Research Online at MacEwan :: Browsing by Author "Elmorsy, Mohammed"

Browsing by Author "Elmorsy, Mohammed"

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Analyzing factors impacting COVID-19 vaccination rates
(2023) Cho, Dongseok; Driedger, Mitchell; Han, Sera; Khan, Noman; Elmorsy, Mohammed; El-Hajj, Mohamad
Since the approval of the COVID-19 vaccine in late 2020, vaccination rates have varied around the globe. Access to a vaccine supply, mandated vaccination policy, and vaccine hesitancy contribute to these rates. This study used COVID-19 vaccination data from Our World in Data and the Multilateral Leaders Task Force on COVID-19 to create two COVID-19 vaccination indices. The first index is the Vaccine Utilization Index (VUI), which measures how effectively each country has utilized its vaccine supply to doubly vaccinate its population. The second index is the Vaccination Acceleration Index (VAI), which evaluates how efficiently each country vaccinated their populations within their first 150 days. Pearson correlations were created between these indices and country indicators obtained from the World Bank. Results of these correlations identify countries with stronger Health indicators such as lower mortality rates, lower age-dependency ratios, and higher rates of immunization to other diseases display higher VUI and VAI scores than countries with lesser values. VAI scores are also positively correlated to Governance and Economic indicators, such as regulatory quality, control of corruption, and GDP per capita. As represented by the VUI, proper utilization of the COVID-19 vaccine supply by country is observed in countries that display excellence in health practices. A country’s motivation to accelerate its vaccination rates within the first 150 days of vaccinating, as represented by the VAI, was largely a product of the governing body’s effectiveness and economic status, as well as overall excellence in health practises.
Analyzing factors impacting COVID-19 vaccination rates
(2023) Cho, Dongseok; Driedger, Mitchell; Han, Sera; Khan, Noman; Elmorsy, Mohammed; El-Hajj, Mohamad
Since the approval of the COVID-19 vaccine in late 2020, vaccination rates have varied around the globe. Access to a vaccine supply, mandated vaccination policy, and vaccine hesitancy contribute to these rates. This study used COVID-19 vaccination data from Our World in Data and the Multilateral Leaders Task Force on COVID-19 to create two COVID-19 vaccination indices. The first index is the Vaccine Utilization Index (VUI), which measures how effectively each country has utilized its vaccine supply to doubly vaccinate its population. The second index is the Vaccination Acceleration Index (VAI), which evaluates how efficiently each country vaccinated their populations within their first 150 days. Pearson correlations were created between these indices and country indicators obtained from the World Bank. Results of these correlations identify countries with stronger Health indicators such as lower mortality rates, lower age dependency ratios, and higher rates of immunization to other diseases display higher VUI and VAI scores than countries with lesser values. VAI scores are also positively correlated to Governance and Economic indicators, such as regulatory quality, control of corruption, and GDP per capita. As represented by the VUI, proper utilization of the COVID-19 vaccine supply by country is observed in countries that display excellence in health practices. A country’s motivation to accelerate its vaccination rates within the first 150 days of vaccinating, as represented by the VAI, was largely a product of the governing body’s effectiveness and economic status, as well as overall excellence in health practises.
