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

In this paper, a new equivalent lumped parameter model is proposed for describing the vibration of beams under the moving load effect. Also, an analytical formula for calculating such vibration for low-speed loads is presented. Furthermore, a MATLAB/Simulink model is introduced to give a simple and accurate solution that can be used to design beams subjected to any moving loads, i.e., loads of any magnitude and speed. In general, the proposed Simulink model can be used much easier than the alternative FEM software, which is usually used in designing such beams. The obtained results from the analytical formula and the proposed Simulink model were compared with those obtained from Ansys R19.0, and very good agreement has been shown. I

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

A new method is characterized by simplicity, accuracy and speed for determination of Oxonuim ion in ionisable inorganic acid such as hydrochloric (0.1 - 10) ,Sulphuric ( 0.1 - 6 ),nitric ( 0.1 - 10 ), perchloric ( 0.1 - 7 ), acetic (0.1 - 100 ) and phosphoric ( 0.1 - 30 ) ( mMol.L-1 )acids. By continuous flow injection analysis. The proposed method was based on generation of bromine from the Bro-3-Br-- H3O+. Bromine reacts with fluorescein to quenches the fluorescence . A sample volume no.1 (31μl) and no.2 (35μl) were used with flow rate of 0.95 mL.min-1 using H2O line no.1as carrier stream and 1.3 mL.min-1 using fluorescein sodium salt line no.2. Linear regression of the concentration ( mMol.L-1 ) Vs quenched fluorescence gives a correla

Liquid electrodes of domperidone maleate (DOMP) imprinted polymer were synthesis based on precipitation polymerization mechanism. The molecularly imprinted (MIP) and non-imprinted (NIP) polymers were synthesized using DOMP as a template. By methyl methacrylate (MMA) as monomer, N,Nmethylenebisacrylamide (NMAA) and ethylene glycol dimethacrylate (EGDMA) as cross-linkers and benzoyl peroxide (BP) as an initiator. The molecularly imprinted membranes were synthesis using acetophenone (APH), di-butyl sabacate (DBS), Di octylphthalate (DOPH) and triolyl phosphate (TP)as plasticizers in PVC matrix. The slopes and limit of detection of l

Metal-organic frameworks (MOFs) have emerged as revolutionary materials for developing advanced biosensors, especially for detecting reactive oxygen species (ROS) and hydrogen peroxide (H₂O₂) in biomedical applications. This comprehensive review explores the current state-of-the-art in MOF-based biosensors, covering fundamental principles, design strategies, performance features, and clinical uses. MOFs offer unique benefits, including exceptional porosity (up to 10,400 m²/g), tunable structures, biocompatibility, and natural enzyme-mimicking properties, making them ideal platforms for sensitive and selective detection of ROS and H₂O₂. Recent advances have shown significant improvements in detection capabilities, with limit

The anatomical study of the epidermis leaflet for seven species and variety wild belonging to the genus Medicago L. species are: M. constricta Dur., M. coronata L., M. intertexta L., M. intertexta.var. ciliaris L., M. laciniata L., M. lupulina L., M. minima L. and M. sativa L. were studied, The search included epidermis characters and stomatal complexes addition to venation system in leaflets. It is revealed through the study, epidermis leaflet type Amphistomatic (the stomata spread on the upper and lower surface) as well as the presence of three types of stomatal complexes namely: Anisocytic (the guard cells surrounded by three unequal cell size), Anomocytic (not differential from subsidiary cells in epidermis) and Anomotetracytic (four ce

During a survey on the helminthic parasites of three species of turtles in the north part of Iraq, five species of nematodes were recorded for the first time in Iraq. They were all found in the intestine. These are, Camallanus microcephalus (Dujardin, 1845) recorvered from the turtle Clemmys caspica; Spironoura japonensis (Yamaguti, 1935) from Triopyx eup¬hraticus and Angusticaecum holopterum (Rudolphi, 1819), and Tachygonetria nicollei (Seurat, 1918) from the turtle Testudo graeca. All of the localities and hosts are newly recorded in Iraq.