Digital Commons Network^™

Exploring Post-Covid-19 Health Effects And Features With Advanced Machine Learning Techniques, Muhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, Iqbal H. Sarker

Research outputs 2022 to 2026

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and …

Navigating Through Chaos, Hoong Chuin Lau

Asian Management Insights

How AI and optimisation models can strengthen supply chain resilience.

Machine Learning Techniques For The Identification Of Risk Factors Associated With Food Insecurity Among Adults In Arab Countries During The Covid-19 Pandemic, Radwan Qasrawi, Maha Hoteit, Reema Tayyem, Khlood Bookari, Haleama Al Sabbah, Iman Kamel, Somaia Dashti, Sabika Allehdan, Hiba Bawadi, Mostafa Waly, Mohammed O. Ibrahim, Stephanny Vicuna Polo, Diala Abu Al-Halawa

All Works

BACKGROUND: A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to …

Detecting Mental Distresses Using Social Behavior Analysis In The Context Of Covid-19: A Survey, Sahraoui Dhelim, Liming Chen, Sajal K. Das, Huansheng Ning, Chris Nugent, Gerard Leavey, Dirk Pesch, Eleanor Bantry-White, Devin Michael Burns

Computer Science Faculty Research & Creative Works

Online social media provides a channel for monitoring people's social behaviors from which to infer and detect their mental distresses. During the COVID-19 pandemic, online social networks were increasingly used to express opinions, views, and moods due to the restrictions on physical activities and in-person meetings, leading to a significant amount of diverse user-generated social media content. This offers a unique opportunity to examine how COVID-19 changed global behaviors regarding its ramifications on mental well-being. In this article, we surveyed the literature on social media analysis for the detection of mental distress, with a special emphasis on the studies published …

Artificial Intelligence Tool For The Study Of Covid-19 Microdroplet Spread Across The Human Diameter And Airborne Space, Hesham H. Alsaadi, Monther Aldwairi, Faten Yasin, Sandra C.P. Cachinho, Abdullah Hussein

All Works

The 2019 novel coronavirus (SARS-CoV-2 / COVID-19), with a point of origin in Wuhan, China, has spread rapidly all over the world. It turned into a raging pandemic wrecking havoc on health care facilities, world economy and affecting everyone’s life to date. With every new variant, rate of transmission, spread of infections and the number of cases continues to rise at an international level and scale. There are limited reliable researches that study microdroplets spread and transmissions from human sneeze or cough in the airborne space. In this paper, we propose an intelligent technique to visualize, detect, measure the distance …

Wearing Masks Implies Refuting Trump?: Towards Target-Specific User Stance Prediction Across Events In Covid-19 And Us Election 2020, Hong Zhang, Haewoon Kwak, Wei Gao, Jisun An

Research Collection School Of Computing and Information Systems

People who share similar opinions towards controversial topics could form an echo chamber and may share similar political views toward other topics as well. The existence of such connections, which we call connected behavior, gives researchers a unique opportunity to predict how one would behave for a future event given their past behaviors. In this work, we propose a framework to conduct connected behavior analysis. Neural stance detection models are trained on Twitter data collected on three seemingly independent topics, i.e., wearing a mask, racial equality, and Trump, to detect people’s stance, which we consider as their online behavior in …

Observing Human Mobility Internationally During Covid-19, Shane Allcroft, Mohammed Metwaly, Zachery Berg, Isha Ghodgaonkar, Fischer Bordwell, Xinxin Zhao, Xinglei Liu, Jiahao Xu, Subhankar Chakraborty, Vishnu Banna, Akhil Chinnakotla, Abhinav Goel, Caleb Tung, Gore Kao, Wei Zakharov, David A. Shoham, George K. Thiruvathukal, Yung-Hsiang Lu

Computer Science: Faculty Publications and Other Works

This article analyzes visual data captured from five countries and three U.S. states to evaluate the effectiveness of lockdown policies for reducing the spread of COVID-19. The main challenge is the scale: nearly six million images are analyzed to observe how people respond to the policy changes.

