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2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Mostert, Werner Malan, Katherine Engelbrecht, Andries Department of Computer Science University of Pretoria Pretoria South Africa Department of Decision Sciences University of South Africa Pretoria South Africa Institute for Big Data and Data Science University of Pretoria Pretoria South Africa

ISBN: (纸本)9781450357647

Feature selection is a complex problem used across many fields, such as computer vision and data mining. Feature selection algorithms extract a subset of features from a greater feature set which can improve algorithm accuracy by discarding features that are less significant in achieving the goal function. Current approaches are often computationally expensive, provide insignificant increases in predictor performance, and can lead to overfitting. This paper investigates the binary feature selection problem and the applicability of using filter and wrapper techniques guided by fitness landscape characteristics. It is shown that using filter methods are more appropriate for problems where the fitness does not provide sufficient information to guide search as needed by wrapper techniques. © 2018 Association for Computing Machinery.

关键词： Feature Selection

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DoT-Net: Document Layout Classification Using Texture-Based CNN

DoT-Net: Document Layout Classification Using Texture-Based ...

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International Conference on Document Analysis and Recognition

作者： Sai Chandra Kosaraju Mohammed Masum Nelson Zange Tsaku Pritesh Patel Tanju Bayramoglu Girish Modgil Mingon Kang Department of Computer Science Kennesaw State University Marietta USA Analytics and Data Science Institute Kennesaw State University Kennesaw USA GE power Atlanta USA

Document Layout Analysis (DLA) is a segmentation process that decomposes a scanned document image into its blocks of interest and classifies them. DLA is essential in a large number of applications, such as Information Retrieval, Machine Translation, Optical Character Recognition (OCR) systems, and structured data extraction from documents. However, identification of document blocks in DLA is challenging due to variations of block locations, inter-and intra-class variability, and background noises. In this paper, we propose a novel texture-based convolutional neural network for document layout analysis, called DoT-Net. DoT-Net is a multiclass classifier that can effectively identify document component blocks such as text, image, table, mathematical expression, and line-diagram, whereas most related methods have focused on the text vs. non-text block classification problem. DoT-Net can capture textural variations among the multiclass regions of documents. Our proposed method DoT-Net achieved promising results outperforming state-of-the-art document layout classifiers on accuracy, F1 score, and AUC. The open-source code of DoT-Net is available at https://***/datax-lab/DoTNet.

关键词： Feature extraction Layout Text analysis Shape Kernel Optical character recognition software Support vector machines

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Temporal trends in hemodialysis access creation during the fistula first era

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European Journal of Vascular & Endovascular Surgery 2024年第6期67卷 e95-e95页

作者： Fitzgibbon, James J., MD Heindel, Patrick, MD, MPH Appah-Sampong, Abena, MD Holden-Wingate, Christopher, BS Hentschel, Dirk M., MD Mamdani, Muhammad, PharmD, MA, MPH Ozaki, C. Keith, MD Hussain, Mohamad A., MD, PhD Department of Surgery Division of Vascular and Endovascular Surgery Brigham and Women’s Hospital/Harvard Medical School Boston MA Center for Surgery and Public Health Department of Surgery Brigham and Women’s Hospital/Harvard Medical School Boston MA Division of Renal Medicine Brigham and Women's Hospital/Harvard Medical School Boston MA Data Science and Advanced Analytics Unity Health Toronto Toronto Ontario Canada Temerty Centre for Artificial Intelligence Research and Education in Medicine University of Toronto Toronto Ontario Canada

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Artificial intelligence reveals brain aging patterns in 27,402 individuals without diagnosed cognitive impairment that are linked to genetics, biomedical measures, and cognitive decline

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Ioanna Skampardoni Ilya M. Nasrallah Junhao Wen Yuhan Cui Ahmed Abdulkadir Zhijian Yang Guray Erus Elizabeth Mamourian Ashish Singh Haochang Shou Li Shen Konstantina Nikita Christos Davatzikos iSTAGING consortium School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Correspondece Department of Radiology University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Laboratory for Research in Neuroimaging Department of Clinical Neurosciences Lausanne University Hospital (CHUV) and University of Lausanne Lausanne Switzerland 1Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA

