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Classification of Apple Slices Treated by Atmospheric Plasma Jet for Post-harvest Processes Using Image Processing and Convolutional Neural Networks
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Abstract<p>Apple slice grading is useful in post-harvest operations for sorting, grading, packaging, labeling, processing, storage, transportation, and meeting market demand and consumer preferences. Proper grading of apple slices can help ensure the quality, safety, and marketability of the final products, contributing to the post-harvest operations of the overall success of the apple industry. The article aims to create a convolutional neural network (CNN) model to classify images of apple slices after immersing them in atmospheric plasma at two different pressures (1 and 5 atm) and two different immersion times (3 and again 6 min) once and in filtered water based on the hardness of the slices using the k-Nearest Neighbors (KNN), Tree, Support Vector Machine (SVM), and Artificial Neural Network (ANN) algorithms. The results showed an inverse relationship between the storage period and the hardness of the apple slices, with the average hardness values gradually decreasing from 4.33 (day 1) to 3.37 (day 5). Treatment with atmospheric plasma at a pressure of 5 atm and an immersion time of 3 min gave the best results for maintaining the hardness of the slices during the storage period, recording values of 4.85 (first day) and 3.68 (fifth day), outperforming other treatments. The average improvement rate was 23.09% over five consecutive days. Regarding the CNN algorithms, the ANN algorithm achieved the highest classification accuracy of 97%, while the Tree algorithm achieved the lowest accuracy of 88.7%. The KNN and SVM algorithms achieved classification accuracies of 94.7% and 95.1%, respectively. The study demonstrated the possibility of using a CNN to classify apple slices based on the degree of hardness. Furthermore, the application of atmospheric plasma at 5 atmospheres with a 3-min immersion improves the firmness of the apple slices by inhibiting degradative enzymes while preserving the cellular structure and tissue quality.</p>
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Publication Date
Fri Dec 03 2021
Journal Name
Journal Of Optoelectronic And Biomedical Materials
Antibacterial activity with eggshell nanoparticles activated by microwave plasma.
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The eggshell cuticle is the proteinaceous outermost layer of the eggshell which regulates water exchange and protects against entry of micro-organisms. Outer eggshell and cuticle protein was extracted from domestic chicken. The aim of the research is to find out the effect of the treated and untreated nano particles of egg shells with micro wave cold plasma on the effectiveness of E. coli (negative bacteria) that infect the skin and measure the diameter of bacterial inhibition zone, the eggshell has been prepared by a chemical method (sol gel) and measure the level of acidity and the PH is neutral. The result of Atomic Force Microscope (AFM) shows that the particles diameters become smaller with nano-particles solution than for egg

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Publication Date
Sat Jan 01 2011
Journal Name
Journal Of Engineering
FILTRATION MODELING USING ARTIFICIAL NEURAL NETWORK (ANN)
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In 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

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Publication Date
Sat May 09 2015
Journal Name
International Journal Of Innovations In Scientific Engineering
USING ARTIFICIAL NEURAL NETWORK TECHNIQUE FOR THE ESTIMATION OF CD CONCENTRATION IN CONTAMINATED SOILS
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The aim of this paper is to design artificial neural network as an alternative accurate tool to estimate concentration of Cadmium in contaminated soils for any depth and time. First, fifty soil samples were harvested from a phytoremediated contaminated site located in Qanat Aljaeesh in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. The inputs are the soil depth, the time, and the soil parameters but the output is the concentration of Cu in the soil for depth x and time t. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Cadmium. The performance of the ANN technique was compared with the traditional laboratory inspecting

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Publication Date
Thu Sep 15 2022
Journal Name
Journal Of Baghdad College Of Dentistry
Immunohistochemical evaluation for integrin binding sialoprotein on healing process of intrabony defect treated by bone sialoprotein
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Background: Bone defect healing is a multidimensional procedure with an overlapping timeline that involves the regeneration of bone tissue. Due to bone's ability to regenerate, the vast majority of bone abnormalities can be restored intuitively under the right physiological conditions. The goal of this study is to examine the immunohistochemistry of bone sialoprotein in order to determine the effect of local application of bone sialoprotein on the healing of a rat tibia generated bone defect. Materials and Methods: In this experiment, 48 albino male rats weighing 300-400 grams and aged 6-8 months will be employed under controlled temperature, drinking, and food consumption settings. The animals will be subjected to a surgical procedure o

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Publication Date
Fri Sep 15 2023
Journal Name
Journal Of Baghdad University College Of Dentistry
Immunohistochemical evaluation for integrin binding sialoprotein on healing process of intrabony defect treated by bone sialoprotein
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Publication Date
Fri Mar 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Comparison Study of Electromyography Using Wavelet and Neural Network
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In this paper we present a method to analyze five types with fifteen wavelet families for eighteen different EMG signals. A comparison study is also given to show performance of various families after modifying the results with back propagation Neural Network. This is actually will help the researchers with the first step of EMG analysis. Huge sets of results (more than 100 sets) are proposed and then classified to be discussed and reach the final.

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Publication Date
Thu Jan 01 2015
Journal Name
Applied And Computational Mathematics
Texture Classification Using Spline, Wavelet Decomposition and Fractal Dimension
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Publication Date
Thu Mar 01 2018
Journal Name
Journal Of Baghdad College Of Dentistry
The Effect of Addition of Combination of Plasma Treated Polyester and Polyamide Fibers on Surface Roughness and Some Mechanical Properties of Heat Cured Acrylic Resin
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Publication Date
Thu Mar 15 2018
Journal Name
Journal Of Baghdad College Of Dentistry
The Effect of Addition of Combination of Plasma Treated Polyester and Polyamide Fibers on Surface Roughness and Some Mechanical Properties of Heat Cured Acrylic Resin
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Background: Poly (methyl methacrylate) has several disadvantages (poor mechanical properties) like impact and transverse strength. In order to overcome these disadvantages, several methods were used to strengthen the acrylic resin by using different fibers or fillers. This study was conducted to evaluate the effect of Plasma treatment of the fiber on mechanical properties Poly (methyl methacrylate) denture base material. Materials and methods: Specimens were prepared from poly methyl metha acrylic (PMMA) divided according to present of fiber into 4 groups (first group without fiber as control group, second group with Plasma treated polyester fibers, third group with Plasma treated polyamide fibers and fourth group Plasma treated combination

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Publication Date
Sun Mar 06 2011
Journal Name
Baghdad Science Journal
Study of opacity broadening in spectral lines for helium like ions in aluminum plasma which produced by laser
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A theoretical analysis studied was performed to study the opacity broadening of spectral lines emitted from aluminum plasma produced by Nd-YLF laser. The plasma density was in the range 1028-1026 )) m-3 with length of plasma about ?300) m) , the opacity was studied as function of plasma density & principle quantum number. The results show that the opacity broadening increases as plasma density increases & decreases with the spacing between energy levels of emission spectral line.

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