Union Parishad (UP) is the lowest tier of the Local Government of Bangladesh which bridges between people and government to ensure people’s participation in governance system. Therefore, from the last two decades government has been emphasizing on enhancing political emancipation of women in local government. To promote female leadership in grass-root level, Ministry of Local Government established a provision in the Local Government Act of reserving 3 (three) seats for women in UPs. This reformation enabled the female leaders to be elected in the UPs by a direct vote of the people. This research intends to explore the roles played by these elected female members of the studied UPs. It was conducted following a qualitative method and interview technique. Face to face interview has been taken from 45 female members of 15 UPs in Dhamrai Upazila, Dhaka with semi structured questionnaire. The study found that most of the respondents are contributing to development related project, social safety net program, controlling early marriage and dowry, family planning and female health-related program, awareness building of hygiene and sanitation, adult literacy etc. At the same time, it is found that significant numbers of the elected female members are not conscious about their responsibilities and even though they do not have any idea about UP manual. Finally, it revealed the obstacles in their workplaces, the challenges in their families, local societies, and recommended probable measures to overcome such situations. Besides, securing female leadership and active representations in the UPs can include the backward women in the mainstream of development and decision-making process which will undoubtedly contribute enormously to women empowerment.
This paper describes an automated selection of the ST segment in 12 leads electrocardiogram (ECG) as well as its classification based on cross correlation. Our proposed method classifies five categories of ST segment which are (a) Up slop (b) Down slop (c) Horizontal (Normal) (d) Concave (e) Convex using cross correlation process. We compare the main ECG (patient ECG) ST segment with the above-mentioned reference ST segments. In this work we have used MIT-BIH ST change database and European ST-T change database where every database contains minimum 30 min and maximum 1-h episode. Our method contains the following steps (1) Filtering ECG signal and Detrending it (2) R peak and S peak detection (3) Starting and ending point detection of ST segment (4) Comparing with ST segment supervised data (5) Classifying the ST segment. We have used total 1,34,879 beats where 58,331 beats from MIT-BIH ST change database and 74,609 beats from European ST-T change database. We have correctly selected total 126,608 ST segments. ST segment classification accuracy is 88.20% for MIT-BIH ST change database and 96.18% for European ST-T change database. The method confirms satisfactory performance with an overall accuracy of 92.1% which is helpful to the detection of major heart diseases like myocardial ischemia.
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