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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 Article
TL;DR: In this article, the influence of the concentration of CdCl 2 -treatment-solution on the microstructural, morphological, and optical properties of thin CdTe thin films was presented.
Abstract: this work presents the influence of the concentration of CdCl 2 -treatment-solution on the microstructural, morphological, and optical properties of CdTe thin films. The CdTe films were deposited on CdS coated borosilicate glass by Close-Spaced Sublimation (CSS) technique using a standard recipe. The CdS films were grown by RF magnetron sputtering. The deposited CdTe films were treated with CdCl 2 solution of 0.20 M, 0.25 M, 0.30 M, 0.35 M, and 0.40 M concentration at 395 °C for 25 minutes. All the films exhibit polycrystalline nature with a predominant (111) Bragg peak of cubic CdTe. The highest crystallite size and smallest micro-strain and dislocation density are found for the treatment solution of 0.35M concentration. The results present that the treatment concentration enhances the S diffusion into the CdTe layer up to a certain limit of molar concentration. The highest S concentration (~2%) among the investigated samples are found for 0.35 M concentration. The SEM micrographs of the as-grown and treated films reveal that the grain size follows an increasing trend with the solution concentration up to 0.35 M. The optical bandgap of all samples are found to be in the range of 1.42 – 1.45 eV estimated from the UV-Vis-NIR transmission data. The lowest bandgap of 1.42 eV is found for the sample treated with 0.35 M solution due to the highest crystallinity among the samples investigated evident from both XRD and SEM results.

5 citations

Journal ArticleDOI
TL;DR: This paper anticipates structuring a machine learning and deep neural network-based mechanized system that can effectively induce religion, sexual orientation utilizing just the names of the masses.

5 citations

Proceedings ArticleDOI
01 Dec 2008
TL;DR: This paper presented a framework for translating the phrasal verbs in English sentence into its equivalent Bangla, and described different types of semantic features that perform semantic based disambiguation of phrAsal verbs.
Abstract: Generally phrasal verbs comprise a verb followed by a preposition that is commonly occurring feature in English. Each of the phrasal verbs acquires absolutely different meanings in different contexts. Having highly context dependent meanings, phrasal verbs may be disambiguated only by devising a technique involving utilization of semantic information pertaining to the context. This paper presented a framework for translating the phrasal verbs in English sentence into its equivalent Bangla. Semantics is necessary in the field of natural language processing to detect the meaning of the individual words in the sentence. This paper also describes different types of semantic features that perform semantic based disambiguation of phrasal verbs. A translation engine works to find out the exact Bangla meaning of the phrasal verb from different English sentences. We have tested our model for different type of English sentences containing phrasal verbs that are taken from the English newspaper and English textbook. We got successful results for most of the test cases.

5 citations

Posted ContentDOI
30 Nov 2020
TL;DR: This paper presents an effective heart disease prediction model through detecting the anomalies in healthcare data using the unsupervised K-means clustering algorithm using the Silhouette method.
Abstract: This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an optimal value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

5 citations

Journal ArticleDOI
TL;DR: In this article, a series of 1:10 physical model tests designed to improve our understanding of the basic failure modes of geobag revetments used as river bank protection is described.
Abstract: Sand-filled geotextile bags (geobags) are commonly used for riverbank protection, however, despite their deployment in a significant number of locations, the failure modes of such structures are not well understood. This paper describes a series of 1:10 physical model tests designed to improve our understanding of the basic failure modes of geobag revetments used as river bank protection. In particular, the aim of the experimental programme is to quantify the relationship between hydraulic shear stress on the revetment surface and geobag displacement. The aspect of the work reported is the use of two analytical techniques, the Conveyance Estimation System (CES) and the membrane analogy method developed by Chow to predict hydraulic shear stress on the geobag surface. It is concluded that both methods produce acceptable shear stress predictions for the initially washed away bags, although the CES is recommended for future use because of its better prediction and ease of application.

5 citations


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