, G. G. Md. Nawaz Ali, Mokhlesur Rahman, Ek Esawi, Yana Samuel. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. . 2020; ():1.

Chicago/Turabian Style

Jim Samuel; G. G. Md. Nawaz Ali; Mokhlesur Rahman; Ek Esawi; Yana Samuel. 2020. "COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification." , no. : 1.

Journal article
Published: 16 March 2020 in IEEE Transactions on Vehicular Technology
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This paper considers the network- and application-level reliabilities for connected vehicles under path loss environments. We derive a probabilistic framework for estimating reliabilities that is applicable with various path loss models. We also build a realistic connected vehicle traffic simulation environment and use it to perform extensive experiments considering semi-urban traffic. The results show that the achievable reliability performances differ significantly with the path loss models considered. For a moderate communication distance between a transmitter and a receiver, with established deterministic and stochastic path loss models, the network-level reliability is around 55%, whereas with the realistic path loss models that consider obstacles and traffic, the reliability falls below 30%. To improve the network- and application-level reliabilities, we propose a feedbackless relaying mechanism that can be deployed on top of IEEE 802.11p, where, the relay vehicle selection is done autonomously. The relaying mechanism improves the network-level and application-level reliabilities by at least 35% for the studied path loss models.

ACS Style

G. G. Md. Nawaz Ali; Beshah Ayalew; Ardalan Vahidi; Noor- A- Rahim. Feedbackless Relaying for Enhancing Reliability of Connected Vehicles. IEEE Transactions on Vehicular Technology 2020, 69, 4621 -4634.

AMA Style

G. G. Md. Nawaz Ali, Beshah Ayalew, Ardalan Vahidi, Noor- A- Rahim. Feedbackless Relaying for Enhancing Reliability of Connected Vehicles. IEEE Transactions on Vehicular Technology. 2020; 69 (5):4621-4634.

Chicago/Turabian Style

G. G. Md. Nawaz Ali; Beshah Ayalew; Ardalan Vahidi; Noor- A- Rahim. 2020. "Feedbackless Relaying for Enhancing Reliability of Connected Vehicles." IEEE Transactions on Vehicular Technology 69, no. 5: 4621-4634.

Journal article
Published: 26 November 2019 in Future Internet
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On-demand broadcast is a scalable approach to disseminating information to a large population of clients while satisfying dynamic needs of clients, such as in vehicular networks. However, in conventional broadcast approaches, only one data item can be retrieved by clients in one broadcast tick. To further improve the efficiency of wireless bandwidth, in this work, we conduct a comprehensive study on incorporating network coding with representative on-demand scheduling algorithms while preserving their original scheduling criteria. In particular, a graph model is derived to maximize the coding benefit based on the clients’ requested and cached data items. Furthermore, we propose a heuristic coding-based approach, which is applicable for all the on-demand scheduling algorithms with low computational complexity. In addition, based on various application requirements, we classify the existing on-demand scheduling algorithms into three groups—real-time, non-real-time and stretch optimal. In view of different application-specific objectives, we implement the coding versions of representative algorithms in each group. Extensive simulation results conclusively demonstrate the superiority of coding versions of algorithms against their non-coding versions on achieving their respective scheduling objectives.

ACS Style

G. G. Md. Nawaz Ali; Victor C. S. Lee; Yuxuan Meng; Peter H. J. Chong; Jun Chen. Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast. Future Internet 2019, 11, 248 .

AMA Style

G. G. Md. Nawaz Ali, Victor C. S. Lee, Yuxuan Meng, Peter H. J. Chong, Jun Chen. Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast. Future Internet. 2019; 11 (12):248.

Chicago/Turabian Style

G. G. Md. Nawaz Ali; Victor C. S. Lee; Yuxuan Meng; Peter H. J. Chong; Jun Chen. 2019. "Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast." Future Internet 11, no. 12: 248.

Journal article
Published: 13 September 2019 in IEEE Transactions on Intelligent Transportation Systems
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Basic Safety Messaging plays a crucial role to provide road safety in vehicular ad-hoc networks (VANETs). To avoid potential accidents, vehicles periodically broadcast safety information to neighboring vehicles. However, due to transmission collisions, fading channels and other factors, vehicular networks usually suffer a low packet delivery ratio (PDR) and a large delay, which are intolerant of many safety applications. To tackle these issues, this paper proposes a hybrid medium access control (MAC) protocol for basic safety message (BSM) dissemination based on the framework of Dedicated Short-Range Communication (DSRC). Its partially centralized and partially distributed characteristic not only can effectively suppress the collisions, but keep compatibility with IEEE 802.11p. In addition, the integration of Physical-Layer Network Coding (PNC) and Random Linear Network Coding (RLNC) further strengthens the reliability and efficiency for BSM dissemination. Both the theoretical analysis and comprehensive simulations indicate that, compared with existing schemes, the proposed protocol can significantly improve the PDR by a range of 20% to 300%. Meanwhile, in terms of normalized throughput, it increases by varying percent between 20% and 160% in different scenarios.

