Study of SMS security as part of an electronic voting system

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2006.Cataloged from PDF version of thesis report.Includes bibliographical references (page 48).E-voting is a new technology in voting system. Recently it was experimented in UK. Basically, this system is proposed to work via Short Message System (SMS). Using secure messaging system we are trying to develop the e-voting system here in our country. Our goal is to develop a system, which will be able to send SMS from a registered cell phone to a server located in the base polling station and cast a vote for a voter. The system must be secured so that while voting, no outside interference can be made to change the vote. So, there will be no tension of casting false vote. By the help of this system our voters will be able to cast their votes in a secure way and also the results will be available immediately when the vote casting finishes. This is going to be a pioneer change in our voting system. Some work on this proposed system has already been done. We wish to carry out the proposed system into further details. That is security aspects and implementation.B. Computer Science and Engineerin

EFFECT OF CREDIT RISK MANAGEMENT ON THE FINANCIAL PERFORMANCE OF BANKING SECTOR OF BANGLADESH: A STUDY ON GENERATION-BASED SELECTED LISTED COMMERCIAL BANKS

The financial sector of the country is mostly comprised of banking institutions; those are leading the economy with great exposure with the contribution to the development process. However, the banking sector of Bangladesh is disturbed by the large-scale amount of non-performing loans while has become an essential part of the finance of the industries. As part of the measurement of credit risk and macro factors average lending rate, inflation, NPL size, capital adequacy ratio, liquidity ratio have been selected to test the influence on the financial performance found through the return on asset of the selected banks. To conduct the study 9 banks of three generations have been selected for the period of 2016 to 2022. Robust least square method of regression and error correction term have been run to oversee the real impact on financial performance while endogenity and random walk in the values are being considered to overcome through a dynamic regression model. NPL has a negative impact on the performance and average lending rate, inflation, liquidity ratio and capital adequacy ratio bring a positive impact on the financial performance of the banks. Breusch-Pagan LM test confirms that cross-sectional dependency exists and VAR serial correlation test finds autocorrelation in the data set. The policy implication of this study suggests that the high NPL ratio must be reduced and CAR and LR must be improved to get the desired results of the performance. Strong fiscal and banking regulation should implement so that governance can be ensured to create responsibility and financial strength.JEL: E4, D81, E33, E44  Article visualizations

Genetic variation among biofortified and late blight tolerant potato (Solanum tuberosum L.) (mini tuber) production in Bangladesh

Biofortified potato could contribute a major role in food security for millions of people. It could help to alleviate worldwide micronutrient malnutrition. An experiment was carried out during 2019-2020 growing season with 49 accessions following randomized complete block design with three replications in order to evaluate and classify agro-morphological traits in Breeder seed production centre (BSPC), Debiganj, Panchagarh. Eight quantitative characters i.e. germination percent, foliage coverage, stem number per hill, plant height, plant vigor, tuber number per plant, tuber weight per plant, yield per plant were measured. Principal components (PC) analysis showed three components explained 72.16 % of the total variation among traits. The first PC assigned 35.22 % and the second PC assigned 58.47 % of total variation between traits. The first PC was more related to yield per plant and weight of tuber. Forty-nine germplasm was placed on three cluster based on cluster analysis using a hierarchical classification (HCA). All accessions were discriminated and high morphological variation was observed. Thus, the outcomes of principal component analysis used in the study have revealed the high level of genetic variation and the traits contributing to the variation were identified. CIP403, CIP404, CIP405, CIP413 and CIP445 accessions identified as superior based on cluster relationship and PCA bi-plot

Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh

The task of locating and classifying different types of vehicles has become a vital element in numerous applications of automation and intelligent systems ranging from traffic surveillance to vehicle identification and many more. In recent times, Deep Learning models have been dominating the field of vehicle detection. Yet, Bangladeshi vehicle detection has remained a relatively unexplored area. One of the main goals of vehicle detection is its real-time application, where `You Only Look Once' (YOLO) models have proven to be the most effective architecture. In this work, intending to find the best-suited YOLO architecture for fast and accurate vehicle detection from traffic images in Bangladesh, we have conducted a performance analysis of different variants of the YOLO-based architectures such as YOLOV3, YOLOV5s, and YOLOV5x. The models were trained on a dataset containing 7390 images belonging to 21 types of vehicles comprising samples from the DhakaAI dataset, the Poribohon-BD dataset, and our self-collected images. After thorough quantitative and qualitative analysis, we found the YOLOV5x variant to be the best-suited model, performing better than YOLOv3 and YOLOv5s models respectively by 7 & 4 percent in mAP, and 12 & 8.5 percent in terms of Accuracy.Comment: Accepted in 25th ICCIT (6 pages, 5 figures, 1 table

