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Water demand management at rural area using Micro-Component Analysis: a case study at Kenyir Lake, Malaysia

IOP Conference Series: Earth and Environmental science 2022年第1期955卷

作者： N N I M Azlan N M Saad S Norhisham M A Malek N S M Shkuri M Zolkepli L W Ean A M Mohamad Department of Civil Engineering College of Engineering Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia Institute of Energy Infrastructures (IEI) Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia College of Computing & Informatics Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia College of Energy Economics & Social Sciences Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia 43400 UPM Serdang Selangor Malaysia Institute of Sustainable Energy Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia School of Law and Centre for Testing Measurement and Appraisal (CeTMA) Universiti Utara Malaysia 06010 Bukit Kayu Hitam Kedah

Water is one of the cornerstones in human survival and a necessity for socio-economic development. Due to that, effective management of water demand especially in the rural area becomes vital. The objective of this study is to conduct a water demand study at Kenyir Lake, Terengganu using Micro-Component Analysis (MCA). Water demand is also being assessed using National Water Services Commissions (SPAN) Guidelines, parallel to water guidelines applied for Malaysia's building and comparison purposes. MCA considers detailed parameters of water use activities of the respective study area, whereas SPAN Guidelines use the fixed values of average daily water demand according to the type of building. The available dataset was interpreted using MCA and SPAN Guidelines, resulting in total daily water demand at Kenyir Lake of 1249.8 m3 and 1254.1 m3 respectively. Estimated water demand using SPAN Guidelines is found to be higher than MCA. The value differences occur due to the average water demand value in the guideline that is limited to a specific type of premises and does not comply with premises there. The estimated water demand can be adopted as a baseline for water companies and the government to improve the demand and supply of water.

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Comparative Analysis of Context Based Classification of Twitter 4

Comparative Analysis of Context Based Classification of Twit...

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4th International Conference on Advances in computing, Communication and Automation, ICACCA 2018

作者： Tiwana, Muhammad Salman Javeed, Farooq Lali, Muhammad Ikramullah Dar, Hafsa Bilal, Muhammad Dept. of CS and IT University of Sargodha Sargodha Pakistan Dept. of Computer Science University of Gujrat Gujrat Pakistan Dept. of Software Engineering University of Gujrat Gujrat Pakistan School of Computing and IT Taylor's University Subang Jaya Malaysia Centre for Data Science and Analytics Taylor's University Subang Jaya Malaysia

ISBN: (纸本)9781538671672

Social media offers online data source where internet users' share their opinions, views and discuss any news or events that happen around the world. Twitter is a popular social media which effectively takes part in any mega event and is used before, during and after live events. Some tweets become trend based on their popularity. The trending tweets are classified by twitter into categories like: Sports (S), Entertainment (E), Politics (P), and Technology (T) which are used in our work. We have used context based meanings to further improve the trending tweet classification results. In this article, we have verified the twitter's automated classification results with manual classification by experts. Furthermore, we compared the classification results with SVM, Linear Regression, Logistic Regression and Naive Bayes implemented in Weka tool. We found that twitter's classifier works with almost 89% accuracy in our dataset. This work would contribute in improving misclassification of data of different categories on social media. © 2018 IEEE.

关键词： Classification (of information)

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Modeling Alzheimer's disease progression using deep recurrent neural networks

Modeling Alzheimer's disease progression using deep recurren...

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2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018

作者： Nguyen, Minh Sun, Nanbo Alexander, Daniel C. Feng, Jiashi Thomas Yeo, B.T. Clinical Imaging Research Centre Singapore Institute for Neurotechnology National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Centre for Cognitive Neuroscience Duke-NUS Medical School Singapore Singapore NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore Singapore

ISBN: (纸本)9781538668597

Multi-step prediction, predicting values multiple steps ahead, is a difficult problem because of the accumulation of error after successive predictions. Recurrent neural networks (RNN) have been applied successfully to multi-step prediction problems such as language modeling where training data are abundant and regular. However, applying RNN to problems in healthcare is still challenging as RNN requires large, feature complete training data in which intervals between time points are fixed which are not reflective of healthcare data. To overcome these problems, we propose strategies to train RNN using missing data which involve using the RNN to fill in the incomplete or missing time points online. The strategies enables the efficient use of training data as well as the adoption of RNN in problems where the variables in a multivariate time series are recorded at irregular intervals or at different frequencies, where there are a lot of missing data. We train RNN using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to model the monthly progression of Alzheimer's disease for seven years into the future which is a multi-step prediction problem. We show that RNN outperforms baseline models. © 2018 IEEE.