ACS Style

Minglong Zhang; G. G. Md. Nawaz Ali; Peter Han Joo Chong; Boon-Chong Seet; Arun Kumar. A Novel Hybrid MAC Protocol for Basic Safety Message Broadcasting in Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems 2019, 21, 4269 -4282.

AMA Style

Minglong Zhang, G. G. Md. Nawaz Ali, Peter Han Joo Chong, Boon-Chong Seet, Arun Kumar. A Novel Hybrid MAC Protocol for Basic Safety Message Broadcasting in Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems. 2019; 21 (10):4269-4282.

Chicago/Turabian Style

Minglong Zhang; G. G. Md. Nawaz Ali; Peter Han Joo Chong; Boon-Chong Seet; Arun Kumar. 2019. "A Novel Hybrid MAC Protocol for Basic Safety Message Broadcasting in Vehicular Networks." IEEE Transactions on Intelligent Transportation Systems 21, no. 10: 4269-4282.

Journal article
Published: 21 August 2019 in IEEE Transactions on Vehicular Technology
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An autonomous V2V communication mode (also known as side-link mode 4), which facilitates V2V communication in out of eNB coverage areas, has recently been introduced into the Long term evolution (LTE) standard. Recent research has studied the performance of this LTE-V2V autonomous mode for a highway use case. However, performance analysis for a highway use case cannot be easily applied to an intersection use case as it may contain non-line-of-sight (NLOS) communication links. In this paper, we analyze and evaluate the safety message broadcasting performance of LTE-V2V autonomous mode in an urban intersection scenario. Considering practical path loss models, we present the impact of NLOS communication link on the overall message dissemination performance. Through the analytical and simulation results, we show that the overall message dissemination performance degrades drastically with increasing vehicle density and increasing distance of the transmitting vehicle from the intersection. To improve the performance, we propose a vehicle-assisted relaying scheme in which the relaying vehicle is selected in an autonomous manner. We also present two resource allocation strategies for the relaying vehicle. For low to medium vehicle density along the street, we observe significant improvement in message dissemination through relaying compared to the scheme without relaying.

ACS Style

Noor- A- Rahim; G. G. Md. Nawaz Ali; Yong Liang Guan; Beshah Ayalew; Peter Han Joo Chong; Dirk Pesch. Broadcast Performance Analysis and Improvements of the LTE-V2V Autonomous Mode at Road Intersection. IEEE Transactions on Vehicular Technology 2019, 68, 9359 -9369.

AMA Style

Noor- A- Rahim, G. G. Md. Nawaz Ali, Yong Liang Guan, Beshah Ayalew, Peter Han Joo Chong, Dirk Pesch. Broadcast Performance Analysis and Improvements of the LTE-V2V Autonomous Mode at Road Intersection. IEEE Transactions on Vehicular Technology. 2019; 68 (10):9359-9369.

Chicago/Turabian Style

Noor- A- Rahim; G. G. Md. Nawaz Ali; Yong Liang Guan; Beshah Ayalew; Peter Han Joo Chong; Dirk Pesch. 2019. "Broadcast Performance Analysis and Improvements of the LTE-V2V Autonomous Mode at Road Intersection." IEEE Transactions on Vehicular Technology 68, no. 10: 9359-9369.

Journal article
Published: 27 February 2019 in Computer Networks
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Network coding has been demonstrated as a promising solution to further enhancing the bandwidth efficiency for on-demand broadcast. In this work, first, we show the performance improvement of a straightforward implementation of coding based on-demand data broadcast algorithms over the traditional on-demand broadcast approaches. Second, as the straightforward implementation of the optimal approach has overwhelming computational overhead, we propose an efficient generalized implementation scheme, which can be applied to all the existing on-demand scheduling algorithms. The proposed scheme reduces the computational overhead while achieves the same performance as the straightforward implementation. Third, to further enhance system scalability, we propose an approximate implementation method with even lower computational overhead while maintaining near optimal performance. Finally, we conduct an extensive simulation study and the results demonstrate that the proposed efficient implementation schemes can improve the system performance over 40% compared with the traditional broadcast approach, and the computational overhead can be reduced by 75% compared with the straightforward implementation. In addition, we show that the proposed approximate implementation can further reduce the computational overhead significantly and it is able to strike a balance between the service performance and system scalability.

ACS Style

G. G. Md. Nawaz Ali; Kai Liu; Victor C.S. Lee; Peter H.J. Chong; Yong Liang Guan; Jun Chen. Towards efficient and scalable implementation for coding-based on-demand data broadcast. Computer Networks 2019, 154, 88 -104.

AMA Style

G. G. Md. Nawaz Ali, Kai Liu, Victor C.S. Lee, Peter H.J. Chong, Yong Liang Guan, Jun Chen. Towards efficient and scalable implementation for coding-based on-demand data broadcast. Computer Networks. 2019; 154 ():88-104.

