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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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DRAODM: Diabetic Retinopathy Analysis Through Optimized Deep learning with Multi Support Vector machine for Classification 2nd

DRAODM: Diabetic Retinopathy Analysis Through Optimized Deep...

引用

2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

作者： Bhatti, Emmy Kaur, Prabhpreet Computer Engineering and Technology Guru Nanak Dev University Amritsar India

ISBN: (纸本)9789811391835

Diabetic retinopathy (DR) is the leading cause of avertable blindness globally. Retinal scanning of eyes is critical for examining the disease at an early stage. The concern of this study is to develop a robust mechanism to automate the process of diabetic retinopathy detection. A person suffering from DR must be referred to an ophthalmologist for an early evaluation in order to reduce the rate of vision loss hence enabling early treatment and prevention of vision loss. The proposed methodology introduces a data-driven novel algorithm using Deep learning for creating a tool for detecting DR. The approach uses DR colored fundus images and classifies them into multiple classes as stages or levels to which the eye is infected. Set of 170 colored fundus images of diabetic patients were used to train and test the model for distinguishing images into multiple classes. Entire simulation is divided into primarily two phases-firstly, the preprocessing phase where the resizing operation is performed since the input layer of the network requires predefined size. Size of 77 × 100 and channel of size 3 is used, indicating RGB image. Gaussian Filtering is used to tackle noise (De-noising) if any within the image. Secondly, the MSVM for segmentation and Classification phase where Multi-class SVM is used to extract critical and non-critical segments from within the training images. After extracting the features, classification is performed on the test images. Parameters used for optimization includes Accuracy, sensitivity, and specificity. Simulation is conducted in MATLAB using image processing and neural network toolbox. The proposed mechanism shows improvement in terms of classification accuracy by the margin of 5–6% which is significant, thereby enhancing the recognition rate. © Springer Nature Singapore Pte Ltd. 2019.

关键词： MATLAB

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Image Super Resolution Reconstruction Algorithm Based on Deep learning

Image Super Resolution Reconstruction Algorithm Based on Dee...

引用

IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Huijing Dou Wenqian Zhang Xiao Liang Beijing University of Technology Beijing China

Super Resolution Convolutional Neural Network (SRCNN) solves the problems of poor robustness and complex calculation of traditional image super-resolution reconstruction algorithm, but its training data set and the number of layers of neural network is relatively small, and the edge and texture detail information are not handled well. For the above problems, the Maxout activation function is adopted in this paper to avoid the problems encountered by traditional activation functions such as gradient disappearance or overflow. Then the combination of Maxout and Dropout can train large data set and deepen neural network. Experimental results show that, compared with the classical algorithm, the algorithm proposed in this paper can train a large amount of data, improve the quality of reconstructed images and the generalization ability of the network model, and can enhance the robustness of the model.

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Analysis of High Resolution Panchromatic Satellite Images, Based on GPGPU Programming 2019

Analysis of High Resolution Panchromatic Satellite Images, B...

引用

proceedings of the 2019 2nd international conference on Sensors, signal and Image processing

作者： Wilver Auccahuasi Aiquipa Jorge del Carpio Jorge Garcia Raul Benites Juan Grados Edward Flores Universidad Nacional del Callao Instituto Peruano de Investigación en Ingenieria Avanzada Lima

ISBN: (纸本)9781450372435

Satellite images are widely used in the study of land cover, being used for many tasks such as: the evaluation of urban growth, the monitoring of cultivation areas, the assessment of natural disasters and other applications, to perform these tasks is resorted to to the use of satellite images, they provide a variety of information that depends on the optical instrument carried on board in their payload, the information in the observation satellites, is provided through a data matrix that can be represented by a image, one of the characteristics is the spatial resolution where a pixel in the image corresponds to a coverage in meters on land, with this spatial resolution we can evaluate large tracts of land, among the spatial resolutions, we have the metrics where a pixel in the image corresponds to more than one meter on the ground and the sub meter, where a pixel in the image corresponds e less than one meter on land. With the sub-metric resolution we have a greater detail of the area of interest on land, having the disadvantage that a smaller area of land is evaluated in comparison with the metric resolution. The image provided by the satellite is composed of a set of matrices commonly called spectral bands, characterized by the bands of colors: red, green, blue, we also have the middle and near infrared bands, among others;additionally, the band of the panchromatic is presented, which is the band where the maximum spatial resolution is identified, therefore the image size is of high resolution. A methodology is presented to process the panchromatic satellite images through the use of the GPGPU programming using the MATLAB tool, a test with a high resolution image and with a weight in 1270 Megabits, with a size of 26012 X 25512 pixels, was performed. which was applied an algorithm where it evaluates the value of the pixel analyzing the whole matrix of the image pixel by pixel, the calculation was made in a Core i7 CPU with a processing time of 2.81 hours, with GPGP