Spatio-Temporal Heterogeneity In The International Trade Resilience During Covid-19, Wei Luo, Lingfeng He, Zihui Yang, Shirui Zhang, Yong Wang, Dianbo Liu, Sheng Hu, Li He, Jizhe Xia, Min Chen

Research Collection School Of Computing and Information Systems

The COVID-19 pandemic and subsequent lockdowns have created immeasurable health and economic crises, leading to unprecedented disruptions to world trade. The COVID-19 pandemic shows diverse impacts on different economies that suffer and recover at different rates and degrees. This research aims to evaluate the spatio-temporal heterogeneity of international trade network vulnerabilities in the current crisis to understand the global production resilience and prepare for the future crisis. We applied a series of complex network analysis approaches to the monthly international trade networks at the world, regional, and country scales for the pre- and post- COVID-19 outbreak period. The spatio-temporal patterns …

An Exploratory Study On Museum Visitor Ship Trends In Singapore, Aldy Gunawan, Chentao Liu, Heranshan S/O Subramaniam, Melissa Tan, Ranice Tan, Clarence Tay, Tasaporn. Visawameteekul

Research Collection School Of Computing and Information Systems

The COVID-19 outbreak has unpredictably disrupted the operations of numerous museums. Museum visitor experience has a physical, personal, and social context, which are not achievable during the pandemic. Despite the depreciation during the Circuit Breaker period, the disruption also presents an opportunity for local museums to develop new strategies of audience engagement to accommodate the altered audience behavior. This exploratory study analyses data from six Singapore-based museums to understand the visitorship patterns across different ages and genders. The impact of COVID-19 is also analysed. Using R-studio and relevant packages, we conducted statistical tests such as hypothesis testing, Chi-square testing and …

Intelligent Health Care And Diseases Management System: Multi-Day-Ahead Predictions Of Covid-19, Ahed Abugabah, Farah Shahid

All Works

The rapidly growing number of COVID-19 infected and death cases has had a catastrophic worldwide impact. As a case study, the total number of death cases in Algeria is over two thousand people (increased with time), which drives us to search its possible trend for early warning and control. In this paper, the proposed model for making a time-series forecast for daily and total infected cases, death cases, and recovered cases for the countrywide Algeria COVID-19 dataset is a two-layer dropout gated recurrent unit (TDGRU). Four performance parameters were used to assess the model’s performance: mean absolute error (MAE), root …

Covid-19 Pandemic And The Cyberthreat Landscape: Research Challenges And Opportunities, Heba Saleous, Muhusina Ismail, Saleh H. Aldaajeh, Nisha Madathil, Saed Alrabaee, Kim Kwang Raymond Choo, Nabeel Al-Qirim

All Works

Although cyber technologies benefit our society, there are also some related cybersecurity risks. For example, cybercriminals may exploit vulnerabilities in people, processes, and technologies during trying times, such as the ongoing COVID-19 pandemic, to identify opportunities that target vulnerable individuals, organizations (e.g., medical facilities), and systems. In this paper, we examine the various cyberthreats associated with the COVID-19 pandemic. We also determine the attack vectors and surfaces of cyberthreats. Finally, we will discuss and analyze the insights and suggestions generated by different cyberattacks against individuals, organizations, and systems.

3d Indoor Modeling And Game Theory Based Navigation For Pre And Post Covid-19 Situation, Jaiteg Singh, Noopur Tyagi, Saravjeet Singh, Babar Shah, Farman Ali, Ahmad Ali Alzubi, Abdulrhman Alkhanifer

All Works

The COVID-19 pandemic has greatly affected human behavior, creating a need for individuals to be more cautious about health and safety protocols. People are becoming more aware of their surroundings and the importance of minimizing the risk of exposure to potential sources of infection. This shift in mindset is particularly important in indoor environments, especially hospitals, where there is a greater risk of virus transmission. The implementation of route planning in these areas, aimed at minimizing interaction and exposure, is crucial for positively influencing individual behavior. Accurate maps of buildings help provide location-based services, prepare for emergencies, and manage infrastructural …

Of Stances, Themes, And Anomalies In Covid-19 Mask-Wearing Tweets, Jwen Fai Low, Benjamin C.M. Fung, Farkhund Iqbal, Ebrahim Bagheri

