Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model based on the Spike Neural Network (SNN) called IoT-Traffic Classification (IoT-TCSNN) to classify IoT devices traffic. The model consists of four phases: data preprocessing, feature extraction, classier and evaluation. The proposed model performance is evaluated according to evaluation metrics: accuracy, precision, recall and F1-score and energy usage in comparison with two models: ML based Support Vector Machine IoT-TCSVM and ML based Deep Neural Network (IoT-TCDNN). The evaluations result has been shown that IoT-TCSNN consumes less energy in contrast to IoT-TCDNN and IoT-TCSVM. Also, it gives high accuracy in comparison with IoT-TCSVM.
The Study aims at evaluating the efficiency of the regional transportation net in Al-mahmoodiya Qadaa center. The bus station of the Qadaa center is suffering from heavy traffic jam, which is due to the ongoing movement of the adjacent provinces, particularly the small cities. They vary in the degree of their link by the regional transportation net that links the province with the centers of big cities. That affects the traffic flow of the civilians of these cities and their daily activities in hierarchical way To achieve the purpose of the study, a questionnaire has been constructed to collect data through selecting a random sample including the passengers who are coming to the bus station in Al-Mahmoodiya center to know the flo
... Show MoreCodes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de
... Show MoreIn general, the importance of cluster analysis is that one can evaluate elements by clustering multiple homogeneous data; the main objective of this analysis is to collect the elements of a single, homogeneous group into different divisions, depending on many variables. This method of analysis is used to reduce data, generate hypotheses and test them, as well as predict and match models. The research aims to evaluate the fuzzy cluster analysis, which is a special case of cluster analysis, as well as to compare the two methods—classical and fuzzy cluster analysis. The research topic has been allocated to the government and private hospitals. The sampling for this research was comprised of 288 patients being treated in 10 hospitals. As t
... Show More
Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob
... Show MoreThis study focusses on the effect of using ICA transform on the classification accuracy of satellite images using the maximum likelihood classifier. The study area represents an agricultural area north of the capital Baghdad - Iraq, as it was captured by the Landsat 8 satellite on 12 January 2021, where the bands of the OLI sensor were used. A field visit was made to a variety of classes that represent the landcover of the study area and the geographical location of these classes was recorded. Gaussian, Kurtosis, and LogCosh kernels were used to perform the ICA transform of the OLI Landsat 8 image. Different training sets were made for each of the ICA and Landsat 8 images separately that used in the classification phase, and used to calcula
... Show MoreArtificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfakedetection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a deeper overview of i) the environment of deepfake creation and detection, ii) how deep learning and forensic tools contributed to the detection
... Show Morecurrent research aims to build an intellectual framework for concept of organizational forgetting, which is considered one of the most important topics in contemporary management thought, which is gain the consideration of most scholars and researchers in field of organizational behavior, which is to be a loss of intentional or unintentional knowledge of any organizational level. It turned out that just as organizations should learn and acquire knowledge, they must also forget, especially knowledge obsolete and worn out. And represented the research problem in the absence of Arab research dealing with organizational forgetting, and highlights the supporting infrastructure core, and show a close relationship with organizational le
... Show More