Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images

Tight reservoirs have attracted the interest of the oil industry in recent years according to its significant impact on the global oil product. Several challenges are present when producing from these reservoirs due to its low to extra low permeability and very narrow pore throat radius. Development strategy selection for these reservoirs such as horizontal well placement, hydraulic fracture design, well completion, and smart production program, wellbore stability all need accurate characterizations of geomechanical parameters for these reservoirs. Geomechanical properties, including uniaxial compressive strength (UCS), static Young’s modulus (Es), and Poisson’s ratio (υs), were measured experimentally using both static and dynamic met

This investigation pertains to the evaluation of water quality in SAWA Lake, located in the Al-Muthanna province of Southern Iraq, from 1977 to 2020. Understanding the water quality and assessments of this Lake is of great importance. The Lake is home to small, transparent, blind fish measuring approximately 10 cm and is often referred to as the "wonderful" or "strange" Lake due to its many unique features. The study focuses on several elements to represent water quality, including total dissolved solids (TDS), electrical conductivity (EC), pH, and temperature (T), which were measured directly in the field. Additionally, scientific concepts such as K⁺, Ca²⁺, Cl^-, HCO

The relationship between costs of environment and costs of product life – cycle.    Boubtlessly when the economical unit exercise their productive works, they lead to pollution in water, air and soil as well as all stages of product life- cycle from Rans Dstage, production stage, packaging stage and finally abandonment stage-             Pollution causes environmental costs. Lgnoring or hiding environmental costs and no taking them in consideration with product cost lead to a wrong account of preduot cost.

Therefore, environmental costs should be included and matched for all stages with in product costs to know which activities, processes

In this investigation , borax (B) (additive I) and chlorinated paraffin (CP.) (additive II) ,were used as flame retardants for each of epoxy and unsaturated polyester resins in the weight ratios of 2,4,6, & 8% by preparing films of (130×130×3) mm dimensions. Also films of these resins with a mixture of [50%(B.)+50%(CP.)] (additive III) in the same weight ratios were prepared in order to study the synergistic effect of these additives on the flammability of the two resins . Three standard test methods were used to measure the flame retardation which are : 1-ASTM : D-2863 2-ASTM : D-635 3-ASTM : D-3014

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de