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检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, SPML 2020"

共 729 条记录，以下是521-530 订阅

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Food Image Recognition for Price Calculation using Convolutional Neural Network 2019

Food Image Recognition for Price Calculation using Convoluti...

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3rd international conference on Digital signal processing (ICDSP) / 2nd international conference on Computer Graphics and Virtuality (ICCGV)

作者： Nordin, Md Jan Aziz, Norshakirah Xin, Ooi Wei Univ Kebangsaan Malaysia Fac Informat Sci & Technol CAIT Bangi Malaysia Univ Teknol PETRONAS High Performance Cloud Comp Ctr HPC3 Comp Informat Sci Dept Seri Iskandar Malaysia Univ Teknol PETRONAS Comp Informat Sci Dept Seri Iskandar Malaysia

ISBN: (纸本)9781450362047

This project is attempting to solve the issue of unfair and inconsistent food price being charged in economy rice or mixed rice that widely seen in the cafe of hawker stall in Malaysia. The main cause of the problem is the absence of standardized price list of the food which causes the pricing of the mixed rice remains unknown. Hence, the authors had decided to propose this project by utilizing convolutional neural network (CNN) algorithm and develop a web application to ease the vendor as well as to provide transparency to the buyer on the food price being charged. CNN model is trained to classify the different types of food. The food price will be stored in a database of the web application in order to calculate the food price with the recognized food in the machine learning model. The outcome of this project is a customized web application for Village 3 Cafe, UTP with a trained CNN classification model at the backend.

关键词： Food image recognition convolutional neural network (CNN) machine learning

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Validation Methods to Promote Real-world Applicability of machine learning in Medicine 20

Validation Methods to Promote Real-world Applicability of Ma...

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2020 3rd international conference on Digital Medicine and Image processing

作者： Riyad Bin Rafiq Francois Modave Shion Guha Mark V. Albert University of North Texas USA University of Florida USA Marquette University USA

ISBN: (纸本)9781450389044

The impact of Artificial Intelligence (AI) on health care has been dramatic; however, there is a considerable degree of skepticism among clinicians about the real-world applicability of advanced predictive models; for this reason, it is particularly important to emphasize the need for proper model validation in machine learning. Often model skepticism is well-placed as modelers may overclaim the real-world replicability for their models, understate the known limitations, or simply not be aware of the hidden limits of the modeling approach. Educational approaches limited to rigorous and thorough justification of all model design decisions may not be practical given model complexity. This also becomes more challenging as state-of-the-art models with the highest benchmark accuracy are becoming less interpretable, e.g. ensemble methods or deep learning. However, in the same way that test-driven development has been a successful paradigm to navigate the complex coding landscape through a focus on testable results, we have observed a similar improvement in modeling strategy when the focus of a predictive model is driven by validation targets rather than more abstract, theoretical concerns. In this study, we provide an overview of the common limitations of model validation methods in medicine. We then present solutions to address such limitations, with a focus on strengthening the validity of predictive models.

关键词： Evaluation metrics Natural variability Precision medicine Validation methods

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machine learning Approaches for Rapid Pothole Detection from 2D Images 3rd

Machine Learning Approaches for Rapid Pothole Detection from...

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3rd international conference on Computational Intelligence, Security and Internet of Things, ICCISIoT 2020

作者： Acharjee, Chandrika Singhal, Somya Deb, Suman Department of Computer Science and Engineering Natioan Institute of Technology Agartala Jirania India

ISBN: (纸本)9783030667627

Roads are inevitable parts of human civilisation, and construction of roads are considered under a Civil Engineering problem;but periodically these roads require maintenance and assessment, which is highly dependent on adequate and timely pavement condition data. Howbeit, in some cases, it has been found that the manual practice of collecting and analysing such data often leads to delay in reporting about the issues and fixing them on time. Also, repairing potholes is time consuming, and locating these manually is a huge task. We want to find out some mechanism which can identify the construction conditions as well as any kind of deformities on the road from the dashboard camera fitted into a car, and at the same time, can analyse the conditions of road surface and formation of potholes on the road. Optimization of manual pothole detection through automation has been a part of scientific research since long. Pothole identification has significantly been adapted in different screening and maintenance systems. But in our country, owing to the large number of road networks and wide variations in the nature of rural and urban road conditions, it is very difficult to identify potholes through an automated system. In this paper, we have looked into several methods of Computer Vision, like image processing techniques and object detection method so as to identify potholes from the video input stream to the system. But these techniques have been found to have different challenges like lighting conditions, interference in the line of vision on waterlogged roads, and inefficiency at night vision. Hence, furthermore, we have explored the viability of Deep learning method for identifying the potholes from the processing of input video streams, and have also analysed the Convolutional Neural Networks approach of Deep learning through a self-built CNN model. In this paper, the expediency of all the methods as well as their drawbacks have been discussed. © 2020, Springer Nature Swi

