Preferred Language
Articles
/
ijs-9632
Enhancing the indication of ancient geologic features by using Seismic Attributes technique extracted along picked horizons of seismic and flattened data
...Show More Authors

Three Seismic Attributes are used to enhance or delineate geologic feature that cannot be detected within seismic resolution limit. These are Instantaneous Amplitude, Instantaneous Phase and Instantaneous Frequency Attributes. These are applied along two defined picked surface horizons within 3D seismic data for an area in southern Iraq. Two geologic features are deduced, the first represents complex channel system at the top of Saadi Formation and the second represents submarine fan within Mishrif Formation. The semblances of these ancient geological features are dramatically enhanced by using flattening technique.

View Publication Preview PDF
Quick Preview PDF
Publication Date
Fri Mar 27 2020
Journal Name
Iraqi Journal Of Science
Uranium Concentration in Some Medical Herbs
...Show More Authors

Uranium concentration and the annual committed effective dose in some selected medicinal plants commonly used in Iraq have been determined using fission tracks technique etch in twelve medical plants samples using CR-39 track detector. The results show that the uranium concentration ranged from 0.044±0.021 ppm in Thyme sample to 0.2±0.03 ppm in Black Pepper and Cardamom samples with an average value of 0.14 ±0.0 4ppm. The average annual effective dose due to ingestion of uranium radionuclide was 13.77x10 -5 mSv/y, which is below the world average annual committed effective dose of 0.3 mSv/y for ingestion of natural radionuclides.

View Publication Preview PDF
Scopus (1)
Scopus Crossref
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