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Traffic Classification of IoT Devices by Utilizing Spike Neural Network Learning Approach
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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.

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Publication Date
Sat Apr 01 2023
Journal Name
Journal Of Engineering
Proposed Face Detection Classification Model Based on Amazon Web Services Cloud (AWS)
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One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Non-linear support vector machine classification models using kernel tricks with applications
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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

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Publication Date
Tue Dec 03 2013
Journal Name
Baghdad Science Journal
Satellite Images Unsupervised Classification Using Two Methods Fast Otsu and K-means
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Publication Date
Mon Aug 31 2015
Journal Name
Journal Of Theoretical And Applied Information Technology
EXAM QUESTIONS CLASSIFICATION BASED ON BLOOM’S TAXONOMY COGNITIVE LEVEL USING CLASSIFIERS COMBINATION
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Scopus
Publication Date
Sat Apr 01 2023
Journal Name
Journal Of Engineering
Proposed Face Detection Classification Model Based on Amazon Web Services Cloud (AWS)
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One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca

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Publication Date
Mon Jan 01 2024
Journal Name
Journal Of Oral Medicine And Oral Surgery
Evaluating dental implant stability using three devices Osstell<sup>®</sup>, Periotest<sup>®</sup>, and AnyCheck<sup>®</sup>: a clinical study
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Introduction: Implant stability is usually measured with resonance frequency analysis (RFA) and damping capacity assessment (DCA). This study aimed to measure primary and secondary stabilities using 3 devices that are based on these methods, namely; RFA (Osstell®) and DCA (Periotest® and AnyCheck®) to assess the correlations of the measurements obtained by these devices and the correlations between implant stability and insertion torque. Material and Methods: This observational prospective study included 35 dental implants. The implant stability was measured using the 3 devices. Mann–Whitney U

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Publication Date
Tue Jan 01 2013
Journal Name
Communications And Network
Link and Cost Optimization of FTTH Network Implementation through GPON Technology
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Publication Date
Thu May 18 2023
Journal Name
Journal Of Engineering
Development of Pavement Maintenance Management System for Baghdad Urban Roadway Network
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The road transportation system is considered as major component of the infrastructure in any country, it affects the developments in economy and social activities. The Asphalt Concrete which is considered as the major pavement material for the road transportation system in Baghdad is subjected to continuous deterioration with time due to traffic loading and environmental conditions, it was felt that implementing a comprehensive pavement maintenance management system (PMMS), which should be capable for preserving the functional and structural conditions of pavement layers, is essential. This work presents the development of PMMS with Visual inspection technique for evaluating the Asphalt Concrete pavement surface condition; common types o

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Publication Date
Sun Jun 30 2019
Journal Name
Journal Of Engineering
Numerical Study of Fluid Flow and Heat Transfer Characteristics in Solid and Perforated Finned Heat Sinks Utilizing a Piezoelectric Fan
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Numerical study is adapted to combine between piezoelectric fan as a turbulent air flow generator and perforated finned heat sinks. A single piezoelectric fan with different tip amplitudes placed eccentrically at the duct entrance. The problem of solid and perforated finned heat sinks is solved and analyzed numerically by using Ansys 17.2 fluent, and solving three dimensional energy and Navier–Stokes equations that set with RNG based k−ε scalable wall function turbulent model. Finite volume algorithm is used to solve both phases of solid and fluid. Calculations are done for three values of piezoelectric fan amplitudes 25 mm, 30 mm, and 40 mm, respectively. Results of this numerical study are compared with previous b

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Publication Date
Sat Nov 30 2024
Journal Name
Iraqi Journal Of Science
Utilizing the Gravity Method to Detect the Potential Effect of the Salt Dome in the Nahr Omar Oilfield, Southern Iraq
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Geophysics is one of the branches of Earth sciences and deals with studying the Earth's interior by studying the variation of physical properties within rock layers. Applied geophysics depends on procedures that involve the measurements of potential fields, such as the gravitational method. One of the significant oil fields in southern Iraq is represented by the Nahr Omar structure. A power spectrum analysis (SPA) technique was used to collect gravity data within the chosen oil field area in order to confirm the salt dome in the subsurface layers. The analysis of SPA resulted from six surfaces representing the gravity variation values of the depths (m)14300, 3780, 3290, 2170, 810, and 93.5. Gravity surfaces have been converted to de

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