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Intrusion detection method for internet of things based on the spiking neural network and decision tree method
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The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices’ power usage. Also, a rand order code (ROC) technique is used with SNN to detect cyber-attacks. The proposed method is evaluated by comparing its performance with two other methods: IDS-DNN and IDS-SNNTLF by using three performance metrics: detection accuracy, latency, and energy usage. The simulation results have shown that IDS-SNNDT attained low power usage and less latency in comparison with IDS-DNN and IDS-SNNTLF methods. Also, IDS-SNNDT has achieved high detection accuracy for cyber-attacks in contrast with IDS-SNNTLF.

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Publication Date
Mon Jun 30 2008
Journal Name
Iraqi Journal Of Science
On the Greedy Ridge Function Neural Networks for Approximation Multidimensional Functions
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The aim of this paper is to approximate multidimensional functions f∈C(R^s) by developing a new type of Feedforward neural networks (FFNS) which we called it Greedy ridge function neural networks (GRGFNNS). Also, we introduce a modification to the greedy algorithm which is used to train the greedy ridge function neural networks. An error bound are introduced in Sobolov space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result in [1]).

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Publication Date
Thu Dec 01 2022
Journal Name
Baghdad Science Journal
Diagnosing COVID-19 Infection in Chest X-Ray Images Using Neural Network
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With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

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Publication Date
Thu Oct 31 2024
Journal Name
Iraqi Geological Journal
Artificial Neural Network Application to Permeability Prediction from Nuclear Magnetic Resonance Log
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Reservoir permeability plays a crucial role in characterizing reservoirs and predicting the present and future production of hydrocarbon reservoirs. Data logging is a good tool for assessing the entire oil well section's continuous permeability curve. Nuclear magnetic resonance logging measurements are minimally influenced by lithology and offer significant benefits in interpreting permeability. The Schlumberger-Doll-Research model utilizes nuclear magnetic resonance logging, which accurately estimates permeability values. The approach of this investigation is to apply artificial neural networks and core data to predict permeability in wells without a nuclear magnetic resonance log. The Schlumberger-Doll-Research permeability is use

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Publication Date
Wed Mar 01 2017
Journal Name
2017 Annual Conference On New Trends In Information & Communications Technology Applications (ntict)
Automatic Iraqi license plate recognition system using back propagation neural network (BPNN)
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Publication Date
Tue Apr 30 2024
Journal Name
Iraqi Journal Of Science
Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network
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     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai

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Publication Date
Wed Oct 16 2013
Journal Name
International Journal Of Pharmacy And Pharmaceutical Sciences
Effects of mucoadhesive polymers combination on the properties of lisinpril buccal tablets prepared by wet granulation method
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Publication Date
Sun Mar 01 2020
Journal Name
Baghdad Science Journal
Indirect Spectrofluorometric Method for the Determination of Cefotaxime Sodium, Ciprofloxacin Hydrochloride and Famotidine in Pharmaceuticals Using Bromate-Bromide and Acriflavine Dye
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An Indirect simple sensitive and applicable spectrofluorometric method has been developed for the determination of Cefotaxime Sodium (CEF), ciprofloxacin Hydrochloride (CIP) and Famotidine (FAM) using reaction system bromate-bromide and acriflavine (AF) as fluorescent dye. The method is based on the oxidation of drugs with known excess bromate-bromide mixture in acidic medium and subsequent determination of unreacted oxidant by quenching fluorescence of AF. Fluorescence intensity of residual AF was measured at 528 nm after excitation at 402 nm. The fluorescence-concentration plots were rectilinear over the ranges 0.1-3.0, 0.05-2.6 and 0.1-3.8 µg ml-1 with lower detection limits of 0.013, 0.018 and 0.021 µg ml-1 an

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Publication Date
Sat Jan 01 2022
Journal Name
Ssrn Electronic Journal
The Prospective of Artificial Neural Network (ANN’s) Model Application to Ameliorate Management of Post Disaster Engineering Projects
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Currently and under the COVID-19 which is considered as a kind of disaster or even any other natural or manmade disasters, this study was confirmed to be important especially when the society is proceeding to recover and reduce the risks of as possible as injuries. These disasters are leading somehow to paralyze the activities of society as what happened in the period of COVID-19, therefore, more efforts were to be focused for the management of disasters in different ways to reduce their risks such as working from distance or planning solutions digitally and send them to the source of control and hence how most countries overcame this stage of disaster (COVID-19) and collapse. Artificial intelligence should be used when there is no practica

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Publication Date
Sun Feb 03 2019
Journal Name
Iraqi Journal Of Physics
Effect of laser energy and pulses on size and concentration of gold nanoparticles in DDDW by LALP method
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The size and the concentration of the gold nanoparticles (GNPs)
synthesized in double distilled deionized water (DDDW) have been
found to be affected by the laser energy and the number of pulses.
The absorption spectra of the nanoparticles DDDW, and the
surface plasmon resonance (SPR) peaks were measured, and found to
be located between (509 and 524)nm using the UV- Vis
spectrophotometer. SPR calculations, images of transmission
electron microscope, and dynamic light scattering (DLS) method
were used to determine the size of GNPs, which found to be ranged
between (3.5 and 27) nm. The concentrations of GNPs in colloidal
solutions found to be ranged between (37 and 142) ppm, and
measured by atomic absorptio

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Publication Date
Fri Sep 05 2014
Journal Name
Engineering And Technology Journal
New Method to Increase the Ability of the Water for Dissolving Total Salts in Soil by Using the Magnetism
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