关键词： Recurrent neural networks

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Biometric Template Security Triggered by Two Dimensional Logistic Sine Map

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Procedia computer science 2018年 143卷 794-803页

作者： Sujarani Rajendran Manivannan Doraipandian Department of Computer Science and Engineering Srinivasa Ramanujan Centre SASTRA Deemed UniversityKumbakonam-612001 India. School of Computing SASTRA Deemed UniversityThanjavur-613401 India.

The distributed computing environment claims high protection of centralized servers. One of the protection mechanism is biometric authentication which provides a reliable solution to control access on servers. The security of user’s data depends on biometric systems, so such biometric templates should be protected from identity theft. Different approaches have been proposed to protect biometric templates, from that chaos based biometric cryptosystem become popular because of its properties such as ergodicity, determinism, high sensitivity on seed values. To enhance the security, this work developed a new two-dimensional Logistic Sine Map (2DLSM) by merging sine map to standard 2D logistic map. The proposed biometric cryptosystem is the sequential arrangement of confusion and diffusion process. Both processes are carried out by utilizing the chaotic stream generated by 2DLSM. Simulation results and analysis proves that the suggested scheme is fast, secure and efficient to implement in real-time applications.

关键词： Biometrics Chaos:Encryption Logistic map Sine map

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Genomic investigations of unexplained acute hepatitis in children

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Nature 2023年第7961期617卷 564-573页

