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检索条件"任意字段=1st International Conference on Image Processing Machine Learning and Pattern Recognition"

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study on temperature measurement of power equipment based on retinex theory and machine learning 1

Study on temperature measurement of power equipment based on...

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1st IEEE China international Youth conference on Electrical Engineering, CIYCEE 2020

作者： He, Yang Ye, Qizheng Du, Wenjiao Li, Wenmao Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Wuhan430074 China Guangdong Power Grid Company Jiangmen Power Supply Bureau Jiangmen China

ISBN: (纸本)9781728196596

Electric power equipment temperature is closely related with its working condition. The current mainstream infrared thermal imagers are expensive, complicated to operate and weak in spatial positioning. Infrared and visible image fusion has a promising application prospect in power system fault location, anomaly monitoring and so on. Visible light radiation temperature measurement has many application cases in the high temperature field. This paper puts forward a method based on image processing and machine learning, and successfully applies visible light temperature measurement technology to the low temperature field such as the temperature detection of power equipment. We used copper plates as the research object, and established a library of visible images under different temperature and light conditions. Four kinds of machine learning algorithms were used to build the temperature prediction model by extracting the gray distribution features from images. We select two algorithms which have good performance in time complexity and prediction accuracy. The average absolute error of predicting temperature is only about 1.5°C. We also have performed Retinex processing on all images to eliminate the interference of different lighting intensity on the grayscale features. After training and calculation, it was found that the average absolute error is reduced to 1.309°C with the same algorithms, which has a better prediction accuracy. © 2020 IEEE.

关键词： machine learning

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On Classification of BMD images Using machine learning (ANN) Algorithm 1st

On Classification of BMD Images Using Machine Learning (ANN)...

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1st international conference on Data Science, machine learning and Applications, 2019

作者： Kumar, Santosh Ansari, Mohd Dilshad Gunjan, Vinit Kumar Solanki, Vijender Kumar Department of Electronics and Communication Engineering Invertis University Bareilly India Department of Computer Science & Engineering CMR College of Engineering & Technology Hyderabad India CMR Institute of Technology Hyderabad India

ISBN: (纸本)9789811514197

Osteoporosis is a disease which has affected major part of human body that is bones. It trends to reduce the mass of the bones and hence degrade the micro architecture of tissues of bones. There are multiple imaging technologies which have been used from the decades in diagnosis and the micro architecture of distressed as well as affected bones in order to find the bone density deficiency. image processing methods are widely used like filtering, segmentation, classification, image enhancement as well as other pre-processing technique to diagnosis the affected bone structure easily. It will help to extract the vital information about deformed micro architecture pattern. In this paper we have collected the basic information of tissues such as osteoblast as well as osteoclast and also have presented a comparative study of various diagnosis technique based on the image processing for the osteoporosis. The main aim of this work is to assess the prevalence of osteoporosis and changes in bone mass with increasing age, compare bone health status of apparently healthy men, premenopausal and postmenopausal women. In this work, we have used ethical database of 260 subject 130 men, 80 women (premenopausal) and 50 women (postmenopausal). Bone mineral density (BMD) has been measured through dual energy X-ray absorptiometry at femoral neck. © 2020, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Face recognition Systems in Math Classroom Through Computer Vision Traditional Techniques 1st

Face Recognition Systems in Math Classroom Through Computer ...

引用

1st international conference on Applied Technologies (ICAT)

作者： Granda, Luis Barba-Guaman, Luis Torres-Carrion, Pablo Cordero, Jorge Univ Tecn Particular Loja Grp Invest Inteligencia Artificial Aplicada Loja Ecuador

ISBN: (纸本)9783030425203;9783030425197

The methods and techniques of detection of the human face and facial recognition have presented a great impulse in recent years, thanks to the advance in areas such as artificial vision and machine learning. Although deep neural network techniques are in vogue, traditional techniques allow you to create applications that do not consume many resources from computing devices. In this research, we present a facial recognition system that implements the Eigenfaces method, developed in C # of Microsoft Visual studio and open-source video processing libraries such as OpenCV as EmguCV. The application is divided into two sections: the first called register where, through an integrated camera, images of the user's face or other means such as video and stored images are captured, and the second section is known as recognition where the user is compared with all the records of the data set, indicating whether this is registered and the recognition percentage. The project was implemented with a universe of the size of twenty-five users, of which six are men (24%) and nineteen are women (76%), developing tests for five weeks.