关键词： CPU GPU panchromatic satellite image processing

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Prediction Based Smart Farming

Prediction Based Smart Farming

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international conference of Computer and Informatics Engineering (IC2IE)

作者： Sai Yasehwanth Chaganti Prajwal Ainapur Mayank Singh Sangamesh Shinta Oktaviana R. NIIT Univertity Rajasthan India Politeknik Negeri Jakarta Indonesia

ISBN: (纸本)9781728123851

The world population is expected to grow by over a third by 2050. Market demand for food will continue to grow. Automated drones and different robots in savvy cultivating applications offer the possibility to screen ranch arrive on a for each plant premise, which thus can diminish the measure of herbicides and pesticides that must be applied. There is a gap between current food productivity growth and needed growth. To boost the yield, farmers switched to extensive use of chemical fertilizers. Excessive fertilizer usage has its negative impact like decreased yield, wastage of fertilizer, damage to soil, and groundwater contamination. Currently, farmers mostly rely on guesswork, estimation, experience when deciding the crop that should grow, and the fertilizer that should be used. In this paper, we have proposed a solution that uses technologies like machine learning, Image processing, and the Internet of things to improvise farm productivity and at the same time, decrease the fertilizer usage. This paper describes the outcomes of a prototype implemented in Rajasthan, India.

关键词： machine learning Drone imagery Satellite imagery Image processing Image processing Satellite Imagery fertilizers world population Artificial Intelligence machine learning Internet of things Rajasthan India

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Non-contact Heart Rate Monitoring Using machine learning

Non-contact Heart Rate Monitoring Using Machine Learning

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international conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

作者： Varsha. A.V Christin Markose Aneesh. R.P Rajadhani Institute Of Engineering And Technology Trivandrum Regional Centre IHRD Trivandrum kerala

Health monitoring is an important parameter to determine the health status of a person. Measuring the heart rate is an easy way to gauge our health. Normal heart rate may vary from person to person and a usually high or low resting heart rate can be a sign of trouble. There are several methods for the measurement of heart rate monitoring such as ecg, ppg etc. Such methods having a disadvantage that these are invasive and have a continuous contact with the human body. In order to overcome this problem a new system is proposed using camera. In this method a blind source separation algorithm is used for extracting the heart rate signal from the face image. Viola jones based face detection algorithm is used to track the face. FastICA algorithm is exploited to separate heart rate signal from noise and artefacts. machine learning algorithm is implemented to standardize the signal. The data is successfully tested with real time video.

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Multi-scale Generative Model for Image Completion 19

Multi-scale Generative Model for Image Completion

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proceedings of the 2019 2nd international conference on Algorithms, Computing and Artificial Intelligence

作者： Jin Yuan Hongliang Yu Computer Science and Technology Department Tsinghua University Beijing China

ISBN: (纸本)9781450372619

Inpainting a large hole of an image is always a challenging task, especially for high-resolution images. Recently, deep learning based approach has brought excellent ability to deal with this issue. However, these methods always generate blurred edge and distorted details, which makes the inpainting images unreal. Besides, assessment for generated images is also challenging. Traditional assessment way uses various formulations, which only shows the partial image condition or distorted degree compared to original images, so this method cannot correctly reflect people's perception. To deal with these problems, we propose a multi-scale generative model which can gradually generate novel text to avoid distorted details, and the multi-scale losses can also eliminate blurred edges between inpainting results and the original region. To evaluate images, we also propose a deep convolutional neural network to do image quality assessment, which is closer to human perception. Experiments on multiple datasets show that our approach can generate more plausible inpainting results both on traditional evaluation criteria and our image quality assessment network.

