A resilient platform for safe execution of software inside a dynamic environment

Tewari, K.; Joshi, Avinash; Garg, Shalini; Goudar, R. H.; Bordoloi, Dibyahash

doi:10.1109/isco.2013.6481201

Search citation statements

Order By: Relevance

Paper Sections

Select...

Data Collection1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2017

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The wine quality dataset is modeled using linear regression algorithm with stochastic gradient descent. The wine quality dataset contains the description of 4,898 white wines which includes the parameters as the acidity and the pH value [6][7][8][9].…”

Section: Data Collectionmentioning

confidence: 99%

Wine Quality Evaluation Using Machine Learning Algorithms

Uniyal¹,

Barthwal²

2017

APJCRI

View full text Add to dashboard Cite

There are many prediction systems available for problems like stock exchange, medical diagnosis, insurance calculation, etc. Wine Quality is one area where there is a big opportunity to recommend a good quality of wine to users based on their preferences as well as in historical data. This paper describes the work to learn and assess whether a given wine sample is of good quality or not. The use of machine learning techniques specifically the linear regression with stochastic gradient descent were explored, and the features that perform well on this classification were engineered. The main aim is to develop a cost-effective system to acquire knowledge using data analysis through machine learning algorithms to predict the quality of wine in a better way.

show abstract

Section: Data Collectionmentioning

confidence: 99%