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Artificial Neural Networks Optimized with Unsupervised Clustering for IDS Classification

Artificial Neural Networks Optimized with Unsupervised Clust...

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international conference on Smart Systems and Data Science (ICSSD)

作者： Ichrak Lafram Naoual Berbiche Jamila El Alami Laboratoire d’Analyse des systèmes et Traitement d’Image et Management Mohammed V University Rabat Agdal Rabat Morocco

The following topics are dealt with: learning (artificial intelligence); data mining; pattern classification; Big Data; production engineering computing; computer aided instruction; Internet; ontologies (artificial in... 详细信息

ISBN: (数字)9781728143682

ISBN: (纸本)9781728143699

关键词： Clustering algorithms Intrusion detection Support vector machines machine learning Data models Classification algorithms Artificial neural networks

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Heritage image classification by convolution neural networks 1

Heritage image classification by convolution neural networks

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1st international conference on Multimedia Analysis and pattern recognition, MAPR 2018

作者： Vu, Manh-Tu Beurton-Aimar, Marie Le, Van-Linh LaBRI-CNRS 5800 France PUF - Pôle Universitaire Francophone Ho Chi Minh City Viet Nam LaBRI-CNRS 5800 Universite Bordeaux Talence France LaBRI-CNRS 5800 France ITDLU - Dalat University France

ISBN: (纸本)9781538641804

In this project, we have explored the use of Convolution Neural Networks for semantic classification of heritage images. Recently four architectures, AlexNet, GoogLeNet, ResNet, and SENet have been proposed. We have tested them with two different learning modalities. Besides conventional training from scratch, we have resorted to pre-trained networks that have been fine-tuned on a huge amount of data in order to avoid overfitting problems and to reduce design/computing time. Experiments on a dataset we have built by ourselves from Heritage repository show that with the help of this step of fine-tuning, one of the simplest (and oldest) networks AlexNet is able to produce similar results than more sophisticated ones as ResNet which is pretty 3 times more consuming in computing time, mainly due to the number of the layer it uses. With this model, we have been able to show that our model can classify in the right way more than 98% of the images belonging to the dataset test. From now, this model can be used to classify the full dataset of the Heritage repository. © 2018 IEEE.

关键词： image classification

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Mapping the Research Landscape of Deep learning from 2001 to 2019

Mapping the Research Landscape of Deep Learning from 2001 to...

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international Informatics and Software Engineering conference (UBMYK)

作者： Fatih Gurcan Setenay Sevik Distance Education Application and Research Center Karadeniz Technical University Trabzon Turkey Career Center Karadeniz Technical University Trabzon Turkey

This paper analyzes recent literature to investigate the emerging topics and trends in deep learning research. In this context, a semantic content analysis based on probabilistic topic modeling has been performed on 22,279 journal articles on the subject of deep learning during the last 19 years between 2001 and 2019. As a consequence of this analysis, 18 topics mapping the research landscape of deep learning was revealed. The topic "Classification" has the highest ratio (10.57%). It is followed by the topics "image feature representing" (8.90%), and "Data applications" (8.71%). In addition, with the purpose of identifying the time-dependent average and acceleration of these topics, the average number of articles for each topic, and the rate of increase in the number of articles for each topic were calculated. In this context, the topics were separated into three groups including "hot topics", "stable topics", and "cold topics". These discovered topics, which map the research landscape of deep learning, are expected to contribute to understanding the present and future of deep learning.

关键词： Deep learning Acceleration Speech recognition Analytical models Market research Signal processing algorithms Feature extraction

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SVBiComm: Sign-Voice Bidirectional Communication System for Normal, 'Deaf/Dumb' and Blind People based on machine learning 1

SVBiComm: Sign-Voice Bidirectional Communication System for ...

引用

1st international conference on Computer Applications and Information Security, ICCAIS 2018

作者： Reda, Mariam Moustafa Mohammed, Nada Gamal Abdel Azeem Abul Seoud, Rania Ahmed Department of Computer Science Faculty of Computers and Information Fayoum University Egypt Department of Electrical Engineering Communication and Electronics Section Faculty of Engineering Fayoum University Fayoum Egypt

ISBN: (纸本)9781538644263

'deaf/dumb' and blind people face problems in communicating with others with difficulties in dealing with the communication technology. The goal of this paper is to design a desktop human computer interface application that is used to facilitate communication between normal, 'deaf/dumb' and blind people. SVBiComm system helps blind person to hear voice saying the word gestured by the' deaf/dumb' while the deaf will receive a gesture representing the word said by the blind. SVBiComm works in two directions, the first direction is processing from video to speech. The animated word gestures are mapped with language knowledge base into text. Then, the relevant audio is generated using Text-to-Speech (TTS) API. The second direction is processing from speech to video. The voice from blind is converted into its corresponding text using Speech-to-Text (stT) API. Then, the natural language is mapped from the database to 'deaf/dumb' in a relevant sign language form by using a 3D graphical model. The system was evaluated using a set of 113 sentences with 244 signs. In voice recognition;system recognized words with a percentage of 90% from 19 different persons. For image recognition the system recognized images with a percentage of 84 % for 21 different persons. SVBiComm system provides many facilities with low cost that could be used in many areas. © 2018 IEEE.