Chicago/Turabian Style

G. G. Md. Nawaz Ali; Kai Liu; Victor C.S. Lee; Peter H.J. Chong; Yong Liang Guan; Jun Chen. 2019. "Towards efficient and scalable implementation for coding-based on-demand data broadcast." Computer Networks 154, no. : 88-104.

Journal article
Published: 05 July 2018 in IEEE Access
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In this paper, the design of error-correcting or channel codes for delay-universal/anytime communication is shown while considering systems with and without a feedback link. We construct practical and low complexity anytime channel codes based on spatially coupled repeat-accumulate (SC-RA) codes. Performance and density evolution analysis are shown for the binary erasure channel (BEC) and the binary input additive white Gaussian noise (BIAWGN) channel. We observe that erasure/error floors exists even at low decoding delay in the following cases: (i) when the code rate is close to the Shannon capacity; and/or (ii) when the code parameters are chosen to target a high decaying rate of erasure/error probability. To mitigate erasure/error floors, we present feedback algorithms for BEC and BIAWGN channels. We show that the proposed feedback strategies can greatly enhance the performance of anytime SC-RA codes. Numerical results also show that feedback strategies significantly reduce the decoder complexity. The proposed feedback approach is applied to an aircraft tracking application to track/calculate/estimate the state information of the aircraft. Based on comparisons of the results obtained from the traditional block and anytime coding scenarios, it is observed that the latter significantly outperforms the former in terms of tracking performance.

ACS Style

Noor- A- Rahim; Mohammad Omar Khyam; Yong Liang Guan; G. G. Md. Nawaz Ali; Khoa D. Nguyen; Gottfried Lechner. Delay-Universal Channel Coding With Feedback. IEEE Access 2018, 6, 37918 -37931.

AMA Style

Noor- A- Rahim, Mohammad Omar Khyam, Yong Liang Guan, G. G. Md. Nawaz Ali, Khoa D. Nguyen, Gottfried Lechner. Delay-Universal Channel Coding With Feedback. IEEE Access. 2018; 6 (99):37918-37931.

Chicago/Turabian Style

Noor- A- Rahim; Mohammad Omar Khyam; Yong Liang Guan; G. G. Md. Nawaz Ali; Khoa D. Nguyen; Gottfried Lechner. 2018. "Delay-Universal Channel Coding With Feedback." IEEE Access 6, no. 99: 37918-37931.

Journal article
Published: 25 April 2018 in IEEE Internet of Things Journal
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Through the embedded processors and communication technologies, vehicles are increasingly being connected with the Internet of Things. Recently, much attentions have been paid to network-coding-assisted data broadcast in vehicular networks. However, majority of the works consider all the accessed data items are the same size. In this work, we have studied the network coding-assisted heterogeneous on-demand real-time data access in vehicular networks. Firstly, we have investigated the less efficiency of conventional coding-assisted approach in accessing heterogeneous data items in real-time vehicular environment. Due to ignoring the impact of heterogeneous data items in decoding, the conventional coding does not achieve expected performance in accessing data items with diverse size. Secondly, based on our observations, for efficiently serving heterogeneous data items, we have proposed a dynamic threshold based coding-assisted real-time data broadcast approach called EDF. Thirdly, we have derived the probabilistic analysis of the system performance of the proposed approach and the state-of-the-art approaches. Fourthly, based on our further investigations, we have proposed another approach, called ISXD. The proposed network-coding-assisted ISXD exploits the different MCSs (Modulation and coding scheme) of IEEE 802.11p physical layer for leveraging the variable serving rate considering the dynamic positions of vehicles along with the vehicle mobility. The comprehensive simulation results demonstrate the efficacy of the proposed approaches over the state-of-the-art approaches in terms of improving the on-demand requests serving capability and reducing the system response time.

ACS Style

G. G. Md. Nawaz Ali; Noor- A- Rahim; Ashiqur Rahman; Syeda Khairunnesa Samantha; Peter Han Joo Chong; Yong Liang Guan. Efficient Real-Time Coding-Assisted Heterogeneous Data Access in Vehicular Networks. IEEE Internet of Things Journal 2018, 5, 3499 -3512.

AMA Style

G. G. Md. Nawaz Ali, Noor- A- Rahim, Ashiqur Rahman, Syeda Khairunnesa Samantha, Peter Han Joo Chong, Yong Liang Guan. Efficient Real-Time Coding-Assisted Heterogeneous Data Access in Vehicular Networks. IEEE Internet of Things Journal. 2018; 5 (5):3499-3512.

Chicago/Turabian Style

G. G. Md. Nawaz Ali; Noor- A- Rahim; Ashiqur Rahman; Syeda Khairunnesa Samantha; Peter Han Joo Chong; Yong Liang Guan. 2018. "Efficient Real-Time Coding-Assisted Heterogeneous Data Access in Vehicular Networks." IEEE Internet of Things Journal 5, no. 5: 3499-3512.