Browsing by Author "Rahman, Tasmia"

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    A review on the prevalence and Detection of Bacterial contamination in Common Food and Associated Health Risk
    (BRAC University, 2021-10) Baidya, Sangita; Rahman, Tasmia; Ahmed, Akash
    Foodborne diseases are becoming a serious public health problem throughout the world, with 48 million illnesses and 3,000 fatalities projected in the United States each year. Over 1.7 billion cases of diarrheal disease-related deaths in children are recorded worldwide each year, with the bulk of these cases attributed to polluted food and water. Food-borne infections cause more medical care and fatalities in children under the age of four than in any other age group. Foodborne illness is caused by two different factors: food intoxication and food infection. Foodborne illness agents are capable of not just decapitating large numbers of people, but also of causing severe mortality and disability. There is increasing evidence that persons from minority racial and ethnic groups are more likely to contract foodborne illnesses. The food and beverage industries constitute a key part of the economy in many countries. Every day, they serve millions of people with a wide range of ready-to-eat (RTE) meals and drinks sold and occasionally prepared in public spaces. Food sources as diverse as meat, fish, natural goods, vegetables, grains, and cereals based on ready-to-eat food variations, frozen produce, and refreshments are included in road-distributed food types. RTE foods are ones that have not been further treated before being ingested in a way that considerably lowers microbial load. According to the World Health Organization, contaminated foods are responsible for up to 70% of diarrheal infections, and food-borne illnesses are the leading cause of death, killing an estimated 2.1 million people globally, the majority of whom are children in developing countries. Clostridium botulinum, E. coli, Salmonella spp., Listeria monocytogenes, Yersinia enterocolitica, Staphylococcus aureus, Shigella spp., Bacillus cereus, and Campylobacter jejuni are the most prevalent foodborne pathogens in the bacterial domain. Escherichia coli, Shigella sp., Staphylococcus sp., Bacillus spp., Klebsiella spp., Listeria monocytogenes, and other foodborne pathogens have all been documented. This review will highlight the Prevalence and
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    Robotics: future of schooling in Bangladesh
    (Brac University Research For Development Club (BURED), 2024-07-03) Noor, Abtahi; Rahman, Tasmia; Ahmed, Monjur; Eshika, Opshora Noshin; Fardin, Faraz; Labby, Ahshanul Mahbub
    Robotics deals with the knowledge of how to design, construct and develop robots. In the present world, technologies are taking over anything because of the efficiency of work over humans. So, children from schools must know robotics to inspire them from childhood to think and develop futuristic technology like robotics. Robotics should be included in the schooling curriculum to influence children to focus on futuristic technologies that will lead the world. Discussing the importance of teaching robotics in school is the main objective of this study. In the present world, robots are taking over anything because of the efficiency of work over human beings. Robotics covers the vast area of knowledge about how to design, construct and develop robots. In the study, the importance of robotics is broadly discussed along with the necessity of the addition of that topic in schooling curriculum. Additionally, the facts and problems associated with the teaching of robotics in schools will be discussed based on statistical data.
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    Using machine learning on a diverse class of problems : from rainfall to criminal actions
    (BRAC University, 2015-12) Munira, Sirajum; Roy, Tonmona Tonny; Rahman, Tasmia; Javed, Md.Aquib
    We intend to compare and analyze certain machine learning algorithms by taking two different datasets tackling separate real world issues. The first one relates to agriculture. Bangladesh, being an agrarian country, is heavily dependent on rain. Being able to predict the rainfall amount accurately would enable successful and sustainable production. Machine learning algorithms can also help predict the category of crimes in a particular area. This will enable law enforcers in a certain region to predict and categorize recent crimes based on past incidents. Our aim was to approach these problems by labelling and processing the data and then comparing the results of different cost functions.