关键词： image recognition

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Fully Convolutional Network for 3D Human Skeleton Estimation from a Single View for Action Analysis

Fully Convolutional Network for 3D Human Skeleton Estimation...

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IEEE international conference on Multimedia and Expo (IEEE ICME)

作者： Lie, Wen-Nung Lin, Guan-Han Shih, Lung-Sheng Hsu, Yuling Thang Huu Nguyen Quynh Nguyen Quang Nhu Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat AIM HI CIRAS Dept Elect Engn Chiayi Taiwan Natl Chung Cheng Univ Dept Elect Engn Chiayi Taiwan Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi Taiwan Natl Chung Cheng Univ CIRAS Chiayi Taiwan Univ Sci & Technol Univ Danang FAST Hanoi Vietnam

ISBN: (纸本)9781538692141

Elderly health monitoring is important nowadays. This paper proposes to extract 3D skeleton for elderly for monitoring their daily behavior (such as walk, fall-down, etc.). Our technique uses two "Fully Convolutional Networks" (FCNs) to estimate 3D Human pose (skeleton) from the corresponding 2D pose (skeleton), which can be estimated from a single RGB-image. Our FCNs contain two-stages: the 1st stage is to estimate a 3D Anchor Pose (i.e., the most similar and frequently occurring pose in the dataset) from the 2D skeleton, while the 2nd stage is to further regress/refine the 3D anchor pose to its final state (parameters). We also apply global or object-centered normalization methods as a pre-processing step so as to be applicable when cameras of different FOV (Field of View), focal length, or object distances, are encountered. According to experiments, our two-stage FCNs are capable of achieving an MPJPE (Mean per joint position error) of 38.84 mm (better than other methods in comparison, 45.5 mm) when a 2D ground truth pose is used as the input. When cascaded with other 2D pose estimator (e.g., stacked Hourglass model), the average MPJPE is about 67.71 mm. Tis can be further improved if a 17-joints human skeleton model is adopted and re-trained based on the dataset H36M.

关键词： 2D human skeleton 3D human skeleton 2D pose 3D pose Deep learning Fully-convolutional network Human-machine interaction Anchor pose

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Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI 1

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1st international Workshop on Graph learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd international conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Yao, Dongren Liu, Mingxia Wang, Mingliang Lian, Chunfeng Wei, Jie Sun, Li Sui, Jing Shen, Dinggang Brainnetome Center & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Radiology and BRIC University of North Carolina at Chapel Hill Chapel HillNC27599 United States College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health Ministry of Health Peking University Beijing100191 China

ISBN: (数字)9783030358174

ISBN: (纸本)9783030358167

Brain functional connectivity (FC) derived from resting-state functional MRI (rs-fMRI) data has become a powerful approach to measure and map brain activity. Using fMRI data, graph convolutional network (GCN) has recently shown its superiority in learning discriminative representations of brain FC networks. However, existing studies typically utilize one specific template to partition the brain into multiple regions-of-interest (ROIs) for constructing FCs, which may limit the analysis to a single spatial scale (i.e., a fixed graph) determined by the template. Also, previous methods usually ignore the underlying high-order (e.g., triplet) association among subjects. To this end, we propose a multi-scale triplet graph convolutional network (MTGCN) for brain functional connectivity analysis with rs-fMRI data. Specifically, we first employ multi-scale templates for coarse-to-fine ROI parcellation to construct multi-scale FCs for each subject. We then develop a triplet GCN (TGCN) model to learn multi-scale graph representations of brain FC networks, followed by a weighted fusion scheme for classification. Experimental results on 1,218 subjects suggest the efficacy or our method. © 2019, Springer Nature Switzerland AG.

关键词： Brain

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Facial Attractiveness Classification using Deep learning 1

Facial Attractiveness Classification using Deep Learning

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1st international conference of the Indonesian-Association-for-pattern-recognition (INAPR)

作者： Anderson, Ricky Gema, Aryo Pradipta Suharjito Isa, Sani M. Bina Nusantara Univ Sch Comp Sci Dept Comp Sci Jakarta 11480 Indonesia Bina Nusantara Univ BINUS Grad Program Dept Comp Sci Comp Sci Jakarta 11480 Indonesia

ISBN: (纸本)9781538694220

Facial attractiveness classification application has many various usabilities, including photo editing, photo beautification, photo grading and dataset labeling. While face attractiveness classification seems to be related to personal preference, building a robust attractiveness classifier is not impossible. There are several studies that have developed a classification system of facial attractiveness using a convolutional neural network and provide satisfactory results. The use of image-net pre-trained convolutional neural network has been largely used by face-related research, yet none of them are related to facial attractiveness. This study aims to compare the famous deep learning architecture, such as VGG, Inception, and ResNet models. This research also aims to analyze the effect of using the Viola-Jones algorithm, as a preprocessing method, to the classification result of the built model. Viola-Jones algorithm will detect faces in image data, and 2 types of cropping will be done to extract the face region from the image, namely loose-crop and tight-crop. This research produces the highest accuracy value of 82.52% by using ResNet50 model and loose-crop preprocessing method.

