The manual classification of oranges according to their ripeness or flavor takes a long time; furthermore, the classification of ripeness or sweetness by the intensity of the fruit’s color is not uniform between fruit varieties. Sweetness and color are important factors in evaluating the fruits, the fruit’s color may affect the perception of its sweetness. This article aims to study the possibility of predicting the sweetness of orange fruits based on artificial intelligence technology by studying the relationship between the RGB values of orange fruits and the sweetness of those fruits by using the Orange data mining tool. The experiment has applied machine learning algorithms to an orange fruit image dataset and performed a comparative study of the algorithms in order to determine which algorithm has the highest prediction accuracy. The results showed that the value of the red color has a greater effect than the green and blue colors in predicting the sweetness of orange fruits, as there is a direct relationship between the value of the red color and the level of sweetness. In addition, the logistic regression model algorithm gave the highest degree of accuracy in predicting sweetness.
One of the most important of satellite image is studying the surface water
according of its distribution and depth. In this work, three images have been taken
for Baghdad and surrounding for year (1991, 1999 and 2014) and by using of envi
program has been used. Different classes have been evaluated for Al-Habania and
Al-Razaza River according to its depth and water reflectance. In the present work
four types of water depth (very shallow, shallow, moderate, and deep area) have
been detected.
Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci
... Show MoreIn this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi
... Show MoreIn this study, Zizphus spina-christi leaf powder was applied for the adsorption of methyl orange. The effect of different operating parameters on the Batch Process adsorption was investigated such as solution pH (2-12), effect of contact time (0-60 min.), initial dye concentration (2-20 mg/L), effect of adsorbent dosage (0-4.5 g) and effect of temperature (20-50ᵒC). The results show a maximum removal rate and adsorption capacity (%R= 23.146, qe = 2.778 mg/g) at pH = 2 and equilibrium was reached at 40 min. The pseudo- second-order kinetics were found to be best fit for the removal process (R2 = 0.997). Different isotherm models (Langmuir, Freundlich, Dubini-Radushkevich,Temkin) were applied in this stud
... Show MoreTime series have gained great importance and have been applied in a manner in the economic, financial, health and social fields and used in the analysis through studying the changes and forecasting the future of the phenomenon. One of the most important models of the black box is the "ARMAX" model, which is a mixed model consisting of self-regression with moving averages with external inputs. It consists of several stages, namely determining the rank of the model and the process of estimating the parameters of the model and then the prediction process to know the amount of compensation granted to workers in the future in order to fulfil the future obligations of the Fund. , And using the regular least squares method and the frequ
... Show MoreApplications of remote sensing are important in improving potato production through the broader adoption of precision agriculture. This technology could be useful in decreasing the potential contamination of soil and water due to the over-fertilization of agriculture crops. The objective of this study was to assess the utility of active sensors (Crop Circle™, Holland Scientific, Inc., Lincoln, NE, USA and GreenSeeker™, Trimble Navigation Limited, Sunnyvale, CA, USA) and passive sensors (multispectral imaging with Unmanned Arial Vehicles (UAVs)) to predict total potato yield and phosphorus (P) uptake. The experimental design was a randomized complete block with four replications and six P treatments, ranging from 0 to 280 kg P ha−1, as
... Show MoreIron is one of the abundant elements on earth that is an essential element for humans and may be a troublesome element in water supplies. In this research an AAN model was developed to predict iron concentrations in the location of Al- Wahda water treatment plant in Baghdad city by water quality assessment of iron concentrations at seven WTPs up stream Tigris River. SPSS software was used to build the ANN model. The input data were iron concentrations in the raw water for the period 2004-2011. The results indicated the best model predicted Iron concentrations at Al-Wahda WTP with a coefficient of determination 0.9142. The model used one hidden layer with two nodes and the testing error was 0.834. The ANN model coul
... Show MoreThe research aims to show the relationship between artificial intelligence in accounting education and its role in achieving sustainable development goals in the Kingdom of Bahrain. The research dealt with the role of artificial intelligence applications in accounting education at the University of Applied Sciences as a model for Bahraini universities to achieve sustainable development goals. The application of artificial intelligence in accounting education achieves seven of the seventeen sustainable development goals. It also concludes that there is an artificial intelligence infrastructure in the Kingdom of Bahrain, as it occupies a leading regional position in digital transformation, as Bahrain ranks first in the Arab world i
... Show MoreThe role of residues in the adsorption process for removing contaminants from their aqueous solution was highlighted in this study. The adsorption capacity of eggshells were used to remove the methyl orange dye from its aqueous solution. The highest dye adsorption was found to range between (62.30% to 62.33%). The results of using adsorption isotherms (Freundlich, Langmuir, and Temkin) have been revealed that the Freundlich model was followed and that the Langmuir model did not match, as well as the partial applicability of Temkin's model at temperatures (298,308,318) K. The process of adsorption is a physical one. Three kinetic models of the adsorption process were also used, with the results demonstrating the applicability of the pseud
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