作者： Morfopoulou, Sofia Buddle, Sarah Torres Montaguth, Oscar Enrique Atkinson, Laura Guerra-Assunção, José Afonso Moradi Marjaneh, Mahdi Zennezini Chiozzi, Riccardo Storey, Nathaniel Campos, Luis Hutchinson, J. Ciaran Counsell, John R. Pollara, Gabriele Roy, Sunando Venturini, Cristina Antinao Diaz, Juan F. Siam, Ala’a Tappouni, Luke J. Asgarian, Zeinab Ng, Joanne Hanlon, Killian S. Lennon, Alexander McArdle, Andrew Czap, Agata Rosenheim, Joshua Andrade, Catarina Anderson, Glenn Lee, Jack C. D. Williams, Rachel Williams, Charlotte A. Tutill, Helena Bayzid, Nadua Martin Bernal, Luz Marina Macpherson, Hannah Montgomery, Kylie-Ann Moore, Catherine Templeton, Kate Neill, Claire Holden, Matt Gunson, Rory Shepherd, Samantha J. Shah, Priyen Cooray, Samantha Voice, Marie Steele, Michael Fink, Colin Whittaker, Thomas E. Santilli, Giorgia Gissen, Paul Kaufer, Benedikt B. Reich, Jana Andreani, Julien Simmonds, Peter Alrabiah, Dimah K. Castellano, Sergi Chikowore, Primrose Odam, Miranda Rampling, Tommy Houlihan, Catherine Hoschler, Katja Talts, Tiina Celma, Cristina Gonzalez, Suam Gallagher, Eileen Simmons, Ruth Watson, Conall Mandal, Sema Zambon, Maria Chand, Meera Hatcher, James De, Surjo Baillie, Kenneth Semple, Malcolm Gracie Bellos, Evangelos Broderick, Claire Channon-Wells, Samuel De, Tisham D’Souza, Giselle Elorrieta, Leire Estramiana Estrada-Rivadeneyra, Diego Galassini, Rachel Habgood-Coote, Dominic Hamilton, Shea Jackson, Heather Kavanagh, James Marjaneh, Mahdi Moradi Menikou, Stephanie Nichols, Samuel Nijman, Ruud Patel, Harsita Pennisi, Ivana Powell, Oliver Reid, Ruth Vito, Ortensia Whittaker, Elizabeth Wilson, Clare Womersley, Rebecca Abdulla, Amina Darnell, Sarah Mustafa, Sobia Georgiou, Pantelis Manzano, Jesus-Rodriguez Moser, Nicolas Carter, Michael Tibby, Shane Cohen, Jonathan Davis, Francesca Kenny, Julia Wellman, Paul White, Marie Fish, Matthew Jennings, Aislinn Shankar-Hari Fidler, Katy Agranoff, Dan Richmond, Vivien Seal, Matthew Faust, Saul Owen, Dan Ensom, Ruth McKay, Sarah Mondo, Diana Shaji, Mariya Schra Infection Immunity and Inflammation Department Great Ormond Street Institute of Child Health University College London London United Kingdom Section for Paediatrics Department of Infectious Diseases Faculty of Medicine Imperial College London London United Kingdom Department of Microbiology Virology and Infection Control Great Ormond Street Hospital for Children NHS Foundation Trust London United Kingdom Section of Virology Department of Infectious Diseases Faculty of Medicine Imperial College London London United Kingdom University College London Mass Spectrometry Science Technology Platform Division of Biosciences University College London London United Kingdom Histopathology Department Great Ormond Street Hospital for Children NHS Foundation Trust London United Kingdom Research Department of Targeted Intervention Division of Surgery and Interventional Science University College London London United Kingdom Division of Infection and Immunity University College London London United Kingdom Gene Transfer Technology Group EGA-Institute for Women’s Health University College London London United Kingdom Genetics and Genomic Medicine Department Great Ormond Street Institute of Child Health University College London London United Kingdom Department of Neurodegenerative Disease Queen Square Institute of Neurology University College London London United Kingdom Wales Specialist Virology Centre Public Health Wales Microbiology Cardiff University Hospital of Wales Cardiff United Kingdom Department of Medical Microbiology Edinburgh Royal Infirmary Edinburgh United Kingdom Public Health Agency Northern Ireland Belfast United Kingdom School of Medicine University of St. Andrews St. Andrews United Kingdom Public Health Scotland Edinburgh United Kingdom West of Scotland Specialist Virology Centre Glasgow United Kingdom Micropathology Ltd University of Warwick Science Park Coventry United Kingdom Molecular and Cellular Immunology Great Ormond Street Institute of C

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK1. Here we report an investigation of 38 cases, 6... 详细信息

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK¹. Here we report an investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator participants, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in the liver, blood, plasma or stool from 27 of 28 cases. We found low levels of adenovirus (HAdV) and human herpesvirus 6B (HHV-6B) in 23 of 31 and 16 of 23, respectively, of the cases tested. By contrast, AAV2 was infrequently detected and at low titre in the blood or the liver from control children with HAdV, even when profoundly immunosuppressed. AAV2, HAdV and HHV-6 phylogeny excluded the emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T cells and B lineage cells. Proteomic comparison of liver tissue from cases and healthy controls identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins. HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV-mediated and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and, in severe cases, HHV-6B may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children. © 2023, The Author(s).

关键词： Clinical microbiology Paediatric research

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Searching for gravitational wave bursts from cosmic string cusps with the Parkes Pulsar Timing Array

