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Development of an ANN Model for RGB Color Classification using the Dataset Extracted from a Fabricated Colorimeter
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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 detection is one of the results of the proposed classifier. The work demanded the collection of about 5000 color codes which in turn were subjected to algorithms for training and testing. The open-source platform TensorFlow for ML and the open-source neural network library Keras were used to construct the algorithm for the study. The results showed an acceptable efficiency of the built classifier represented by an accuracy of 90% which can be considered applicable, especially after some improvements in the future to makes it more effective as a trusted colorimeter.

 

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Publication Date
Wed Feb 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
A comparison between the logistic regression model and Linear Discriminant analysis using Principal Component unemployment data for the province of Baghdad
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     The objective of the study is to demonstrate the predictive ability is better between the logistic regression model and Linear Discriminant function using the original data first and then the Home vehicles to reduce the dimensions of the variables for data and socio-economic survey of the family to the province of Baghdad in 2012 and included a sample of 615 observation with 13 variable, 12 of them is an explanatory variable and the depended variable is number of workers and the unemployed.

     Was conducted to compare the two methods above and it became clear by comparing the  logistic regression model best of a Linear Discriminant  function written

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Publication Date
Sun Jun 01 2014
Journal Name
Baghdad Science Journal
Clouds Height Classification Using Texture Analysis of Meteosat Images
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In the present work, pattern recognition is carried out by the contrast and relative variance of clouds. The K-mean clustering process is then applied to classify the cloud type; also, texture analysis being adopted to extract the textural features and using them in cloud classification process. The test image used in the classification process is the Meteosat-7 image for the D3 region.The K-mean method is adopted as an unsupervised classification. This method depends on the initial chosen seeds of cluster. Since, the initial seeds are chosen randomly, the user supply a set of means, or cluster centers in the n-dimensional space.The K-mean cluster has been applied on two bands (IR2 band) and (water vapour band).The textural analysis is used

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Publication Date
Tue Jul 31 2018
Journal Name
Journal Of Theoretical And Applied Information Technology
Classification and monitoring of autism using svm and vmcm
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Autism is a lifelong developmental deficit that affects how people perceive the world and interact with each others. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. The commonly used tools for analyzing the dataset of autism are FMRI, EEG, and more recently "eye tracking". A preliminary study on eye tracking trajectories of patients studied, showed a rudimentary statistical analysis (principal component analysis) provides interesting results on the statistical parameters that are studied such as the time spent in a region of interest. Another study, involving tools from Euclidean geometry and non-Euclidean, the trajectory of eye patients also showed interesting results. In this

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Publication Date
Wed Sep 12 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Hiding Data in Color Image Using Least Significant Bits of Blue Sector
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Concealing the existence of secret hidden message inside a cover object is known as steganography, which is a powerful technique. We can provide a secret communication between sender and receiver using Steganography. In this paper, the main goal is for hiding secret message into the pixels using Least Significant Bit (LSB) of blue sector of the cover image. Therefore, the objective is by mapping technique presenting a model for hiding text in an image. In the model for proposing the secret message, convert text to binary also the covering (image) is divided into its three original colors, Red, Green and Blue (RGB) , use the Blue sector convert it to binary,  hide two bits from the message in  two bits of the least significant b

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Publication Date
Sun Apr 01 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Science
Hiding Data in Color Image Using Least Significant Bits of Blue Sector
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Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
A Novel Technique for Secure Data Cryptosystem Based on Chaotic Key Image Generation
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The advancements in Information and Communication Technology (ICT), within the previous decades, has significantly changed people’s transmit or store their information over the Internet or networks. So, one of the main challenges is to keep these information safe against attacks. Many researchers and institutions realized the importance and benefits of cryptography in achieving the efficiency and effectiveness of various aspects of secure communication.This work adopts a novel technique for secure data cryptosystem based on chaos theory. The proposed algorithm generate 2-Dimensional key matrix having the same dimensions of the original image that includes random numbers obtained from the 1-Dimensional logistic chaotic map for given con

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Publication Date
Sun Feb 05 2017
Journal Name
Iraqi Journal Of Market Research And Consumer Protection
STUDY THE OPTIMUM CONDITIONS FOR THE REMOVAL OF HEAVY METAL ELEMENTS FROM AQUEOUS SOLUTIONS USING CONTAMINATED BACTERIA Bacillus subtilis LOCALLY ISOLATED: STUDY THE OPTIMUM CONDITIONS FOR THE REMOVAL OF HEAVY METAL ELEMENTS FROM AQUEOUS SOLUTIONS USING CONTAMINATED BACTERIA Bacillus subtilis LOCALLY ISOLATED
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We studied the effect of certain environmental conditions for removing heavy metal elements from contaminated aqueous solutions (Cd, Cu, Pb, Fe, Zn, Ni, Cr) using the bacterium Bacillus subtilis to appoint the optimal conditions for removal ,The best optimum temperature range for two isolate was 30-35○C while the hydrogen number for the maximum mineral removal range was 6-7. The best primary mineral removal was 100 mg/L, while the maximum removal for all minerals was obtained after 6 hrs of Cu element time and the maximum removal efficiency was obtained after 24 hrs of Cu element. The results have proved that the best aeration for maximum removal was obtained at rotation speed of 150 rpm/minute. Inoculums of 5ml/100ml which contained 1

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Publication Date
Tue Apr 30 2024
Journal Name
Iraqi Journal Of Science
Adsorption of the Color Pollutant onto NiO Nanoparticles Prepared by a New Green Method
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Green synthesis methods have emerged as favorable techniques for the synthesis of nano-oxides due to their simplicity, cost-effectiveness, eco-friendliness, and non-toxicity. In this study, Nickel oxide nanoparticles (NiO-NPs) were synthesized using the aqueous extract of Laurus nobilis leaves as a natural capping agent. The synthesized NiO-NPs were employed as an adsorbent for the removal of Biebrich Scarlet (BS) dye from aqueous solution using adsorption technique. Comprehensive characterization of NiO-NPs was performed using various techniques such as atomic force microscopy (AFM), Fourier transform infrared (FTIR), X-ray diffraction (XRD), Brunauer-Emmett and Teller (BET) analysis, and scanning electron microscopy (SEM). Additionally, o

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Publication Date
Thu Nov 02 2017
Journal Name
Iraqi Journal Of Laser
Enhancement the Sensitivity of Humidity Sensor Based on an Agarose Coating Transmission-Type Photonic Crystal Fiber Interferometer
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Photonic Crystal Fiber Interferometers (PCFIs) are widely used for sensing applications. This work presents the fabrication and the characterization of a relative humidity sensor based on a polymer-coated photonic crystal fiber that operates in a Mach- Zehnder Interferometer (MZI) transmission mode. The fabrication of the sensor involved splicing a short (1 cm) length of Photonic Crystal Fiber (PCF) between two single-mode fibers (SMF). It was then coated with a layer of agarose solution. Experimental results showed that a high humidity sensitivity of 29.37 pm/%RH was achieved within a measurement range of 27–95%RH. The sensor also showed good repeatability, small size, measurement accuracy and wide humidity range. The RH sensitivity o

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Publication Date
Thu Jun 30 2022
Journal Name
Journal Of Economics And Administrative Sciences
Using Genetic Algorithm to Estimate the Parameters of the Gumbel Distribution Function by Simulation
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In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as  the Bayes method. The comparison was made using the mean error squares (MSE), where the best  estimator  is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).

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