When images are customized to identify changes that have occurred using techniques such as spectral signature, which can be used to extract features, they can be of great value. In this paper, it was proposed to use the spectral signature to extract information from satellite images and then classify them into four categories. Here it is based on a set of data from the Kaggle satellite imagery website that represents different categories such as clouds, deserts, water, and green areas. After preprocessing these images, the data is transformed into a spectral signature using the Fast Fourier Transform (FFT) algorithm. Then the data of each image is reduced by selecting the top 20 features and transforming them from a two-dimensional matrix to a one-dimensional vector matrix using the Vector Quantization (VQ) algorithm. The data is divided into training and testing. Then it is fed into 23 layers of deep neural networks (DNN) that classify satellite images. The result is 2,145,020 parameters, and the evaluation of performance measures was accuracy = 100%, loopback = 100%, and the result F1 = 100 %.
The study aimed to reach the best rating for the views and variables in the totals characterized by qualities and characteristics common within each group and distinguish them from aggregates other for the purpose of distinguishing between Iraqi provinces which suffer from deprivation, for the purpose of identifying the status of those provinces in the early allowing interested parties and regulators to intervene to take appropriate corrective action in a timely manner. Style has been used cluster analysis Cluster analysis to reach the best rating to those totals from the provinces that suffer from problems, where the provinces were classified, based on the variables (Edu
... Show MoreProjects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postpo
The study has used Geographic Information Systems (GIS) data and Arc GIS software, to produce a new map. This map contains a spatial database of the newly constructed buildings, in the area of water resources engineering college, and using the above tool for updating contains the college in any time, also the map and browse all buildings individually or in clustered, for example, browsing geometric data, statistical data and the plans of drawings, and make any necessary changes to these sectors immediately, or in the future, like the expansion of the study halls by constructed a new floor, or add supplements, prepare any electrical to any building, and anything else. The paper also, updates the old Google Earth image, f
... Show MoreThe aim of the study is the assessment of changes in the land cover within Mosul City in the north of Iraq using Geographic Information Systems (GIS) and remote sensing techniques during the period (2014-2018). Satellite images of the Landsat 8 on this period have been selected to classify images in order to measure normalized difference vegetation index (NDVI) to assess land cover changes within Mosul City. The results indicated that the vegetative distribution ratio in 2014 is 4.98% of the total area under study, decreased to 4.77% in 2015 and then decreased to 4.54
Triticale is a hybrid of wheat and rye grown for use as animal feed. In Florida, due to its soft coat, triticale is highly vulnerable to Sitophilus oryzae L. (rice weevil) and there is interest in development of methods to detect early-instar larvae so that infestations can be targeted before they become economically damaging. The objective of this study was to develop prediction models of the infestation degree for triticale seed infested with rice weevils of different growth stages. Spectral signatures were tested as a method to detect rice weevils in triticale seed. Groups of seeds at 11 different levels (degrees) of infestation, 0–62%, were obtained by combining different ratios of infested and uninfested seeds. A spectrophotometer wa
... Show MoreMachine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims
... Show MoreGravity and magnetic data are used to study the tectonic situation of Al-Kut- Al-
Hai and surrounding areas in central Iraq. The study included application of many
processing and interpretation programs. The window method with different spacing
was used to separate the residual from regional anomalies for gravity and magnetic
data. The Total Horizontal Derivative (THDR) techniques used to identify the fault
trends in the basement and sedimentary cover rocks depending upon gravity and
magnetic data. The identified faults in the study area show (NW-SE), (NE-SW) (NS)
and (E-W) trends. It is believed that these faults extending from the basement to
the upper most layer of the sedimentary cover rocks.