Browsing by Author "Hossain, M.A.,"

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Effect of core material draft ratio and denier on core spun yarn and denim fabric properties pre and post washing
(Elsevier Ltd, 2024-01-17) Hossain, M.A.; Hossain, M.A.,; Emon, J.H.; Islam, M.T.
Core-spun yarn (CSY) is utilized for better fabric characteristics like stretchability, durability, and comfortability. The study aims to investigate the influence of spandex drafts of core-spun yarn on denim fabric characteristics before and after washing treatment. Two types of denim fabrics were produced from two types of core-spun yarn, namely 16 + 40D, and 16 + 70D by applying 2.8, 3.0, 3.20 spandex drafts for 16 + 40D, and 3.40, 3.50, 3.60 spandex drafts for 16 + 70D. Prepared denim fabrics were desized, and acid-washed and the properties of denim fabric before and after washing were investigated as a function of spandex drafts and deniers. Accurate count, twist, and better elongation percentage were observed at 2.80 draft for 16 + 40D CSY and 3.4 draft for 16 + 70D CSY, but a higher imperfection index (IPI) value was obtained on those drafts. The strength of the denim fabric prepared with 16 + 40D CSY and 16 + 70D CSY were higher at 2.8 and 3.6 drafts, respectively. Higher shrinkage (%), ends per inch (EPI), and fabric weight of denim fabric was obtained after washing compared to before washing. The width of both fabrics decreased when the fabric was washed. Exploring various drafts of core material and their correlations with yarn and fabric properties provides valuable insights for textile manufacturers seeking to produce denim fabrics with optimum quality.
Evaluating the Performance of State-of-the-art Methods and Classifying Covid-19 Infected Tissues
(Institute of Electrical and Electronics Engineers Inc., 2022) Kamruzzaman, M.M.; Moinuddin, M.,; Liton, A.I.,; Azad, M.M.,; Hossain, M.A.,; Rahman, W.
In this study, the Traditional Convolution Neural Network (TCNN) and state-of-the-art approaches were applied to the datasets of Chest X-ray and CT scan imaging modalities and trained them concurrently. The TCNN's performance for detecting COVID-19 infected tissues was determined through a comparison examination using state-of-the-art approaches. The accuracy of the models has been improved by lowering the model's losses and overfitting. Finally, the training data size has been enhanced utilizing various picture augmentation methods such as flip-up-down, flip-down-left-right, and so on. VGG19 and InceptionV3 were tested in this work, and accuracy scores of 97 percent (X-ray images) and 96 percent (CT-scan images) were obtained. The model's loss functions, Precision, Recall, and F1-Score, were extracted and interpreted in the study. We examined the researchers' modified DL models and discovered that they were 65 percent accurate on X-ray data and 62 percent accurate on CT scan images. Experiments have demonstrated that when the number of sample images rises, the VGG19 and InceptionV3 perform well.
Investigations of the spinning consistency index (SCI) and its impact on yarn quality
(Emerald Publishing, 2025-02-14) Hossain, M.A.,; Srijan, S.K.,; Rinaz, K.R.; Khan, S.Q.; Jalal Uddin, A.
Purpose: This study aims to analyze the spinning consistency index (SCI) and its impact on yarn quality. It focuses on the relationship between SCI and the final properties of 30 Ne carded hosiery yarn. By evaluating key quality parameters, the research seeks to determine how SCI can serve as a predictive tool for optimizing yarn production. Design/methodology/approach: The study involved producing 30 Ne carded hosiery yarn and rigorously testing its properties using the Uster Evenness Tester-5. Various quality parameters, including neps content, mass irregularity, yarn imperfections and tensile behavior, were evaluated at each processing stage. Findings: The findings reveal that Type 1 yarn demonstrated a count strength product value approximately 15% higher than the other two types. In addition, it exhibited a CVm% that was 18% lower, highlighting reduced mass variation and enhanced uniformity. Regarding imperfections, Type 1 yarn showed a 25% reduction compared to the other two types, reflecting improved fiber spinnability and overall yarn performance. Originality/value: This research thoroughly examines SCI’s role in yarn production, highlighting its predictive value for optimizing yarn quality. The study offers valuable insights for textile manufacturers seeking to enhance the efficiency and effectiveness of their spinning processes, ultimately contributing to the development of higher-quality textile products. © 2025, Emerald Publishing Limited.