Research Profiles

In The State of Qatar, municipalities are responsible for solid waste collection both directly, using their own logistics, and indirectly through private sector contracts. Waste collection and transport is carried out by a large fleet of trucks that collect wastes from thousands of collection points scattered across the country. The conventional waste collecting method (leaving waste in waste containers or trash bins prior to collection by a waste collector) is not convenient. Sometimes, waste collectors take trips to garbage bins that are either partially or completely empty. This increases the service time, number of trips from/to collection points, increases unnecessary traffic on the streets, increases fuel consumption, and increases the number of people working than might otherwise be needed. In contrast, overfilled bins result in rubbish falling out while being collected, and if containing organic waste may cause contamination of surrounding soil. In addition, in hot countries like Qatar, hazardous rubbish like batteries, lighters, empty petrol or oil cans can cause fires igniting other trash in the bin. The Ministry of Municipality and Environment in Qatar, which is responsible for waste collection and management, strives to find solutions for smart and safe waste collection specially that Qatar is going to host The 2022 World Cup event where the number of people will be increased drastically. A literature review reveals that several researchers have attempted to address the problem of waste collection. One smart solution proposed includes the use of implant sensors in the trash cans to monitor the trash cans' status (full or empty), followed by the use of a central system to receive the signals from the sensors resulting in an optimal schedule to collect the waste. Researchers in another project also developed a set of algorithms to optimize the waste collection routes. Though both of these proposals allow for an optimal collection route based on waste can fill patterns, neither address the importance of prioritizing the collection of waste containing flammable materials nor altering collection routes to maximize collection of more frequently filled trash cans. Utilizing machine learning models our project aims to take both of these factors into consideration. Six CNAQ students propose a smart cross platform mobile application that can not only monitor the fill status of trash cans but also predict which trash cans are the busiest and which ones frequently contain flammable materials. Our App will be designed to prioritize the collection of such cans to reduce the possibilities of fire ignition, especially during summer when temperature can reach as high as 50 Celsius degree in the State of Qatar. To decide if a waste in the trash can is flammable or not, we propose using a combination of two types of data collected using a temperature detector and a gas detector. With these goals in mind, we will build a machine learning model that can use previously collected data to predict which trash cans require the most urgent collection and to predict the busiest areas, allowing the preparation of an optimal collection plan. The objective of this project is twofold. Firstly, to design and develop an intelligent cross-platform mobile application to optimize the waste collection at the State of Qatar. Secondly, to promote good research integrity and to emphasize the importance of inquiry, investigation, and immersion. In addition to the above-mentioned goal, our application provides the following features: 1. Workers Management System The system will manage the attendance and schedule of workers. 2. Map and tracking System The tracking system will provide collecting line to the truck drivers in order to manage the best way. 3. Report/Ticket Users can report any problems that arises, and an agent/admin can handle this ticket and provide solutions to the problem.

In The State of Qatar, municipalities are responsible for solid waste collection. The conventional waste collecting method is not convenient. Sometimes, waste collectors take trips to garbage bins that are either partially or completely empty. In contrast, overfilled bins result in rubbish falling out while being collected, and if containing organic waste may cause contamination of surrounding soil. In addition, in hot countries like Qatar, hazardous rubbish like batteries, lighters, empty petrol or oil cans can cause fires igniting other trash in the bin. This project aims to design and develop an intelligent cross- platform mobile application to optimize the waste collection in the State of Qatar.

Recently parking management systems have gained huge attention by the business companies. After all, parking plays a major role in a customer choice where to shop. According to Planning and Statistics Authority in The Sate of Qatar, the number of registered vehicles has increased by 41.2% just in one month [1]. Moreover, the number of vehicles in Qatar are expected to reach more than 912,000 units by the end of 2020. Such increase is the highest annualize growth in the gulf region[2]. Based on this fact, queues, difficulties in finding a parking spot has extended to educational institutions and College of the North Atlantic in Qatar (CNAQ) is no exception. Currently CNAQ has more than 300 instructors and approximately 3000 students. At present, there are 10 parking areas with a total of 1653 parking spaces. Finding a free parking spot has become a major concern for many students. Therefore, a group of third year students in the Department of Computer Sciences has decided to create a smart solution to help manage the parking process at the college. Finally, it has been said that [3] “Research and inquiry is not just for those who choose to pursue an academic career. It is central to professional life in the twenty-first century.” Thus, the present research project aims at: 1- Provide a motivational and enjoyable learning experience for the students as researchers through research and inquiry. 2- Design and implement of a machine learning model to accurately predict the waiting time for the next available parking lot. 3- Design and implement an algorithm to facilitate and encourage carpooling between employees and students. 4- Design and implement of the back-end of the mobile app. 5- Design and implement of the look-and-feel user interface of the mobile app. 6- Test the app using simulated data at the first stage and then test it with a real data. Deliverable: In addition to the smart parking mobile app, we are planning to write a conference paper once the research is finished and submit it to a national scientific conference. References [1] Planning and Statistics Authority, “https://www.psa.gov.qa/en/statistics1/Pages/LatestStats/10102017a.aspx” [2] Alpen Capital, “www.alpencapital.com” [3] Brew, A., 2007, April. Research and teaching from the students’ perspective. In International policies and practices for academic enquiry: An international colloquium (pp. 19-21).