NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Large scale systems.")'

Dynamic Games Theory and Applications /

By Alain Haurie. 0387246010

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Sustainable urbanism : urban design with nature /

By Farr, Douglas.. Hoboken, N.J. : Wiley, 2008 . 304 p. : 24 cm.. 9780471777519 (cloth) | 047177751X (cloth)

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Sustainable urbanism : urban design with nature /

By Farr, Douglas.. Hoboken, N.J. : Wiley, 2008 . 304 p. : 24 cm.. 9780471777519 (cloth) | 047177751X (cloth)

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Manmade modular megastructures /

London : Wiley ; 2006 . 128 p. : 28 cm.. 047001623X (paperback) | 9780470016237 (paperback)

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Energy from the desert. CAS-EVery large scale photovoltaic power--state of the art and into the future /

2013 . xiv, 225 pages : 31 cm. 9780415639828 | 9780203081402

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Alternative energy for dummies /

By DeGunther, Rik,. Hoboken, NJ : Wiley, 2009 . xviii, 362 p. : , Includes index. 24 cm.. 9780470430620 (pbk.) | 0470430621 (pbk.)

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Photoelectrochemistry, Photocatalysis and Photoreactors Fundamentals and Developments /

By Schiavello, Mario.. Dordecht: D. reidel publishing compony, 1984 9789401577250 | 9789048184149 (print)

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Modeling and Control of Fuel Cells Distributed Generation Applications

By Nehrir, M. H.. Hoboken : Wiley-IEEE Press [Imprint] | John Wiley & Sons, Incorporated 2009 9780470233283 | 0470233281

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Rooftop garden.

Berkeley, CA : Gingko Press, 2013 . 347 p. : , Concept and compilation by HKASP ; graphic design by Meiguang Hou ; copy editing by Danielle Tsao.--Colophon. 30 cm.. 9781584235323 | 1584235322

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Algorithms in advanced artificial intelligence /

New Yprk CRC Press 2024 . xxiii,520 Pages.; 9781032867984

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Automated Karyotyping: Segmentation and Classification /

By Umbreen, Neelam. . 130p. , Karyotyping continues to be the bedrock of cytogenetic diagnosis, providing key information on chromosomal abnormalities causative of a broad range of genetic disorders, developmental abnormalities, and cancers. But standard karyotyping is time-consuming, requires extensive specialist interpretation, and is vulnerable to human mistake and inefficiency, especially in high-volume clinical settings. Despite advances in medical image analysis, the automation of karyotyping faces persistent challenges, including the lack of large-scale annotated datasets, difficulties in segmenting overlapping chromosomes, and variability in chromosome morphology and staining. These challenges define a significant research gap in developing scalable, accurate, and clinically deployable deep learning models for automated chromosome analysis. In order to overcome this gap, we first present a large-scale, clinically annotated cytogenetic database, built from 1,311 patients and consisting of 10,057 karyograms with 514,949 manually annotated chromosome singlets. Furthermore, 3,935 metaphase images are annotated at the instance level in COCO format. This data set reflects true-world diversity, ranging from normal and abnormal karyotypes to different Giemsa (G-banding) staining intensities, structural abnormalities, and overlapping difficult cases, and thus presents a solid basis for the creation and testing of deep learning models within automated cytogenetics. Based on this work, we create two primary methodologies aimed at the fundamental tasks of karyotyping. For segmentation of chromosomes, we introduce a variant Mask R-CNN model involving an Attention-based Feature Pyramid Network (AttFPN), spatial attention, and a LastLevelMaxPool component to improve multi-scale feature representation and contextual perception. It enhances performance in difficult situations, including overlapping chromosomes and weak banding patterns, and gains considerable improvements in mean Average Precision (mAP) compared to standard baselines. For chromosome classification, we present the Dual Attention Multiscale Pyramid Network (DAMP), a specifically designed model that combines channel and spatial attention mechanisms to concentrate on discriminative features, as well as a multiscale pyramid architecture to cope with size, orientation, and quality variation in chromosomes. DAMP's highest classification accuracy is 96.76% on both public and commercial datasets, performingxv better than state-of-the-art models like ResNet-50, Vision Transformers, and Siamese Networks. Overall, this thesis provides interpretable and scalable deep learning models for automating chromosome classification and segmentation. Through the closure of key gaps in dataset quality, model resilience, and clinical utility, the work facilitates the insertion of clever decision-support systems into cytogenetic pipelines, ultimately leading to improved diagnostic reliability and efficiency in the face of chromosomal disorders. 30cm.

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