关键词： Image quality assessment Image processing Generative model

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A Simple and Mighty Arrowhead Detection Technique of Bangla Sign Language Characters with CNN 2nd

A Simple and Mighty Arrowhead Detection Technique of Bangla ...

引用

2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

作者： Islam, Md. Sanzidul Mousumi, Sadia Sultana Sharmin Rabby, Akm Shahariar Azad Hossain, Syed Akhter Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9789811391804

Sign Language is argued as the first Language for hearing impaired people. It is the most physical and obvious way for the deaf and dumb people who have speech and hearing problems to convey themselves and general people. So, an interpreter is wanted whereas a general people needs to communicate with a deaf and dumb person. In respect to Bangladesh, 2.4 million people uses sign language but the works are extremely few for Bangladeshi Sign Language (BdSL). In this paper, we attempt to represent a BdSL recognition model which are constructed using of 50 sets of hand sign images. Bangla Sign alphabets are identified by resolving its shape and assimilating its structures that abstract each sign. In proposed model, we used multi-layered Convolutional Neural Network (CNN). CNNs are able to automate the method of structure formulation. Finally the model gained 92% accuracy on our dataset. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Privacy Disclosures Detection in Natural-Language Text Through Linguistically-Motivated Artificial Neural Networks 2nd

Privacy Disclosures Detection in Natural-Language Text Throu...

引用

2nd EAI international conference on Security and Privacy in New Computing Environments, SPNCE 2019

作者： Mehdy, Nuhil Kennington, Casey Mehrpouyan, Hoda Boise State University BoiseID83702 United States

ISBN: (纸本)9783030213725

An increasing number of people are sharing information through text messages, emails, and social media without proper privacy checks. In many situations, this could lead to serious privacy threats. This paper presents a methodology for providing extra safety precautions without being intrusive to users. We have developed and evaluated a model to help users take control of their shared information by automatically identifying text (i.e., a sentence or a transcribed utterance) that might contain personal or private disclosures. We apply off-the-shelf natural language processing tools to derive linguistic features such as part-of-speech, syntactic dependencies, and entity relations. From these features, we model and train a multichannel convolutional neural network as a classifier to identify short texts that have personal, private disclosures. We show how our model can notify users if a piece of text discloses personal or private information, and evaluate our approach in a binary classification task with 93% accuracy on our own labeled dataset, and 86% on a dataset of ground truth. Unlike document classification tasks in the area of natural language processing, our framework is developed keeping the sentence level context into consideration. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

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An Introduction to Deep learning Applications In MRI Images

An Introduction to Deep Learning Applications In MRI Images

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international conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Priyanka Datta Rajesh Rohilla Delhi Technological University Delhi

Neural networks have progressively used at a surprising rate now a days. Deep neural networks are majorly used machine learning models in different areas, from analysis of image to natural language processing (NLP) and broadly conveyed in scholarly community and industry. These progresses to have enormous possibilities for medical imaging, analysis of medical data, medical diagnostics. This paper illustrates a thorough literature review of deep learning techniques. This paper illustrates brief discussion about current deep learning architectures used for medical imaging and magnetic resonance imaging (MRI) for image classification, detection, segmentation, registration, etc. This review mainly focuses on the applications of deep learning methods in medical diagnosis that uses MRI modality along with the recent developments and various challenges in deep learning related to analysis of medical images.

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Generative Object Detection: Erasing the Boundary via Adversarial learning with Mask

Generative Object Detection: Erasing the Boundary via Advers...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Heeoh Jang Dongkyu Kim Wonhyuk Ahn Heung-Kyu Lee School of Computing Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Samsung Electronics Suwon Republic of Korea

Recently, object detection has presented superior performance using the deep convolutional neural network (CNN). However, most CNN-based models need the bounding box information of the input image in pairs. To overcome this limitation, we propose the Generative Object Detection which learns with only cropped images that are not in pairs. Our model based on Generative Adversarial Networks (GAN) creates cropped images by making a mask that represents the object region. To achieve this goal, we devise a novel mask mean loss (MML) that helps the GAN be able to estimate the distribution of training data and uses dilated convolution for a wider reception field in the generator. The experimental results show that Generative Object Detection improves the mIoU and accuracy.

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