scholarly journals DETECÇÃO E RECONHECIMENTO DE PLACAS DE LICENCIAMENTO VEICULAR EM TEMPO REAL USANDO CNN

2021 ◽  
Vol 13 (1) ◽  
pp. 89-99
Author(s):  
Marcelo Eidi Imamura ◽  
Francisco Assis da Silva ◽  
Leandro Luiz de Almeida ◽  
Danillo Roberto Pereira ◽  
Almir Olivette Artero ◽  
...  
Keyword(s):  
Real Time ◽  
Character Recognition ◽  
Character Segmentation ◽  
Machine Learning Techniques ◽  
Hit Rate ◽  
Learning Techniques ◽  
Last Stage ◽  
The Individual ◽  
Control And Management ◽  
Computational Solution

Brazil has a large fleet of vehicles running daily along urban roads and highways, which requires the use of some computational solution to assist in control and management. In this work we developed an application to detect and recognize real-time licenseplates with various application possibilities. The methodology developed in this work has three main stages: plate detection, character segmentation and recognition. For the detection step we used the YOLO library, which makes use of machine learning techniques to detect objects in real time. YOLO was trained using a dataset with plate images in different environments. In the segmentation stage, the individual characters contained in the plate were separated, using image processing methods. In the last stage, character recognition was performed using two convolutional neural networks, obtaining a hit rate of 83.33%.

scholarly journals Near real-time air quality forecasts using the NASA GEOS model

2021 ◽  
Author(s):  
K. Emma Knowland ◽  
Christoph Keller ◽  
Krzysztof Wargan ◽  
Brad Weir ◽  
Pamela Wales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Real Time ◽  
Weather Forecasting ◽  
Atmospheric Composition ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Wide Range ◽  
Reactive Trace Gases ◽  
Assimilation System

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

User Profiles for Personalizing Digital Libraries

2009 ◽  
pp. 149-158 ◽  
Author(s):  
Giovanni Semeraro ◽  
Pierpaolo Basile ◽  
Marco de Gemmis ◽  
Pasquale Lops
Keyword(s):  
Machine Learning ◽  
Digital Libraries ◽  
Machine Learning Techniques ◽  
Linguistic Knowledge ◽  
User Profiles ◽  
User Interests ◽  
Learning Techniques ◽  
Document Filtering ◽  
The Individual ◽  
Machine Readable

Exploring digital collections to find information relevant to a user’s interests is a challenging task. Information preferences vary greatly across users; therefore, filtering systems must be highly personalized to serve the individual interests of the user. Algorithms designed to solve this problem base their relevance computations on user profiles in which representations of the users’ interests are maintained. The main focus of this chapter is the adoption of machine learning to build user profiles that capture user interests from documents. Profiles are used for intelligent document filtering in digital libraries. This work suggests the exploiting of knowledge stored in machine-readable dictionaries to obtain accurate user profiles that describe user interests by referring to concepts in those dictionaries. The main aim of the proposed approach is to show a real-world scenario in which the combination of machine learning techniques and linguistic knowledge is helpful to achieve intelligent document filtering.

Machine Learning Techniques Application

2021 ◽  
pp. 1396-1417
Author(s):  
Karthikeyan P. ◽  
Karunakaran Velswamy ◽  
Pon Harshavardhanan ◽  
Rajagopal R. ◽  
JeyaKrishnan V. ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Modern World ◽  
Interdisciplinary Field ◽  
Sound Image ◽  
Learning Techniques

Machine learning is the part of artificial intelligence that makes machines learn without being expressly programmed. Machine learning application built the modern world. Machine learning techniques are mainly classified into three techniques: supervised, unsupervised, and semi-supervised. Machine learning is an interdisciplinary field, which can be joined in different areas including science, business, and research. Supervised techniques are applied in agriculture, email spam, malware filtering, online fraud detection, optical character recognition, natural language processing, and face detection. Unsupervised techniques are applied in market segmentation and sentiment analysis and anomaly detection. Deep learning is being utilized in sound, image, video, time series, and text. This chapter covers applications of various machine learning techniques, social media, agriculture, and task scheduling in a distributed system.

