Browsing by Author "Hasan, Md. Sabid"

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Impact of Digital Marketing on Customer Satisfaction:
(Comilla University, 29-Jun-2025) Hasan, Md. Sabid
This internship report, titled “Impact of Digital Marketing on Customer Satisfaction: A Study on Easyfie,” explores how digital marketing strategies influence customer satisfaction in the context of a growing Bangladeshi e-commerce platform. As digital interaction becomes the primary channel of customer engagement, understanding how specific digital marketing elements affect satisfaction has become essential for businesses like Easyfie.
Riot Perception and Safety Navigation of Autonomous Vehicles Using Deep Learning
(Springer Nature, 2024-03-30) Komol, Md. Mostafizur Rahman; Hasan, Md. Sabid; Md. Razon Hossain; Arafat, Md. Eaysir; Arefin, Mohammad Shamsul; Rahman, Md. Mahfujur
Rioting is an act of participating in a violent public disturbance, which involves multiple individuals engaging in destructive activities. Such activities can include vandalism, theft from both public and private property, physical assaults on others, and looting. Riots can significantly harm both government and public property, resulting in losses of life, injuries, and property damage. Most of the time, it has been observed that private and public transport turned into the major targets of riots. By detecting potential threats and responding quickly, autonomous vehicles equipped with riot prevention features can help to prevent harm to both individuals and property during a riot. Moreover, riot threat-detecting features can contribute to minimizing the economic impact of riots, which is particularly important for businesses and communities that rely on tourism, trade, and commerce. Despite the development of various safety features in autonomous vehicles, there is currently a lack of effective measures to detect riots and violent public disturbances on roads and highways. In this study, we propose a solution for leveraging the You Only Look Once (YOLO) algorithm to detect six types of road objects and one class of threats for Rioting is an act of participating in a violent public disturbance, which involves multiple individuals engaging in destructive activities. Such activities can include vandalism, theft from both public and private property, physical assaults on others, and looting. Riots can significantly harm both government and public property, resulting in losses of life, injuries, and property damage. Most of the time, it has been observed that private and public transport turned into the major targets of riots. By detecting potential threats and responding quickly, autonomous vehicles equipped with riot prevention features can help to prevent harm to both individuals and property during a riot. Moreover, riot threat-detecting features can contribute to minimizing the economic impact of riots, which is particularly important for businesses and communities that rely on tourism, trade, and commerce. Despite the development of various safety features in autonomous vehicles, there is currently a lack of effective measures to detect riots and violent public disturbances on roads and highways. In this study, we propose a solution for leveraging the You Only Look Once (YOLO) algorithm to detect six types of road objects and one class of threats for autonomous vehicles. The YOLO version 8 model was trained and assessed on a dataset of road objects including riot threats, and it achieved a maximum accuracy of 97.71%. Additionally, the proposed solution can be coupled with ground robots and unmanned aerial vehicles technology to enable real-time monitoring and treatment of chaotic and risky zones of riot.. The YOLO version 8 model was trained and assessed on a dataset of road objects including riot threats, and it achieved a maximum accuracy of 97.71%. Additionally, the proposed solution can be coupled with ground robots and unmanned aerial vehicles technology to enable real-time monitoring and treatment of chaotic and risky zones of riot.
Riot Perception and Safety Navigation of Autonomous Vehicles Using Deep Learning
(Scopus, 2024-03-30) Komol, Md. Mostafizur Rahman; Hasan, Md. Sabid; Hossain, Md. Razon; Arafat, Md. Eaysir; Arefin, Mohammad Shamsul; Rahman, Md. Mahfujur
Rioting is an act of participating in a violent public disturbance, which involves multiple individuals engaging in destructive activities. Such activities can include vandalism, theft from both public and private property, physical assaults on others, and looting. Riots can significantly harm both government and public property, resulting in losses of life, injuries, and property damage. Most of the time, it has been observed that private and public transport turned into the major targets of riots. By detecting potential threats and responding quickly, autonomous vehicles equipped with riot prevention features can help to prevent harm to both individuals and property during a riot. Moreover, riot threat-detecting features can contribute to minimizing the economic impact of riots, which is particularly important for businesses and communities that rely on tourism, trade, and commerce. Despite the development of various safety features in autonomous vehicles, there is currently a lack of effective measures to detect riots and violent public disturbances on roads and highways. In this study, we propose a solution for leveraging the You Only Look Once (YOLO) algorithm to detect six types of road objects and one class of threats for autonomous vehicles. The YOLO version 8 model was trained and assessed on a dataset of road objects including riot threats, and it achieved a maximum accuracy of 97.71%. Additionally, the proposed solution can be coupled with ground robots and unmanned aerial vehicles technology to enable real-time monitoring and treatment of chaotic and risky zones of riot.