| 51. |
- Abd El‑Hameed, Mona M., et al.
(författare)
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Phycoremediation of contaminated water by cadmium (Cd) using two cyanobacterial strains (Trichormus variabilis and Nostoc muscorum)
- 2021
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Ingår i: Environmental Sciences Europe. - Germany : Springer Nature. - 2190-4707 .- 2190-4715. ; 33:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundWater pollution with heavy metals is a severe dilemma that concerns the whole world related to its risk to natural ecosystems and human health. The main objective was to evaluate the removal efficiency of Cd of various concentrations from contaminated aqueous solution by use of two cyanobacterial strains (Nostoc muscorum and Trichormus variabilis). For this purpose, a specially designed laboratory pilot-scale experiment was conducted using these two cyanobacterial strains on four different initial concentrations of Cd (0, 0.5, 1.0 and 2.0 mg L−1) for 21 days.ResultsN. muscorum was more efficient than T. variabilis for removing Cd (II), with the optimum value of residual Cd of 0.033 mg L−1 achieved by N. muscorum after 21 days with initial concentration of 0.5 mg L−1, translating to removal efficiency of 93.4%, while the residual Cd (II) achieved by T. variabilis under the same conditions was 0.054 mg L−1 (89.13% removal efficiency). Algal growth parameters and photosynthetic pigments were estimated for both cyanobacterial strains throughout the incubation period.ConclusionsHigh Cd concentration had a more toxic impact on algal growth. The outcomes of this study will help to produce treated water that could be reused in agrarian activities.
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| 52. |
- Abd-Ellah, Mahmoud Khaled, et al.
(författare)
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A Review on Brain Tumor Diagnosis from MRI Images : Practical Implications, Key Achievements, and Lessons Learned
- 2019
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Ingår i: Magnetic Resonance Imaging. - : Elsevier. - 0730-725X .- 1873-5894. ; 61, s. 300-318
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Tidskriftsartikel (refereegranskat)abstract
- The successful early diagnosis of brain tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a difficult process. Thus, there is a crucial need for computer-aided methods with better accuracy for early tumor diagnosis. Computer-aided brain tumor diagnosis from MRI images consists of tumor detection, segmentation, and classification processes. Over the past few years, many studies have focused on traditional or classical machine learning techniques for brain tumor diagnosis. Recently, interest has developed in using deep learning techniques for diagnosing brain tumors with better accuracy and robustness. This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis. This review paper identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes. In addition, this study discusses the key findings and draws attention to the lessons learned as a roadmap for future research.
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| 53. |
- 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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| 54. |
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| 55. |
- 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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| 56. |
- 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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| 57. |
- Abd-Ellah, Mahmoud Khaled, et al.
(författare)
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TPUAR-Net : Two Parallel U-Net with Asymmetric Residual-Based Deep Convolutional Neural Network for Brain Tumor Segmentation
- 2019
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Ingår i: Image Analysis and Recognition. - Cham : Springer. ; , s. 106-116
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Konferensbidrag (refereegranskat)abstract
- The utilization of different types of brain images has been expanding, which makes manually examining each image a labor-intensive task. This study introduces a brain tumor segmentation method that uses two parallel U-Net with an asymmetric residual-based deep convolutional neural network (TPUAR-Net). The proposed method is customized to segment high and low grade glioblastomas identified from magnetic resonance imaging (MRI) data. Varieties of these tumors can appear anywhere in the brain and may have practically any shape, contrast, or size. Thus, this study used deep learning techniques based on adaptive, high-efficiency neural networks in the proposed model structure. In this paper, several high-performance models based on convolutional neural networks (CNNs) have been examined. The proposed TPUAR-Net capitalizes on different levels of global and local features in the upper and lower paths of the proposed model structure. In addition, the proposed method is configured to use the skip connection between layers and residual units to accelerate the training and testing processes. The TPUAR-Net model provides promising segmentation accuracy using MRI images from the BRATS 2017 database, while its parallelized architecture considerably improves the execution speed. The results obtained in terms of Dice, sensitivity, and specificity metrics demonstrate that TPUAR-Net outperforms other methods and achieves the state-of-the-art performance for brain tumor segmentation.