scholarly journals Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5504
Author(s):  
Hyang-A Park ◽  
Gilsung Byeon ◽  
Wanbin Son ◽  
Hyung-Chul Jo ◽  
Jongyul Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Industrial Revolution ◽  
Storage System ◽  
Research Field ◽  
Optimization Techniques ◽  
Virtual Space ◽  
Machine Learning Techniques ◽  
Time Data ◽  
Digital Twin ◽  
Learning Techniques

Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, causing issues such as difficulty in decision making and system abnormalities. Using digital twin technology, which is one of the technologies representing the fourth industrial revolution, it is possible to overcome these problems by changing the microgrid configuration and operating algorithms of virtual space in various ways and testing them in real time. In this study, we proposed an energy storage system (ESS) operation scheduling model to be applied to virtual space when constructing a microgrid using digital twin technology. An ESS optimal charging/discharging scheduling was established to minimize electricity bills and was implemented using supervised learning techniques such as the decision tree, NARX, and MARS models instead of existing optimization techniques. NARX and decision trees are machine learning techniques. MARS is a nonparametric regression model, and its application has been increasing. Its performance was analyzed by deriving performance evaluation indicators for each model. Using the proposed model, it was found in a case study that the amount of electricity bill savings when operating the ESS is greater than that incurred in the actual ESS operation. The suitability of the model was evaluated by a comparative analysis with the optimization-based ESS charging/discharging scheduling pattern.

scholarly journals Ensemble Learning to Improve the Prediction of Fetal Macrosomia and Large-for-Gestational Age

2020 ◽  
Vol 9 (2) ◽  
pp. 380 ◽  
Author(s):  
Shangyuan Ye ◽  
Hui Zhang ◽  
Fuyan Shi ◽  
Jing Guo ◽  
Suzhen Wang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Gestational Age ◽  
Prediction Accuracy ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Fetal Macrosomia ◽  
Large For Gestational Age ◽  
Empirical Formulas ◽  
Learning Techniques ◽  
The Individual

Background: The objective of this study was to investigate the use of ensemble methods to improve the prediction of fetal macrosomia and large for gestational age from prenatal ultrasound imaging measurements. Methods: We evaluated and compared the prediction accuracies of nonlinear and quadratic mixed-effects models coupled with 26 different empirical formulas for estimating fetal weights in predicting large fetuses at birth. The data for the investigation were taken from the Successive Small-for-Gestational-Age-Births study. Ensemble methods, a class of machine learning techniques, were used to improve the prediction accuracies by combining the individual models and empirical formulas. Results: The prediction accuracy of individual statistical models and empirical formulas varied considerably in predicting macrosomia but varied less in predicting large for gestational age. Two ensemble methods, voting and stacking, with model selection, can combine the strengths of individual models and formulas and can improve the prediction accuracy. Conclusions: Ensemble learning can improve the prediction of fetal macrosomia and large for gestational age and have the potential to assist obstetricians in clinical decisions.

scholarly journals CUSTOMER SEGMENTATION IN PRIVATE BANKING SECTOR USING MACHINE LEARNING TECHNIQUES

2013 ◽  
Vol 14 (5) ◽  
pp. 923-939 ◽  
Author(s):  
Ion Smeureanu ◽  
Gheorghe Ruxanda ◽  
Laura Maria Badea
Keyword(s):  
Machine Learning ◽  
Character Recognition ◽  
Foreign Exchange Market ◽  
Banking Sector ◽  
Customer Segmentation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Fraud Prediction ◽  
Private Banking

Machine learning techniques have proven good performance in classification matters of all kinds: medical diagnosis, character recognition, credit default and fraud prediction, and also foreign exchange market prognosis. Customer segmentation in private banking sector is an important step for profitable business development, enabling financial institutions to address their products and services to homogeneous classes of customers. This paper approaches two of the most popular machine learning techniques, Neural Networks and Support Vector Machines, and describes how each of these perform in a segmentation process.

Sign in / Sign up

Export Citation Format

Share Document