Analyzing factors that lead to NBA regular season success
(2024) El-Hajj, Mohamad; Steed, Jackson; Gore, Victor; Infante, Craeg; Flores, Raniel; Wakista, Danindu; Elmorsy, Mohammed
The National Basketball Association (NBA) values regular-season success and acknowledges the crucial role of a team’s roster composition in determining overall performance. This study uses machine learning techniques, specifically unsupervised learning clustering and decision tree models, to predict the composition of a winning roster. Our research identified three distinct clusters based on win percentage and the distribution of players across different skill levels. Successful teams typically have more top-tier players and a significant representation of players in the lowest skill level. In contrast, teams that spread their talent across the entire roster are less successful. We have noticed that players with average to above-average skills are notably affected by excessive playing time in the previous game, which leads to decreased performance and potential losses for the team in the next game. Considering the time of year and the gap between games, we recommend prioritizing the rest and recovery of top players, especially in the latter half of the season. It’s crucial to ensure that players who are not as skilled as the top players but still make significant contributions to the team maintain consistent performance, especially during the first half of the season. Analyzing height’s impact on basketball player performance has revealed practical insights that can empower coaches and management. We found that the shortest and tallest players often perform less than those of average height. Most top performers in the NBA tend to have heights closer to the average. However, for players who frequently operate near the net and encounter numerous rebound opportunities, it is generally preferable to have an average or taller player for slightly enhanced overall performance compared to below-average height players. Teams can use these insights to improve their roster construction and maximize player utilization by coaches from one game to the next. This research provides practical strategies that can be immediately implemented to enhance team performance.
Breach path detection reliability in energy harvesting wireless sensor networks
(2021) Abougamila, Salwa; Elmorsy, Mohammed; Elmallah, Ehab S.
In this paper, we consider reliability assessment of energy harvesting wireless sensor networks (EH-WSNs) deployed to guard a geographic area against intruders that can enter and exit the network through a known set of entry-exit perimeter sides. To handle energy fluctuations during different time slots, a node may reduce its transmission power. Using a probabilistic graph model, we formalize a problem denoted EH-BPDREL (for breach path detection reliability). The problem calls for estimating the likelihood that any such intrusion can be detected and reported to a sink node. Due to the hardness of the problem, bounding algorithms are needed. We devise an efficient algorithm to solve a core problem that facilitates the design of various lower bounding algorithms. We obtain numerical results on the use of Monte Carlo simulation to estimate the probabilistic graph parameters, and illustrate the use of our devised algorithm to bound the solutions.
Charging optimization in multi-app wireless sensor networks through reinforcement learning
(2025) Hamacher, Neal; Lawrence, Benjamin; Elmorsy, Mohammed
Wireless sensor networks are becoming increasingly prevalent in modern systems. These networks can be outfitted with a mobile charger that travels the network and replenishes the energy of the nodes within. This paper introduces a novel resource management problem for controlling mobile chargers in rechargeable wireless sensor networks shared among multiple applications. A reinforcement learning approach is developed to optimize the charger's actions, increasing the network's lifetime while ensuring that each application's throughput and coverage requirements are met to the best of the charger's ability. The resultant algorithm optimizes mobile charger network traversal and energy usage to maximize the network's lifespan while meeting application Quality of Service (QoS) requirements. It can also adjust the mobile charger behaviour when some applications are assigned higher priority than others, ensuring critical network operations are maintained more effectively. Numerical results show that the proposed approach ensures minimum QoS requirements are met through network node energy level maintenance and prolonged network up-time.
Flow sharing reliability in energy harvesting wireless sensing networks
(2024) Abougamila, Salwa; Elmorsy, Mohammed; Elmallah, Ehab S.
This paper introduces a new resource sharing problem in wireless sensor networks (WSNs) that employ energy harvesting for prolonged network uptime. The problem is on managing a given infrastructure of EH-WSNs by supporting concurrent applications. Each application is characterized by a set of traffic generating nodes, a sink node, and a minimum required traffic rate that should be periodically delivered to its sink node. The overall EH-WSN is modelled by a probabilistic graph where energy fluctuation over time in each node is described by a probability distribution and handled by adjusting the flow relaying capacity of a node. Performance of the obtained network management scheme is assessed by a reliability metric on the formulated probabilistic graph. We call the formulated problem the flow sharing reliability (FS-REL) problem in EH-WSNs. We present a heuristic algorithm to cope with the problem using ideas from minimum cost multi-commodity flows in networks and approximation of flow reliability using a factoring algorithm. We also present numerical results that give more insights into the problem and the proposed solution.