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| 58. |
- Abdel-Hameed, Amal Mohamed, et al.
(författare)
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Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions
- 2024
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Ingår i: Potato Research. - : Springer Nature. - 0014-3065 .- 1871-4528.
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Tidskriftsartikel (refereegranskat)abstract
- Precise assessment of water footprint to improve the water consumption and crop yield for irrigated agricultural efficiency is required in order to achieve water management sustainability. Although Penman-Monteith is more successful than other methods and it is the most frequently used technique to calculate water footprint, however, it requires a significant number of meteorological parameters at different spatio-temporal scales, which are sometimes inaccessible in many of the developing countries such as Egypt. Machine learning models are widely used to represent complicated phenomena because of their high performance in the non-linear relations of inputs and outputs. Therefore, the objectives of this research were to (1) develop and compare four machine learning models: support vector regression (SVR), random forest (RF), extreme gradient boost (XGB), and artificial neural network (ANN) over three potato governorates (Al-Gharbia, Al-Dakahlia, and Al-Beheira) in the Nile Delta of Egypt and (2) select the best model in the best combination of climate input variables. The available variables used for this study were maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tave), wind speed (WS), relative humidity (RH), precipitation (P), vapor pressure deficit (VPD), solar radiation (SR), sown area (SA), and crop coefficient (Kc) to predict the potato blue water footprint (BWF) during 1990–2016. Six scenarios (Sc1–Sc6) of input variables were used to test the weight of each variable in four applied models. The results demonstrated that Sc5 with the XGB and ANN model gave the most promising results to predict BWF in this arid region based on vapor pressure deficit, precipitation, solar radiation, crop coefficient data, followed by Sc1. The created models produced comparatively superior outcomes and can contribute to the decision-making process for water management and development planners.
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| 59. |
- Abdel-Hameed, Amal Mohamed, et al.
(författare)
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Winter Potato Water Footprint Response to Climate Change in Egypt
- 2022
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Ingår i: Atmosphere. - : MDPI. - 2073-4433 .- 2073-4433. ; 13:7
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Tidskriftsartikel (refereegranskat)abstract
- The limited amount of freshwater is the most important challenge facing Egypt due to increasing population and climate change. The objective of this study was to investigate how climatic change affects the winter potato water footprint at the Nile Delta covering 10 governorates from 1990 to 2016. Winter potato evapotranspiration (ETC) was calculated based on daily climate variables of minimum temperature, maximum temperature, wind speed and relative humidity during the growing season (October–February). The Mann–Kendall test was applied to determine the trend of climatic variables, crop evapotranspiration and water footprint. The results showed that the highest precipitation values were registered in the northwest governorates (Alexandria followed by Kafr El-Sheikh). The potato water footprint decreased from 170 m3 ton−1 in 1990 to 120 m3 ton−1 in 2016. The blue-water footprint contributed more than 75% of the total; the remainder came from the green-water footprint. The findings from this research can help government and policy makers better understand the impact of climate change on potato crop yield and to enhance sustainable water management in Egypt’s major crop-producing regions to alleviate water scarcity.
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| 60. |
- Abdel-Khalek, N.A., et al.
(författare)
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Effect of starch type on selectivity of cationic flotation of iron ore
- 2012
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Ingår i: Mineral Processing and Extractive Metallurgy. - 0371-9553 .- 1743-2855. ; 121:2, s. 98-102
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Tidskriftsartikel (refereegranskat)abstract
- Cationic flotation is one of the most widely accepted technologies for upgrading siliceous iron ore using polysaccharides (mainly starches) as depressing agents for iron bearing minerals while floating silica with amines. In this paper, a group of starches are investigated as depressants for haematite. These starches are wheat, corn, rice, potato and dextrin. The role of starch type on the selectivity of the separation process has been studied through zeta potential, adsorption measurements as well as flotation tests. The effects of type of starch and pH of the medium have been studied. The results indicate that the selectivity of the separation process is strongly affected by the type of starch used, where better results are obtained with corn starch or wheat starch in comparison to the other types. Fourier transform infrared spectroscopy measurements indicated that the interaction between starches and haematite surface is intermolecular interaction.
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