All Works

COVID-19 is an opportunity to study public acceptance of a ‘‘new’’ healthcare intervention, universal masking, which unlike vaccination, is mostly alien to the Anglosphere public despite being practiced in ages past. Using a collection of over two million tweets, we studied the ways in which proponents and opponents of masking vied for influence as well as the themes driving the discourse. Pro-mask tweets encouraging others to mask up dominated Twitter early in the pandemic though its continued dominance has been eroded by anti-mask tweets criticizing others for their masking behavior. Engagement, represented by the counts of likes, retweets, and replies, …

Implications Of Cyberbiosecurity In Advanced Agriculture, Simone Stephen, Keitavius Alexander, Lucas Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

The world is currently undergoing a rapid digital transformation sometimes referred to as the fourth industrial revolution. During this transformation, it is increasingly clear that many scientific fields are not prepared for this change. One specific area is agriculture. As the sector which creates global food supply, this critical infrastructure requires detailed assessment and research via newly developed technologies (Millett et al, 2019; Peccoud et al, 2018) . Despite its fundamental significance to modern civilization, many aspects of industrial agriculture have not yet adapted to the digital world. This is evident in the many vulnerabilities currently present within agricultural systems, …

Science, Technology, Society, And Law, Paolo Davide Farah, Justo Corti Varela

Book Chapters

Traditionally, science and technology have been granted as sources of knowledge and objective truth. However, much more recently, they are also seen as human activities, conducted in a social environment. This new approach focuses on the intersections between science, technology and society, and particularly their regulation by the law. Concerns on how to best regulate the interaction come up in modern societies, and when either their use or their impacts are global, international law and international organizations become involved. The impact of the fourfold relation is so high that science and technology are seen as one of the reasons for …

Leveraging Machine Learning To Analyze Sentiment From Covid-19 Tweets: A Global Perspective, Md Mahbubar Rahman, Nafiz Imtiaz Khan, Iqbal H. Sarker, Mohiuddin Ahmed, Muhammad Nazrul Islam

Research outputs 2022 to 2026

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID-19 pandemic from a cross-country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective …

Smartphone Usage Before And During Covid-19: A Comparative Study Based On Objective Recording Of Usage Data, Khansa Chemnad, Sameha Alshakhsi, Mohamed Basel Almourad, Majid Altuwairiqi, Keith Phalp, Raian Ali

All Works

Most studies that claimed changes in smartphone usage during COVID-19 were based on self-reported usage data, e.g., that collected through a questionnaire. These studies were also limited to reporting the overall smartphone usage, with no detailed investigation of distinct types of apps. The current study investigated smartphone usage before and during COVID-19. Our study used a dataset from a smartphone app that objectively logged users’ activities, including apps accessed and each app session start and end time. These were collected during two periods: pre-COVID-19 (161 individuals with 77 females) and during COVID-19 (251 individuals with 159 females). We report on …

A Comparative Analysis Of Anti-Vax Discourse On Twitter Before And After Covid-19 Onset, Tareq Nasralah, Ahmed El Noshokaty, Omar El-Gayar, Mohammad A. Al-Ramahi, Abdullah Wahbeh

Computer Information Systems Faculty Publications

This study aimed to identify and assess the prevalence of vaccine-hesitancy-related topics on Twitter in the periods before and after the Coronavirus Disease 2019 (COVID-19) outbreak. Using a search query, 272,780 tweets associated with anti-vaccine topics and posted between 1 January 2011, and 15 January 2021, were collected. The tweets were classified into a list of 11 topics and analyzed for trends during the periods before and after the onset of COVID-19. Since the beginning of COVID-19, the percentage of anti-vaccine tweets has increased for two topics, “government and politics” and “conspiracy theories,” and decreased for “developmental disabilities.” Compared to …

Predicting The Level Of Respiratory Support In Covid-19 Patients Using Machine Learning, Hisham Abdeltawab, Fahmi Khalifa, Yaser Elnakieb, Ahmed Elnakib, Fatma Taher, Norah Saleh Alghamdi, Harpal Singh Sandhu, Ayman El-Baz

