Browsing by Author "Mahmud, Rashed"

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    Characteristics of Deshi chicken and Duck Trading Patterns and their Impact on Zoonotic Disease Transmission
    (A thesis submitted in the partial fulfilment of the requirements for the degree of Master of Science in Epidemiology Department of Medicine and Surgery Faculty of Veterinary Medicine Chittagong Veterinary and Animal Sciences University Chittagong-4225, Bangladesh, 2018) Mahmud, Rashed
    Live bird markets (LBMs) may function as hubs for the dissemination of pathogens and thus a potential source of avian influenza. Under a cross-sectional study 22 stallholders and 40 middlemen were selected and interviewed from two wholesale LBMs in Chittagong City Corporation (CCC), in order to describe the flow of deshi chickens and ducks and to assess the risk of release of avian influenza viruses (AIVs) in 2 LBMs at CCC through deshi chickens and ducks trade. A total of 44 environmental pooled samples were collected from middleman storage facilities from outside of 6 LBMs in CCC. Real Time Reverse Transcriptase Polymerase Chain Reaction (rRT-PCR) was used to detect M gene then followed by H5 and H9 subtypes in order to determine whether deshi chickens and ducks were infected by AIVs prior to their arrival at CCC LBMs. In the present cross-sectional study we found that most of the deshi chickens were handled by 2 to 3 middleman and 1 or 2 either wholesaler or retailer. It was rarely found the involvement of 2 whole¬salers in the desi chickens transaction chain. In case of ducks transaction chain between farms and end customer is more likely that 1 to 2 middlemen and 1 whole¬saler or retailer were involved but did not find the involvement of 2 wholesalers in the transaction chain. Two main pathways (P1and P2) of release assess¬ment were identified for AIVs transmission. In pathway1 (P1) deshi chickens or ducks sold by farmers being infected at the farm gate, risk of release of AIVs to the susceptible poultry at LBMs in CCC was high, whereas in the pathway 2 (P2) of deshi chickens or ducks sold by farmers being not infected at the farm, but infected any step of the pathway (storage or transport) and arrives as infectious at CCC LBMs, risk of release of AIVs to the susceptible poultry at CCC LBMs was low, medium and high depending upon when and at which step of the pathway susceptible deshi chickens and ducks become getting infected in the transaction chain and dissemination of AIVs to the susceptible poultry at CCC LBMs. We found overall prevalence of AIV in the environmental sample sites was 26.3% for M gene, 19.5% for H5 and 12.8% for H9 (95% CI: N=44). The proportion of positive samples in the present study was identical (p- 0.81) to the results obtained from the BALZAC project cross sectional study. It was therefore likely that the poultry entering the LBMs may be already contaminated with AIVs. Knowledge from this study could provide a new under¬standing of the deshi chickens and ducks value chain and release of avian influenza through LBMs at CCC in Bangladesh. These findings could be used to develop a programme concerning biosecurity and hygienic management to reduce the risk of release of AIVs and spreading of avian influenza through LBMs in Bangladesh via deshi chickens and ducks trade.
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    Heart Attack Prediction Using Machine Learning Technique
    (Daffodil International University, 23-01-29) Afrin, Shomaiya; Mahmud, Rashed
    The subject of AI known as machine learning has been at the forefront of several recent statistical and technological advances. It’s a branch of AI. Improved health outcomes can be achieved with the help of machine learning since it can increase patient participation in the treatment process. Machine learning methods can improve the diagnosis accuracy at every level by identifying the most likely reason for all similar patients' test findings. People over the age of 60 have a higher risk of experiencing a heart attack, and the prevalence of heart attacks increases with age. In order to foretell the onset of a heart attack, researchers are using a number of machine learning techniques. The goal of this study is to describe in depth the methods we use to predict cardiovascular disease, including Decision Tree, K-nearest neighbors, Logistic Regression, XGBoost, Support Vector Machine, and Random Forest. Predictive data mining techniques have been tested on the same dataset with varying degrees of success, and Random Forest methods have been shown to yield the highest accuracy of 87%.