Leveraging machine learning to predict factors that drive successful basketball team formation
(2025) El-Hajj, Mohamad; Kwon, Benjamin; Jethro Infante, Craeg; Steed, Jackson; Gore, Victor; Phan, Nhi; Elmorsy, Mohammed; Pang, Xiaodan
This study delves deep into the key factors affecting the likelihood of NCAA basketball players getting drafted into the NBA. The study highlights the importance of offensive metrics such as points scored and offensive ratings in predicting an NCAA player’s chances of being drafted into the NBA by utilizing an unsupervised learning clustering model and a supervised decision tree model. This underscores the significance of offensive statistics in a player’s skill set and suggests that players and coaches should prioritize improving these metrics to enhance a player’s draft potential. The study found that defensive metrics like defensive ratings and blocks have less impact on overall draft potential than offensive metrics. A crucial point to note is that a team’s success often relies on having its top players actively participating on the court. This research enhances our understanding of the factors influencing the draft prospects of NCAA basketball players. It underscores the advancement of basketball analytics and paves the way for further research on player performance metrics and their influence on the scouting and selection of professional athletes.
On flow reliability in energy harvesting wireless sensor networks
(2021) Elmorsy, Mohammed; Elmallah, Ehab S.
A basic wireless sensor networks (WSNs) reliability problem calls for finding the likelihood that a sink node receives at least a certain amount of traffic generated periodically by sensor nodes that can either operate or fail. When the nodes rely on harvesting energy from the ambient environment, a node can be in any one of a possible number of energy states with probabilities that can be estimated using measured environmental data. A node’s energy management unit can work by controlling the amount of data that can be periodically transmitted in each state. In this context, we formalize a flow reliability problem (denoted FLOWREL) in EH-WSNs. We present a method for computing lower bounds on exact solutions using an iterative algorithmic framework. Numerical results are presented to examine the performance of the devised methodology. Further, we discuss its use in a sample application that asks for determining the best sink location among a set of candidate locations.
On slicing weighted energy-harvesting wireless sensing networks with transmission range uncertainty
(2022) Abougamila, Salwa; Elmorsy, Mohammed; Elmallah, Ehab S.
In this paper, we deal with a wireless sensor network (WSN) infrastructure management problem where a provider wants to partition a network into a given number of node-disjoint subgraphs (called slices) for running different user applications. Nodes in the given infrastructure use energy harvesting for prolonged service time. The nodes manage fluctuations in their stored energy by adjusting their transmission range. We assume that each node is assigned an importance weight, and model the overall network using a probabilistic graph. In this context, we formalize a problem, denoted k-WBS-RU (for k weighted balanced slices with range uncertainty), to partition the network into k slices subject to some connectivity and operation constraints. We devise a solution to the problem, and present numerical results on the quality of the obtained slices. We also discuss an application of the proposed framework and solution when the assigned weights are derived from an area coverage application.
Reinforcement learning for self driving racing car games
(2025) Beaunoyer, Adam; Beaunoyer, Cory; Elmorsy, Mohammed; Saleh, Hanan
This research aims to create a reinforcement learning agent capable of racing in challenging simulated environments with a low collision count. We present a reinforcement learning agent that can navigate challenging tracks using both a Deep Q-Network (DQN) and a Soft Actor-Critic (SAC) method. A challenging track includes curves, jumps, and varying road widths throughout. Using open-source code on Github, the environment used in this research is based on the 1995 racing game WipeOut. The proposed reinforcement learning agent can navigate challenging tracks rapidly while maintaining low racing completion time and collision count. The results show that the SAC model outperforms the DQN model by a large margin. We also propose an alternative multiple-car model that can navigate the track without colliding with other vehicles on the track. The SAC model is the basis for the multiple-car model where it can complete the laps quicker than the single-car model but has a higher collision rate with the track wall.

Browsing by Author "Elmorsy, Mohammed"

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