Multivariate Time Series Classification of Sensor Data from an Industrial Drying Hopper: A Deep Learning Approach

In recent years, the advancement of industry 4.0 and smart manufacturing has made a large number of industrial process data attainable with the use of sensors installed in the machineries. This thesis proposes an experimental predictive maintenance framework for an industrial drying hopper so that it can detect any unusual event in the hopper which reduces the risk of erroneous fault diagnosis in the manufacturing shop floor. The experimental framework uses Deep Learning (DL) algorithms in order to classify Multivariate Time Series (MTS) data into two categories- failure or unusual events and regular events, thus formulating the problem as binary classification. As classification is a supervised learning technique, any DL algorithm needs labeled data for classification. Moreover, raw data extracted from the sensors contain missing values. Therefore, necessary preprocessing is performed to make it usable for DL algorithms and the dataset is self-labeled after defining two categories precisely. To tackle the imbalanced data issue, data balancing techniques like Ensemble Learning with undersampling and Synthetic Minority Oversampling Technique (SMOTE) are used. Moreover, along with DL algorithms like Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), Machine Learning (ML) algorithms like Support Vector Machine (SVM), K Nearest Neighbor (KNN), etc. have also been used to perform a comparative analysis on the result obtained from these algorithms. The result shows that CNN is arguably the best algorithm for classifying this dataset into two categories and outperforms other traditional approaches as well as deep learning algorithms

The epidemiology of osteoarthritis and its association with cardiovascular disease and diabetes

Background: Osteoarthritis (OA) is a highly prevalent chronic condition and the most common form of rheumatic disease. The relationship between OA and cardiovascular disease (CVD) and diabetes has not been observed prospectively and the data on descriptive epidemiology of administratively defined OA are limited. Objectives: 1) to determine whether OA increases the risk of CVD (myocardial infarction, ischemic heart disease, congestive heart failure, and stroke) and diabetes; 2) to examine the association between OA and prevalent CVD; 4) to estimate the prevalence, incidence, and trends of OA; and 5) to validate the administrative diagnosis of OA. Methods: Using a random sample (n = 640,000) from the British Columbia administrative database during the period 1991-2009, the crude and age-standardized incidence rates and the prevalence of OA were calculated. Administrative OA Definition 1 required at least one physician diagnosis or hospital admission, and Definition 2 required, at least two physician diagnoses in two years or one hospital admission. The relative risks (RR) of CVD and diabetes in persons with OA, compared to age-sex matched non-OA individuals, were estimated using Cox proportional hazards models. Based on the Canadian Community Health Survey (CCHS) data, odds ratio (OR) between OA and heart disease was obtained. The validity of the two administrative definitions was determined using four clinical reference standards. Results: The overall prevalence of OA on March 2009, was 19.7%, and the incidence rate in the year 2008/09 was 14.6/1000 person-years under Definition 1. The adjusted RRs (95% CI) for CVD were 1.26 (1.13-1.42), 1.17 (1.07-1.26), 1.08 (0.97-1.19), and 1.15 (1.04-1.27), among younger women, older women, younger men, and older men, respectively. For diabetes, adjusted RRs (95% CI) were 1.27 (1.18-1.38), 1.23 (1.12-1.34), 1.19 (1.09-1.29), and 0.94 (0.82-1.09) for younger women, older women, younger men, and older men, respectively. In the CCHS sample, ORs (95% CI) for heart disease were 1.35 (1.21-1.50) among men and 1.51 (1.39-1.64) among women. Conclusions: These novel findings update current knowledge of OA epidemiology and highlight the risks of CVD and diabetes among persons with OA. These data are useful in formulating public health policies around OA treatment and prevention.Medicine, Faculty ofPopulation and Public Health (SPPH), School ofGraduat

