Multiclass SVM coupled with optimization techniques for segmentation and classification of medical images

Author(s):  
S.N. Kumar ◽  
A. Lenin Fred ◽  
Parasuraman Padmanabhan ◽  
Balazs Gulyas ◽  
Ajay Kumar Haridhas ◽  
...  
Solar Energy ◽  
2021 ◽  
Vol 218 ◽  
pp. 296-311
Author(s):  
Arslan A. Rizvi ◽  
Syed N. Danish ◽  
Abdelrahman El-Leathy ◽  
Hany Al-Ansary ◽  
Dong Yang

2018 ◽  
Vol 24 (6) ◽  
pp. 720-752 ◽  
Author(s):  
Aldric Vives ◽  
Marta Jacob ◽  
Marga Payeras

Pricing and revenue management (RM) techniques have become a popular field of research in hotel management literature. The sector’s background framework and evolution and the widespread use of new technologies have allowed a customer-oriented approach to be taken to pricing and the development of RM tools, while also contributing to better processes in hotel management performance at individual hotel level. Thus, price optimization (PO) methods that seek to maximize hotel revenue are based on inventory scarcity, customer segmentation and pricing. In the hotel sector, as in the airline industry, different pricing policies have a greater impact than competition measurement effects. This is mainly as differentiation strategies and specific policies at hotels can reduce the pressure of a competitive environment. The main contributions of the article are the presentation, description and classification of the principal RM and PO techniques in hotel sector literature.


2021 ◽  
pp. 243-266
Author(s):  
D. Devasena ◽  
M. Jagadeeswari ◽  
B. Sharmila ◽  
K. Srinivasan

2018 ◽  
Vol 14 (11) ◽  
pp. 1488-1498
Author(s):  
Ramzi Ben Ali ◽  
Ridha Ejbali ◽  
Mourad Zaied

Author(s):  
Angeliki Skoura ◽  
Vasileios Megalooikonomou ◽  
Athanasios Diamantopoulos ◽  
George C. Kagadis ◽  
Dimitrios Karnabatidis

2020 ◽  
Vol 130 ◽  
pp. 207-215 ◽  
Author(s):  
Jian Wang ◽  
Jing Li ◽  
Xian-Hua Han ◽  
Lanfen Lin ◽  
Hongjie Hu ◽  
...  

2019 ◽  
Vol 10 (2) ◽  
pp. 63-85
Author(s):  
Ramani Selvanambi ◽  
Jaisankar N.

Quality analysis of the treatment of cancer has been an objective of e-health services for quite some time. The objective is to predict the stage of breast cancer by using diverse input parameters. Breast cancer is one of the main causes of death in women when compared to other tumors. The classification of breast cancer information can be profitable to anticipate diseases or track the hereditary of tumors. For classification, an artificial neural network (ANN) structure was carried out. In the structure, nine training algorithms are used and the proposed is the Levenberg-Marquardt algorithm. For optimizing the hidden layer and neuron, three optimization techniques are used. In the result, the best approval execution is anticipated and the diverse execution evaluation estimation for three optimization algorithms is researched. The correlation execution diagram for an accuracy of 95%, a sensitivity of 98%, and a specificity of 89% of a social spider optimization (SSO) algorithm are shown.


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