Browsing by Author "Islam, Md Jahidul"

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Internship report on audit procedures followed by ACNABIN & Co, Chartered Accountants.
(2014-12-27) Islam, Md Jahidul
This report is titled “Audit Procedures of a Chartered Accountant Firm – A Study on ACNABIN” is an outcome of BBA internship program. This report contains the details of the audit practice followed by ACNABIN. I have divided this report into seven sections. First section contains the background of the study. Section two will depict a clean picture of ACNABIN. In section three I have organized and discussed all my knowledge that I have gathered about auditing during my studies at the Department of Business Administration, Daffodil International University. Section four will provide the details of the overall audit procedures of ACNABIN. In section five, I have made a comparison between the Audit Procedures followed by ACNABIN and Emile Woolf’s Chronological Sequences of Audit Procedures. Section six contains the problems that I have identified to carry out audit engagement in ACNABIN and some recommendations to minimize such problems. In section seven, I have drawn an overall conclusion.
Physical, Mechanical, and Durability Properties of Concrete with Class F Fly Ash
(Research and Development Wing, MIST, 2023-12) Islam, Md Jahidul; Ahmed, Tasnia; Salehin, Md Riadus; Sadman Sakib, Mohammad; Shariar, Md Shakil; Hossain, Monowar
Concrete is one of the most used manufactured materialsin the world. Fly ash (FA) is a byproduct produced from pulverized coal combustion in power generation. A total of 0.08 million tons of class F fly ash is produced from a coal-based power plant yearly in Barapukuria, Bangladesh, whose disposal is of a great issue. Therefore, this study aimsto explore the possibility of using class F FA in concrete construction as a supplementary cementitious material. In this study, two different water-to-cement ratios (0.4 and 0.5), each with five cement replacement levels numerically, 0%, 10%, 20%, 30%, and 40% with FAare used. Various tests are performed on cylinder and beam specimens to assess physical, mechanical, and durability properties, such as workability, density, compressive strength (CS), splitting tensile strength (STS), flexural strength (FS), chloride ion penetrability (CIP), and shrinkage. Analyzing the results, it is reported that workability increases and density decreases with the increasingFA replacement. Mechanical properties mostly decrease with increasing FA content. However, the strength gained with age is higher for concrete with FA compared to the control concrete. The CIP reduces with FA replacement, especially at 56 days of age. Shrinkage value reduces 82% at 40% replacement FA replacement and w/c ratio 0.4. However, at 10% FA replacement and concrete age of 56 days, mechanical strength loss is infinitesimal or even better compared to the control concrete. Thus, a low replacement percentage of FA with a high curing period is a suitable concrete cement alternative.
Selectively Oversampling Difficult Positive Samples from Imbalanced Data for Preprocessing
(22nd International Conference on Computer and Information Technology, ICCIT 2019, IEEE, 2020-03-19) Mahin, Md.; Rukhsara, Lamia; Kabir, Md. Yasin; Rahman, H M Mostafizur; Islam, Md Jahidul; Khatun, Ayesha; Kabir, Sumaiya
Oversampling is a procedure traditionally has been applied to train machine learning classifiers for a better performance in presence of class imbalance. This work suggests a new insight for oversampling imbalanced data. In literature Borderline samples are mainly focused for oversampling. How-ever, because of low number of samples within the positive class a huge percentage of samples can be labeled as Rare and Outliers. These samples are often overlooked by the traditional oversampling methods or the nearest negative samples are often removed to increase positive prediction rate- while sacrificing the negative prediction rate. This work demonstrates that by only oversampling the Borderline, Rare and Outlier samples at different rate, better performance can be achieved than all other pre-processing methods. The proposed method is applied on four datasets- Abalone, CMC, Solar Flare and Seismic Bump, collected from the UCL digital library and compared with four traditional pre-processing methods ADYSYN, SMOTE, Border-line SMOTE 1 and 2 from imbalanced learn toolkit python. The result analysis shows that with fine tuning better performance can be achieved for all known performance measurements: Accuracy, True Positive Rate, True Negative Rate, Geometric Mean, Area Under the Curve measure and F-measure.