关键词： Facial recognition Computer vision Eigenfaces image processing

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

International Conference on Smart Sustainable Intelligent Co...

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

The proceedings contain 43 papers. The topics discussed include: edge detection using guided image filtering and enhanced ant colony optimization;estimation of parameters in fractional order financial chaotic system with nature inspired algorithms;performance analysis of various server hosting techniques;flood damage analysis using machine learning techniques;asymmetrically slotted, tunable and single mode circularly polarized microstrip antenna for IoT application;robustness in multilayer networks under strategically and random attacks;credit card fraud detection using pipelining and ensemble learning;comparison of various scale invariant crowd count analysis methods;effect of influential nodes on time varying opinion formation models;investigation of artificial intelligence techniques in finance and marketing;hand gesture recognition using image processing and feature extraction techniques;and comparative analysis of bagging and boosting algorithms for sentiment analysis.

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Analysis of Federated learning as a Distributed Solution for learning on Edge Devices 1

Analysis of Federated Learning as a Distributed Solution for...

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1st international conference on Intelligent Data Science Technologies and Applications, IDstA 2020

作者： Lameh, Saba F. Noble, Wade Amannejad, Yasaman Afshar, Arash University of Tabriz Tabriz Iran Mount Royal University Calgary Canada Aranite Inc. Calgary Canada

ISBN: (纸本)9781728183763

Sensors and smart devices are continuously collecting massive amounts of data. Today's state-of-the-art machine learning (ML) techniques, such as deep networks, are typically trained using cloud platforms, leveraging the elastic scalability of the cloud. For such processing, data from various sources need to be transferred to a cloud server. While this works well for some application domains, it is not suitable for all due to privacy and network overhead concerns. Sharing life logging photos and videos from cell phones and wearable devices can cause privacy concerns for users. To respond to the needs of such applications, federated learning (FL) is proposed as a distributed ML solution for learning on edge devices, such as cellphones. In FL, data does not leave users' devices. All users collaboratively train a model without sharing their data. Each user trains a local model with their data and shares the model updates with a FL server to aggregate and build a global model. Such training respects users' privacy while reducing the network overhead for transferring data to a central server. In this paper, we aim to study the FL approach for learning on edge devices. We investigate the performance of the FL model with various parameter settings and compare its accuracy and training time to a traditional learning technique that is trained on a central server. For this purpose, we train a convolutional deep learning network for image recognition in traditional and federated format. The results confirm that it is possible to conduct a complex learning task on distributed devices without sharing their data with a central server, and such federated models can achieve comparable accuracy to central models. © 2020 IEEE.

关键词： image recognition

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Global Prediction of COVID-19 Cases and Deaths using machine learning

Global Prediction of COVID-19 Cases and Deaths using Machine...

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international conference on image Information processing

作者： Sumit Bhardwaj Harshit Bhardwaj Jyoti Bhardwaj Punit Gupta Amity University Noida Uttar Pradesh Govt. PG Degree College Dharamshala Himachal Pradesh Manipal University Jaipur Jaipur Rajasthan

ISBN: (纸本)9781665433624

Coronavirus Disease or COVID-19 pandemic has taken over the world by storm. It has horrifying effect on the health of the people. Continuously rising number of COVID-19 cases has and still creating huge stress on the governing bodies of all countries, and they are finding it hard to find solution for the situation. This project's goal is to explore machine learning and develop a COVID-19 model that can predict number of cases with high accuracy. The proposed study employs SVR and PR models to forecast the number of recovered cases, confirmed cases, deaths, and daily case count. The data is collected from the 1st of March to the 30th of April 2020. The confirmed number of cases as of April 30th were 35043, with 1147 total deaths and 8889 recovered patients. The model was created in Python 3.8.5. We will look at various machine learning prediction algorithms and compare them. In conclusion, supervised learning algorithms proved to be better than unsupervised learning algorithms. These prediction models can help us to brace for another COVID-19 wave and to ensure the availability of the required resources.

关键词： COVID-19 Support vector machines machine learning algorithms Supervised learning machine learning Predictive models Prediction algorithms

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A New Facial image Deviation Estimation and image Selection Algorithm (Fide-Isa) for Facial image recognition Systems: The Mathematical Models

A New Facial Image Deviation Estimation and Image Selection ...

