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

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

Every so often, a confluence of novel technologies emerges that radically transforms every aspect of the industry, the global economy, and finally, the way we live. These sharp leaps of human ingenuity are known as industrial revolutions, and we are currently in the midst of the fourth such revolution, coined Industry 4.0 by the World Economic Forum. Building on their guideline set of technologies that encompass Industry 4.0, we present a full set of pillar technologies on which Industry 4.0 project portfolio management rests as well as the foundation technologies that support these pillars. A complete model of an Industry 4.0 factory which relies on these pillar technologies is presented. The full set of pillars encompasses cyberph

The construction industry in Iraq suffers from many problems, perhaps the most important of which is the delay in time and the increase in costs. Therefore, it was necessary to try to adopt a new methodology that would help in overcoming these problems. It was suggested to combine building information modeling with the agile management approach because this technique and methodology is modern and helps in reducing time and cost and improving quality. This paper aims to know the status of using Building Information Modeling (BIM) and Agile Project management (APM) in Iraq and to shed light on the merging of this integration, explaining the benefits, difficulties, and workflow practices, finding the most influencing factors on the tim

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

Research includes three axes, the first is the average estimate time of achievement (day) to work oversight, to five supervisory departments in the Office of Financial Supervision Federal and then choose the three control outputs and at the level of each of the five departments above, and after analyzing the data statistically back to us that the distribution of the times of achievement It is the exponential distribution (Exponential Distribution) a parameter (q), and the distribution of normal (Normal Distribution) with two parameters (μ, σ²), and introduced four methods of parameter estimation (q) as well as four modalities parameter to estimate (

The simulation have been made for  3D flow structure and heat  transfer with and without

longitudinal riblet upstream of leading edge vane endwall junction of first stage nozzle guide vane .The research explores concept of weakening the secondary flows and reducing their harmful effects.Numerical investigation involved examination of the secondary flows ,velocity and heat transfer rates by solving the governing equations (continuity, Navier -stokes and energy equations ) using the known package FLUENT version (12.1).The governing equations were solved for three dimentional, turbulent flowe, incompressible with an appropriate turbulent model (k-ω,SST) .The numerical solution was carried out for 25 mode

Abstract: This study aims to investigate the backscattering electron coefficient for SixGe1-x/Si heterostructure sample as a function of primary electron beam energy (0.25-20 keV) and Ge concentration in the alloy. The results obtained have several characteristics that are as follows: the first one is that the intensity of the backscattered signal above the alloy is mainly related to the average atomic number of the SixGe1-x alloy. The second feature is that the backscattering electron coefficient line scan shows a constant value above each layer at low primary electron energies below 5 keV. However, at 5 keV and above, a peak and a dip appeared on the line scan above Si-Ge alloy and Si, respectively, close to the interfacing line