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21 results on '"Brown, Mary N"'

2. Classification of Banana Leaf Diseases Using Enhanced Gabor Feature Descriptor

Author

Ani Brown Mary, N., Robert Singh, A., Athisayamani, Suganya, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ranganathan, G., editor, Chen, Joy, editor, and Rocha, Álvaro, editor

Published
2021
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3. The effect of AZD9567 vs. prednisolone on glycaemic control in patients with type 2 diabetes mellitus: Results from a phase 2a clinical trial

Author

Ambery, Philip, primary, Zajac, Grzegorz, additional, Almquist, Joachim, additional, Prothon, Susanne, additional, Astbury, Carol, additional, Brown, Mary N., additional, Nemes, Szilard, additional, Nsabimana, Joselyne, additional, Edman, Karl, additional, Öberg, Lisa, additional, Lepistö, Matti, additional, Edenro, Goran, additional, Dillmann, Inken, additional, Mitra, Suman, additional, Belfield, Graham, additional, Keen, Christina, additional, and Heise, Tim, additional

Published
2024
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12. Response

Author

Usmani, Omar S., primary, Han, MeiLan K., additional, Kaminsky, David A., additional, Hogg, James, additional, Hjoberg, Josephine, additional, Patel, Naimish, additional, Hardin, Megan, additional, Keen, Christina, additional, Rennard, Stephen, additional, Blé, François-Xavier, additional, and Brown, Mary N., additional

Published
2022
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13. Banana leaf diseased image classification using novel HEAP auto encoder (HAE) deep learning

Author

Suganya Athisayamani, Ani Brown Mary N, and A. Robert Singh

Subjects
Training set, Contextual image classification, Computer Networks and Communications, Computer science, business.industry, Deep learning, 020207 software engineering, Pattern recognition, 02 engineering and technology, Autoencoder, Hardware and Architecture, 0202 electrical engineering, electronic engineering, information engineering, Media Technology, Artificial intelligence, business, Software, Heap (data structure)
Abstract

Among the fruit harvest, banana is one of the most significant crops in the international business for yielding the foreign exchange in many African countries. Nowadays, banana leaf diseases are the most significant factors regarding agricultural goods. These diseases result in a severe decline in the quantity and quality of agricultural food. Moreover, early recognition and classification of these banana leaf diseases are much needed. In this work, a novel deep learning technique called Heap Auto Encoders (HAEs) has been proposed. This proposed method can straightly extract important features and reduce the exhausted use of handcrafted features. Besides, over fitting difficulty in the training method is reduced, and the performance for a small training set is improved. The dropout method and the Rectified Linear Units (ReLU) activation function are also applied in HAE. The results of the proposed technique indicate that the proposed method is superior to traditional techniques. This framework provides the highest classification accuracy of 99.35% for real data sets.

Published
2020

14. Seven Pillars of Small Airways Disease in Asthma and COPD

Author

Usmani, Omar S., primary, Han, MeiLan K., additional, Kaminsky, David A., additional, Hogg, James, additional, Hjoberg, Josephine, additional, Patel, Naimish, additional, Hardin, Megan, additional, Keen, Christina, additional, Rennard, Stephen, additional, Blé, François-Xavier, additional, and Brown, Mary N., additional

Published
2021
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16. Coral reef image/video classification employing novel octa-angled pattern for triangular sub region and pulse coupled convolutional neural network (PCCNN)

Author

Dejey Dharma and Ani Brown Mary N

Subjects
geography, geography.geographical_feature_category, Contextual image classification, Computer Networks and Communications, business.industry, Computer science, Feature vector, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020207 software engineering, Pattern recognition, 02 engineering and technology, Coral reef, Convolutional neural network, Hardware and Architecture, Histogram, 0202 electrical engineering, electronic engineering, information engineering, Media Technology, Feature descriptor, 020201 artificial intelligence & image processing, Artificial intelligence, business, Classifier (UML), Time complexity, Software
Abstract

Coral reef image classification with the help of its texture features is a challenging task, due to its variation in class samples. This is achieved with the proposed feature descriptor termed as Octa-angled Pattern for Triangular sub region (OPT) which selects the neighbor in a triangular pattern in clockwise and counter-clockwise directions. The proposed method reduces the size of feature vector by reducing the bin size of histogram besides improving accuracy. For classification, a novel classifier, named Pulse Coupled Convolutional Neural Network (PCCNN) is employed. The performance of OPT is estimated using F-score. Experiments carried out with a variety of coral images and video data sets, diseased coral data sets and texture data sets to show that OPT technique gets on better than existing feature descriptors. Experimental result shows that the time complexity is reduced and accuracy is improved from 2 to 5% for all coral data sets used.

