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Intelligent Dust Monitoring System Based on IoT
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Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system train and test part was applied to dust phenomena historical data. Its data has been collected through the Iraqi Meteorological Organization and Seismology (IMOS) raw dataset with 170237 of 17023 rows and 10 columns. The LSTM model achieved small time, computationally complexity of, and layers number while being effective and accurate for dust prediction. The simulation results reveal that the model's mean square error test reaches 0.12877 and Mean Absolute Error (MAE) test is 0.07411 at the same rates of learning and exact features values of vector in the dense layer, representing a neural network layer deeply is connected to the LSTM training proposed model. Finally, the model suggested enhances monitoring performance.

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Publication Date
Sat Apr 01 2017
Journal Name
Image & Video Processing
Enhancement of LBP-based face identification system by adopting preprocessing techniques
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Face Identification system is an active research area in these years. However, the accuracy and its dependency in real life systems are still questionable. Earlier research in face identification systems demonstrated that LBP based face recognition systems are preferred than others and give adequate accuracy. It is robust against illumination changes and considered as a high-speed algorithm. Performance metrics for such systems are calculated from time delay and accuracy. This paper introduces an improved face recognition system that is build using C++ programming language with the help of OpenCV library. Accuracy can be increased if a filter or combinations of filters are applied to the images. The accuracy increases from 95.5% (without ap

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Publication Date
Mon Dec 14 2020
Journal Name
2020 13th International Conference On Developments In Esystems Engineering (dese)
Anomaly Based Intrusion Detection System Using Hierarchical Classification and Clustering Techniques
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With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect

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Publication Date
Wed Jun 30 2021
Journal Name
International Journal Of Intelligent Engineering And Systems
Airborne Computer System Path-Tracking Based Multi-PID-PSO Controller Design
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Publication Date
Sun Jan 01 2023
Journal Name
Computers, Materials & Continua
Severity Based Light-Weight Encryption Model for Secure Medical Information System
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Publication Date
Fri May 01 2020
Journal Name
Journal Of Engineering
Shrinkage and Strength Behavior of Highly Plastic Clay Improved by Brick Dust
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Highly plastic soils exhibit unfavorited properties upon saturation, which produce different defects in engineering structures. Attempts were made by researchers to proffer solutions to these defects by experimenting in practical ways. This included various materials that could possibly improve the soil engineering properties and reduce environmental hazards. This paper investigates the strength behavior of highly plastic clay stabilized with brick dust. The brick dust contents were 10%, 20%, and 30% by dry weight of soil. A series of linear shrinkage and unconfined compression tests were carried out to study the effect of brick dust on the quantitative amount of shrinkage experienced by highly plastic clay and the undra

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Publication Date
Mon Nov 01 2010
Journal Name
Iraqi Journal Of Physics
New Technique for Treatment of the dust accumulation from PV solar panels surface
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One of the most important problems facing the world today is the energy problem. The solution was in finding renewable energy sources such as solar energy. The solar energy applications in Iraq is facing many problems . One of the most important problems is the accumulation of dust on the solar panels surface which causes decreasing its performance sharply. In the present work, a new technique was presented by using two-axis solar tracking system to reduce the accumulated dust on the solar panel surface and compared it with the fixed solar panels which installed at tilt angles 30° and 45°. The results indicated that the maximum losses of the output power due to accumulation of dust on the fixed solar panels is about 31.4% and 23.1% res

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Publication Date
Sat Apr 02 2022
Journal Name
The International Conference On Science In Engineering And Technology (icosiet) 2020 (virtually)
The Possible Application of Intelligent Systems in Traditional Courtyard House.
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Publication Date
Sun Aug 01 2021
Journal Name
International Journal Of Electrical And Computer Engineering
Smart element aware gate controller for intelligent wheeled robot navigation
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<p>The directing of a wheeled robot in an unknown moving environment with physical barriers is a difficult proposition. In particular, having an optimal or near-optimal path that avoids obstacles is a major challenge. In this paper, a modified neuro-controller mechanism is proposed for controlling the movement of an indoor mobile robot. The proposed mechanism is based on the design of a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. This controller is updated to overcome the rigid and dynamic barriers in the indoor area. The proposed controller is implemented with a mobile robot known as Khepera IV in a practical manner. The practical results demonstrate that the propo

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Publication Date
Sun Jan 01 2017
Journal Name
Journal Of Engineering
Detection and Diagnosis of Induction Motor Faults by Intelligent Techniques
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This paper presents a complete design and implementation of a monitoring system for the operation of the three-phase induction motors. This system is built using a personal computer and  two types of sensors (current, vibration) to detect some of the mechanical faults that may occur in the motor. The study and examination of several types of faults including (ball bearing and shaft misalignment faults) have been done through the extraction of fault data by using fast Fourier transform (FFT) technique. Results showed that the motor current signature analysis (MCSA) technique, and measurement of vibration technique have high possibility in the detection and diagnosis of most mechanical faults with high accuracy. Subsequently, diagnosi

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Publication Date
Fri Nov 15 2024
Journal Name
Iraqi Journal Of Science
Spatial and Temporal Analysis of Severe Dust Storm in Iraq in May 2022
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Dust storms are a common ecological occurrence in many world‘s countries, mainly in dry and semi-dry parts. Dust storms tremendously influence human health, the environment, the climate, and numerous social aspects. In this paper, spatial and temporal analysis, metrological triggers, and trajectory, dust exporting areas of a severe dust storm that occurred in Iraq on May 16, 2022, were investigated. The dust storm's backward trajectory was determined using HYSPLIT model, which is then compared with MODIS and Meteosat satellite images. The weather is then analyzed using the NCEP/NCAR Reanalysis model, and the approximate area of these sources was determined using Landsat 8 satellite image classification method. The results revealed

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