An Efficient Palm-Dorsa-Based Approach for Vein Image Enhancement and Feature Extraction in Cloud Computing Environment

Author(s):  
Kamta Nath Mishra
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jing Yu ◽  
Hang Li ◽  
Desheng Liu

Medical data have the characteristics of particularity and complexity. Big data clustering plays a significant role in the area of medicine. The traditional clustering algorithms are easily falling into local extreme value. It will generate clustering deviation, and the clustering effect is poor. Therefore, we propose a new medical big data clustering algorithm based on the modified immune evolutionary method under cloud computing environment to overcome the above disadvantages in this paper. Firstly, we analyze the big data structure model under cloud computing environment. Secondly, we give the detailed modified immune evolutionary method to cluster medical data including encoding, constructing fitness function, and selecting genetic operators. Finally, the experiments show that this new approach can improve the accuracy of data classification, reduce the error rate, and improve the performance of data mining and feature extraction for medical data clustering.


2011 ◽  
Vol 4 (3) ◽  
pp. 132-133
Author(s):  
Krishnakumar L Krishnakumar L ◽  
◽  
Jimy Joy

Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.


2018 ◽  
Vol 5 (2) ◽  
pp. 1 ◽  
Author(s):  
SHAFI'I MUHAMMAD ABDULHAMID ◽  
NAFISAT ABUBAKAR SADIQ ◽  
ABDULLAHI MOHAMMED ◽  
NADIM RANA ◽  
HARUNA CHIROMA ◽  
...  

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