Preferred Language
Articles
/
ijs-6062
The Theoretical Solving of Intersection Point of the Horizontal and Vertical Gravity Gradients in Order to Estimate the Depth of Causative Source of Gravity Anomaly
...Show More Authors

The depth of causative source of gravity is one of the most important parameter
of gravity investigation. Present study introduces the theoretical solve of the
intersection point of the horizontal and vertical gradients of gravity anomaly. Two
constants are obtained to estimate the depth of causative source of gravity anomaly,
first one is 1.7807 for spherical body and the second is 2.4142 for the horizontal
cylinder body. These constants are tested for estimating the depth of three actual
cases and good results are obtained. It is believed that the constants derived on
theoretical bases are better than those obtained by empirical experimental studies.

View Publication Preview PDF
Quick Preview PDF
Publication Date
Sun Jun 01 2014
Journal Name
Journal Of Economics And Administrative Sciences
Design Sampling Plan when Life Time Follows Logistic Distribution
...Show More Authors

Design sampling plan was and still one of most importance subjects because it give lowest cost  comparing with others, time live statistical distribution should be known to give best estimators for  parameters of sampling plan and get best sampling plan.

Research dell with design sampling plan when live time distribution follow Logistic distribution with () as location and shape parameters, using these information can help us getting (number of groups, sample size) associated with reject or accept the Lot

Experimental results for simulated data shows the least number of groups and sample size needs to reject or accept the Lot with certain probability of

... Show More
View Publication Preview PDF
Crossref
Publication Date
Sat Jan 01 2022
Journal Name
Turkish Journal Of Physiotherapy And Rehabilitation
classification coco dataset using machine learning algorithms
...Show More Authors

In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho

... Show More
Publication Date
Thu Sep 30 2021
Journal Name
Iraqi Journal Of Science
PFDINN: Comparison between Three Back-propagation Algorithms for Pear Fruit Disease Identification
...Show More Authors

     The diseases presence in various species of fruits are the crucial parameter of economic composition and degradation of the cultivation industry around the world. The proposed pear fruit disease identification neural network (PFDINN) frame-work to identify three types of pear diseases was presented in this work. The major phases of the presented frame-work were as the following: (1) the infected area in the pear fruit was detected by using the algorithm of K-means clustering. (2) hybrid statistical features were computed over the segmented pear image and combined to form one descriptor. (3) Feed forward neural network (FFNN), which depends on three learning algorithms of back propagation (BP) training, namely Sca

... Show More
View Publication Preview PDF
Scopus Crossref