Background Understanding heterogeneity of structural brain changes in aging may provide insights into susceptibility to neurodegenerative diseases. We characterize the genetics underlying brain structural heterogeneity within cognitively unimpaired (CU) individuals using data-driven machine learning applied to a diverse dataset of 27,402 individuals from 11 neuroimaging studies from the iSTAGING consortium. Method Structural brain morphologic patterns of CU individuals were independently examined in four decade-long intervals spanning ages 45 to 85. Within each interval, Smile-GAN (Yang et al., 2021) was trained on baseline anatomic and white matter hyperintensity (WMH) volumes. Smile-GAN probability scores were used as phenotypes in genome-wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for confounders (e.g., age) via Plink (Purcell et al., 2007). We observed longitudinal clustering stability across decades, so individuals from adjacent age groups were combined into broader age groups ([45,65), [65,85)) due to the large sample requirement of GWAS. Genomic loci, represented by the top leading single nucleotide polymorphisms (SNPs), were defined considering linkage disequilibrium. We investigated associations of SNPs with clinical traits and mapped them to genes using the GWAS Catalog (Buniello et al., 2019). Result Three structural brain aging patterns, relative to resilient agers (A0), consistent across decades, emerged: A1, or ‘typical’ aging with low atrophy and WMHs, and two ‘advanced’ aging patterns, one showing elevated WMHs and modest atrophy (A2) and the other displaying severe, widespread atrophy and moderate WMH load (A3) (Figure 1). GWAS discovered eight and six genomic loci in [45,65) and [65,85) age groups, respectively (Table 1, Figure 2). The lead SNPs for A1 and A2 were previously associated with several cardiometabolic risk factors, WMHs, and regional brain volumes. Interestingly, rs4843552, previo

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Speckle-based optical cryptosystem and its application for human face recognition via deep learning

arXiv

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arXiv 2022年

作者： Zhao, Qi Li, Huanhao Yu, Zhipeng Woo, Chi Man Zhong, Tianting Cheng, Shengfu Zheng, Yuanjin Liu, Honglin Tian, Jie Lai, Puxiang Department of Biomedical Engineering Hong Kong Polytechnic University Hong Kong Shenzhen Research Institute Hong Kong Polytechnic University Shenzhen518057 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Key Laboratory for Quantum Optics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medical Science and Engineering Beihang University Beijing100191 China Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing100190 China Photonics Research Institute Hong Kong Polytechnic University Hong Kong

Face recognition has recently become ubiquitous in many scenes for authentication or security purposes. Meanwhile, there are increasing concerns about the privacy of face images, which are sensitive biometric data that should be carefully protected. Software-based cryptosystems are widely adopted nowadays to encrypt face images, but the security level is limited by insufficient digital secret key length or computing power. Hardware-based optical cryptosystems can generate enormously longer secret keys and enable encryption at light speed, but most reported optical methods, such as double random phase encryption, are less compatible with other systems due to system complexity. In this study, a plain yet high-efficient speckle-based optical cryptosystem is proposed and implemented. A scattering ground glass is exploited to generate physical secret keys of gigabit length and encrypt face images via seemingly random optical speckles at light speed. Face images can then be decrypted from the random speckles by a well-trained decryption neural network, such that face recognition can be realized with up to 98% accuracy. The proposed cryptosystem has wide applicability, and it may open a new avenue for high-security complex information encryption and decryption by utilizing optical speckles. Copyright © 2022, The Authors. All rights reserved.

关键词： Speckle

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The properties of property alignment on the semantic web

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International Journal of Metadata, Semantics and Ontologies 2018年第1期13卷 42-56页

作者： Cheatham, Michelle Pesquita, Catia Oliveira, Daniela McCurdy, Helena B. Computer Science and Engineering Wright State University DaytonOH United States Department of Informatics University of Lisbon Lisbon Portugal Insight Centre for Data Analytics National University of Ireland Galway Ireland

The performance of alignment systems on property matching lags behind that on class and instance matching. This work seeks to understand the reasons for this and consider avenues for improvement. The paper contains an exploration of the performance of current alignment systems on the only commonly accepted alignment benchmark that involves matches between properties. A second benchmark involving properties from DBPedia and YAGO, scaled to be within the capabilities of most existing alignment systems, is also proposed. A basic approach focused on aligning properties is then presented and evaluated using both benchmarks to serve as a baseline against which to compare more complex matchers on the property alignment task. The results show that even a relatively simplistic approach can achieve a significantly higher F-measure than current matchers. Finally, an existing full-featured alignment system is augmented with the basic property matching approach and the difference in performance is assessed. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Ontology

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Predicting parkinson's disease using latent information extracted from deep neural networks

arXiv

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arXiv 2019年

作者： Kollia, Ilianna Stafylopatis, Andreas-Georgios Kollias, Stefanos Big Data and Analytics Center IBM Hellas Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece School of Computer Science University of Lincoln Lincoln United Kingdom

