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

Gingival crevicular fluid (GCF) may reflect the events associated with orthodontic tooth movement. Attempts have been conducted to identify biomarkers reflecting optimum orthodontic force, unwanted sequallea (i.e. root resorption) and accelerated tooth movement. The aim of the present study is to find out a standardized GCF collection, storage and total protein extraction method from apparently healthy gingival sites with orthodontics that is compatible with further high-throughput proteomics. Eighteen patients who required extractions of both maxillary first premolars were recruited in this study. These teeth were randomly assigned to either heavy (225g) or light force (25g), and their site specific GCF was collected at baseline and aft

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.

This work describes the development of new spectrophotometric techniques for 3-aminophenol assessment. The first technique involves using benzidine in an alkaline solution to convert 3-aminophenol into a colored complex. The produced complex has a red color with an absorbance of 462 nm. Between the concentration range 5–14 μg mL−1, Beer's law is obeyed with a correlation coefficient (R2) of 0.99781, a limit of detection (LOD) of 0.0423 μg mL−1, and a limit of quantification (LOQ) of 0.1411 μg mL−1. The recovery was between 87.2–95.43%, the relative standard deviation (%RSD) was 2.40–3.31% and the molar absorptivity was 3.545 × 103 L mol−1 cm−1. Secondly, cloud point extraction (CPE) was used to determ

Oil from Brassca campestris (local variety) was extracted with hexane using Soxhlet. The extracted oil was characterized and its antimicrobial activity was determined as well. The content of extracted oil was 40% with 0.5% of volatile oil .Oil was immiscible with polar solvent such as ethanol, acetone and water, while it was easily miscible with chloroform due to its hydrophobicity. The result of organoleptic tests revealed that the oil is clear yellow in color and odorless with acceptable taste. The oil was stable at 4 -25 C? for a month. Refractive index (RI) of oil was 1.4723 with density of 0.914, [both at 4-25 C?]. Boiling point 386 C?. Infra red spectroscopy (IR) indicated the presence of different chemical groups (C=C

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

Effects of Ozonated Water on Micro Leakage between Enamel and Fissure Sealants Prepared by Different Etching Technique (An in vitro Study), Baraa M Jabar*, Muna S Khalaf

In this study, the water treatment plants located on the Tigris River within Baghdad city were subjected to qualitative and quantitative assessments. Based on location, the plants from upstream to downstream are Al-Karkh, East Tigris, Al-Karamah, Al-Wathbah, Al-Wehdah, Al-Kadiseyah, Al-Dora, and Al-Rashid. Data from 2009 to 2020 on the turbidity, total dissolved solids, Alkalinity, hardness, chloride, calcium, and temperature were used in the qualitative assessment while data on the treated water production and population served were used in the quantitative assessment. The above Data was acquired from the Municipality of Baghdad. The turbidity was mainly used as a fair gauge to assess the performance of the water treatment plants in Baghda