All Works

In this paper, a machine learning-based system for the prediction of the required level of respiratory support in COVID-19 patients is proposed. The level of respiratory support is divided into three classes: class 0 which refers to minimal support, class 1 which refers to non-invasive support, and class 2 which refers to invasive support. A two-stage classification system is built. First, the classification between class 0 and others is performed. Then, the classification between class 1 and class 2 is performed. The system is built using a dataset collected retrospectively from 3491 patients admitted to tertiary care hospitals at the …

Role Of Imaging And Ai In The Evaluation Of Covid-19 Infection: A Comprehensive Survey, Mayada Elgendy, Hossam Magdy Balaha, Mohamed Shehata, Ahmed Alksas, Mahitab Ghoneim, Fatma Sherif, Ali Mahmoud, Ahmed Elgarayhi, Fatma Taher, Mohammed Sallah, Mohammed Ghazal, Ayman El-Baz

All Works

Coronavirus disease 2019 (COVID-19) is a respiratory illness that started and rapidly became the pandemic of the century, as the number of people infected with it globally exceeded 253.4 million. Since the beginning of the pandemic of COVID-19, over two years have passed. During this hard period, several defies have been coped by the scientific society to know this novel disease, evaluate it, and treat affected patients. All these efforts are done to push back the spread of the virus. This article provides a comprehensive review to learn about the COVID-19 virus and its entry mechanism, its main repercussions on …

Overview Of The Clef-2022 Checkthat! Lab Task 2 On Detecting Previously Fact-Checked Claims, Preslav Nakov, Giovanni Da San Martino, Firoj Alam, Shaden Shaar, Hamdy Mubarak, Nikolay Babulkov

Natural Language Processing Faculty Publications

We describe the fourth edition of the CheckThat! Lab, part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting three tasks related to factuality, and it covers seven languages such as Arabic, Bulgarian, Dutch, English, German, Spanish, and Turkish. Here, we present the task 2, which asks to detect previously fact-checked claims (in two languages). A total of six teams participated in this task, submitted a total of 37 runs, and most submissions managed to achieve sizable improvements over the baselines using transformer based models such as BERT, RoBERTa. In this paper, we …

Overview Of The Clef-2022 Checkthat! Lab Task 1 On Identifying Relevant Claims In Tweets, Preslav Nakov, Alberto Barrón-Cedeño, Giovanni Da San Martino, Firoj Alam, Mucahid Kutlu, Wajdi Zaghouani, Mucahid Kutlu, Wajdi Zaghouani, Chengkai Li, Shaden Shaar, Hamdy Mubarak, Alex Nikolov

Natural Language Processing Faculty Publications

We present an overview of CheckThat! lab 2022 Task 1, part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). Task 1 asked to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics in six languages: Arabic, Bulgarian, Dutch, English, Spanish, and Turkish. A total of 19 teams participated and most submissions managed to achieve sizable improvements over the baselines using Transformer-based models such as BERT and GPT-3. Across the four subtasks, approaches that targetted multiple languages (be it individually or in conjunction, in general obtained the best performance. We describe the …

Analyzing The Impact Of Covid-19 Control Policies On Campus Occupancy And Mobility Via Wifi Sensing, Camellia Zakaria, Amee Trivedi, Emmanuel Cecchet, Michael Chee, Prashant Shenoy, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Mobile sensing has played a key role in providing digital solutions to aid with COVID-19 containment policies, primarily to automate contact tracing and social distancing measures. As more and more countries reopen from lockdowns, there remains a pressing need to minimize crowd movements and interactions, particularly in enclosed spaces. Many COVID-19 technology solutions leverage positioning systems, generally using Bluetooth and GPS, and can theoretically be adapted to monitor safety compliance within dedicated environments. However, they may not be the ideal modalities for indoor positioning. This article conjectures that analyzing user occupancy and mobility via deployed WiFi infrastructure can help institutions …

Sel-Covidnet: An Intelligent Application For The Diagnosis Of Covid-19 From Chest X-Rays And Ct-Scans, Ahmad Al Smadi, Ahed Abugabah, Ahmad Mohammad Al-Smadi, Sultan Almotairi

All Works

COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). In which …

Asian Hate Speech Detection On Twitter During Covid-19, Amir Toliyat, Sarah Ita Levitan, Zeng Peng, Ronak Etemadpour