Published
2018

18. The development of AZD7624 for prevention of exacerbations in COPD: a randomized controlled trial

Author

Patel,Naimish, Cunoosamy,Danen, Fagerås,Malin, Taib,Ziad, Asimus,Sara, Hegelund-Myrbäck,Tove, Lundin,Sofia, Pardali,Katerina, Kurian,Nisha, Ersdal,Eva, Kristensson,Cecilia, Korsback,Katarina, Palmér,Robert, Brown,Mary N, Greenaway,Steven, Siew,Leonard, Clarke,Graham W, Rennard,Stephen I, Make,Barry J, Wise,Robert A, Jansson,Paul, Patel,Naimish, Cunoosamy,Danen, Fagerås,Malin, Taib,Ziad, Asimus,Sara, Hegelund-Myrbäck,Tove, Lundin,Sofia, Pardali,Katerina, Kurian,Nisha, Ersdal,Eva, Kristensson,Cecilia, Korsback,Katarina, Palmér,Robert, Brown,Mary N, Greenaway,Steven, Siew,Leonard, Clarke,Graham W, Rennard,Stephen I, Make,Barry J, Wise,Robert A, and Jansson,Paul

Abstract

Naimish R Patel,1,2 Danen M Cunoosamy,1 Malin Fagerås,1 Ziad Taib,1 Sara Asimus,1 Tove Hegelund-Myrbäck,1 Sofia Lundin,1 Katerina Pardali,1 Nisha Kurian,1 Eva Ersdal,1 Cecilia Kristensson,1 Katarina Korsback,1 Robert Palmér,1 Mary N Brown,3 Steven Greenaway,4 Leonard Siew,4 Graham W Clarke,4,5 Stephen I Rennard,6,7 Barry J Make,8 Robert A Wise,9 Paul Jansson11Innovative Medicines and Early Development, AstraZeneca, Gothenburg, Sweden; 2Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Hospital, Boston, MA, 3Innovative Medicines and Early Development, AstraZeneca, Boston, MA, USA; 4Quintiles Drug Research Unit at Guy’s Hospital, London, 5Department of Cardiothoracic Pharmacology, NHLI, Imperial College London, London, UK; 6Division of Pulmonary, Critical Care, Sleep and Allergy, University of Nebraska, Omaha, NE, USA; 7Clinical Discovery Unit, Innovative Medicines and Early Development, AstraZeneca, Cambridge, UK; 8Division of Pulmonary Sciences and Critical Care Medicine, National Jewish Health, University of Colorado, Denver, CO, 9Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, MD, USABackground: p38 mitogen-activated protein kinase (MAPK) plays a central role in the regulation and activation of pro-inflammatory mediators. COPD patients have increased levels of activated p38 MAPK, which correlate with increased lung function impairment, alveolar wall inflammation, and COPD exacerbations.Objectives: These studies aimed to assess the effect of p38 inhibition with AZD7624 in healthy volunteers and patients with COPD. The principal hypothesis was that decreasing lung inflammation via inhibition of p38α would reduce exacerbations and improve quality of life for COPD patients at high risk for acute exacerbations.Methods: The p38 isoform most relevant to lung inflammation was assessed using an in situ proximity ligation assay in severe COPD pat

Published
2018

20. The development of AZD7624 for prevention of exacerbations in COPD: a randomized controlled trial

Author

Patel, Naimish, primary, Cunoosamy, Danen, additional, Fagerås, Malin, additional, Taib, Ziad, additional, Asimus, Sara, additional, Hegelund-Myrbäck, Tove, additional, Lundin, Sofia, additional, Pardali, Katerina, additional, Kurian, Nisha, additional, Ersdal, Eva, additional, Kristensson, Cecilia, additional, Korsback, Katarina, additional, Palmér, Robert, additional, Brown, Mary N, additional, Greenaway, Steven, additional, Siew, Leonard, additional, Clarke, Graham W, additional, Rennard, Stephen I, additional, Make, Barry J, additional, Wise, Robert A, additional, and Jansson, Paul, additional

Published
2018
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21. Classification of Coral Reef Submarine Images and Videos Using a Novel Z with Tilted Z Local Binary Pattern (Z⊕TZLBP).

Author

Ani Brown Mary, N. and Dejey, Dharma

Subjects
CORAL reefs & islands, CORAL reef ecology, REEF ecology, CORAL bleaching, COMPUTATIONAL complexity
Abstract

In this paper, a novel feature descriptor named Z with Tilted Z Local Binary Pattern (Z⊕TZLBP) is proposed for extracting coral reef image features efficiently. The aim is to reduce LBP's computational complexity by reducing the size of the feature vector. This is achieved in the proposed Z⊕TZLBP by dividing the neighborhood pixels into two non overlapped groups of Z and TZ (Tilted Z), and computing LBP wherein the centre pixel is also treated as one of the neighbors. KNN classification with four different distance metrices has been used for classification purpose. Metric F-measure is used to evaluate the performance of the proposed system. Experiments conducted with various coral image and video data sets show that the proposed feature descriptor outperforms Local Binary Pattern (LBP), Uniform Pattern, Center-Symmetric Local Binary Pattern and Orthogonal Combination of Local Binary Pattern and also guarantees accurate and efficient feature extraction. [ABSTRACT FROM AUTHOR]

Published
2018
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