A contribution to the volumetric estimation of mercury

1930 ◽  
Vol 2 ◽  
pp. 593-608
Author(s):  
O. Pročke
Keyword(s):  
Volumetric Estimation

Method for the volumetric estimation of titanium

1908 ◽  
Vol s4-25 (146) ◽  
pp. 130-132 ◽  
Author(s):  
H. D. Newton
Keyword(s):  
Volumetric Estimation

On the volumetric estimation of cerium

1899 ◽  
Vol s4-8 (48) ◽  
pp. 451-459
Author(s):  
P. E. Browning
Keyword(s):  
Volumetric Estimation

Oxidimetric methods for the volumetric estimation of sulphite

1955 ◽  
Vol 13 ◽  
pp. 313-322 ◽  
Author(s):  
K.Bhaskara Rao ◽  
G.Gopala Rao
Keyword(s):  
Volumetric Estimation

Femur Bone Volumetric Estimation for Osteoporosis Classification Using Optimization-Based Deep Belief Network in X-Ray Images

2019 ◽  
Vol 62 (11) ◽  
pp. 1656-1670
Author(s):  
N Shankar ◽  
S Sathish Babu ◽  
C Viswanathan
Keyword(s):  
Gradient Descent ◽  
Harmony Search ◽  
Search Method ◽  
Deep Belief Network ◽  
Shape Model ◽  
Belief Network ◽  
Femur Bone ◽  
Volumetric Estimation ◽  
Real Time Database ◽  
Improved Accuracy

AbstractOsteoporosis classification is a significant requirement in the medical field to automatically classify the patients with skeleton disorder that occurs as a result of aging. The classification algorithms required improved accuracy and computationally less complexity. Accordingly, this paper proposes a classification method using the proposed gradient harmony search (GHS) optimization-based deep belief network. The GHS is developed by integrating the harmony search (HS) in the gradient descent (GD) algorithm. The osteoporosis classification is progressed as five major steps: preprocessing, segmentation using active shape model, geometric estimation using the proposed template search method, feature extraction for extracting the medical and image level features, and osteoporosis classification using the proposed GHS based deep belief network. The proposed template search method updates the geometric points of the femur segment effectively and automatically. Experimentation using the real-time database ensures the effectiveness of the proposed method in terms of accuracy, sensitivity, and specificity. The proposed method acquired the accuracy of 0.9539, proving that the osteoporosis classification using the proposed algorithm seems to be effective in taking accurate decisions regarding the patients.

Efficient volumetric estimation from plenoptic data

2013 ◽  
Author(s):  
Paul Anglin ◽  
Stanley J. Reeves ◽  
Brian S. Thurow
Keyword(s):  
Volumetric Estimation

scholarly journals The volumetric estimation of lead

The Analyst ◽  
1916 ◽  
Vol 41 (486) ◽  
pp. 270
Author(s):  
John Waddell
Keyword(s):  
Volumetric Estimation
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