Polyaniline Multi wall Carbon nanotube (PANI/MWCNTs) nanocomposite thin films have been prepared by Plasma jet polymerization at low frequency on glass substrate with preliminary deposited aluminum electrodes to form Al/PANI-MWCNT/Al surface-type capacitive humidity sensors, the gap between the electrodes about 50 μm and the MWCNTs weight concentration varied between 0, 1, 2, 3, 4%. The diameter of the MWCNTs was in the range of 8-15 nm and the length 10-55 μm. The capacitance-humidity relationships of the sensors were investigated at humidity levels from 35 to 90% RH. The electrical properties showed that the capacity increased with increasing relative humidity, and that the sensitivity of the sensor increases with the increase of the additive (MWCNTs); while each of the response time and the recovery time increasing with concentration. The change in MWCNTs concentration leads to a change in the energy gap as well as the initial capacity. The capacitance increases linearly with the relative humidity at MWCNTs concentration of 3% for thus the possibility of manufacturing humidity sensor with good specifications at this concentration.
Merging images is one of the most important technologies in remote sensing applications and geographic information systems. In this study, a simulation process using a camera for fused images by using resizing image for interpolation methods (nearest, bilinear and bicubic). Statistical techniques have been used as an efficient merging technique in the images integration process employing different models namely Local Mean Matching (LMM) and Regression Variable Substitution (RVS), and apply spatial frequency techniques include high pass filter additive method (HPFA). Thus, in the current research, statistical measures have been used to check the quality of the merged images. This has been carried out by calculating the correlation a
... Show MoreContent-based image retrieval has been keenly developed in numerous fields. This provides more active management and retrieval of images than the keyword-based method. So the content based image retrieval becomes one of the liveliest researches in the past few years. In a given set of objects, the retrieval of information suggests solutions to search for those in response to a particular description. The set of objects which can be considered are documents, images, videos, or sounds. This paper proposes a method to retrieve a multi-view face from a large face database according to color and texture attributes. Some of the features used for retrieval are color attributes such as the mean, the variance, and the color image's bitmap. In add
... Show MoreAn image retrieval system is a computer system for browsing, looking and recovering pictures from a huge database of advanced pictures. The objective of Content-Based Image Retrieval (CBIR) methods is essentially to extract, from large (image) databases, a specified number of images similar in visual and semantic content to a so-called query image. The researchers were developing a new mechanism to retrieval systems which is mainly based on two procedures. The first procedure relies on extract the statistical feature of both original, traditional image by using the histogram and statistical characteristics (mean, standard deviation). The second procedure relies on the T-
... Show MoreThe transition of customers from one telecom operator to another has a direct impact on the company's growth and revenue. Traditional classification algorithms fail to predict churn effectively. This research introduces a deep learning model for predicting customers planning to leave to another operator. The model works on a high-dimensional large-scale data set. The performance of the model was measured against other classification algorithms, such as Gaussian NB, Random Forrest, and Decision Tree in predicting churn. The evaluation was performed based on accuracy, precision, recall, F-measure, Area Under Curve (AUC), and Receiver Operating Characteristic (ROC) Curve. The proposed deep learning model performs better than othe
... Show MoreToday with increase using social media, a lot of researchers have interested in topic extraction from Twitter. Twitter is an unstructured short text and messy that it is critical to find topics from tweets. While topic modeling algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are originally designed to derive topics from large documents such as articles, and books. They are often less efficient when applied to short text content like Twitter. Luckily, Twitter has many features that represent the interaction between users. Tweets have rich user-generated hashtags as keywords. In this paper, we exploit the hashtags feature to improve topics learned
Image pattern classification is considered a significant step for image and video processing.Although various image pattern algorithms have been proposed so far that achieved adequate classification,achieving higher accuracy while reducing the computation time remains challenging to date. A robust imagepattern classification method is essential to obtain the desired accuracy. This method can be accuratelyclassify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism.Moreover, to date, most of the existing studies are focused on evaluating their methods based on specificorthogonal moments, which limits the understanding of their potential application to various DiscreteOrthogonal Moments (DOMs). The
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