85 research outputs found


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    Modern artificial intelligence and machine learning opens up new era towards video surveillance system. Next generation video surveillance in Internet of Things (IoT) environment is an emerging research area because of high bandwidth, big-data generation, resource constraint video surveillance node, high energy consumption for real time applications. In this thesis, various opportunities and functional requirements that next generation video surveillance system should achieve with the power of video analytics, artificial intelligence and machine learning are discussed. This thesis also proposes a new video surveillance system architecture introducing fog computing towards IoT based system and contributes the facilities and benefits of proposed system which can meet the forthcoming requirements of surveillance. Different challenges and issues faced for video surveillance in IoT environment and evaluate fog-cloud integrated architecture to penetrate and eliminate those issues. The focus of this thesis is to evaluate the IoT based video surveillance system. To this end, two case studies were performed to penetrate values towards energy and bandwidth efficient video surveillance system. In one case study, an IoT-based power efficient color frame transmission and generation algorithm for video surveillance application is presented. The conventional way is to transmit all R, G and B components of all frames. Using proposed technique, instead of sending all components, first one color frame is sent followed by a series of gray-scale frames. After a certain number of gray-scale frames, another color frame is sent followed by the same number of gray-scale frames. This process is repeated for video surveillance system. In the decoder, color information is formulated from the color frame and then used to colorize the gray-scale frames. In another case study, a bandwidth efficient and low complexity frame reproduction technique that is also applicable in IoT based video surveillance application is presented. Using the second technique, only the pixel intensity that differs heavily comparing to previous frame’s corresponding pixel is sent. If the pixel intensity is similar or near similar comparing to the previous frame, the information is not transferred. With this objective, the bit stream is created for every frame with a predefined protocol. In cloud side, the frame information can be reproduced by implementing the reverse protocol from the bit stream. Experimental results of the two case studies show that the IoT-based proposed approach gives better results than traditional techniques in terms of both energy efficiency and quality of the video, and therefore, can enable sensor nodes in IoT to perform more operations with energy constraints

    Corruption and its diverse effect on credit risk: global evidence

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    Corruption has a complex relationship with economic growth. We have explored the impact of corruption on credit risk from a global perspective. The sample consists of 178 countries and covers 18 years that range from 2000 to 2017. Non-performing loan (NPL) is used as a proxy for credit risk and data regarding NPL is collected from the World Bank Database. Corruption scores are collected from the Transparency International reports. Panel regression results provide a positive association between corruption and credit risk for the global sample. Generalized Methods of Moments (GMM) regression and robustness tests validate the findings. However, sub-sample analysis provides support for “grease the wheel” hypothesis for high corruption countries and indicate that corruption is beneficial in a weak form of governance and excessive regulatory pressure. This study advocate for the importance of strong governance mechanisms in high corruption countries that can minimize the impact of corruption on banking sector profitability and ensure economic development. Unlike past literature, we provide global evidence on the association between corruption and credit risk for the banking sector which allows generalizability

    Job Satisfaction of Private Banking Sector Employees in Bangladesh

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    The intention of this study is to explore the job satisfaction level in Private Banking Sector officers of Bangladesh. This study highlights the factors of job satisfaction of bank employees in our country also try to evaluate the influences of these factors on the ultimate job satisfaction. So, Private Banks employees are the target population of this research paper. This paper reveled some institutional factors like working environment, work condition, pay, fair treatment of employees, provision of loan, promotion policy and acknowledgement for good performance meaningfully effect on job satisfaction of private Bank employees. On the other hand, employee’s individual factors such as age and gender, marital status minorly effect job satisfaction of Bank employees. The study conveys the message to Bank management that they should give more attention to stimulate and uphold the human resources in banking sector in Bangladesh. For attaining decisive mission of the banking division, the Bank Administration should satisfy the bank officers and utilize their maximum effort by confirming comprehensive superiority of their institutions. We think the results and academic consultation of this research may assist the relevant people for additional research and formulation of plan

