Browsing by Author "Rahman, A. K. M. Mahbubur"

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    IMPLICATION OF FUZZY LOGIC IN GA FOR SOLVING MATHEMATICAL PROBLEMS
    (2007-01-01) Rahman, A. K. M. Mahbubur; Khan, Md. Bashir Uddin; Bhuiyan, Md. Al-Amin
    In this paper we present a novel method for obtaining fast software implementation of the Elliptic Curve Digital Signature Algorithm in the finite field GF(p) with an arbitrary prime modulus p of arbitrary length. The most important feature of the method is that it avoids bit-level operations which are slow on microprocessors and performs word-level operations which are significantly faster. The algorithms used in the implementation perform word-level operations, trading them off for bit-level operations and thus resulting in much higher speeds. We provide the timing results of our implementations on a 2.8 GHz Pentium 4 processor, supporting our claim that ECDSA is appropriate for constrained environments.
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    Morphological Classification of Radio Galaxies using Semi-Supervised Group Equivariant CNNs
    (Independent University, Bangladesh, 2023-05) Hossain, Mir Sazzat; Roy, Sugandha; Asad, K. M. B.; Momen, Arshad; Ali, Amin Ahsan; Amin, M Ashraful; Rahman, A. K. M. Mahbubur
    Out of the estimated few trillion galaxies, only around a million have been detected through radio frequencies, and only a tiny fraction, approximately a thousand, have been manually classified. We have addressed this disparity between labelled and unlabeled images of radio galaxies by employing a semi-supervised learning approach to classify them into the known FRI and FRII types. A Group Equivariant Convolutional Neural Network was used as an encoder that preserves the equivariance for the Euclidean Group E(2) to learn the representation of globally oriented feature maps through new SelfSupervised Learning (SSL) techniques SimCLR and BYOL. After representation learning, we trained a fully-connected classifier and fine-tuned the trained encoder with labelled data. We have found that this semi-supervised approach helps our method outperform a state-of-the-art method of classifying radio galaxies in many metrics. Our work reiterates the importance of semisupervised learning in radio galaxy classification, where labelled data are scarce, but prospects are immense.