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Dhaka, Bangladesh

The International Islamic University Chittagong or IIUC for short is a private university in Bangladesh located in Chittagong the major port and second-largest city of the country. It was founded in 1995 under the Private Universities Act of 1992 . Islamic University Chittagong Trust is the founder organization of this university. IIUC is still governed by IUCT. It got government approval on February 11, 1995. Wikipedia.


Sobhani F.A.,International Islamic University Chittagong | Amran A.,Universiti Sains Malaysia | Zainuddin Y.,Universiti Malaysia Pahang
Journal of Cleaner Production | Year: 2012

This study aims to describe the status of disclosure practices of corporate sustainability in the annual reports and corporate websites of the banking industry in Bangladesh. It is revealed in the study that, to varying degrees, all listed banks practice sustainability disclosure in an unstructured manner in both the annual reports and corporate websites. The annual report surpasses the corporate website in the disclosure of all categories of corporate sustainability disclosure (CSD) practices except product responsibility disclosure. Unlike the environmental and economic dimensions, issues concerning the social dimension are generally disclosed. Islamic banks disclose more sustainability information in comparison to conventional banks. It is also found that among the three generation, the older bank does not outperform the younger bank in terms of the sustainability disclosure. © 2011 Elsevier Ltd. All rights reserved. Source


Uddin M.T.,International Islamic University Chittagong
2015 4th International Conference on Informatics, Electronics and Vision, ICIEV 2015 | Year: 2015

Automated human gestures analysis has a wide range of promising applications in many advanced fields including human-computer interaction, motion analysis, and security surveillance. However, automatic gesture segmentation is still a very challenging task due to the spatio-temporal variation and endpoint localization issues, and the variation of gestures based on performers, topics and performance sessions. This paper presents a novel framework for segmenting gesture unit based on Ada-Boost and extremely randomized trees algorithms from video streams. In this approach, an Ada-Boost feature selection algorithm is applied to select compact feature subsets from the numerous raw extracted features to reduce the computational time as well as to improve the segmentation rate of the gesture segmentation model; then, selected features are fed to a robust extremely randomized trees classifier, given their capability to handle complex and unbalanced data, to segment gesture unit. The evaluation results of the experiments conducted on the publicly available benchmark gesture segmentation data set indicate that the proposed technique improve the segmentation metric by as much as 5.2% over the previously applied techniques. © 2015 IEEE. Source


Uddin M.T.,International Islamic University Chittagong
2015 4th International Conference on Informatics, Electronics and Vision, ICIEV 2015 | Year: 2015

The automatic recognition of facial expressions has a tremendous impact in many research fields, especially in the field of sign language since facial expressions contribute towards the formation of grammatical structure of the language that reduce the ambiguity of the sign language understanding. However, the automatic recognition of grammatical facial expressions is still a very challenging task due to the signer-based variation of the grammatical facial expressions, and the co-occurrence of manual and non-manual signs. This paper presents a novel Ada-Random Forests framework for recognizing the grammatical facial expressions used in Brazilian sign language. In this approach, an Ada-Boost feature selection algorithm is applied to select compact feature subsets from the numerous raw extracted features to reduce the computational time as well as to improve the recognition rate of the system; then, selected features are fed to a robust random forests classifier, given their capability to handle high-dimensional and unbalanced data, to recognize the grammatical facial expressions. The evaluation results of the experiments conducted on the first publicly available benchmark data set on Brazilian sign language indicate that the proposed technique improve the recognition metric by as much as 7.5% over the previously applied technique. © 2015 IEEE. Source


Uddin M.T.,International Islamic University Chittagong
2015 4th International Conference on Informatics, Electronics and Vision, ICIEV 2015 | Year: 2015

Automated analysis of social media documents has a tremendous impact in our day to day life since we extensively use social media to share our thoughts, feelings, tastes etc. However, the automatic social media analysis is still a very challenging task due to the massive amount of social media documents as well as the uncontrolled, dynamic and rapidly-changing content of social media documents. To automate social media analysis, this paper presents an automatic feedback prediction model based on novel Ada-Boost learning algorithm for blog documents considering realistic scenario. In this approach, an Ada-Boost classifier is applied to the numerous features extracted from crawled blog document to predict whether someone comments on a blog document or not in the next 24 hours of its publication in blogs. The evaluation results of the experiments conducted on the publicly available benchmark blog feedback data set indicate that the proposed technique is efficient both in terms of feedback prediction accuracy and computational time; the proposed approach yielded the maximum feedback prediction rate of 91.4%. © 2015 IEEE. Source


Rahaman S.,International Islamic University Chittagong
2nd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2015 | Year: 2015

Diabetes is a knotty disease and very common in the world. It can affect almost every organ of the body. The diagnosis of diabetes is determining from some medical and clinical data associate with diabetes. However, all the data are associated various types of uncertainty, which can cause for time delay, inaccuracy of the diagnosis. A system for automated medical diagnosis would enhance the accuracy of the diagnosis and reduce the cost effects. This paper presents an effective approach for diagnosing diabetes using Belief Rule Base (BRB) with evidential reasoning, which can handle the errors and uncertainties. This paper used the medical and clinical real data to develop and test this system. It has been observed that, this system provides user interactive and reliable results of diabetes diagnosis in percentage. © 2015 IEEE. Source

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