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1st international conference on Smart Sustainable Intelligent Computing and Applications, ICITETM 2020

作者： Sharma, Ashish Mittal, Anmol Singh, Savitoj Awatramani, Vasudev Maharaja Agrasen Institute of Technology Guru Gobind Singh Indraprastha University New Delhi Delhi110085 India

image identification is becoming a crucial step in most of the modern world problem-solving systems. Approaches for image detection, analysis and classification are available in glut, but the difference between such approaches is still arcane. It essential that proper distinctions between such techniques should be interpreted and they should be analyzed. standard American Sign Language (ASL) images of a person's hand photographed under several different environmental conditions are taken as the dataset. The main aim is to recognize and classify such hand gestures to their correct meaning with the maximum accuracy possible. A novel approach for the same has been proposed and some other widely popular models have compared with it. The different preprocessing techniques used are Histogram of Gradients, Principal Component Analysis, Local Binary patterns. The novel model is made using canny edge detection, ORB and bag of word technique. The preprocessed data is passed through several classifiers (Random Forests, Support Vector machines, Naïve Bayes, Logistic Regression, K-Nearest Neighbours, Multilayer Perceptron) to draw effective results. The accuracy of the new models has been found significantly higher than the existing model. © 2020 The Authors. Published by Elsevier B.V.

关键词： Local binary pattern

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Bird Species Identification using Deep learning on GPU platform 1

Bird Species Identification using Deep Learning on GPU platf...

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1st international conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020

作者： Gavali, Pralhad Banu, J.Saira Rajarambapu Institute of Technology Sakharale Computer Science Information Technology Department Sangli Maharashtra India

ISBN: (数字)9781728141428

ISBN: (纸本)9781728141428

Today, many species of birds are rarely found, and it is difficult to classify bird species when found. For example, for different scenarios, birds come with different sizes, forms, colors and from a human viewpoint with different angles. Indeed, the images show different differences that need to be recorded as audio recognition of bird species. It is also easier for people to identify birds in the pictures. Today, using deep convolutional neural network (DCNN) on GoogLeNet framework bird species classification is possible. For this experiment, a bird image was converted into a gray scale format that generated the autograph. After examining each and every autograph that calculates the score sheet from each node and predicts the respective bird species after the score sheet analysis. In this experiment, the Caltech-UCSD Birds 200 [CUB-200-2011] data collection was used for both training and testing purposes. For training purpose 500 labeled data are used and 200 unlabeled data are used for testing. For classification, Deep Convolutional Neural Networks are used and parallel processing was carried out using GPU technology. Final results show that the DCNN algorithm can be predicted at 88.33% of bird species. The experimental research is performed on the linux operating systems with Tensor flow library and using a NVIDIA Geforce GTX 680 with 2 GB RAM. © 2020 IEEE.

关键词： Birds

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Research on Text Detection and recognition Based on OCR recognition Technology

Research on Text Detection and Recognition Based on OCR Reco...

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international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Yuming He Software Engineering Shanghai Starriver Bilingual School Shanghai China

ISBN: (数字)9781728183046

ISBN: (纸本)9781728194608

Optical character recognition (OCR) is an important branch in the field of machine vision. It involves pattern recognition, image processing, digital signal processing, artificial intelligence and other disciplines. It is a comprehensive. It has important use value and theoretical significance in high-tech fields such as word information processing, office automation, machine translation and real-time monitoring system. In the 21st century, with the popularity of smart phones with high-definition cameras, OCR has a new pursuit in its development: more and more people pick up their mobile phones to photograph the things and scenes they see and obtain the text information in the pictures. Therefore, the recognition of characters in natural scenes has become a brand-new topic. In the past, text detection and text recognition algorithms were basically based on artificially designed features and traditional image processing methods. These features and algorithms were difficult to design and needed a lot of professional knowledge and experience support, so the accuracy was not high and they were not generalized. In recent years, with the rapid development of deep learning technology, breakthroughs have been made in the fields of computer vision such as image classification, object detection and semantic segmentation. Deep learning algorithm is a data-driven algorithm. The algorithm based on deep learning can automatically discover and learn the hidden feature rules in a large number of data through iterative training, without too much human intervention, so it has better generalization than traditional image processing related algorithms.

