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Syed Hamad Shirazi

Researcher at Hazara University

Publications -  32
Citations -  534

Syed Hamad Shirazi is an academic researcher from Hazara University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 10, co-authored 26 publications receiving 319 citations. Previous affiliations of Syed Hamad Shirazi include COMSATS Institute of Information Technology.

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Offline cursive Urdu-Nastaliq script recognition using multidimensional recurrent neural networks

TL;DR: An implicit segmentation based recognition system for Urdu text lines in Nastaliq script that relies on sliding overlapped windows on lines of text and extracting a set of statistical features is presented.
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Urdu Nasta'liq text recognition system based on multi-dimensional recurrent neural network and statistical features

TL;DR: A robust feature extraction approach that extracts feature based on right-to-left sliding window that significantly reduce the label error for Urdu Nasta’liq text lines and outperforms the state-of-the-art results.
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Deep transfer learning for alzheimer neurological disorder detection

TL;DR: This paper has tested 13 differnt flavours of different pre-trained CNN models using a fine-tuned approach of transfer learning across two different domain on ADNI dataset, and achieved remarkable accuracy on benchmark ADNI datasets.
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Innovation performance in digital economy: does digital platform capability, improvisation capability and organizational readiness really matter?

TL;DR: This study explores the role of digital platform capability, improvisational capability and organizational readiness for achieving innovation performance in digital economy by exploring the relationship between digital platforms capability and innovation performance link and how organizational readiness acts as mediator.
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Efficient leukocyte segmentation and recognition in peripheral blood image.

TL;DR: An efficient strategy to segment cell images by using Wiener filter along with Curvelet transform for image enhancement and noise elimination in order to elude false edges and overcome the problem of overlapping cells.