Pulsed-laser deposition (PLD) technique was used to prepare thin films from cerium oxide (CeO2) doped with erbium and indium oxides. In order to characterize the morphology and sensing features, these films were deposited on glass and/or single silicon substrates, respectively, with erbium and indium oxides. The morphological investigation conducted using atomic force microscope has shown an increase in the non-regularity of particle diameter with increasing doping ratios of both oxides. The maximum particle diameter of 61.45nm was obtained for 9% Eu2O3-doped CeO2. The surface roughness value of Eu2O3-doped ceria was higher than those of In2O3-doped ceria. The gas sensing measurements were done at

Tetragonal compound CuAl0.4Ti0.6Se2 semiconductor has been prepared by
melting the elementary elements of high purity in evacuated quartz tube under low
pressure 10-2 mbar and temperature 1100 oC about 24 hr. Single crystal has been
growth from this compound using slowly cooled average between (1-2) C/hr , also
thin films have been prepared using thermal evaporation technique and vacuum 10-6
mbar at room temperature .The structural properties have been studied for the powder
of compound of CuAl0.4Ti0.6Se2u using X-ray diffraction (XRD) . The structure of the
compound showed chalcopyrite structure with unite cell of right tetragonal and
dimensions of a=11.1776 Ao ,c=5.5888 Ao .The structure of thin films showed

Cadmium sulfide (CdS) thin films with n-type semiconductor characteristics were prepared by flash evaporating method on glass substrates. Some films were annealed at 250 ^oC for 1hr in air. The thicknesses of the films was estimated to be 0.5µ by the spectrometer measurement. Structural, morphological, electrical, optical and photoconductivity properties of CdS films have been investigated by X-ray diffraction, AFM, the Hall effect, optical transmittance spectra and photoconductivity analysis, respectively. X-ray diffraction (XRD) pattern shows that CdS films are in the stable hexagonal crystalline structure. Using Debye Scherrerś formula, the average grain size for the samples was found to be 26 nm. The transmittance of the

The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

In this work, a deep computational study has been conducted to assign several qualities for the graph ⁠. Furthermore, determine the amount of the dihedral subgroups in the Held simple group He through utilizing the attributes of gamma.

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes