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Applying Scikit-learn of Machine Learning to Predict Consumed Energy in Al-Khwarizmi College of Engineering, Baghdad, Iraq
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Globally, buildings use about 40% of energy. Many elements, such as the physical properties of the structure, the efficiency of the cooling and heating systems, the activity of the occupants, and the building’s sustainability, affect the energy consumption of a building. It is really difficult to predict how much energy a building will need. To improve the building’s sustainability and create sustainable energy sources to reduce carbon dioxide emissions from fossil fuel combustion, estimating the building's energy use is necessary. This paper explains the energy consumed in the lecture building of the Al-Khwarizmi College of Engineering, University of Baghdad (UOB), Baghdad, Iraq. The weather data and the building construction information were collected for a specific period and put into a specific data set. That data was used to find the value of energy consumption in the building using artificial intelligence and data analysis. A Python library called Scikit-learn is used to implement machine learning algorithms. In particular, the Multi-layer Perceptron regressor (MLPRegressor) algorithm was used to predict the consumption. The importance of this work lies in predicting the amount of energy consumed. The outcomes of this work can be used to predict the energy consumed by any building before it is built. The used methodology shows the ability to predict energy performance in educational buildings using previous results and train the model on them, and prediction accuracy depends on the amount of data available for the training in artificial intelligence (AI) steps to give the highest accuracy. The prediction was checked using root-mean-square error (RMSE) and coefficient of determination (R²) and we arrived at 0.16 and 0.97 for RMSE and R², respectively.

Publication Date
Tue Mar 01 2022
Journal Name
Iraqi Journal Of Physics
Effect of Copper on Tensile and Hardness of Al-Si Alloy in Automotive Application
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In current research Copper was employed for preparing a ternary system of Al–Si alloy in different (0.2–2.5 wt. %) the best was taken is (1.5%wt) of copper that circumstances of solidification for improving the mechanical performance of the available in aluminium alloy. Cast iron molds were prepared to obtain tensile strength testing specimens. Alloys were prepared by employing gas furnaces. The molten metal was poured into a preheated cast-iron mold. The obtained alloy structures were studied using an X-ray diffractometer and optical microscopy. The mechanical performance of the prepared alloys was examined under the influence of different hardening conditions in both heat and non-heat-treated conditions. The outcomes showed at the

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Publication Date
Sat Jan 01 2022
Journal Name
Geotechnical Engineering And Sustainable Construction
Stability and Seismic Performance of Tall Steel Structures with Hybrid Energy Absorbers Including P-Delta Effect
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Publication Date
Wed Dec 09 2020
Journal Name
Civil Engineering Journal
Torsional Strengthening of Reinforced Concrete Beams with Externally-Bonded Fibre Reinforced Polymer: An Energy Absorption Evaluation
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The impacts of numerous important factors on the Energy Absorption (EA) of torsional Reinforced Concrete (RC) beams strengthened with external FRP is the main purpose and innovation of the current research. A total of 81 datasets were collected from previous studies, focused on the investigation of EA behaviour. The impact of nine different parameters on the Torsional EA of RC-beams was examined and evaluated, namely the concrete compressive strength (f’c), steel yield strength (fy), FRP thickness (tFRP), width-to-depth of the beam section (b/h), horizontal (ρh) and vertical (ρv) steel ratio, angle of twist (θu), ultimate torque (Tu), and FRP ultimate strength (fy-FRP). For the evaluation of the energy absorption capacity at di

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Publication Date
Sun Sep 01 2019
Journal Name
Journal Of Materials Research And Technology
The effect of laser pulse energy on ZnO nanoparticles formation by liquid phase pulsed laser ablation
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Zinc Oxide nanoparticles were prepared using pulsed laser ablation process from a pure zinc metal placed inside a liquid environment. The latter is composed of acetyltrimethylammonium bromide (CTAB) of 10−3 molarity and distilled water. A Ti:Sapphire laser of 800 nm wavelength, 1 kHz pulse repetition rate, 130 fs pulse duration is used at three values of pulse energies of 0.05 mJ, 1.11 mJ and 1.15 mJ. The evaluation of the optical properties for the obtained suspension was applied through ultraviolet–visible absorption spectroscopy test (UV/VIS). The result showed peak wavelengths at 210 nm, 211 nm and 213 nm for the three used pulse energies 0.05 mJ, 1.11 mJ and 1.15 mJ respectively. This indicates a blue shift,

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Engineering
Efficient Energy Management for a Proposed Integrated Internet of Things-Electric Smart Meter (2IOT-ESM) System
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In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these measurement

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Engineering
Efficient Energy Management for a Proposed Integrated Internet of Things-Electric Smart Meter (2IOT-ESM) System
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In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these

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Publication Date
Mon Sep 30 2024
Journal Name
Al-mustansiriyah Journal Of Science
A Transfer Learning Approach for Arabic Image Captions
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Publication Date
Sat Dec 31 2022
Journal Name
International Journal On “technical And Physical Problems Of Engineering”
Age Estimation Utilizing Deep Learning Convolutional Neural Network
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Estimating an individual's age from a photograph of their face is critical in many applications, including intelligence and defense, border security and human-machine interaction, as well as soft biometric recognition. There has been recent progress in this discipline that focuses on the idea of deep learning. These solutions need the creation and training of deep neural networks for the sole purpose of resolving this issue. In addition, pre-trained deep neural networks are utilized in the research process for the purpose of facial recognition and fine-tuning for accurate outcomes. The purpose of this study was to offer a method for estimating human ages from the frontal view of the face in a manner that is as accurate as possible and takes

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Publication Date
Tue Dec 21 2021
Journal Name
Mendel
Hybrid Deep Learning Model for Singing Voice Separation
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Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
Arabic Sentiment Analysis (ASA) Using Deep Learning Approach
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Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

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