Wine Quality Analysis Using Machine Learning

2019 ◽  
pp. 239-247
Author(s):  
Bipul Shaw ◽  
Ankur Kumar Suman ◽  
Biswarup Chakraborty
Keyword(s):  
Machine Learning ◽  
Quality Analysis ◽  
Wine Quality

Wine Quality Analysis Using Machine Learning Algorithms

2020 ◽  
pp. 11-18
Author(s):  
Mahima ◽  
Ujjawal Gupta ◽  
Yatindra Patidar ◽  
Abhishek Agarwal ◽  
Kushall Pal Singh
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Quality Analysis ◽  
Wine Quality

Air quality analysis in critical zones of Mexico using unsupervised machine learning

2020 ◽  
Author(s):  
Eli G. Pale-Ramon ◽  
Luis J. Morales-Mendoza ◽  
Sonia L. Mestizo-Gutierrez ◽  
Mario Gonzalez-Leee ◽  
Rene F. Vazquez-Bautista ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Quality Analysis ◽  
Unsupervised Machine Learning ◽  
Critical Zones

The Machine of Teaching Quality Valuation Studies an Analytical Method

2013 ◽  
Vol 303-306 ◽  
pp. 1510-1513
Author(s):  
Yun Xia Wang
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Teaching Quality ◽  
Experimental Results ◽  
Quality Analysis ◽  
Positive Contribution ◽  
Learning Framework ◽  
Machine Learning Methods ◽  
Network Method ◽  
Artificial Neural Network Method

This text describes the research work in machine learning framework for the assessment of teaching quality , mainly focused on the analysis of data on information technology in the teaching process , and the use of artificial neural network method, the experiment , the experimental results reflect the level of teaching quality analysis . Experimental results show that the use of machine learning methods can indeed make a positive contribution to the teaching quality assessment .

DelibAnalysis: Understanding the quality of online political discourse with machine learning

2019 ◽  
Vol 46 (6) ◽  
pp. 810-822
Author(s):  
Eleonore Fournier-Tombs ◽  
Giovanna Di Marzo Serugendo
Keyword(s):  
Machine Learning ◽  
Quality Index ◽  
Political Discourse ◽  
Large Datasets ◽  
Quality Analysis ◽  
Political Discussion ◽  
Systematic Framework ◽  
Insight Into ◽  
Discourse Quality

This article proposes an automated methodology for the analysis of online political discourse. Drawing from the discourse quality index (DQI) by Steenbergen et al., it applies a machine learning–based quantitative approach to measuring the discourse quality of political discussions online. The DelibAnalysis framework aims to provide an accessible, replicable methodology for the measurement of discourse quality that is both platform and language agnostic. The framework uses a simplified version of the DQI to train a classifier, which can then be used to predict the discourse quality of any non-coded comment in a given political discussion online. The objective of this research is to provide a systematic framework for the automated discourse quality analysis of large datasets and, in applying this framework, to yield insight into the structure and features of political discussions online.

scholarly journals A Study and Analysis of Machine Learning Techniques in Predicting Wine Quality

2021 ◽  
Vol 10 (1) ◽  
pp. 314-321
Author(s):  
Mohit Gupta ◽  
Vanmathi C
Keyword(s):  
Machine Learning ◽  
Human Heart ◽  
Machine Learning Techniques ◽  
Wine Quality ◽  
High Quality ◽  
Wine Consumption ◽  
Independent Variables ◽  
Quantitative Metrics ◽  
Learning Techniques

In today’s trend consumers are very much concern about the quality of the product in turn, Industries are all working on various methodologies to ensure the high quality in their products. Most of consumers judge the quality of the product based on the certification obtained for the product. In Earlier days, the quality is measured and validated only through human experts. Nowadays most of the validation tasks are automated through software and this ease the burden of human experts by assisting with them in predicting the quality of the product and that leads to greater a reduction of time spent. Wine consumption has increased rapidly over the last few decades, not only for recreational purposes but also due of its inherent health benefits especially to human heart. This chapter demonstrates the usage of various machine learning techniques in predicting the quality of wine and results are validated through various quantitative metrics. Moreover the contribution of various independent variables facilitating the final outcome is precisely portrayed.

scholarly journals Improving Water Quality Assessment through Anomaly Detection Using Hybrid Convolutional Neural Network Approach

2021 ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Quality ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Machine Learning Algorithms ◽  
Quality Analysis

<p>Water being a precious commodity for every person around the world needs to be quality monitored continuously for ensuring safety whilst usage. The water data collected from sensors in water plants are used for water quality assessment. The anomaly present in the water data seriously affects the performance of water quality assessment. Hence it needs to be addressed. In this regard, water data collected from sensors have been subjected to various anomaly detection approaches guided by Machine Learning (ML) and Deep Learning framework. Standard machine learning algorithms have been used extensively in water quality analysis and these algorithms in general converge quickly. Considering the fact that manual feature selection has to be done for ML algorithms, Deep Learning (DL) algorithm is proposed which involve implicit feature learning. A hybrid model is formulated that takes advantage of both and presented it is data invariant too. This novel Hybrid Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) approach is used to detect presence of anomalies in sensor collected water data. The experiment of the proposed CNN-ELM model is carried out using the publicly available dataset GECCO 2019. The findings proved that the model has improved the water quality assessment of the sensor water data collected by detecting the anomalies efficiently and achieves F1 score of 0.92. This model can be implemented in water quality assessment.</p>

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