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Performance Prediction in EDM Process for Al 6061 Alloy Using Response Surface Methodology and Genetic Algorithm
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The Electric Discharge (EDM) method is a novel thermoelectric manufacturing technique in which materials are removed by a controlled spark erosion process between two electrodes immersed in a dielectric medium. Because of the difficulties of EDM, determining the optimum cutting parameters to improve cutting performance is extremely tough. As a result, optimizing operating parameters is a critical processing step, particularly for non-traditional machining process like EDM. Adequate selection of processing parameters for the EDM process does not provide ideal conditions, due to the unpredictable processing time required for a given function. Models of Multiple Regression and Genetic Algorithm are considered as effective methods for determining the optimal processing variables of Electrical Discharge Machining.

The material removal rate (MRR) and tool wear (Tw) were investigated using the process variables of pulse on time (Ton), pulse off time (Toff), and current intensity (Ip). The established empirical models were used to perform Genetic Algorithm (GA) to maximize (MRR) and minimize (Tw). The optimization results were utilized to establish machining conditions, validate empirical models, and obtain optimization outcomes. The optimal result that appears in this work was the pulse on (176.261 μs), pulse off (39.42 μs), and current intensity (23.62 Amp.) to maximize the MRR to (0.78391 g/min) and reduce tool wear to (0.0451 g/min).

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Publication Date
Mon May 01 2017
Journal Name
Journal Of Stored Products Research
Detection and prediction of Sitophilus oryzae infestations in triticale via visible and near-infrared spectral signatures
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Triticale is a hybrid of wheat and rye grown for use as animal feed. In Florida, due to its soft coat, triticale is highly vulnerable to Sitophilus oryzae L. (rice weevil) and there is interest in development of methods to detect early-instar larvae so that infestations can be targeted before they become economically damaging. The objective of this study was to develop prediction models of the infestation degree for triticale seed infested with rice weevils of different growth stages. Spectral signatures were tested as a method to detect rice weevils in triticale seed. Groups of seeds at 11 different levels (degrees) of infestation, 0–62%, were obtained by combining different ratios of infested and uninfested seeds. A spectrophotometer wa

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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction
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Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

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Publication Date
Sat Apr 30 2022
Journal Name
Eastern-european Journal Of Enterprise Technologies
Improvement of noisy images filtered by bilateral process using a multi-scale context aggregation network
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Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

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Publication Date
Fri Feb 08 2019
Journal Name
Journal Of The College Of Education For Women
Rightness in Evalating Basra and Kufa’s Views in The Work Of Al _ Anbary, Al - Insaf Fi Masael Al - Khilaf
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This paper deals with the Kufis and basris agreements in Abo Al _ Barakat Al _ anbary’s
work “ Al- Insaf Fi Masael Al - Khilaf.It attempts to settle the disputes and find out the right
views.
The book presents the issnes that the two schools of Kufa and Basra dispute upon and try to
evaluate them so as to support the correct ones .The present researcher has noticed that the
author of the above work was not always right or accurate in picturing a number of issues as
disputable whereas they implicitly shared the same views .Accordingly ,this papor sheds light
on such cases and drew acadernic researchers, attention to the invalidity of Al –anbary’s
misjudged views.

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Publication Date
Sun Jan 01 2023
Journal Name
Aip Conference Proceedings
Using the artificial TABU algorithm to estimate the semi-parametric regression function with measurement errors
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Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing artificial TABU algorithm to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as sport, chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement.

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Publication Date
Thu Aug 01 2019
Journal Name
مجلة العلوم الاقتصادية والإدارية
Improving" Jackknife Instrumental Variable Estimation method" using A class of immun algorithm with practical application
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Improving" Jackknife Instrumental Variable Estimation method" using A class of immun algorithm with practical application

Publication Date
Sun Jun 30 2024
Journal Name
Iraqi Journal Of Science
Genetic Diversity and Polymorphism of Eucalyptus L՚He҆r Speciesby ITS Sequence
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      Genus Eucalyptus belongs to the family Myrtaceae that consists of more than 700 species, various hybrids and varieties. The majorly distributed species that are grown in Iraq are Eucalyptus alba, E. macarthurii, E. siderophloia and E. camaldulensis, E. tereticornis, E. vicina. Most Eucalyptus species are highly dependent on rainfall, and this is challenged by climatic changes owing to global warming making it difficult to effectively match the availability of mature trees and the market demand, especially for use as power transmission poles. With the widespread availability of other naturally occurring Eucalyptus species, it has become important to determine the genetic diversity and to analyze the phenotypic tra

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Publication Date
Mon Nov 09 2020
Journal Name
Heat Transfer
Experimental performance of cooling photovoltaic panels using geothermal energy in an arid climate
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Publication Date
Mon Sep 06 2021
Journal Name
Journal Of Mechanical Engineering Research And Developments
Using Micro-Ceramic as an Abrasive Material to Remove Surface Roughness by Magnetic Abrasive Finishing
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Due to the rapid advancement of technology and the technology of things, modern industries start to need a highprecision equipment and surface finishing, so many finishing processes began to develop. One of the modern processes is Magnetic Abrasive Finishing (MAF), which is a high-precision process for internal and external finishing under the influence of a magnetic field of abrasive particles. Boron Carbide (B4C) ceramics was tested by mixing it with iron (Fe) and produced abrasive particles to reduce the intensity of scraping on the surface, reduce the economic cost and achieve a high finishing addition to remove the edges at the same time. The material selected for the samples was mild steel (ASTM E415) under (Quantity of Abrasives, Mac

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Publication Date
Wed Mar 01 2017
Journal Name
Neural Computing And Applications
The potential of nonparametric model in foundation bearing capacity prediction
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