Browsing by Author "Tahmid, Ahnaf"

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    Analysing neural network models for detecting panic attacks with uncertainty analysis
    (BRAC University, 2024-01) Tahmid, Ahnaf; Zamil, Rafsan; Mubin, MD. Muhimenul; Mohammad, Nafis; Noor, Jannatun
    In our society and around the world a lot of people suffer from panic attacks. These panic attacks can be mild or very intense physical stimulations that may incapacitate an individual at the spot when the panic attack occurs. The problem in this case is, if the person suffers from a panic attack outside their house and loses control over themselves, they might be subjected to external environmental hazard such as getting into a car accident, etc. Therefore, if we can effectively track and detect whether a person had a panic attack via their spatiotemporal and biometric data, steps can be taken to help them recover from the panic attack or send help to them, as quickly as possible. Keeping this in our mind, in this study we analysed the performance of different neural network models and techniques to detect panic attacks of individuals from their spatiotemporal and biometric data. Since detection of panic attacks is an emergency use-case, model reliability is essential. To ensure model reliability, we also represented the uncertainty analysis of these neural network models using Monte Carlo Dropout. During our study, we found that among all the models that were used, GRU (Gated Recurrent Unit) had the highest accuracy of 95.56%, and GRU also had one of the least amount of uncertainty. However, the ensemble model had the least amount of uncertainty among all the models that were used.
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    Comprehensive analysis of accident severity determinants in Bangladesh using machine learning
    (Department of Industrial & Production Engineering (IPE), BUET, 2024-11-27) Tahmid, Ahnaf; Mahbub, Dr. Nafisa
    Road traffic accidents are a major cause of fatalities in developing countries like Bangladesh, with the country's accident fatality rate significantly exceeding that of neighboring countries. By leveraging police reported accident data from the Accident Research Institute (ARI) at BUET, this study conducts a comprehensive analysis of the determinants of accident severity (AS) in Bangladesh using machine learning (ML) techniques. However, the dataset has been clustered based on area (urban/rural), vehicle involvement (single/two vehicles) and road class (Highways, other roads). Previous studies analyzing AS primarily use traditional statistical models, which are limited by assumptions about data distribution and linear relationships. These studies rarely employ explainable AI methods or cluster-wise analysis to identify significant factors within each cluster. To address these limitations, this study employed Explainable Artificial Intelligence approaches: permutation importance, and SHapley Additive exPlanations (SHAP) method across clusters, using tree-based Random Forest (RF), Extreme Gradient Boosting (XGBoost); classification-based K-Nearest Neighbor (KNN); and hybrid Stack model ML approaches. Analysis depicts that, stack model most effectively capture the complex structure of data for all. The result of the study indicates that, vehicle type, collision type, district, divider, surface quality, location type, time and driver age are the key variables for predicting AS. Based on further analysis this research concludes that common collision scenarios on Bangladeshi roads include hit pedestrian, head on collision, collision between heavy and light vehicles, and incidents involving drivers aged between 31 and 45 years. Based on the analysis, this study provides valuable insights for key organizations in Bangladesh, including the Bangladesh Road Transport Authority (BRTA), Roads and Highway Department (RHD), Bangladesh Police (BP), and Local Government Engineering Department (LGED).
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    Effect of Fatigue on Traffic Violations of Bus Drivers
    (Department of Civil and Environment Engineering, Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh, 2017-11-15) Mahamud, Hayat; Saif, Adib; Rimol, Abu Hasan; Tahmid, Ahnaf
    Road accident has become a daily and deadly phenomenon in Bangladesh which has one of the worst crash rates in the world, at more than 60 per 10,000 registered motor vehicles. The official death toll for road traffic accidents is about 4,000 a year, but Nirapad Sarak Chai gave a higher figure of 5162 accident related deaths in 2013, which also include deaths en route to hospital and deaths after release from hospitals. Road Accident and casualties Statistics of Bangladesh Road Transport Authority (BRTA) shows that around 19,450 number of accident has occurred during the year 2009-2016.In those accident 18,510 people lost their life and about 14,442 people became injured (Bangladesh Road Transport Authority, BRTA). In Bangladesh, according to the official statistics (police statistics), at least 2437 accidents were reported in 2010 of which 1911 were directly fatigue related fatal accidents. The fatigue related fatality is increasing tremendously and it has become an alarming event causing huge loss of lives and assets. A study revealed that about 83% drivers in Bangladesh were directly or indirectly involved in fatigue related disability and faced psycho-social disorders (Talukder et al., 2013). However, bus drivers does not take the matter seriously. Even the bus company owners overlook the drivers’ fatigue fact. II This study aims for identifying and analyzing factors that cause fatigue-induced road violations to isolate the most extreme group of drivers. Previously fatigue related study has not been explored much in Bangladesh. This study will help to create public awareness and attain mitigation measures to improve the current situation by addressing the ‘fatigue’ issue. In our study we conducted a questionnaire survey on selected locations. Based on the survey data the model was developed to predict the number of violations by fatigue induced drivers. The questionnaire was prepared on other studies, literature review and local context. The study will help to identify the factors which affects the violations of drivers due to fatigue. Alcohol consumption, breaks between trips, fitness of vehicle, drivers’ monthly income, work pattern, trip distance etc. are significant factors which have influence on drivers’ fatigue induced violations This study will help policy makers to set up an environment to minimize traffic casualties and ensure road safety by reducing fatigue