Publications and Research

Coronavirus disease 2019 (COVID-19) started in Wuhan, China, in late 2019, and after being utterly contagious in Asian countries, it rapidly spread to other countries. This disease caused governments worldwide to declare a public health crisis with severe measures taken to reduce the speed of the spread of the disease. This pandemic affected the lives of millions of people. Many citizens that lost their loved ones and jobs experienced a wide range of emotions, such as disbelief, shock, concerns about health, fear about food supplies, anxiety, and panic. All of the aforementioned phenomena led to the spread of racism and …

The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics, El Hussain Shamsa, Kezhong Zhang

Medical Student Research Symposium

Background: Despite more than 60% of the United States population being fully vaccinated, COVID-19 cases continue to spike in a temporal pattern. These patterns in COVID-19 incidence and mortality may be linked to short-term changes in environmental factors.

Methods: Nationwide, county-wise measurements for COVID-19 cases and deaths, fine-airborne particulate matter (PM_2.5), and maximum temperature were obtained from March 20, 2020 to March 20, 2021. Multivariate Linear Regression was used to analyze the association between environmental factors and COVID-19 incidence and mortality rates in each season. Negative Binomial Regression was used to analyze daily fluctuations of COVID-19 cases …

Gauging The Acceptance Of Contact Tracing Technology: An Empirical Study Of Singapore Residents’ Concerns With Sharing Their Information And Willingness To Trust, Ee-Ing Ong, Wee Ling Loo

Research Collection Yong Pung How School Of Law

In response to the COVID-19 pandemic, governments began implementing various forms of contact tracing technology. Singapore’s implementation of its contact tracing technology, TraceTogether, however, was met with significant concern by its population, with regard to privacy and data security. This concern did not fit with the general perception that Singaporeans have a high level of trust in its government. We explore this disconnect, using responses to our survey (conducted pre-COVID-19) in which we asked participants about their level of concern with the government and business collecting certain categories of personal data. The results show that respondents had less concern with …

Communicative Strategies For Building Public Confidence In Data Governance: Analyzing Singapore's Covid-19 Contact-Tracing Initiatives, Gordon Kuo Siong Tan, Sun Sun Lim

Research Collection College of Integrative Studies

Effective social data governance rests on a bedrock of social support. Without securing trust from the populace whose information is being collected, analyzed, and deployed, policies on which such data are based will be undermined by a lack of public confidence. The COVID-19 pandemic has accelerated digitalization and datafication by governments for the purposes of contact tracing and epidemiological investigation. However, concerns about surveillance and data privacy have stunted the adoption of such contact-tracing initiatives. This commentary analyzes Singapore's contact-tracing initiative to uncover the reasons for public resistance and efforts by the state to address them. The government's contact-tracing program …

Interpretable Deep Learning For The Prediction Of Icu Admission Likelihood And Mortality Of Covid-19 Patients, Amril Nazir, Hyacinth Kwadwo Ampadu

All Works

The global healthcare system is being overburdened by an increasing number of COVID-19 patients. Physicians are having difficulty allocating resources and focusing their attention on high-risk patients, partly due to the difficulty in identifying high-risk patients early. COVID-19 hospitalizations require specialized treatment capabilities and can cause a burden on healthcare resources. Estimating future hospitalization of COVID-19 patients is, therefore, crucial to saving lives. In this paper, an interpretable deep learning model is developed to predict intensive care unit (ICU) admission and mortality of COVID-19 patients. The study comprised of patients from the Stony Brook University Hospital, with patient information such …

A Non-Invasive Interpretable Diagnosis Of Melanoma Skin Cancer Using Deep Learning And Ensemble Stacking Of Machine Learning Models, Iftiaz A. Alfi, Mahfuzur Rahman, Mohammad Shorfuzzaman, Amril Nazir

All Works

A skin lesion is a portion of skin that observes abnormal growth compared to other areas of the skin. The ISIC 2018 lesion dataset has seven classes. A miniature dataset version of it is also available with only two classes: malignant and benign. Malignant tumors are tumors that are cancerous, and benign tumors are non-cancerous. Malignant tumors have the ability to multiply and spread throughout the body at a much faster rate. The early detection of the cancerous skin lesion is crucial for the survival of the patient. Deep learning models and machine learning models play an essential role in …