Investigating factors influencing E-hrm implementation in government organisations: a multiple case study of Bangladesh

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis study focuses on investigating factors influencing the implementation of electronic human resource management (e-HRM) in Bangladesh government organisations. In recent years, research evidence has shown that e-HRM played a significant role in enhancing the performance of human resource (HR) departments in private sector organisations. While it is expected that similar contribution would be found in public sector (government) organisations, there is no empirical research evidence to support this proposition. Given the significant technological advancements today, e-HRM as a strategic tool has grown to encompass seamless functionality to support organisations in providing tactical capability, reducing cost and continuous service improvement as well as the quality of service delivery. Although several private and public sector organisations have implemented e-HRM, its application within public (government) sector organisation in developing nations is quite limited. In a sense, this may reflect the bureaucratic nature of public sector organisation, which are often slow to adopt changes. The low level in the number of public organisations that have implemented e-HRM applications in developing countries may be responsible for the dearth of empirical studies in this area. The dearth of empirical studies and the need to understand the factors that influence the implementation of e-HRM in public sector organisations led the-the author to investigate the factors influencing the implementation of e-HRM in public sector organisation. It is hoped that the findings provide insights into the myth surrounding the lack of and slow implementation of e-HRM among public sector organisation in developing economies but also serve as empirical data for further research. This thesis advances and contributes to the body of knowledge as it examines factors influencing the implementation of e-HRM in Bangladesh public sector organisations and proposes a model for e-HRM implementation in government organisations in developing economies (see Figure 3.3). The e-HRM implementation research presented in this thesis extends recognised standards for e-HRM implementation, by bringing together influential factors – benefits, barriers and risks, thus, enabling government organisations in developing nations to produce more vigorous suggestions for e-HRM implementation. The study adopts a qualitative and multiple case study research approach. Findings from two case organisations exhibit that such an approach contributes towards more healthy and robust decisions for e-HRM implementation and specifies that it is acceptable by the case study organisations. The findings in this study may not be generalised because of the nature of data collected. However, that it should serve as a source of understanding the particular context in which the study was undertaken. Moreover, it should serve as a catalyst for understanding organisations in similar socio-politico and economic context. This piece of research thus proposes, validates and presents a new model for e-HRM implementation in government organisations in developing economies and contributes to literature and the body of knowledge in this field

Bayesian curve fitting with roughness penalty prior distributions

In statistical research with populations having a multilevel structure, hierarchical models can play significant roles. The use of the Bayesian approach to hierarchical models has numerous advantages over the classical approach. For example, a spline with roughness penalties can easily be expressed as a hierarchical model and the model parameters can be estimated by the Bayesian techniques. Splines are sometimes useful to express the rapid fluctuating relationship between response and the covariate. In smoothing spline problems, usually one smoothing parameter (variance component in Bayesian context) is considered for the whole data set. But to deal with rapidly fluctuating or wiggly data sets, it is more logical to consider different smoothing parameters at different knot points in order to find more efficient estimates of the the regression functions under consideration. In this study, we have proposed the roughness penalty prior distribution considering local variance components at different knot points and call it Prior 2. Prior 2 is compared with Prior 1, where a single global variance component is considered for the whole data set, and with Prior 3, where no roughness penalty terms are considered ( i.e., the parameters at different knot points are assumed independent). Performance of the proposed prior distributions are checked for three different data sets of different curvature. Similar performance of Prior 1 and Prior 2 is observed for all three data sets under the assumption of piecewise linear spline. The application has been extended to the case of natural cubic spline, where the modification of Prior 1 and Prior 3 are straightforward. However, for Prior 2, the modification becomes very tedious. We have proposed an approximate roughness penalty matrix for Prior 2. Parameters corresponding to the smoothing splines are estimated using MCMC techniques. We carefully compare the inferential procedures in simulation studies and illustrate them for two data sets. Similarity among the curves produced by Prior 1 and Prior 2 are observed, and they are much smoother than the curve estimated by Prior 3 for both piecewise linear and natural cubic splines. Therefore, in the context of Bayesian curve fitting, both local and global roughness penalty priors produce equally smooth curves in dealing with wiggly data.Science, Faculty ofStatistics, Department ofGraduat