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Bangladesh

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Performance Evaluation of Machine Learning Algorithm for Classification of Unintended Pregnancy among Married Women in Bangladesh.

Journal of healthcare engineering
Intended pregnancy is one of the significant indicators of women's well-being. Globally, 74 million women become pregnant every year without planning. Unintended pregnancies account for 28% of all pregnancies among married women in Bangladesh. This s...

Predicting risks of low birth weight in Bangladesh with machine learning.

PloS one
BACKGROUND AND OBJECTIVE: Low birth weight is one of the primary causes of child mortality and several diseases of future life in developing countries, especially in Southern Asia. The main objective of this study is to determine the risk factors of ...

Prediction of monthly dry days with machine learning algorithms: a case study in Northern Bangladesh.

Scientific reports
Dry days at varied scale are an important topic in climate discussions. Prolonged dry days define a dry period. Dry days with a specific rainfall threshold may visualize a climate scenario of a locality. The variation of monthly dry days from station...

An investigation into epidemiological situations of COVID-19 with fuzzy K-means and K-prototype clustering methods.

Scientific reports
The ten countries with the highest population during the pandemic were analyzed for clustering based on the quantitative numbers of COVID-19 and policy plans. The Fuzzy K-Means (FKM) and K-prototype algorithms were used for clustering, and various pe...

Tea leaf disease detection and identification based on YOLOv7 (YOLO-T).

Scientific reports
A reliable and accurate diagnosis and identification system is required to prevent and manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and affecting yield quality and productivity. This study aims to present an arti...

Assessment of coastal vulnerability using integrated fuzzy analytical hierarchy process and geospatial technology for effective coastal management.

Environmental science and pollution research international
The vulnerability of coastal regions to climate change is a growing global concern, particularly in Bangladesh, which is vulnerable to flooding and storm surges due to its low-lying coastal areas. In this study, we used the fuzzy analytical hierarchy...

Application of machine learning and multivariate approaches for assessing microplastic pollution and its associated risks in the urban outdoor environment of Bangladesh.

Journal of hazardous materials
Microplastics (MPs) are an emerging global concern due to severe toxicological risks for ecosystems and public health. Therefore, this is the first study in Bangladesh to assess MP pollution and its associated risks for ecosystems and human health in...

Exploring post-COVID-19 health effects and features with advanced machine learning techniques.

Scientific reports
COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effec...

Empowering real-time flood impact assessment through the integration of machine learning and Google Earth Engine: a comprehensive approach.

Environmental science and pollution research international
Floods cause substantial losses to life and property, especially in flood-prone regions like northwestern Bangladesh. Timely and precise evaluation of flood impacts is critical for effective flood management and decision-making. This research demonst...

The Rohingya refugee crisis in Bangladesh: assessing the impact on land use patterns and land surface temperature using machine learning.

Environmental monitoring and assessment
Bangladesh, a third-world country with the seventh highest population density in the world, has always struggled to ensure its residents' basic needs. But in recent years, the country is going through a serious humanitarian and financial crisis that ...