Browsing by Author "Hasan, M.K."
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Item Detection of non-culturable Vibrio cholerae O1 associated with a cyanobacterium from an aquatic environment in Bangladesh(1994-05) Islam, M. Sirajul; Miah, M.A.; Hasan, M.K.; Sack, R.B.; Albert, M.J.Item Detection of Shigellae from Stools of Dysentery Patients by Culture and Polymerase Chain Reaction Techniques(2007-09-02T03:26:26Z) Islam, M.S.; Hosssin, M.S.; Hasan, M.K.; Rahman, M.M.; Fuchs, G.; Mahalanabis, D.; Baqui, A.H.; Albert, M. JhonItem Effects of Time on Dyeing of Different Cotton Fabrics with Reactive Dye(Daffodil International University, 2012) Monira, Shirajum; Haque, Md.Rashedul; Hasan, M.K.Aim of this project is to evaluate the effect of time, absorbency of dyed material reflectance (%) value of different cellulose fabrics by dyeing of 100% reactive dye. We have cotton knit and woven fabrics for dyeing. These will specifically address the subject of dyeing at single stage and consideration to the selection of dyeing agent. It is to be hoped that by the end of theis paper the reader will have a better idea about the time, what are the importance of time in a dye bath and which time is better and widely used in the dyeing operation.Item Growth and survival of Shigella flexneri in common Bangladeshi foods under various conditions of time and temperature(1993-02) Islam, M.S.; Hasan, M.K.; Khan, S.I.Item Isolation of Vibrio cholerae O139 Bengal from water in Bangladesh[letter](1993) Islam, M.S.; Hasan, M.K.; Miah, M.A; Qadri, F.; Yunus, M.; Sack, R.B.; Albert, M.J.Item Isolation of Vibrio cholerae O139 synonym Bengal from the aquatic environment in Bangladesh: implications for disease transmission(1994-05) Islam, M.S.; Hasan, M.K.; Miah, M.A.; Yunus, M.; Zaman, K.; Albert, M.J.Item Scientific Article Classification: Harnessing Hybrid Deep Learning Models for Knowledge Discovery(Institute of Electrical and Electronics Engineers Inc., 2023-11-03) Haque, R.; Parameshachari, B.D.; Hasan, M.K.; Sakib, A.H.; Rahman, A.U.; Islam, M.B.In an era defined by the explosive growth of the scientific literature, the imperative for effective methods of organizing, categorizing, and accessing scholarly articles has become paramount. This study addresses this crucial need by delving into the realm of scientific article classification, aiming to enhance accuracy through the innovative integration of hybrid deep neural networks. Focusing on domains including Computer Science, Mathematics, Physics, and Statistics, the research sought to improve categorization using advanced techniques. A comprehensive dataset of 20,006 abstracts was curated through rigorous data collection and preprocessing. Experimental models, spanning various RNN architectures and CNN-RNN based hybrids, were employed to assess the efficacy of the approach. The innovative integration of CNNs and RNNs pioneered new horizons in feature extraction. Key findings reveal the proficiency of hybrid models in capturing both local nuances and sequential dependencies within abstracts. Notably, the CNN-BiGRU model trained on Word2Vec embeddings exhibited highest F1 score of 91.32%. Furthermore, comparative analysis between GloVe and Word2Vec embeddings underscored the pivotal role of embeddings in extracting semantic information for accurate classification. © 2023 IEEE.Item Specificity of Cholera Screen test during an epidemic of cholera-like disease due to Vibrio cholerae O139 synonym Bengal[short report](1994-07) Islam, M.S.; Hasan, M.K.; Miah, M.A.; Huq, A.; Bardhan, P.K.; Sack, R.B.; Albert, M.J.Item Use of the polymerase chain reaction and fluorescent-antibody methods for detecting viable but nonculturable Shigella dysenteriae type 1 in laboratory microcosms(1993-02) Islam, M.S.; Hasan, M.K.; Miah, M.A.; Sur, G.C.; Felsenstein, A.; Venkatesan, M.; Sack, R.B.; Albert, M.J.Item Yoga posture recognition by detecting human joint points in real time using microsoft kinect(Institute of Electrical and Electronics Engineers Inc., 2018-02-09) Islam, M.U.,; Mahmud, H.,; Bin Ashraf, F.,; Hossain, I.,; Hasan, M.K.Musculoskeletal disorder is increasing in humans due to accidents or aging which is a great concern for future world. Physical exercises can reduce this disorder. Yoga is a great medium of physical exercise. For doing yoga a trainer is important who can monitor the perfectness of different yoga poses. In this paper, we have proposed a system which can monitor human body parts movement and monitor the accuracy of different yoga poses which aids the user to practice yoga. We have used Microsoft Kinect to detect different joint points of human body in real time and from those joint points we calculate various angles to measure the accuracy of a certain yoga poses for a user. Our proposed system can successfully recognize different yoga poses in real time.