关键词： Deep learning image recognition Text recognition Signal processing algorithms Classification algorithms Optical character recognition software Character recognition

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Application of deep learning and compressed sensing for reconstruction of images 1

Application of deep learning and compressed sensing for reco...

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1st international conference on Advances in Physical Sciences and Materials 2020, ICAPSM 2020

作者： Hanumanth, P. Bhavana, P. Subbarayappa, Shreyanka Department of Computer Science and Engineering M S Ramaiah University of Applied Sciences #470-P 4th Phase Peenya Industrial Area Bengaluru KA560058 India Department of Electronic and Communication Engineering M S Ramaiah University of Applied Sciences #470-P 4th Phase Peenya Industrial Area Bengaluru KA560058 India Electrical Engineering Department University of Texas at Arlington ArlingtonTX76019 United States

Compressed sensing (CS) is a technique in signal processing which reconstructs any given signal at a rate less than that of Nyquist's' rate given that the signal is sparse and incoherent in nature. The main focus of CS is to find a random matrix which reconstructs the original signal using as few samples as possible. In recent years there has been a lot of interest in using CS to reconstruct 2D images. Several machine learning and deep learning algorithms have been proposed for finding the random matrix. However, using deep learning techniques to generate the random matrix is still an emerging concept. There are several papers exploring the concepts of deep learning with compressed sensing for images and have produced promising results. The results obtained in these papers warrant a comparison to analyze their performances against more traditional methods to determine the best approach to reconstruct images. These results are also compared with the popular JPEG and JPEG2000 codecs. This paper focuses on surveying deep learning algorithms for reconstruction of images using CS. The comparison reveals that deep learning algorithms perform significantly better than traditional methods and perform well when compared to any image compression codec like JPEG. Possible methods to improve the existing algorithms have been suggested. © Published under licence by IOP Publishing Ltd.

关键词： Compressed sensing

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Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations

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BMC MEDICAL GENOMICS 2020年第5-Sup期13卷 41-41页

作者： Huang, Zhi Johnson, Travis S. Han, Zhi Helm, Bryan Cao, Sha Zhang, Chi Salama, Paul Rizkalla, Maher Yu, Christina Y. Cheng, Jun Xiang, Shunian Zhan, Xiaohui Zhang, Jie Huang, Kun Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Indiana Univ Sch Med Dept Med Indianapolis IN 46202 USA Indiana Univ Purdue Univ Dept Elect & Comp Engn Indianapolis IN 46202 USA Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA Indiana Univ Sch Med Dept Med & Mol Genet Indianapolis IN 46202 USA Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen 518060 Peoples R China Shenzhen Univ Sch Biomed Engn Guangdong Key Lab Biomed Measurements & Ultrasoun Shenzhen 518060 Peoples R China

Background Recent advances in kernel-based Deep learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep learning models are now applied to survival prognosis of cancer patients. Specifically, Deep learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients. Methods In this study, a broad analysis was performed on TCGA cancers using a variety of Deep learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances. Results All models show competitive results across 12 cancer types. The last hidden layers of the Deep learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy. Conclusions Deep learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level.

关键词： Deep learning Cancer prognosis Survival analysis Tumor mutation burden Cox regression

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Examining the Capability of GANs to Replace Real Biomedical images in Classification Models Training 14th

Examining the Capability of GANs to Replace Real Biomedical ...

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14th international conference on pattern recognition and Information processing (PRIP)

作者： Kovalev, Vassili Kazlouski, Siarhei United Inst Informat Problems Surganova St 6 Minsk 220012 BELARUS

ISBN: (纸本)9783030354305;9783030354299

In this paper, we explore the possibility of generating artificial biomedical images that can be used as a substitute for real image datasets in applied machine learning tasks. We are focusing on generation of realistic chest X-ray images as well as on the lymph node histology images using the two recent GAN architectures including DCGAN and PGGAN. The possibility of the use of artificial images instead of real ones for training machine learning models was examined by benchmark classification tasks being solved using conventional and deep learning methods. In particular, a comparison was made by replacing real images with synthetic ones at the model training stage and comparing the prediction results with the ones obtained while training on the real image data. It was found that the drop of classification accuracy caused by such training data substitution ranged between 2.2% and 3.5% for deep learning models and between 5.5% and 13.25% for conventional methods such as LBP + Random Forests.

