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Institution

Chittagong University of Engineering & Technology

EducationChittagong, Bangladesh
About: Chittagong University of Engineering & Technology is a education organization based out in Chittagong, Bangladesh. It is known for research contribution in the topics: Renewable energy & Dielectric. The organization has 1200 authors who have published 1444 publications receiving 10418 citations. The organization is also known as: Engineering College, Chittagong & Bangladesh Institute of Technology, Chittagong.


Papers
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Journal ArticleDOI
TL;DR: In this article, a finite element based model of the heat transfer problem is presented for an 800mm long pipe with 26mm inner diameter and 5mm thickness, and simulations have been completed for both stationary and time dependent conditions with Reynolds number 1600∼2400.

11 citations

Journal ArticleDOI
13 Jul 2018
TL;DR: This paper presents an optimal KNearest Neighbor (Opt-KNN) learning based prediction model based on patient’s habitual attributes in various dimensions that determines the optimal number of neighbors with low error rate for providing better prediction outcome in the resultant model.
Abstract: Nowadays, eHealth service has become a booming area, which refers to computer-based health care and information delivery to improve health service locally, regionally and worldwide. An effective disease risk prediction model by analyzing electronic health data benefits not only to care a patient but also to provide services through the corresponding data-driven eHealth systems. In this paper, we particularly focus on predicting and analysing diabetes mellitus, an increasingly prevalent chronic disease that refers to a group of metabolic disorders characterized by a high blood sugar level over a prolonged period of time. K-Nearest Neighbor (KNN) is one of the most popular and simplest machine learning techniques to build such a disease risk prediction model utilizing relevant health data. In order to achieve our goal, we present an optimal KNearest Neighbor (Opt-KNN) learning based prediction model based on patient’s habitual attributes in various dimensions. This approach determines the optimal number of neighbors with low error rate for providing better prediction outcome in the resultant model. The effectiveness of this machine learning eHealth model is examined by conducting experiments on the real-world diabetes mellitus data collected from medical hospitals.

11 citations

Journal ArticleDOI
20 Jul 2011
TL;DR: In this article, the effects of Ground Granulated Blast Furnace Slag (GGBFS) on strength development of mortar and the optimum use of slag in mortar were investigated.
Abstract: This paper presents an experimental investigation carried out to study the effects of Ground Granulated Blast Furnace Slag (GGBFS) on strength development of mortar and the optimum use of slag in mortar Cement was partially replaced with seven percentages (10%, 20%, 30%, 40%, 50%, 60% and 70%) of slag by weight Ordinary Portland cement (OPC) mortar was also prepared as reference mortar A total of 400 cube and briquet mortar specimens were cast and compressive as well as tensile strength of the mortar specimens were determined at curing age of 3, 7, 14, 28, 60, 90 and 180 days Test results show that strength increases with the increase of slag up to an optimum value, beyond which, strength values start decreasing with further addition of slag Among the seven slag mortars, the optimum amount of cement replacement is about 40%, which provides 19% higher compressive strength and 25% higher tensile strength as compared to OPC mortar K EY W ORDS : Slag; Cement; Mortar; Compressive Strength; Tensile Strength; Hydration DOI: http://dxdoiorg/103329/mistv3i08053

11 citations

Journal ArticleDOI
TL;DR: Samples of several brands of tiles popularly used in Bangladeshi dwellings have been evaluated in terms of their efficacy towards shielding of ionizing radiation as discussed by the authors, and the results showed that these tiles were different from those used in other tiles popular in Bangladesh.
Abstract: Samples of several brands of tiles popularly used in Bangladeshi dwellings have been evaluated in terms of their efficacy towards shielding of ionizing radiation. These decorative tile samples, dif...

11 citations

Proceedings ArticleDOI
01 Feb 2019
TL;DR: An approach to detect distraction real time by analyzing driver's visual feature from the face region using visual features such as movement of eye and head to extract critical information to detect driver attention states and to classify it as either attentive or distracted.
Abstract: Driver's distraction has been listed as the leading contributing factor to traffic accidents for the past decades. This paper focuses on developing an approach to detect distraction real time by analyzing driver's visual feature from the face region. The proposed approach uses visual features such as movement of eye and head to extract critical information to detect driver attention states and to classify it as either attentive or distracted. Deviation of eye center and head from their standard position for a period of time is considered to be useful cues for detecting lack of attention in this approach. At first face detection is performed after which region of interest (ROI) - eye and head region, are extracted using facial landmarks and lastly, head and eye movements are detected to classify attention state. To evaluate the system performance, we conducted an experiment in a real driving environment with subjects having different characteristics. Our system achieved on average 92% accuracy in detecting attention state for all tested scenarios.

11 citations


Authors

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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20234
202240
2021243
2020241
2019228
2018119