引用

Multidisciplinary Engineering and Applied Science (ICMEAS), international conference on

作者： Hassan Opotu Siyaka Olumide Owolabi I. Hashim Bisallah Department of Computer Science University of Abuja

ISBN: (数字)9781665434935

ISBN: (纸本)9781665434942

Deep learning models have been successful and shown to perform better in terms of accuracy and efficiency for facial recognition applications. However, they require huge amount of data samples that were well annotated to be successful. Their data requirements have led to some complications which include increased processing demands of the systems where such systems were to be deployed. Reducing the training sample sizes of deep learning models is still an open problem. This paper proposes the reduction of the number of samples required by the convolutional neutral network used in training a facial recognition system using a new Facial image Deviation Estimation and image Selection Algorithm (FIDE-ISA). The algorithm was used to select appropriate facial image training samples incrementally based on their facial deviation. This will reduce the need for huge dataset in training deep learning models. Preliminary results indicated a 100% accuracy for models trained with 54 images (at least 3 images per individual) and above.

关键词： Training Deep learning image recognition Face recognition Estimation Mathematical models Convolutional neural networks

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Iris recognition Using Convolutional Neural Network 10

Iris Recognition Using Convolutional Neural Network

引用

10th IEEE international conference on System Engineering and Technology (ICSET)

作者： Zhuang, Yuan Chuah, Joon Huang Chow, Chee Onn Lim, Marcus Guozong Univ Malaya Fac Engn Dept Elect Engn VIP Res Lab Kuala Lumpur Malaysia

ISBN: (纸本)9781728199108

The design of a pragmatic user authentication system is vital to provide accurate detection of personal identity. Iris recognition as a form of biometric identification technology has been actively researched for decades and is gaining wider popularity considering the increasing awareness of personal privacy. The rise of artificial intelligence provides a great opportunity to further elevate the penetration of iris recognition in safeguarding one's private data. Convolutional neural network is a practical algorithm that is highly suitable for image processing and pattern recognition, its effectiveness and flexibility have seen it being applied in many fields. This study focuses on the development of an iris recognition system based on convolutional neural network with high precision and efficiency. A total of iris samples from 20 individuals with both sides of the eyes included are used to train the deep recognition system. The model shows an early sign of underfitting and little convergence with inadequate number of training epoch. However, as the training epochs are increased, the trained model managed to achieve a testing accuracy of 99%.

关键词： Iris recognition Biometric Identification Convolutional Neural Network (CNN) Deep learning machine learning

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Detection of Economic Minerals in Beach Placer Samples by machine learning-Based Microscopic image processing 1

Detection of Economic Minerals in Beach Placer Samples by Ma...

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

作者： Basu, Rikin Rao, D.S. Das, Debi Prasad School of Electronics Engineering VIT University Summer Intern at CSIR-IMMT Vellore India CSIR-Institute of Minerals and Materials Technology MP Department Bhubaneswar India

ISBN: (纸本)9781728141428

Beach sand minerals (BSM) contains different economic minerals, out of which seven minerals viz. Ilmenitev, Rutile, Leucoxene, Zircon, Sillimanite, Garnet and Monazite are important ones. These minerals have different physical properties based on which these are separated conventionally. In this paper, a separation process using visual information is proposed. Accordingly, microscopic images of sand particles are taken and using machine learning algorithm, the particular economic mineral is accurately detected in the microscopic images. Aggregated Channel Features (ACF) detector-based learning algorithm is used in this paper. © 2020 IEEE.

关键词： learning algorithms

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EEG-based Motor imagery Classification through Transfer learning of the CNN 1

EEG-based Motor Imagery Classification through Transfer Lear...

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1st international conference on machine Vision and image processing, MVIP 2020

作者： Taheri, Samaneh Ezoji, Mehdi Babol Noshirvani University of Technology Faculty of Electrical and Computer Engineering Babol Iran

ISBN: (纸本)9781728168326

Brain computer interface (BCI) is a system which is able to translate EEG signals into comprehensive commands for the computers. EEG-based motor imagery (MI) signals are one of the most widely used signals in this topic. In this paper, an efficient algorithm to classify 2-class MI signals based on the convolutional neural network (CNN) through the transfer learning is introduced. To this end, different 3D representations of EEG signals are injected into the CNN. These proposed 3D representations are prepared by combination of some frequency and time-frequency algorithms such as Fourier Transform, CSP, DCT and EMD. Then, CNN will be trained to classify MI-EEG signals. The average accuracy of classification for 5 subjects achieved 98.5% on the BCI competition iii database IVa. © 2020 IEEE.

关键词： Convolutional neural networks

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