| 1. |
- Chabuk, Ali, et al.
(författare)
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Application ArcGIS on Modified-WQI Method to Evaluate Water Quality of the Euphrates River, Iraq, Using Physicochemical Parameters
- 2021
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Ingår i: Proceedings of Sixth International Congress on Information and Communication Technology. - Singapore : Springer. ; , s. 657-675
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Konferensbidrag (refereegranskat)abstract
- The global interest of the water bodies due to the water scarcity crisis encourages researchers to study the details water environment in different aspects. Consequently, this study objective to evaluate the water quality in the Euphrates River through adopted 11 physicochemical parameters measured at 16 locations during the 3 years (2009–2011) for both seasons (dry and wet). In this study, the water quality index model (WQIM) was calculated after modifying the weighted arithmetic method to define as MWQI. The chosen parameters were comprised of Cl, SO4, HCO3, NO3, Na, K, Ca, Mg, TH, TDS, and EC. For the river section of locations (L.1–L.10), all readings of the selected parameters (expected HCO3) were increased more and more. Then, all concentrations of parameters were recorded the high increasing after location (L.10) at locations (L.11–L.14). The concentrations situation of HCO3 were verse vice at all locations. For the average values of 3 years (wet, dry, total), the MWQI of section length of the Euphrates River at locations (L.1–L.10) were classified as good water quality (class, C-II). The river section at locations (L.11–L.16, excepted L.13) was classified as poor water quality (class, C-III), while the location (L.13) was classified as very poor (class, C-IV). The interpolation prediction maps of the average readings (total, dry, and wet) of the Euphrates River were output in GIS using the interpolation model of IDWM.
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| 2. |
- Chabuk, Ali, et al.
(författare)
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Application GIS Software to Determine the Distribution of T.D.S. Concentrations Along the Tigris River
- 2021
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Ingår i: 2nd Virtual International Scintific Agrticultural Conference 21-22 January 2021, Iraq. - : Institute of Physics (IOP).
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Konferensbidrag (refereegranskat)abstract
- Tigris River is a major source to supply water for a big part of Iraq. Lately, Iraq has experienced water shortage problems such as variability in climate and the building of huge dams in the upstream countries (Turkey and Iran). In this work, the total dissolved salts (T.D.S.) were measured at fourteen sites on the Tigris River in two periods of the year 2014. The first period consisted of six rainy-months (April–September) and the second period covered non-rainy-months (October-March). Interpolation technique of inverse-distance-weighting (I.D.W.) in ArcGIS was applied to create the prediction maps of the river for (T.D.S.) concentration in both periods. The findings revealed that the (T.D.S.) levels continued to the last site in Al-Qurnah-city (Basrah) from the first site in Fish-Khabur-city (S-1). In the first period, the (T.D.S.) levels at fourteen selected sites were over the levels in the second period. According to World-Health-Organization (2003), the (T.D.S.) concentration on the Tigris River in both periods in 2014 was graded into five classes, then, the prediction maps of the (T.D.S.) classifications were created.
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| 3. |
- Chabuk, Ali, et al.
(författare)
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Noise Level in Textile Industries: Case Study Al-Hillah Textile Factory-Company for Textile Industries, Al-Hillah-Babylon-Iraq
- 2021
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Ingår i: First International Virtual Conference on Environment & Natural Resources. - : Institute of Physics (IOP).
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Konferensbidrag (refereegranskat)abstract
- In this study, Al-Hillah Textile Factory, in Al-Hillah city-Iraq follows to State Company for Textile Industries was selected to study the intensity of noise in 2014. Measurements of the noise level were carried out in different workshops for each of the production stages including the spinning machinery workshop (parts 1 and 2), the rotating machinery room, the preparations room, and the textile machinery room (Roti model), weaving machines: Techmash model room Russian-made model room, Sheets' machinery room, and operator machines room; using two noise meters (model 2237 Fulfici). Fifty samples were collected in each part of these rooms to give realistic results for the noise level. After recording the noise level data, the highest and lowest values and the average of noise intensity readings were calculated in each of the rooms and compared with the global standards permitted by the EPA for industrial facilities. The results of this study showed that the general rate of noise intensity in all rooms exceeded the permissible limits, which impose a noise level of 65-70dB for such industrial establishments according to EPA recommendations in 2008.
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| 4. |
- Hommadi, Ali H., et al.