关键词： Generative Adversarial Networks X-ray images Histology images

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The Effectiveness Research of Different Methods of a user Identification in the Noisy Channel Case 1

The Effectiveness Research of Different Methods of a user Id...

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1st international conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency, SUMMA 2019

作者： Pashentsev, A.V. Vedishchev, V.V. Lipetsk State Technical University Lipetsk Russia

ISBN: (纸本)9781728149110

Nowadays the biometric identification is a part of our life and there are reasons for determining the possibility of applying different methods of a user identification in noisy channel case by using the machine learning approach and the apache spark framework for processing input data such as a user voice or a face image. © 2019 IEEE.

关键词： machine learning

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CPU vs GPU Performance of MATLAB Clustering Algorithms 23rd

CPU vs GPU Performance of MATLAB Clustering Algorithms

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23rd international conference on Distributed Computer and Communication Networks: Control, Computation, Communications , DCCN 2020

作者： Ivanov, Andrey Natalia, Ziazina Veronika, Antonova BMSTU ul. Baumanskaya 2-ya 5/1 Moscow Russia ISP RAS Alexander Solzhenitsyn St. 25 Moscow Russia IRE RAS Mokhovaya 11-7 Moscow Russia

ISBN: (纸本)9783030662417

Clustering is one of machine learning’s tasks when given objects must be split into specific groups based on distance between them. Its applications include different fields such as pattern matching, data compression and image analysis. Many programing languages allow to create clustering algorithms, though using already implemented ones is much easier. MATLAB includes a few of them. Knowing the performance of MATLAB’s cluster analysis algorithms may help choose the more optimal hardware for a given problem. © 2020, Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Collecting Retail Data Using a Deep learning Identification Experience 2nd

Collecting Retail Data Using a Deep Learning Identification ...

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2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on image Analysis and processing, ICIAP 2019

作者： La Porta, Salvatore Marconi, Fabrizio Lazzini, Isabella JEF Srl Civitanova Marche Italy Consumer Business Group Italia Huawei Milan Italy

ISBN: (纸本)9783030307530

The aim of the paper is to present a part of an architecture realized by Huawei, that propose the first Christmas tree endowed with artificial intelligence. Its ability is to identify facial expressions from images acquired by a mobile application and then recognize the sentiment of the subject. So, basing on the prevailing sentiment the tree lights up itself with different special effects. Our task in the project was testing the performances of the neural networks employed in the mobile application for the recognition of facial emotion. We used a convolutional neural networks model-based and created a purposely dedicated dataset of images for testing the recognition performances. © 2019, Springer Nature Switzerland AG.

关键词： Emotion recognition

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Using Convolutions and image processing Techniques to Segment LungsÂ from CT Data 1st

Using Convolutions and Image Processing Techniques to Segmen...

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1st international conference on Emerging Technology in Modelling and Graphics, IEMGraph 2018

作者： Ghosh, Souvik Sil, Sayan Gomes, Rohan Mark Dey, Monalisa Institute of Engineering and Management Salt Lake Electronics Complex Gurukul Y-12 Sector V Kolkata700091 India

ISBN: (纸本)9789811374029

In this paper, we put forward a novel approach of lung segmentation using convolutional neural networks (CNNs). CNNs have outperformed traditional techniques in many visual recognition tasks over the years. In the traditional methods, the features of the images to be recognized and then segmented are often hand-coded. This might seem quite simple at first and easy to implement but then as the complexity of the problems increases, these methods start to crumble. Moreover, these methods do not handle edge cases accurately as the programs do not know what to do when the images deviate from the expected nature. This is where deep learning methods (CNNs in our case) come to the rescue. Instead of hard-coding, we have built a CNN which is fed with a dataset consisting of CT images and the segmented images of the lungs. CNN automatically detects the features, and the accuracy is increased by tuning the parameters as well as using other techniques. The model once trained is then tested using the test dataset. As evident from the results, this approach gives a much higher accuracy compared to traditional methods, ultimately paving the way to a better analysis of the lungs. © Springer Nature Singapore Pte Ltd 2020.

关键词： Convolutional neural networks

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