This paper presents a new method for medical diagnosis of neurodegenerative diseases, such as Parkinson's, by extracting and using latent information from trained Deep convolutional, or convolutional-recurrent Neural Networks (DNNs). In particular, our approach adopts a combination of transfer learning, k-means clustering and k-Nearest Neighbour classification of deep neural network learned representations to provide enriched prediction of the disease based on MRI and/or DaT Scan data. A new loss function is introduced and used in the training of the DNNs, so as to perform adaptation of the generated learned representations between data from different medical environments. Results are presented using a recently published database of Parkinson's related information, which was generated and evaluated in a hospital environment. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Constructing a Comprehensive Clinical database Integrating Patients' data from Intensive Care Units and General Wards 12

Constructing a Comprehensive Clinical Database Integrating P...

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12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Liu, Tongbo Liu, Xiaoli Fan, Yong Xu, Haoran Ng, Yeuk Lam Li, Peiyao Xue, Wanguo Zhang, Zhengbo Chinese PLA General Hospital Department of Computer Management and Application Beijing China School of Biological Science and Medical Engineering Beihang University Beijing China Chinese PLA General Hospital Department of Biomedical Engineering Beijing China University of Toronto Faculty of Arts Science Toronto Canada Medical Big Data Center Chinese PLA General Hospital Beijing China

ISBN: (纸本)9781728148526

Collection and analysis of large volumes of ICU data are invaluable to the advancement of clinical knowledge, and large-scale ICU databases have been effective resources to understand risk factors and perform predictive analysis by using machine learning. This paper introduces the construction of a comprehensive clinical database: PLAGH-ICU database, which integrated data from nine intensive care units, emergency department and general wards. data from several sources were extracted and integrated in the database, including patient demographics, hospital administrative data, physiological data, medications, lab test, fluid balance data, notes and reports etc. Detailed information about the database, such as patient characteristics, disease distribution, category of data, data records were illustrated. As far as we know, this is the first comprehensive ICU database developed and used as a research database in datathon event in China. Such kind of database will promote the research progress in critical care medicine, as well as the development of modern hospital information system in China. © 2019 IEEE.

关键词： Intensive care units

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Research on Pre-view Method of Safety Level of Cascading Trip for Power Grid 6th

Research on Pre-view Method of Safety Level of Cascading Tri...

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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Deng, Huiqiong Li, Qinbin Zheng, Rongjin Li, Peiqiang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China Fujian Provincial University Engineering Research Center of Smart Grid Simulation Analysis and Integrated Control Fuzhou Fujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China College of Mechanical & Electrical Engineering National Taipei University of Technology No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan Department of Business Administration North Borneo University College Lot 47 Block-F Alamesra Permai Plaza 2 Jln Sulaman Kota Kinabalu Sabah88400 Malaysia

ISBN: (纸本)9783030697167

This article introduces a method by which the power grid's security level can be observed in advance based on the expected initial failure. Firstly, based on the general form of cascading trip, this paper gives a mathematical expression of grid tripping based on cascading trip relay protection action behavior. According to the mathematical expression, the relationship between grid tripping and grid node injection power is discussed. According to this relationship, the distance between the node injection power under the current operation state and the nearest critical state node injection power is taken as the index to measure the power grid's security for cascading trip. According to this index, combined with various power grid constraints before and after the initial fault, a mathematical model for obtaining this safety index is given. This model is an optimized mathematical model. For the model, this paper gives a solution idea based on particle swarm. Finally, a few cases are performed in the IEEE-14 node system, and the calculations of the cases proved the accuracy of the proposed model and method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electric load flow

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Extracting valid indoor semantic trajectories using movement constraints

Extracting valid indoor semantic trajectories using movement...

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IEEE International Conference on big data

作者： Hani Ramadhan Yoga Yustiawan Joonho Kwon Big Data Department Pusan National University Busan South Korea Computer Science and Engineering Department Pusan National University Busan South Korea

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

An indoor semantic trajectory is a sequence of timestamped semantic positions inside a building. However, its extraction depends on the erroneous indoor positioning. The error leads to an invalid trajectory that has distant consecutive positions. This invalid trajectory may lead to an issue of the non-sensical patterns when analyzing a big semantic trajectory data. To prevent extracting invalid trajectories, we apply the movement constraints to infer only close positions to the current position. We extend the constraints to several indoor positioning techniques, such as Hidden Markov Model, K-Nearest Neighbor, or Deep Neural Network. We show that our approach can effectively extract valid indoor semantic trajectories.

关键词： Semantics Trajectory Hidden Markov models Machine learning big data Two dimensional displays data mining

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