scholarly journals Determination of Pear Cultivars (Pyrus communis L.) Based on Colour Change Levels by Using Data Mining

2020 ◽  
Vol 8 (6) ◽  
pp. 1291-1296
Author(s):  
Dilara Gerdan ◽  
Abdullah Beyaz ◽  
Mustafa Vatandaş
Keyword(s):  
Data Mining ◽  
Room Temperature ◽  
False Negative ◽  
Colour Change ◽  
Machine Learning Algorithms ◽  
Majority Voting ◽  
Supervised Machine Learning ◽  
Negative Effects ◽  
True Negative ◽  
Majority Voting Method

Colour is an essential parameter at product quality control stages, and finally, it is necessary for the consumer marketing decision. It is possible to damage the products during the process from collection to storage. Also, it is a well-known condition, cold environmental conditions protect fruits from deformations negative effects, but most of the time, most of the consumers keep the fruits at room temperature in open packs during the consumption process. Also, this condition affects the product storage time. In this study, it is aimed that to determine the behaviours of the fruits in room temperature and humidity conditions. For this aim the colour change of the damaged pears were determined, in another term, colour change value from red to green and yellow to blue at the damaged pears were determined with lightness values by using image analysis technique and analysed with data mining methods. For this purpose, 100 “Akça” pear and 100 “Deveci” local pear cultivar used for experiments. Fruits were equally damaged by using a pendulum mechanism. The damaged fruits were kept at room temperature. Colour change areas on fruits were evaluated with X-rite Ci60 spectrophotometer, and the hardness of fruits was measured by using a fruit penetrometer. The colour (L, a, b) and ΔE values were analysed for the fruit cultivars. The relationship between fruit hardness and colour change were also demonstrated. The predictions were done supervised machine learning algorithms (Decision Tree and Neural Networks with Meta-Learning Techniques; Majority Voting and Random Forest) by using KNIME Analytics software. The classifier performance (accuracy, error, F-Measure, Cohen's Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values were given at the conclusion section of the research. The best prediction were found at the Majority Voting method (MAVL) 98.458 % success given with 70% partitioning.

scholarly journals Automated multi-classifier recognition of atmospheric turbulent structures obtained by Doppler lidar

2020 ◽  
Vol 223 ◽  
pp. 03013
Author(s):  
Anton Sokolov ◽  
Egor Dmitriev ◽  
Ioannis Cheliotis ◽  
Hervé Delbarre ◽  
Elsa Dieudonne ◽  
...  
Keyword(s):  
Regional Scale ◽  
Machine Learning Algorithms ◽  
Majority Voting ◽  
Supervised Machine Learning ◽  
Doppler Lidar ◽  
Turbulent Structures ◽  
Lidar Measurements ◽  
Automated Processing ◽  
Measuring Campaign ◽  
Voting Strategy

We present algorithms and results of automated processing of LiDAR measurements obtained during VEGILOT measuring campaign in Paris in autumn 2014 in order to study horizontal turbulent atmospheric regimes on urban scales. To process images obtained by horizontal atmospheric scanning using Doppler LiDAR, the method is proposed based on texture analysis and classification using supervised machine learning algorithms. The results of the parallel classification by various classifiers were combined using the majority voting strategy. The obtained estimates of accuracy demonstrate the efficiency of the proposed method for solving the problem of remote sensing of regional-scale turbulent patterns in the atmosphere.

scholarly journals A NEW OBJECT-BASED FRAMEWORK TO DETECT SHODOWS IN HIGH-RESOLUTION SATELLITE IMAGERY OVER URBAN AREAS

2015 ◽  
Vol XL-1-W5 ◽  
pp. 713-717 ◽  
Author(s):  
N. Tatar ◽  
M. Saadatseresht ◽  
H. Arefi ◽  
A. Hadavand
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Majority Voting ◽  
Supervised Machine Learning ◽  
Object Based ◽  
High Resolution Satellite Images ◽  
Ground Truth Generation ◽  
High Resolution Satellite Imagery

In this paper a new object-based framework to detect shadow areas in high resolution satellite images is proposed. To produce shadow map in pixel level state of the art supervised machine learning algorithms are employed. Automatic ground truth generation based on Otsu thresholding on shadow and non-shadow indices is used to train the classifiers. It is followed by segmenting the image scene and create image objects. To detect shadow objects, a majority voting on pixel-based shadow detection result is designed. GeoEye-1 multi-spectral image over an urban area in Qom city of Iran is used in the experiments. Results shows the superiority of our proposed method over traditional pixel-based, visually and quantitatively.

scholarly journals Grid Data Mining Strategies for Outcome Prediction in Distributed Intensive Care Units

2013 ◽  
pp. 87-101
Author(s):  
Manuel Filipe Santos ◽  
Filipe Portela ◽  
Miguel Miranda ◽  
José Machado ◽  
António Abelha ◽  
...  
Keyword(s):  
Data Mining ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Prediction Models ◽  
Majority Voting ◽  
Data Sources ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Learning Classifier System ◽  
Majority Voting Method

