Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven classifiers. A hybrid supervised learning system that takes advantage of rich intermediate features extracted from deep learning compared to traditional feature extraction to boost classification accuracy and parameters is suggested. They provide the same set of characteristics to discover and verify which classifier yields the best classification with our new proposed approach of “hybrid learning.” To achieve this, the performance of classifiers was assessed depending on a genuine dataset that was taken by our camera system. The simulation results show that the support vector machine (SVM) has a mean square error of 0.011, a total accuracy ratio of 98.80%, and an F1 score of 0.99. Moreover, the results show that the LR classifier has a mean square error of 0.035 and a total ratio of 96.42%, and an F1 score of 0.96 comes in the second place. The ANN classifier has a mean square error of 0.047 and a total ratio of 95.23%, and an F1 score of 0.94 comes in the third place. Furthermore, RF, WKNN, DT, and NB with a mean square error and an F1 score advance to the next stage with accuracy ratios of 91.66%, 90.47%, 79.76%, and 75%, respectively. As a result, the main contribution is the enhancement of the classification performance parameters with images of varying brightness and clarity using the proposed hybrid learning approach.
The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA
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Nanomaterials have an excellent potential for improving the rheological and tribological properties of lubricating oil. In this study, oleic acid was used to surface-modify nanoparticles to enhance the dispersion and stability of Nanofluid. The surface modification was conducted for inorganic nanoparticles (NPs) TiO₂ and CuO with oleic acid (OA) surfactant, where oleic acid could render the surface of TiO2-CuO hydrophobic. Fourier transform infrared spectroscopy (FTIR), and Scanning electron microscopy (SEM) were used to characterize the surface modification of NPs. The main objective of this study was to investigate the influence of adding modified TiO₂-CuO NPs with weight ratio 1:1 on thermal-physical propertie
... Show Moreconventional FCM algorithm does not fully utilize the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership functions in the neighborhood of each pixel under consideration. The advantages of the method are that it is less
sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. This technique is a powerful method for noisy image segmentation.
Background: Educational environment is one of the most important determinants of an effective curriculum. Students' perceptions of their educational environment have a significant impact on their behavior and academic progress. Objective: 1. To identify students’ perception to the educational environment.2. To identify any gender or class level differences in the students’ perception.Type of the study: This is a descriptive cross-sectional studyMethodology: The study was carried out on convenient sample of 150 students of 2nd and 5th grade. This study was done in Al Kindy Medical College, Baghdad, Iraq and conducted during the period from the 1st of October 2013 till the end of March 2014, by using DREEM questionnaire a validated uni
... Show MoreBackground: Penetrating neck injuries are common problem in our country due to increasing violence, terrorist bombing and military operations.
These injuries are potentially life threating and need great attention and proper management.
Objective: The aim of this study is to focus on the importance of anatomical zonal classification of the neck in the management of penetrating injuries of the visceral compartment of the Neck.
Methods :70 patients with various injuries who were managed at causality unit and Otolaryngology department in Al-Kindy Teaching Hospital during aperiod from January 1st 2015 to October 31st 2015.
The study carried on those patient depending on proper clinical examination and their urgent management.
New bidentate dithiocarbamate ligand (NaL) namely [Sodium-2-(((3-methyl -4- “(2,2,2-tri fluoro ethoxy) pyridin-2”-yl) methyl) sulfinyl)-1H-benzoimidazole -1-carbodithioate] was prepared. This free ligand was synthesized from the reaction of a (RS)-2-([3-methyl -4-(2,2,2-tri fluoroethoxy) pyridin-2-yl] methyl sulfinyl)-1H benzoimidazole, CS2 and NaOH in methanol as solvent. From reaction of dithiocarbamate salt (NaL) with metal ions (M); Co(II), Ni(II), Cu(II), Zn(II), Cd(II) and Pd(II)”, have obtained the DTC complexes at general molecular formula [M(L)2(H2O)2] and [Pd(L)2]. To characterize the ligand and its complexes, used different analyses methods such FTIR, UV-Vis, elemental microanalysis, atomic absoreption, magnetic susceptibil
... Show MoreA simplified parallel key was presented in this work for the Taxa of Stackys L. wildly grown in Iraq. Three records within this genus were newly recorded to our country in the present work and they are S. kermanshahansis Rech S. setifera C.A. Mey. subsp setifera, S. setifera ssp iranica (Reck.) The characteristics of these new records were also given with some representative specimens.
Acetophenone sulfamethoxazole and 3-Nitrobenzophenone sulfamethoxazole were prepared from the reaction of sulfamethoxazole with two ketones. The prepared ligands were identified by (C.H.N) analysis and UV-VIS, FT-IR spectroscopic techniques. Metal complexes of the two ligands were prepared in an aqueous alcohol with Zn (II), Mn (II) and Cu (II) ions with a molar ratio1:1. The proposed general formula for the resulting complexes was [ML.CL2.H2O]H2O .The complexes were characterized by (C.H.N) technique , spectroscopic methods ,conductivity, atomic absorption ,magnetic susceptibility measurements and melting point. According to the results obtained, the suggested geometry is to be octahedral for all the complexes.