    A study of the generalizability of self-supervised representations

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    Recent advancements in self-supervised learning (SSL) made it possible to learn generalizable visual representations from unlabeled data. The performance of Deep Learning models fine-tuned on pretrained SSL representations is on par with models fine-tuned on the state-of-the-art supervised learning (SL) representations. Irrespective of the progress made in SSL, its generalizability has not been studied extensively. In this article, we perform a deeper analysis of the generalizability of pretrained SSL and SL representations by conducting a domain-based study for transfer learning classification tasks. The representations are learned from the ImageNet source data, which are then fine-tuned using two types of target datasets: similar to the source dataset, and significantly different from the source dataset. We study generalizability of the SSL and SL-based models via their prediction accuracy as well as prediction confidence. In addition to this, we analyze the attribution of the final convolutional layer of these models to understand how they reason about the semantic identity of the data. We show that the SSL representations are more generalizable as compared to the SL representations. We explain the generalizability of the SSL representations by investigating its invariance property, which is shown to be better than that observed in the SL representations

    Complexities of waqf development in Bangladesh / Rashedul Hasan and Siti Alawiah Siraj.

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    Bangladesh is coping with the problem of poverty since its independence. Recent success stories of the country in alleviating poverty have been the outcome of the efforts of several public and private initiatives. As a Muslim-majority country, Bangladesh is yet to incorporate Islamic vehicles of poverty reduction in the national development strategies. Waqf has been sidelined as a mere charity even though it has historical success in producing continuous income and thus reducing poverty levels in the Muslim countries. This paper is divided into three sections. In the first part, it provides a historical overview of Waqf in Bangladesh followed by a brief description of Waqf institutions that are responsible for managing Waqf in Bangladesh. The final section discusses contemporary issues that are affecting the efficient management of Waqf funds towards making its mark in alleviating poverty from Bangladesh. In describing various problems, issues concerning governance and information disclosure were addressed that provide significant insight for different stakeholders. Information gathered through systematic literature review process indicated that Waqf institutions in Bangladesh are lacking appropriate governance mechanisms that are resulting in information asymmetry. Future research on the governance mechanisms applied by Waqf institutions and their impact on the extent and quality of information disclosure will provide further insight for the need of reform in this voluntary sector to ensure their sustainable future and contribution towards economic development in Bangladesh

    Influence of Internal and External Governance Mechanisms on Corporate Governance Disclosure among Islamic and Conventional Banks

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    The purpose of this paper is to investigate the extent of corporate governance disclosure in the annual reports of listed conventional and Islamic banks in Bangladesh. Out of fifty-six scheduled banks in Bangladesh, a sample of thirty-nine banks is selected, and data for the sample is extracted from the annual reports covering a period of 2011 to 2014. As such, the study focused on the extent of CGC after the stock market crisis in 2010 in Bangladesh. This results in the final observations of 116 which were used to perform balanced panel regression analysis. Fixed effect model is found significant for the balanced panel model which indicates that appointment of large audit firms negatively affects the extent of corporate governance compliance. Pooled OLS regression established that while profitability has a negative influence, the size of banks positively affects the extent of CGC. This study has focused on the commercial banks and thus results obtained from the study may not be representative for public and foreign banks operating in Bangladesh. Statistical evidence provided by the study provides guidelines for the policymakers toward necessary governance reforms required for banks to successfully operate in a post-crisis environment. Factors established by the study that influences corporate governance compliance using a balanced panel model are unique in the context of developing countries. Evidence of a difference in governance compliance between Islamic and conventional banks in Bangladesh establishes a new research arena and a necessary shift from the traditional performance comparisons

    Impact of intellectual capital on profitability : conventional versus Islamic banks

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    Intellectual capital has been found to have a significant association with profitability in the financial sector of various parts of the world. As a result, this study aims to empirically investigate the relationship between intellectual capital and financial performance of twentyseven private commercial banks for the year 2013 in Bangladesh. Annual reports for the relevant year of the selected banks have been used to gather secondary information for the empirical models based on Pulic’s VAIC model. Stepwise regression was performed for the full sample, conventional and Islamic banks separately. The analysis indicates that both VIAC and its components have a significant association with profitability. Results for conventional and Islamic banks established different components of VIAC as a significant predictor of bank’s profitability. A future study including all financial institutions could provide a better estimate of the impact of intellectual capital on profitability for the finance sector.peer-reviewe
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