Previous work developed to predict the outcome of patients in the context of intensive care units brought to the light some requirements like the need to deal with distributed data sources. Those data sources can be used to induce local prediction models, and those models can in turn be used to induce global models more accurate and more general than the local models. This chapter introduces a distributed data mining approach suited to grid computing environments based on a supervised learning classifier system. Five different tactics are explored for constructing the global model in a Distributed Data Mining (DDM) approach: Generalized Classifier Method (GCM), Specific Classifier Method (SCM), Weighed Classifier Method (WCM), Majority Voting Method (MVM), and Model Sampling Method (MSM). Experimental tests were conducted with a real world data set from intensive care medicine. The results demonstrate that the performance of DDM methods is very competitive when compared with the centralized methods.

Discovering News Frames

2016 ◽  
Vol 7 (4) ◽  
pp. 45-62 ◽  
Author(s):  
Loretta H. Cheeks ◽  
Tracy L. Stepien ◽  
Dara M. Wald ◽  
Ashraf Gaffar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Large Scale ◽  
Online News ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Small Scale ◽  
Water Insecurity ◽  
News Content ◽  
Mathematical Techniques

The Internet is a major source of online news content. Current efforts to evaluate online news content including text, storyline, and sources is limited by the use of small-scale manual techniques that are time consuming and dependent on human judgments. This article explores the use of machine learning algorithms and mathematical techniques for Internet-scale data mining and semantic discovery of news content that will enable researchers to mine, analyze, and visualize large-scale datasets. This research has the potential to inform the integration and application of data mining to address real-world socio-environmental issues, including water insecurity in the Southwestern United States. This paper establishes a formal definition of framing and proposes an approach for the discovery of distinct patterns that characterize prominent frames. The authors' experimental evaluation shows the proposed process is an effective approach for advancing semi-supervised machine learning and may assist in advancing tools for making sense of unstructured text.

scholarly journals The Classification of Customers’ Sentiment using Data Mining Approaches

2019 ◽  
Vol IV (IV) ◽  
pp. 146-156
Author(s):  
Dost Muhammad Khan ◽  
Tariq Aziz Rao ◽  
Faisal Shahzad
Keyword(s):  
Data Mining ◽  
Service Providers ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Supervised Machine Learning ◽  
Market Trends ◽  
Using Data

Data mining is a procedure of extracting the requisite information from unprocessed records by using certain methodologies and techniques. Data having sentiments of customers is of utmost importance for managers and decision-makers who intend to monitor the progress, to maintain the quality of their products or services and to observe the latest market trends for business support. Billions of customers are using micro-blogging websites and social media for sharing their opinions about different topics on daily basis. Therefore, it has become a source of acquiring information but to identify a particular feature of a product is still an issue as the information retrieves from varied sources. We proposed a framework for data acquisition, preprocessing, feature extraction and used three supervised machine-learning algorithms for classification of customers’ sentiments. The proposed framework also tested to evaluate the system’s performance. Our proposed methodology will be helpful for researchers, service providers, and decisionmakers.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

The role of serum periostin in the diagnosis of asthma: A meta-analysis

2020 ◽  
Vol 41 (4) ◽  
pp. 240-247
Author(s):  
Lei Yang ◽  
Qingtao Zhao ◽  
Shuyu Wang
Keyword(s):  
Diagnostic Accuracy ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
False Negative ◽  
Summary Receiver Operating Characteristic ◽  
True Negative ◽  
Negative Results ◽  
Noninvasive Biomarker ◽  
Sensitivity Specificity ◽  
Serum Periostin

Background: Serum periostin has been proposed as a noninvasive biomarker for asthma diagnosis and management. However, its accuracy for the diagnosis of asthma in different populations is not completely clear. Methods: This meta-analysis aimed to evaluate the diagnostic accuracy of periostin level in the clinical determination of asthma. Several medical literature data bases were searched for relevant studies through December 1, 2019. The numbers of patients with true-positive, false-positive, false-negative, and true-negative results for the periostin level were extracted from each individual study. We assessed the risk of bias by using Quality Assessment of Diagnostic Accuracy Studies 2. We used the meta-analysis to produce summary estimates of accuracy. Results: In total, nine studies with 1757 subjects met the inclusion criteria. The pooled estimates of sensitivity, specificity, and diagnostic odds ratios for the detection of asthma were 0.58 (95% confidence interval [CI], 0.38‐0.76), 0.86 (95% CI, 0.74‐0.93), and 8.28 (95% CI, 3.67‐18.68), respectively. The area under the summary receiver operating characteristic curve was 0.82 (95% CI, 0.79‐0.85). And significant publication bias was found in this meta‐analysis (p = 0.39). Conclusion: Serum periostin may be used for the diagnosis of asthma, with moderate diagnostic accuracy.

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