Background: The most common reason for re-making a maxillofacial prosthesis is the degradation of the mechanical properties of the silicone. Aim of this study: To assess some mechanical properties of VST-50F maxillofacial silicone reinforced with a composite of silicon dioxide nanoparticle and polyamide-6 microparticle before and after artificial aging. Material and Method: Preparing 240 samples tested for tear strength, tensile strength and elongation percentage, hardness, and roughness before and after aging. The Silicon dioxide was added in concentrations of 1% by weight and Polyamide-6 in the concentration of 0.25% and 0.5% by weight to the VST-50F RTV maxillofacial silicone. The one-way ANOVA and post hoc tests were used for inferentia

This study investigates the influence of silver oxide (Ag₂O) nanoparticles on the gas-sensing properties of carbon nanotube (CNT) and poly(3-hexylthiophene) (P3HT) composite films. The goal is to enhance the sensitivity and stability of CNT/P3HT-based gas sensors for nitrogen dioxide (NO₂) detection. CNTs and P3HT are composited at various mass ratios, followed by integrating Ag₂O nanoparticles to improve gas interaction and electronic properties. Structural and morphological analyses, including x-ray diffraction, Fourier transform infrared spectroscopy, field emission scanning electron microscopy, and energy dispersive x-ray spectroscopy, ascertain the successful incorporation of Ag₂O into the composites and reveal its role in impr

Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to