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 class

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

The aim of this study was to investigate the effect of operating variables on, the percentage of removed sludge (PSR) obtained during re-refining of 15W-40 Al-Durra spent lubricant by solvent extraction-flocculation treatment method. Binary solvents were used such as, Heavy Naphtha (H.N.): MEK (N:MEK), H.N. : n-Butanol (N:n-But), and H.N. : Iso-Butanol (N:Iso:But). The studied variables were mixing speed (300-900, rpm), mixing time (15-60, min), and operating  temperature (2540, oC). This study showed that the studied operating variables have effects where, increasing the mixing time up to 45 min for H.N.: MEK, H.N.: n-Butanol  and 30 min for H.N.: Iso-Butanol increased the PSR, after that percentage was decreased; increasing t

The study is based on the selective binding ability of the drug compound procaine (PRO) on a surface imprinted with nylon 6 (N6) polymer. Physical characterization of the polymer template was performed by X-ray diffraction and DSC thermal analysis. The imprinted polymer showed a high adsorption capacity to trap procaine (237 µg/g) and excellent recognition ability with an imprinted factor equal to 3.2. The method was applied to an extraction column simulating a solid-phase extraction to separate the drug compound in the presence of tinoxicam and nucleosimide separately and in a mixture of them with a recovery rate more than the presence of tinoxicam and nucleosimide separately and in a mixture of them with a recovery rate of more t

Background: Non-small cell lung cancer (NSCLC) is caused of 85% of all lung cancers. Among the most important factors for lung tumor growth and proliferation are the tyrosine kinase receptors that coded by the epidermal growth factor recep-tor (EGFR) gene. Activation of EGFR ultimately leads to developing of lung cancer. The present study was undertaken with an objective to detect EGFR mutations in bronchial wash from Iraqi patients with NSCLC before treatment. Methods: DNA was extracted from bronchial wash samples collected from 50 patients with NSCLC by using a Qiamp DNA Mini Kit (Qiagen, Hilden, Germany). Then, EGFR mutations were determined by using real-time RCR combined with two technologies, Amplification Refractory Mutation System (

Background: Non-small cell lung cancer (NSCLC) is caused of 85% of all lung cancers. Among the most important factors for lung tumor growth and proliferation are the tyrosine kinase receptors that coded by the epidermal growth factor recep-tor (EGFR) gene. Activation of EGFR ultimately leads to developing of lung cancer. The present study was undertaken with an objective to detect EGFR mutations in bronchial wash from Iraqi patients with NSCLC before treatment. Methods: DNA was extracted from bronchial wash samples collected from 50 patients with NSCLC by using a Qiamp DNA Mini Kit (Qiagen, Hilden, Germany). Then, EGFR mutations were determined by using real-time RCR combined with two technologies, Amplification Refractory Mutation System (

From a large number of bacterial samples collected from different hospital in Iraq in central  health laboratory ,only ten isolates were identified primary as Vibrio. A number of  morphology and biochemical test were carried out to complete this identification that showed all bacterial isolates were related to Vibrio cholerae .In this study  all Vibrio isolates were investigated for Bio typing and the result showed that all (10) isolate were related to (Eltor biotypes) .Also, the susceptibility test towards eight antibiotics were carried  out .

Results shows that  ciprofloxacin , Norfloxacin, Erythromycin, Ampicillin,  ceftriaxone  and Amikacin were the most effective

     In this work, the detection of zinc (Zn) ions that cause water pollution is studied using the CSNPs- Linker-alkaloids compound that was prepared by linking extracted alkaloids from Iraqi Catharanthus roseus plant with Chitosan nanoparticles (CSNPs) using maleic anhydride. This compound is characterized by an X-ray diffractometer (XRD) which shows that it has an orthorhombic structure with crystallite size in the nano dimension. Zeta Potential results show that the CSNPs-Linker-alkaloids carried a positive charge of 54.4 mV, which means it possesses high stability.  The Fourier transform infrared spectroscopy (FTIR) shows a new distinct band at 1708.93 cm-1 due to C=O esterification. Scanning electron microscope (SEM) image