A Comparative Study of FCA-Based Supervised Classification Algorithms

2004 ◽  
pp. 313-320 ◽  
Author(s):  
Huaiyu Fu ◽  
Huaiguo Fu ◽  
Patrik Njiwoua ◽  
Engelbert Mephu Nguifo
Keyword(s):  
Comparative Study ◽  
Supervised Classification ◽  
Classification Algorithms

scholarly journals Predicting the Demand for Fmcg using Machine Learning

2021 ◽  
Vol 10 (3) ◽  
pp. 169-171
Author(s):  
Anish Mebal. P ◽  
Hema. S ◽  
Jothika. S.J ◽  
Manochitra M
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Consumer Goods ◽  
Fast Moving ◽  
Statistical Information ◽  
Classification Algorithms ◽  
Perishable Products ◽  
Fresh Food ◽  
Fast Moving Consumer Goods ◽  
Competitive Factor

Now-a-days the more accurate prediction of the demand for fast-moving consumer goods (FMCG) is a competitive factor for both the manufacturers and retailers, especially in the super markets, wholesale manufacturers and fresh food sectors and other consumable industries. This proposed system presents the benefits of Machine Learning in sales forecasting for short shelf-life and highly-perishable products, as it predict the statistical information as a result, improves inventory balancing throughout the chain, improving availability to consumers and increasing profitability. This performance is done with various classification algorithms and comparative study is done with some metrics like accuracy, precision, recall and f-score. So that it helps in finding customer need and to increase the profit of the manufacturers

scholarly journals The State-of-the-Art Technology of Currency Identification

2017 ◽  
Vol 9 (3) ◽  
pp. 58-72 ◽  
Author(s):  
Guangyu Wang ◽  
Xiaotian Wu ◽  
WeiQi Yan
Keyword(s):  
Feature Extraction ◽  
New Zealand ◽  
Comparative Study ◽  
State Of The Art ◽  
The State ◽  
Classification Algorithms ◽  
Security Issue ◽  
The Public ◽  
Us Dollar ◽  
Chinese Yuan

The security issue of currency has attracted awareness from the public. De-spite the development of applying various anti-counterfeit methods on currency notes, cheaters are able to produce illegal copies and circulate them in market without being detected. By reviewing related work in currency security, the focus of this paper is on conducting a comparative study of feature extraction and classification algorithms of currency notes authentication. We extract various computational features from the dataset consisting of US dollar (USD), Chinese Yuan (CNY) and New Zealand Dollar (NZD) and apply the classification algorithms to currency identification. Our contributions are to find and implement various algorithms from the existing literatures and choose the best approaches for use.

scholarly journals CAN A MACHINE LEARNING ALGORITHM IDENTIFY SARS-COV-2 VARIANTS BASED ON CONVENTIONAL rRT-PCR? PROOF OF CONCEPT

2021 ◽  
Author(s):  
jorge cabrera Alvargonzalez ◽  
Ana Larranaga Janeiro ◽  
Sonia Perez ◽  
Javier Martinez Torres ◽  
Lucia martinez lamas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Supervised Classification ◽  
Learning Algorithm ◽  
Support Vector ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Proof Of Concept ◽  
The Past ◽  
Number Of Cycles

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges humanity has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Finally, the results obtained from the classification show how the appearance of each wave is coincident with the surge of each of the variants present in the region of Galicia (Spain) during the development of the SARS-CoV-2 pandemic and clearly identified with the classification algorithm.

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