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

Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving ener

Quantum dots (QDs) of zinc sulfide (ZnS) was prepared by chemical reaction with different potential of hydrogen (pH) and used to fabricate organic quantum dot hybrid junction device. The optical properties of QDs were characterized by ultraviolet-visible (UV-Vis.) and photoluminescence (PL) spectrometer. The results show that the prepared QDs were nanocrystalline with defects formation. The energy gap (E_g)calculated from PL were 3.64, 3.53 and 3.35 eV for pH=8, 10 and 12 respectively. This decreasing of energy gaps is results of the effect the pH solution increases, which in turn leads to the shifted of the PL spectrum toward red shifted, which makes the energy bands at surface states are shallow ban

Objective: Benzoxazole derivatives have antifungal, anticancer, antibacterial, and anticonvulsant function. Encouraged by this comment, we agreed to synthesize new Benzoxazole compounds connected to the bases of Schiff's. Methods: 2,4-diaminophenol (1) was prepared by the reaction of 2,4-dinitrophenol and sodium dithionate. Compound (1) reacted with either acetic acid to afford compound (2) or with formic acid to afford compound (3). The Schiff bases were preparation from the reaction condensing reaction of compound (2) or (3) and aromatic aldehydes or ketone; [p-nitrobenzaldehyde, p-hydroxybenzaldehyde, p-chlorobenzaldehyde, p-bromoacetophenone and terephthaldehyde]. Results: FTIR and 1H-NMR spectroscopy characterized all of the pr