(författare)
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Scheduling the Laterals of Shattulhilla River by Utilizing the Genetic Algorithm as Water Sustainability Technique
- 2024
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Ingår i: Proceedings of the 4th International Conference on Recent Innovation in Engineering ICRIE 2023, University of Duhok, College of Engineering, 13th – 14th September 2023. - : University of Garmian. ; , s. 84-93
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Konferensbidrag (refereegranskat)abstract
- Open channels are very important to deliver water from main sources to laterals especially for developing countries. Production is subjective by the way that the water is scheduled, and this scheduling is subject to several irrigation constraints. In open channel projects, for instance, maximum discharge of the laterals and main channels, depending on the size of their dimensions and the water requirements for fields. The current paper shows how efficient water scheduling, regarding the delivering water from the main channel to laterals in consequent time slots, can be done by utilizing a genetic algorithm optimisation technique. This research is intended to be applied for scheduling the Shattulhilla River in Babylon City and has broad applications for open channel projects in Iraq. The obtained results clarify how the genetic algorithm optimisation modelling is a sophisticated tool which operators of irrigation projects could now utilize to timetable open channels of irrigation systems.
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| 5. |
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| 6. |
- Chabuk, Ali, et al.
(författare)
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Water Quality Variation Along the Tigris River
- 2022
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Ingår i: CAJG 2019: New Prospects in Environmental Geosciences and Hydrogeosciences. - Cham : Springer. ; , s. 447-450
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Konferensbidrag (refereegranskat)
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| 7. |
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| 8. |
- Abd-Ellah, Mahmoud Khaled, et al.
(författare)
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Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine
- 2016
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Ingår i: Building Sustainable Health Ecosystems. - Cham : Springer International Publishing. - 9783319446714 - 9783319446721 ; , s. 151-160
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Konferensbidrag (refereegranskat)abstract
- The use of medical images has been continuously increasing, which makes manual investigations of every image a difficult task. This study focuses on classifying brain magnetic resonance images (MRIs) as normal, where a brain tumor is absent, or as abnormal, where a brain tumor is present. A hybrid intelligent system for automatic brain tumor detection and MRI classification is proposed. This system assists radiologists in interpreting the MRIs, improves the brain tumor diagnostic accuracy, and directs the focus toward the abnormal images only. The proposed computer-aided diagnosis (CAD) system consists of five steps: MRI preprocessing to remove the background noise, image segmentation by combining Otsu binarization and K-means clustering, feature extraction using the discrete wavelet transform (DWT) approach, and dimensionality reduction of the features by applying the principal component analysis (PCA) method. The major features were submitted to a kernel support vector machine (KSVM) for performing the MRI classification. The performance evaluation of the proposed system measured a maximum classification accuracy of 100 % using an available MRIs database. The processing time for all processes was recorded as 1.23 seconds. The obtained results have demonstrated the superiority of the proposed system.
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| 9. |
- Abd-Ellah, Mahmoud Khaled, et al.
(författare)
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Design and implementation of a computer-aided diagnosis system for brain tumor classification
- 2017
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Ingår i: 2016 28th International Conference on Microelectronics (ICM). - 9781509057214 ; , s. 73-76
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Konferensbidrag (refereegranskat)abstract
- Computer-aided diagnosis (CAD) systems have become very important for the medical diagnosis of brain tumors. The systems improve the diagnostic accuracy and reduce the required time. In this paper, a two-stage CAD system has been developed for automatic detection and classification of brain tumor through magnetic resonance images (MRIs). In the first stage, the system classifies brain tumor MRI into normal and abnormal images. In the second stage, the type of tumor is classified as benign (Noncancerous) or malignant (Cancerous) from the abnormal MRIs. The proposed CAD ensembles the following computational methods: MRI image segmentation by K-means clustering, feature extraction using discrete wavelet transform (DWT), feature reduction by applying principal component analysis (PCA). The two-stage classification has been conducted using a support vector machine (SVM). Performance evaluation of the proposed CAD has achieved promising results using a non-standard MRIs database.
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| 10. |
- Abd-Ellah, Mahmoud Khaled, et al.
(författare)
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Parallel Deep CNN Structure for Glioma Detection and Classification via Brain MRI Images
- 2019
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Ingår i: IEEE-ICM 2019 CAIRO-EGYPT. - : IEEE. ; , s. 304-307
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Although most brain tumor diagnosis studies have focused on tumor segmentation and localization operations, few studies have focused on tumor detection as a time- and effort-saving process. This study introduces a new network structure for accurate glioma tumor detection and classification using two parallel deep convolutional neural networks (PDCNNs). The proposed structure is designed to identify the presence and absence of a brain tumor in MRI images and classify the type of tumor images as high-grade gliomas (HGGs, i.e., glioblastomas) or low-grade gliomas (LGGs). The introduced PDCNNs structure takes advantage of both global and local features extracted from the two parallel stages. The proposed structure is not only accurate but also efficient, as the convolutional layers are more accurate because they learn spatial features, and they are efficient in the testing phase since they reduce the number of weights, which reduces the memory usage and runtime. Simulation experiments were accomplished using an MRI dataset extracted from the BraTS 2017 database. The obtained results show that the proposed parallel network structure outperforms other detection and classification methods in the literature.
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