关键词： Deep learning Facial Attractiveness Face recognition

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Graph Hyperalignment for Multi-subject fMRI Functional Alignment 1

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作者： Li, Weida Chen, Fang Zhang, Daoqiang College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9783030358174

ISBN: (纸本)9783030358167

In fMRI analysis, the scientist seeks to aggregate multi-subject fMRI data so that inferences shared across subjects can be achieved. The challenge is to eliminate the variability of anatomical structure and functional topography of the human brain, which calls for aligning fMRI data across subjects. However, the existing methods do not exploit the geometry of the stimuli, which can be inferred by using certain domain knowledge and then serve as a priori. In this paper, such geometry is encoded in a graph matrix, and we propose an algorithm named Graph Hyperalignment for leveraging it. Specifically, a kernel-based optimization is developed to allow for non-linear feature extraction. To tackle overfitting caused by the high-spatial-and-low-temporal resolution of fMRI, the data in the new feature space are assumed to lie in a low-dimensional affine subspace, which can be implicitly integrated into the proposed optimization. Unlike other iterative existing methods, GHA reaches an optimal solution directly. Examining over four real datasets, Graph Hyperaligment achieves superior results to other methods. © 2019, Springer Nature Switzerland AG.

关键词： Domain Knowledge

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A Fish Classification on images using Transfer learning and Matlab 1

A Fish Classification on Images using Transfer Learning and ...

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1st international conference of the Indonesian-Association-for-pattern-recognition (INAPR)

作者： Liawatimena, Suryadiputra Heryadi, Yaya Lukas Trisetyarso, Agung Wibowo, Antoni Abbas, Bahtiar Saleh Barlian, Erland Bina Nusantara Univ Fac Engn Comp Sci Dept Comp SciBINUS Grad Program Jakarta 11480 Indonesia Bina Nusantara Univ Fac Engn Dept Comp Engn Jakarta 11480 Indonesia Univ Katolik Indonesia Atma Jaya Fac Engn CERG Jakarta 12930 Indonesia Bina Nusantara Univ Comp Sci BINUS Grad Program Dept Comp Sci Jakarta 11480 Indonesia Bina Nusantara Univ Fac Engn Dept Ind Engn Jakarta 11480 Indonesia Bina Nusantara Univ Dept Management BINUS Business Sch Res Management Jakarta 11480 Indonesia

ISBN: (纸本)9781538694220

The Ministry of Marine Affairs and Fisheries (MMAF) carry out the responsibilities and features associated with the policy of marine and fisheries. MMAF carry out the responsibilities and features associated with marine and fisheries policy. One of their duties and functions are organized marine and fishery statistics, in accordance to Law of The Republic of Indonesia Number sixteen of 1997 regarding statistics. The main problem is the number of enumerators at each Basis Landing Of Fish. This paper proposed a fish classification on fish images using transfer learning and Matlab as the first stage of tackling the problem. FishNet is a modification from AlexNet to classify Katsuwonus Pelamis (Skipjack tuna or Cakalang), Euthynnus Affinis (Tongkol) and Coryphaena Hippurus (Mahi-mahi) that caught by fishermen. There are 15.120 images of 3 type of fishes, 5.040 for each fish. Data is split into 70: 30 for training and validation set. The training process is done using Matlab 2018a on Windows 7 operating system in a notebook with single CPU i7 with 8 GB RAM for 124 minutes. The validation accuracy is 99.63%.

关键词： image classification machine learning Marine animals

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An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN 24

An Efficient Approach for Polyps Detection in Endoscopic Vid...

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24th international conference on pattern recognition (ICPR)

作者： Mo, Xi Tao, Ke Wang, Quan Wang, Guanghui Univ Kansas Dept Elect Engn & Comp Sci Lawrence KS 66045 USA Jilin Univ Hosp 1 Changchun 130000 Jilin Peoples R China

ISBN: (纸本)9781538637883

Polyp has long been considered as one of the major etiologies to colorectal cancer which is a fatal disease around the world, thus early detection and recognition of polyps plays an crucial role in clinical routines. Accurate diagnoses of polyps through endoscopes operated by physicians becomes a chanllenging task not only due to the varying expertise of physicians, but also the inherent nature of endoscopic inspections. To facilitate this process, computer-aid techniques that emphasize on fully-conventional image processing and novel machine learning enhanced approaches have been dedicatedly designed for polyp detection in endoscopic videos or images. Among all proposed algorithms, deep learning based methods take the lead in terms of multiple metrics in evolutions for algorithmic performance. In this work, a highly effective model, namely the faster region-based convolutional neural network (Faster R-CNN) is implemented for polyp detection. In comparison with the reported results of the state-of-the-art approaches on polyps detection, extensive experiments demonstrate that the Faster R-CNN achieves very competing results, and it is an efficient approach for clinical practice.

关键词： Endoscopy

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