Quality Assessment of Crops using Machine Learning Techniques

2019 ◽  
Author(s):  
Tenzin Chokey ◽  
Sarika Jain
Keyword(s):  
Machine Learning ◽  
Quality Assessment ◽  
Machine Learning Techniques ◽  
Learning Techniques

Traditional vs. Machine-Learning Techniques for OSM Quality Assessment

2019 ◽  
pp. 469-487
Author(s):  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Padraig Corcoran ◽  
Amerah Alghanim
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Quality Assessment ◽  
Spatial Data ◽  
Current Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Data Quality Assessment ◽  
Expensive Process

Nowadays an ever-increasing number of applications require complete and up-to-date spatial data, in particular maps. However, mapping is an expensive process and the vastness and dynamics of our world usually render centralized and authoritative maps outdated and incomplete. In this context crowd-sourced maps have the potential to provide a complete, up-to-date, and free representation of our world. However, the proliferation of such maps largely remains limited due to concerns about their data quality. While most of the current data quality assessment mechanisms for such maps require referencing to authoritative maps, we argue that such referencing of a crowd-sourced spatial database is ineffective. Instead we focus on the use of machine learning techniques that we believe have the potential to not only allow the assessment but also to recommend the improvement of the quality of crowd-sourced maps without referencing to external databases. This chapter gives an overview of these approaches.

Traditional vs. Machine-Learning Techniques for OSM Quality Assessment

2017 ◽  
pp. 47-64
Author(s):  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Padraig Corcoran ◽  
Amerah Alghanim
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Quality Assessment ◽  
Spatial Data ◽  
Current Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Data Quality Assessment ◽  
Expensive Process

Nowadays an ever-increasing number of applications require complete and up-to-date spatial data, in particular maps. However, mapping is an expensive process and the vastness and dynamics of our world usually render centralized and authoritative maps outdated and incomplete. In this context crowd-sourced maps have the potential to provide a complete, up-to-date, and free representation of our world. However, the proliferation of such maps largely remains limited due to concerns about their data quality. While most of the current data quality assessment mechanisms for such maps require referencing to authoritative maps, we argue that such referencing of a crowd-sourced spatial database is ineffective. Instead we focus on the use of machine learning techniques that we believe have the potential to not only allow the assessment but also to recommend the improvement of the quality of crowd-sourced maps without referencing to external databases. This chapter gives an overview of these approaches.

scholarly journals Visual Perceptual Quality Assessment Based on Blind Machine Learning Techniques

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 175
Author(s):  
Ghislain Takam Tchendjou ◽  
Emmanuel Simeu
Keyword(s):  
Machine Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Visual Quality ◽  
Machine Learning Techniques ◽  
Reference Image ◽  
Training Phase ◽  
Visual Quality Assessment ◽  
Learning Techniques ◽  
Direct Learning

This paper presents the construction of a new objective method for estimation of visual perceiving quality. The proposal provides an assessment of image quality without the need for a reference image or a specific distortion assumption. Two main processes have been used to build our models: The first one uses deep learning with a convolutional neural network process, without any preprocessing. The second objective visual quality is computed by pooling several image features extracted from different concepts: the natural scene statistic in the spatial domain, the gradient magnitude, the Laplacian of Gaussian, as well as the spectral and spatial entropies. The features extracted from the image file are used as the input of machine learning techniques to build the models that are used to estimate the visual quality level of any image. For the machine learning training phase, two main processes are proposed: The first proposed process consists of a direct learning using all the selected features in only one training phase, named direct learning blind visual quality assessment DLBQA. The second process is an indirect learning and consists of two training phases, named indirect learning blind visual quality assessment ILBQA. This second process includes an additional phase of construction of intermediary metrics used for the construction of the prediction model. The produced models are evaluated on many benchmarks image databases as TID2013, LIVE, and LIVE in the wild image quality challenge. The experimental results demonstrate that the proposed models produce the best visual perception quality prediction, compared to the state-of-the-art models. The proposed models have been implemented on an FPGA platform to demonstrate the feasibility of integrating the proposed solution on an image sensor.

scholarly journals Suitability Determination of Machine Learning Techniques for the Operational Quality Assessment of Geophysical Survey Results

2020 ◽  
Vol 25 (2) ◽  
pp. 153-162
Author(s):  
Kirill Abramov ◽  
Janis Grundspenkis
Keyword(s):  
Machine Learning ◽  
Quality Assessment ◽  
Geophysical Methods ◽  
Well Logging ◽  
Geophysical Survey ◽  
Machine Learning Techniques ◽  
Drilling Process ◽  
Learning Techniques ◽  
Operational Quality ◽  
Geophysical Surveys

AbstractWell logging, also known as a geophysical survey, is one of the main components of a nuclear fuel cycle. This survey follows directly after the drilling process, and the operational quality assessment of its results is a very serious problem. Any mistake in this survey can lead to the culling of the whole well. This paper examines the feasibility of applying machine learning techniques to quickly assess the well logging quality results. The studies were carried out by a reference well modelling for the selected uranium deposit of the Republic of Kazakhstan and further comparing it with the results of geophysical surveys recorded earlier. The parameters of the geophysical methods and the comparison rules for them were formulated after the reference well modelling process. The classification trees and the artificial neural networks were used during the research process and the results obtained for both methods were compared with each other. The results of this paper may be useful to the enterprises engaged in the geophysical well surveys and data processing obtained during the logging process.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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