关键词： Computer vision

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An Effective Remote Data Disaster Recovery Plan for the Space TT&C System 1

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3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Han, Weijie Xue, Jingfeng Zhang, Fuquan Sun, Zhonghui School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Space Information Space Engineering University Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China Baicheng Weapon Test Center of China Baicheng137001 China

ISBN: (数字)9783030624606

ISBN: (纸本)9783030624590

The critical asset data of the Space Tracking Telemetry and Command (TT&C) System plays an important role in fulfilling space missions. According to analyze the current storing methods and disaster recovery requirements of the data, the remote data disaster recovery techniques are studied based on the remote replication capability of the Oracle database, and the remote data disaster recovery plan is developed for the space TT&C system. Furthermore, the experiment is conducted to validate the plan by building a simulation environment. The experiment results demonstrate that the plan can reach the fifth degree of the disaster recovery level and satisfy the following three performance requirements including recoverability, reliability and real-time performance, and therefore realize remote disaster recovery of the critical asset data for the space TT&C system efficiently. © 2020, Springer Nature Switzerland AG.

关键词： Disasters

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RAASID: A Multipurpose Crowd Sensing Smart System With Sentimental Analysis

RAASID: A Multipurpose Crowd Sensing Smart System With Senti...

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international conference on signal processing and Information Security (ICSPIS)

作者： Maha Al Hosani Hamda Al Marzouqi Shoug Al Junaibi Amna Al Hmoudi Jamal N. Al-Karaki Amjad Gawanmeh Abu Dhabi Polytechnic Abu Dhabi UAE College of Engineering and IT University of Dubai Dubai UAE

The following topics are dealt with: medical image processing; learning (artificial intelligence); deep learning (artificial intelligence); Internet of Things; medical computing; neural nets; feature extraction; disea... 详细信息

ISBN: (数字)9781728189987

ISBN: (纸本)9781728189994

关键词： Streaming media signal processing Rendering (computer graphics) Real-time systems Sensors Monitoring Business

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A machine learning Approach for Detection Plant Disease - Taking Orchid as Example 3

A Machine Learning Approach for Detection Plant Disease - Ta...

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3rd international conference on Vision, Image and signal processing (ICVISP)

作者： Li, Li-Hua Chu, Yu-Sheng Chu, Jui-Yuan Guo, Shian-Hau Chaoyang Univ Technol Taichung Taiwan

ISBN: (纸本)9781450376259

The export value of flower production is very high in many countries. Among these export flowers, orchids are considered one of the most valuable flowers. In order to increase orchid production, it is important to take closely care of orchid in the cultivation period. Due to the plant diseases may cause the economic losses in agricultural industry, the daily inspection and early recognition of plant diseases are necessary. Detection and prevention of plant diseases are a worldwide agricultural problem. Many researchers have proposed biocontrol or IT technology to handle plant disease problems. Some researchers applied image recognition to find out leaf problems such as banana leaves, alfalfa leaves, and citrus leaves. However, these researches all focus on trees or fruits in the lab. They did not provide the long-distance leaf identification for orchid flowers. To help flower farmers and to enhance the quality of production, this research has proposed a machine learning method to capture the image of orchid leaf and to identify the leaf disease. This research analyzes the leaf image with feature space, i.e., HSI, RGB, and grayscale. We use histogram to analysis the leaf color and we analyze the green color threshold so that the image can be classified into various color zones. Based on the generated threshold, this research is able to segment the leaf image into healthy area and unhealthy area. Finally, we use the Artificial Neural Network (ANN) and Deep learning ANN to learn the image patterns of orchid leaf. Our proposed method is then applied to identify the orchid leaves and to determine whether the orchid is healthy or sick. With our proposed model, the accuracy of recognizing the leaf disease can achieve 100% for training data and 90% for testing data. This research enables flower farmers to recognize the orchid disease and can prevent the disease in early stage. As a result, the farmer can take better care to the orchid plants and enhance the cultivation of o

关键词： Neural Network Plant Disease Image processing Orchid Deep learning machine learning

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TAG: Nucleus Detection in Colorectal Adenocarcinomas Histology Images using Local Texture, Appearance, and Gradient Features 3

TAG: Nucleus Detection in Colorectal Adenocarcinomas Histolo...

