College of Information Technology

Khaled Edhah Salem Aljabri

Dr. Munkhjargal Gochoo 

Ali Monir Haji Ali 

Abstract

Attending to patient needs and meeting schedules have been a rising issue in hospitals in the past period as the number of patients can surge drastically in any public health crisis such as the Novel Corona Virus epidemic of 2019-2020 or the exponential rise of senior citizens, to a volume that exceeds the capacity of health care systems. The inability of a system to accommodate a crisis will hamper the workflow of hospitals and will exhaust its staff and further cause patient dissatisfaction with the quality of healthcare they are receiving. Delivering medicine is one of many repetitive tasks that burden hospital staff and shift the focus of their efforts from patient care to logistical operations, in addition to being a sensitive and critical task that could be compromised in times of crisis. Automating such tasks will improve the efficiency and flow of operations, assist and carry some workload from hospital staff, and produce better deliverables and outcome. We propose the use of robotics as a solution, and more specifically Ranger Mobile robots, to automate medicine delivery, assist with hospital staff work, and produce a more efficient operation and a better experience for patients.

Saarah Mohamed Rashed Ali Aldhanhani 

Dr. Mohammad Mehedy Masud

Tarfa Abdulla Khamis Abdulla Alazeezi 

Shamma Humaid Salem Hamad Alalawi 

Aaesha Aadel Yousef Ali Almansoori 

Abstract

In the past several months, we have witnessed a non-precedent disease which is COVID-19, known initially as Corona Virus Disease of 2019 and later declared as a pandemic. It has resulted in unprecedented pressures on each country for controlling the population by properly utilizing available resources. And it is now becoming a source of depression, stress, and anxiety because of information posted on social media. In this project, we designed and developed a web application for mining large amounts of Twitter data for pandemic crisis management. The twitter text data is targeted specifically for the COVID-19 pandemic. We analyzed twitter text data using mining techniques such as natural language processing (NLP) to find the sentiment of the text. The data was passed through several processing steps, starting from collecting the relevant messages and tweets and storing them in data files for further processing. We then performed filtering based on user inputs such as keywords, date ranges, to retrieve tweets from the data files based on the filtering terms. We then combined the analysis results with the associated metadata, such as geolocation, for high-level analysis and visual inspection. For example, users can view sentiments of a given keyword (e.g., "Vaccine") for a given date frame on the world map. Thus, the web application did provide a user-friendly and interactive graphical interface. We believed that by correlating tweet sentiments with corresponding users' geolocations, we could identify and locate crises in different geographic locations and the severity. Therefore, we hope the proposed technique can answer questions like, "Which city is facing the highest level of difficulty in the food supply?" or "what is the most pressing issue of a particular area?" and so on. We believed the proposed work would have a significant impact on pandemic crisis management. It will be helpful for different entities, such as government or non-government organizations, who strive to provide maximum support to the citizens to help them during the crisis. We already performed extensive testing of the application and obtained interesting results. For example, for different search terms(e.g., “Coronavirus”, “Vaccine” etc.) we have identified the top 10 most negative and top 10 most positive countries and cities. Also, we have demonstrated how the running time varies with different search terms due to the frequency of the term found in the Tweets.