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.
Heart diseases are diverse, common, and dangerous diseases that affect the heart's function. They appear as a result of genetic factors or unhealthy practices. Furthermore, they are the leading cause of mortalities in the world. Cardiovascular diseases seriously concern the health and activity of the heart by narrowing the arteries and reducing the amount of blood received by the heart, which leads to high blood pressure and high cholesterol. In addition, healthcare workers and physicians need intelligent technologies that help them analyze and predict based on patients’ data for early detection of heart diseases to find the appropriate treatment for them because these diseases appear on the patient without pain or noticeable symptoms,
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15 sediment samples were collected; 8 samples from the eastern bank, and 7 samples from the western bank of Al-Wind River in Diyala governorate to assess the sediment pollution in some trace elements such as Fe, Ni, Cd, Zr, Zn and Cu in addition to some oxides such as Al2O3, CaO, Na2O and K2O to find the effect of anthropogenic pollution and the industrial production on the sediment closed especially Naftkhana by using some geochemical pollution indices such as: geoaccumulation factor (I-geo), enrichment factor (EF),contamination factor (CF), pollution loud index (PLI) and to evaluate the degree of weathering by Applying the Chemical Index of Alteration (CIA)in both banks of Al-Wind River. The
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The aim of the research is to identify both the re-engineering of management processes and the strategic decision-making process in the research community and determine the nature of the correlation between the two variables and know the relationship between them to achieve the research goal. The researcher used a descriptive and analytical method. The research community consists of a group of professors and staff of the College of Education affiliated to the University of Mustansiriya in Baghdad, which their number were (45), the researcher has distributed the forms to all members of the sample, only (3) forms were excluded for invalidity and thus the number of forms approved in the analysis were (42) forms. The rese
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Soil is a crucial component of environment. Total soil analysis may give information about possible enrichment of the soil with heavy metals. Heavy metals, potentially contaminate soils, may have been dumped on the ground. The concentrations of soil heavy metals (Cd, As, Pb, Cr, Ni, Zn and Cu) were measured in three zones thought to be deeply contaminated at different depths (5, 25, 50 cm) at Ibn Al-Haitham College. The highest concentration of heavy metals Pb (63.3ppm), Cr (90.7ppm), Ni (124ppm) and Cu (75.7ppm) were found in zone (A) location-1, where the highest concentration of Zn (111.7ppm) was found in zone (C). Cd and As were detected in small amounts in all zones. PH value, organic matters, carbonat
... Show MoreAgricultural loans play an important role in the growth and stimulation of agricultural investment opportunities in Iraq, as well as the sustainability and development of existing agricultural projects. The agricultural sector is characterized by the specific conditions of seasonal production and fluctuations in production conditions, which makes the situation of uncertainty more acute in this sector, the need for any agricultural project for financing is urgent and continuous if it wants to continue production and development at all stages. The study proved the impact of agricultural loans in increasing investment and agricultural production at specific times, However, the fluctuation of funding
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HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023
Lately, a growing interest has been emerging in age estimation from face images because of the wide range of potential implementations in law enforcement, security control, and human computer interactions. Nevertheless, in spite of the advances in age estimation, it is still a challenging issue. This is due to the fact that face aging process is not only set by distinct elements, such as genetic factors, but by extrinsic factors, such as lifestyle, expressions, and environment as well. This paper applied machine learning technique to intelligent age estimation from facial images using J48 classifier on FG_NET dataset. The proposed work consists of three phases; the first phase is image preprocessing which include
... Show MoreIn recent years, predicting heart disease has become one of the most demanding tasks in medicine. In modern times, one person dies from heart disease every minute. Within the field of healthcare, data science is critical for analyzing large amounts of data. Because predicting heart disease is such a difficult task, it is necessary to automate the process in order to prevent the dangers connected with it and to assist health professionals in accurately and rapidly diagnosing heart disease. In this article, an efficient machine learning-based diagnosis system has been developed for the diagnosis of heart disease. The system is designed using machine learning classifiers such as Support Vector Machine (SVM), Nave Bayes (NB), and K-Ne
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The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system
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