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

Abstract

 Metal cutting processes still represent the largest class of manufacturing operations. Turning is the most commonly employed material removal process. This research focuses on analysis of the thermal field of the oblique machining process. Finite element method (FEM) software DEFORM 3D V10.2 was used together with experimental work carried out using infrared image equipment, which include both hardware and software simulations. The thermal experiments are conducted with AA6063-T6, using different tool obliquity, cutting speeds and feed rates. The results show that the temperature relatively decreased when tool obliquity increases at different cutting speeds and feed rates, also it

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

The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

The Asphalt cement is produced as a by-product from the oil industry; the asphalt must practice further processing to control the percentage of its different ingredients so that it will be suitable for paving process. The objective of this work is to prepare different types of modified Asphalt cement using locally available additives, and subjecting the prepared modified Asphalt cement to testing procedures usually adopted for Asphalt cement, and compare the test results with the specification requirements for the modified Asphalt cement to fulfill the paving process requirements. An attempt was made to prepare the modified Asphalt cement for pavement construction in the laboratory by digesting each of the two penetration grade Asphalt c

In this work ,the modified williamos-Hall method was used to analysis the x-ray diffraction lines for powder of magnesium oxide nanoparticles (Mgo) .and for diffraction lines (111),(200),(220),(311) and (222).where by used special programs such as origin pro Lab and Get Data Graph ,to calculate the Full width at half maximum (FWHM) and integral breadth (B) to calculate the area under the curve for each of the lines of diffraction .After that , by using modified Williamson –Hall equations to determin the values of crystallite size (D),lattice strain (ε),stress( σ ) and energy (U) , where was the results are , D=17.639 nm ,ε =0.002205 , σ=0.517 and U=0.000678 respectively. And then using the scherrer method can by calculated the crystal

This study aimed to determine obesity level of some population in Baghdad by using Bio-electrical impedance analysis (BIA) and compared with anthropometric measurements such as body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR). Statistical analysis results of linear correlation coefficients for obesity indicators showed that BIA correlation 0.92 was most significant and reliable for obesity measurement.

Results of BIA method for age group 20-29 years showed that 44.4% of females were healthy body while 37.8% of males suffer from increased body fat. Results of age group 30-39 year showed that 32.6 of females were in healthy body and 42% of males were obese. In case age group 40-4