Browsing by Author "Hasan, Md. Hasib"

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    Machine Vision–Based Classification of Rare Fruits in Bangladesh Using Transfer Learning and Custom CNN Models
    (IUB, 2026-04) Hasan, Md. Hasib; Islam, Afsana; Bosri, Rabeya
    This study presents a deep learning framework for recognizing six rare Bangladeshi fruits using image classification. A dataset of 1,800 real-world images was created, and a lightweight Custom CNN was compared with transfer-learning models including MobileNetV2, InceptionV3, ResNet-50, and DenseNet-121. Experimental results show that the proposed Custom CNN achieved the best performance with 97.40% accuracy, along with superior ROC–AUC and PR–AUC scores. The findings demonstrate that domain-specific lightweight CNN architectures can outperform deeper pre-trained models while remaining computationally efficient. The proposed system has potential applications in digital agriculture, biodiversity conservation, and educational awareness related to Bangladesh’s rare fruit heritage.
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    Personal information from Bangla speech signal using MFCC and GMM
    (BRAC University, 2019-08) Hridy, Maisha Munawara; Hasan, Md. Hasib; Emon, Mahfuz Al; Uddin, Jia
    Our system extracts personal information from bangla speech. Dataset that was used consists real-life voice inputs from di erent age and gender groups. A set of Bengali speech samples from YouTube were used as input dataset. This system is based on basic machine learning algorithms. Mel frequency cepstral coe cient was used to train and construct this system. While calculating gender and age detection part, we will be using GMM to calculate the nal scores on the samples having the MFCCs of the extracted speech samples. GMM model basically congregates some subsets among the whole set based on probability. Along with the gender determination process, age detection process will also be simulated using fundamental frequency of speech. Python is the programming language used to write the coding. Our system was successful in giving 88% accuracy for gender recognition and 75% accuracy for age detection.