A comparative study of classification algorithms for risk prediction in pregnancy

Author(s):  
Lakshmi B.N. ◽  
Indumathi T.S. ◽  
Nandini Ravi
1965 ◽  
Vol 48 (1) ◽  
pp. 14-22 ◽  
Author(s):  
S. A. Aboul-Khair ◽  
J. Crooks

ABSTRACT Studies of iodine metabolism have been carried out in 15 pregnant women, 33 cases with sporadic goitre and 11 with thyrotoxicosis. A low plasma inorganic iodine was common to the three groups. In pregnancy and sporadic goitre the thyroid clearance of iodine was elevated and the absolute iodine uptake normal. A high thyroid clearance of iodine in thyrotoxicosis was associated with a high absolute iodine uptake. The results suggest that both pregnancy and sporadic goitre are physiological responses to an iodine deficiency state while the iodine deficiency state of thyrotoxicosis is secondary to increased thyroid activity.


Author(s):  
Anish Mebal. P ◽  
Hema. S ◽  
Jothika. S.J ◽  
Manochitra M

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


2017 ◽  
Vol 9 (3) ◽  
pp. 58-72 ◽  
Author(s):  
Guangyu Wang ◽  
Xiaotian Wu ◽  
WeiQi Yan

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.


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