Financial analysis of HSBC (2008-2012)

This internship report is submitted in a partial fulfillment of the requirements for the degree of Bachelor of Business Administration,2013.Cataloged from PDF version of Internship report.Includes bibliographical references (page 42).This report elaborates some key financial findings of HSBC from the 2008 to 2012 and tries to give a meaningful interpretation of the major findings. The findings will provide a snap shot of HSBCs performance over the last couple of years and how they have been doing as a global bank. The report also contains my role as an intern in HSBC Bangladesh and what I have done in these four months of my stay in the management office of HSBC. I have tried to relate my academic learning in the practicality of an office premises and also used the opportunities to learn the things outside the theories of academic books. The report is broken down into several parts for the ease of reading. This report showcases my four months experience in HSBC.Md. Ishtiaque ShamsB. Business Administratio

GENDER ADAPTATION TO CLIMATIC VULNERABILITIES IN COASTAL REGIONS OF BANGLADESH

Master'sMASTER OF SCIENCE (ENVIRONMENTAL MANAGEMENT) (MEM

An End-to-End Deep Learning Framework for Real-Time Denoising of Heart Sounds for Cardiac Disease Detection in Unseen Noise

The heart sound signals captured via a digital stethoscope are often distorted by environmental and physiological noise, altering their salient and critical properties. The problem is exacerbated in crowded low-resource hospital settings with high noise levels which degrades the diagnostic performance. In this study, we present a novel deep encoder-decoder-based denoising architecture (LU-Net) to suppress ambient and internal lung sound noises. Training is done using a large benchmark PCG dataset mixed with physiological noise, i.e., breathing sounds. Two different noisy datasets were prepared for experimental evaluation by mixing unseen lung sounds and hospital ambient noises with the clean heart sound recordings. We also used the inherently noisy portion of the PASCAL heart sound dataset for evaluation. The proposed framework showed effective suppression of background noises in both unseen real-world data and synthetically generated noisy heart sound recordings, improving the signal-to-noise ratio (SNR) level by 5.575 dB on an average using only 1.32 M parameters. The proposed model outperforms the current state-of-the-art U-Net model with an average SNR improvement of 5.613 dB and 5.537 dB in the presence of lung sound and unseen hospital noise, respectively. LU-Net also outperformed the state-of-the-art Fully Convolutional Network (FCN) by 1.750 dB and 1.748 dB for lung sound and unseen hospital noise conditions, respectively. In addition, the proposed denoising method model improves classification accuracy by 38.93% in the noisy portion of the PASCAL heart sound dataset. The results presented in the paper indicate that our proposed architecture demonstrated a robust denoising performance on different datasets with diverse levels and characteristics of noise. The proposed deep learning-based PCG denoising approach is a pioneering study that can significantly improve the accuracy of computer-aided auscultation systems for detecting cardiac diseases in noisy, low-resource hospitals and underserved communities