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3rd international conference on Imaging, signal processing and Communication (ICISPC)

作者： Domoguen, Jansen Keith L. Suarez, Jessie James P. Naval, Prospero C., Jr. Univ Philippines Comp Vis & Machine Intelligence Grp Diliman 1 Quezon City Philippines

ISBN: (纸本)9781728136639

This work considers the problem of detecting nuclei in H&E stained images. The high intra-class and inter-image variability of nuclei of the images itself calls for a more robust method that is able to handle this high variability. Recently, deep learning has become the most popular method in computer vision with many systems achieving state-of-the-art results for a wide array tasks. However, as a trade-off, deep learning methods require tons of data and computational resources that some may not posses. Thus, this work proposes a traditional computer vision pipeline along with TAG, a local feature-based approach that combines texture, appearance, and gradient features which is used in the task of pixel-per-pixel prediction in the virtue of semantic segmentation. Based from the results, the method was shown to be effective despite the challenges of high variability in the dataset.

关键词： nucleus detection nuclei detection histology image texture appearance gradient multi-layer perceptron

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Long Short-Term Memory based Conversation Modelling

Long Short-Term Memory based Conversation Modelling

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r Engineering: machine learning and Internet of Things (ICETCE), 2020 3rd international conference on

作者： Vipasha Chandwani Sandeep Kumar Parikshit Kishor Singh Department of CSE IIIT KOTA India

ISBN: (数字)9781728116839

ISBN: (纸本)9781728116846

Conversation models are becoming more popular with every day. Major companies like Apple, Microsoft, Google and Amazon have invested major funds and time and effort into such models. However, at present, these conversation models are predominantly voice chat. This does not create much difference because all the algorithms and methodologies all of the deep natural language processing techniques are all applicable to both text and voice. Our model predicts the next sentence given the previous sentence. We have developed a neural network-based model to answer questions in the form of generating sequences about the data it has been trained upon. Our model makes use of two popular neural network architecture: Sequential learning using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM).

关键词： Computer architecture Microprocessors Decoding Recurrent neural networks Logic gates Natural language processing

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Estimation of Motor Imagination Based on Consumer-Grade EEG Device 3rd

Estimation of Motor Imagination Based on Consumer-Grade EEG ...

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3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Luo, Zhenzhen Hu, Zhongyi Li, Zuoyong College of Computer and Artificial Intelligence Wenzhou University Wenzhou China Intelligent Information Systems Institute Wenzhou University Wenzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9783030624590

Nowadays, classifying electroencephalogram (EEG) signals based motor imagery tasks is extensively used to control brain-computer interface applications, as a communication bridge between humans and computers. In this paper, we propose signal-to-image transformation and feature extraction methods for the classification of motor imagery. Specifically, a continuous wavelet transform is applied to decompose EEG signals into five rhythms and generate time-frequency images. Then, a gray-level co-occurrence matrix is used to extract global texture features on time-frequency images. Finally, the SVM classification model is optimized by using a grid search algorithm to select optimal parameter pairs. To confirm the validity of the proposed methods, we experimented on self-collected data, which is obtained using a consumer-grade EEG device. The experimental result showed that this proposed method can achieve an acceptable classification accuracy of 90% for a two-class problem (left/right-hand motor imagery). © 2020, Springer Nature Switzerland AG.

关键词： Electroencephalography

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SDUW-Net: An Effective Retinal Vessel Segmentation Model 1

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3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Lin, Hongkai Kang, Hongliang Cao, Xinrong College of Computer and Control Engineering Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (数字)9783030624637

ISBN: (纸本)9783030624620

Retinal vessel segmentation is the main step in the analysis of fundus images. However, gray-scales’ uneven distribution, complex structure, and serious noise interference bring difficulties of automatic retinal vessels segmentation on fundus images. To solve these problems, we present an effective retinal vessel segmentation model, SDUW-Net, in this paper. The same scale dense connection is designed to improve U-Net’s structure and remove different scales dense connections to accelerate the training speed. We use skip connections to merges the features between shallow layers and deep layers to retain more features that may be lost in the process of down sampling and convolution. Experimental results on the DRIVE dataset show that the retinal vessels can be effectively segmented out by our proposed SDUW-Net, which has AUC of 0.9811 with low computation and short training time. © 2020, Springer Nature Switzerland AG.

关键词： Ophthalmology

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