arXiv

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arXiv 2020年

作者： Yonemaru, N. Kuroyanagi, S. Hobbs, George Takahashi, K. Zhu, X.-J. Coles, W.A. Dai, S. Howard, E. Manchester, R. Reardon, D. Russell, C. Shannon, R. Thyagarajan, N. Spiewak, R. Wang, J.-B. Kumamoto University Graduate School of Science and Technology Kumamoto860-8555 Japan CSIRO Astronomy and Space Science PO Box 76 EppingNSW1710 Australia Nagoya University Graduate School of Science Nagoya464-8601 Japan Instituto de Física Teórica UAM-CSIC Universidad Autonóma de Madrid Cantoblanco Madrid28049 Spain International Research Organization for Advanced Science and Technology Kumamoto University Kumamoto860-8555 Japan School of Physics and Astronomy Monash University VIC3800 Australia OzGrav: Australian Research Council Centre of Excellence for Gravitational Wave Discovery Electrical and Computer Engineering University of California at San Diego San diegoCA United States Department of Physics and Astronomy Macquarie University Sydney2109 Australia Centre for Quantum Dynamics Griffith University BrisbaneQLD4111 Australia Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia CSIRO Scientific Computing Services Australian Technology Park Locked Bag 9013 AlexandriaNSW1435 Australia National Radio Astronomy Observatory 1003 Lopezville Rd SocorroNM87801 United States Xinjiang Astronomical Observatory Chinese Academy of Science 150 Science 1-Street UrumqiXinjiang830011 China Key Laboratory of Radio Astronomy Chinese Academy of Science 150 Science 1-Street UrumqiXinjiang830011 China

Cosmic strings are potential gravitational wave (GW) sources that can be probed by pulsar timing arrays (PTAs). In this work we develop a detection algorithm for a GW burst from a cusp on a cosmic string, and apply it to Parkes PTA data. We find four events with a false alarm probability less than 1%. However further investigation shows that all of these are likely to be spurious. As there are no convincing detections we place upper limits on the GW amplitude for different event durations. From these bounds we place limits on the cosmic string tension of G μ∼ 10−5, and highlight that this bound is independent from those obtained using other techniques. We discuss the physical implications of our results and the prospect of probing cosmic strings in the era of Square Kilometre Array (SKA). © 2020, CC BY.

关键词： Pulsars

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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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Joint inference on structural and diffusion MRI for sequence-adaptive Bayesian segmentation of thalamic nuclei with probabilistic atlases

arXiv

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arXiv 2019年

作者： Iglesias, Juan Eugenio van Leemput, Koen Golland, Polina Yendiki, Anastasia Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London United Kingdom Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Computer Science and Artificial Intelligence Laboratory MIT United States Department of Health Technology Technical University of Denmark

Segmentation of structural and diffusion MRI (sMRI/dMRI) is usually performed independently in neuroimaging pipelines. However, some brain structures (e.g., globus pallidus, thalamus and its nuclei) can be extracted more accurately by fusing the two modalities. Following the framework of Bayesian segmentation with probabilistic atlases and unsupervised appearance modeling, we present here a novel algorithm to jointly segment multi-modal sMRI/dMRI data. We propose a hierarchical likelihood term for the dMRI defined on the unit ball, which combines the Beta and Dimroth-Scheidegger-Watson distributions to model the data at each voxel. This term is integrated with a mixture of Gaussians for the sMRI data, such that the resulting joint unsupervised likelihood enables the analysis of multi-modal scans acquired with any type of MRI contrast, b-values, or number of directions, which enables wide applicability. We also propose an inference algorithm to estimate the maximum-a-posteriori model parameters from input images, and to compute the most likely segmentation. Using a recently published atlas derived from histology, we apply our method to thalamic nuclei segmentation on two datasets: HCP (state of the art) and ADNI (legacy) - producing lower sample sizes than Bayesian segmentation with sMRI alone. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Large margin multi-modal multi-task feature extraction for image classification

arXiv

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arXiv 2019年

作者： Luo, Yong Wen, Yonggang Tao, Dacheng Gui, Jie Xu, Chao School of Electronics Engineering and Computer Science Peking University Beijing China Division of Networks and Distributed Systems School of Computer Engineering Nanyang Technological University Singapore Centre for Quantum Computation & Intelligent Systems Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Street UltimoNSW2007 Australia Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We therefore propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization and each sub-problem can be efficiently solved. Experiments on two challenging real-world image datasets demonstrate the effectiveness and superiority of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

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