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  • Abbas, Zainab Dekan, et al. (författare)
  • Locating Dam Sites For Water Harvesting Case Study Of Najaf Province, Iraq
  • 2019
  • Ingår i: Journal of Environmental Hydrology. - Canada : The International Association of Environmental Hydrology. - 1058-3912 .- 1996-7918. ; 27, s. 1-8
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>The Middle East is considered as an arid area. Iraq was an exception due to the presence of the Tigris  and  Euphrates  Rivers. After  1970,  the  flow  of  these  rivers  started  to  decrease  due  to  climate change  and  building  of  dams  in  the  upper  parts  of  the  catchments  of  the  rivers.  Now,  Iraq  is experiencing  water  shortage  problems.  Rain  water  harvesting  will  definitely  minimize  the  effect  of water shortage problems. In this research an arid area was selected (al Najaf) to find out the best sites for water harvesting using GIS techniques. The good agreement between the results from a simple GIS model  and  observations  in  cases  such  as  al  Najaf  Sea  is  indicating  a  promising  future  for  GIS application  in  hydrological  modeling.  The  present  study  proposed  a  function  formula  of  estimating suitable dam site using existing geographic information map such as the digital elevation maps. It is expected that it will save time, cost and work force. Finally, through the contour map of the study area, the lowest three elevation values at the governorate level were observed (20, 40, 60m). Based on these values, three possibilities were suggested to select the dam sites.</p>
  • Abd-Ellah, Mahmoud Khaled, et al. (författare)
  • A Review on Brain Tumor Diagnosis from MRI Images : Practical Implications, Key Achievements, and Lessons Learned
  • 2019
  • Ingår i: Magnetic Resonance Imaging. - Elsevier. - 0730-725X .- 1873-5894. ; 61, s. 300-318
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>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.</p>
  • Abd-Ellah, Mahmoud Khaled, et al. (författare)
  • Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine
  • 2016
  • Ingår i: Building Sustainable Health Ecosystems : 6th International Conference on Well-Being in the Information Society, WIS 2016, Tampere, Finland, September 16-18, 2016, Proceedings. - Springer International Publishing. - 978-3-319-44671-4 - 978-3-319-44672-1 ; s. 151-160
  • Konferensbidrag (refereegranskat)abstract
    • <p>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.</p>
  • Abd-Ellah, Mahmoud Khaled, et al. (författare)
  • Design and implementation of a computer-aided diagnosis system for brain tumor classification
  • 2017
  • Ingår i: 2016 28th International Conference on Microelectronics (ICM). - 978-1-5090-5721-4 ; s. 73-76
  • Konferensbidrag (refereegranskat)abstract
    • <p>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.</p>
  • Abd-Ellah, Mahmoud Khaled, et al. (författare)
  • Parallel Deep CNN Structure for Glioma Detection and Classification via Brain MRI Images
  • 2019
  • Ingår i: IEEE-ICM 2019 CAIRO-EGYPT : The 31st International Conference on Microelectronics. - IEEE. ; s. 304-307
  • Konferensbidrag (övrigt vetenskapligt)abstract
    • <p>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.</p>
  • Abdel-Khalek, N.A., et al. (författare)
  • Effect of starch type on selectivity of cationic flotation of iron ore
  • 2012
  • Ingår i: Transactions of the Institution of Mining and Metallurgy Section C - Mineral Processing and Extractive Metallurgy. - 0371-9553 .- 1743-2855. ; 121:2, s. 98-102
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>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</p>
  • Abdelaziz, Ahmed, et al. (författare)
  • Survey on network virtualization using openflow : Taxonomy, opportunities, and open issues
  • 2016
  • Ingår i: KSII Transactions on Internet and Information Systems. - 1976-7277. ; 10:10, s. 4902-4932
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>The popularity of network virtualization has recently regained considerable momentum because of the emergence of OpenFlow technology. It is essentially decouples a data plane from a control plane and promotes hardware programmability. Subsequently, OpenFlow facilitates the implementation of network virtualization. This study aims to provide an overview of different approaches to create a virtual network using OpenFlow technology. The paper also presents the OpenFlow components to compare conventional network architecture with OpenFlow network architecture, particularly in terms of the virtualization. A thematic OpenFlow network virtualization taxonomy is devised to categorize network virtualization approaches. Several testbeds that support OpenFlow network virtualization are discussed with case studies to show the capabilities of OpenFlow virtualization. Moreover, the advantages of popular OpenFlow controllers that are designed to enhance network virtualization is compared and analyzed. Finally, we present key research challenges that mainly focus on security, scalability, reliability, isolation, and monitoring in the OpenFlow virtual environment. Numerous potential directions to tackle the problems related to OpenFlow network virtualization are likewise discussed</p>
  • Abdelnasser, Amr, et al. (författare)
  • REE geochemical characteristics and satellite-based mapping of hydrothermal alteration in Atud gold deposit, Egypt
  • 2018
  • Ingår i: Journal of African Earth Sciences. - Elsevier. - 0899-5362. ; 145, s. 317-330
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>New geochemical data of the hydrothermal alteration zones associated with gold-bearing quartz veins at Atud mine are used for better understanding the ore evolution and exploration vectoring. ASTER and Landsat 8 OLI data are used to elucidate the distribution of gold-associated alteration zones. Three alteration zones are defined; zone 1 (sericite-kaolinite-quartz-pyrite), zone 2 (quartz-sericite-albite-pyrite), and zone 3 (chlorite-carbonate-epidote ± pyrite). Sericite and hydrothermal quartz are confined to the mineralized quartz veins. Fe-OH and OH-bearing minerals are observed along NW- and NE-trending shear zones in the Main Atud mine. The association of gold-bearing quartz veins and sericite alteration is constrained by processing ASTER- and OLI-imagery data. The geochemical data of the ore-enveloping hydrothermally altered rocks are used to assess the behavior of the REEs during the mineralization process. Mild enrichment in LREE and significant enrichment in the HREE are associated with sericite in zones (1) and (2) alterations. Carbonate alteration (zone 3) is enriched in LREE and in immobile HREE. Moreover, LREE and Eu anomalies have negative correlated with the Alteration Index (A.I.) and K<sub>2</sub>O index (K.I.) in zones 1 and 2, suggesting high mobility of LREE in K-rich hydrothermal fluids. On the other hand, HREE anomalies with increasing MgO index (M.I.) in alteration zone 3 may imply low solubility of these elements in alkaline solutions. Au anomalies linked to sericite/silica alteration is a rather meaningful vector for further exploration in the area.</p>
  • Abdikalikova, Zamira, et al. (författare)
  • Boundedness and compactness of the embedding between spaces with multiweighted derivatives when 1
  • 2011
  • Ingår i: Czechoslovak Mathematical Journal. - 0011-4642 .- 1572-9141. ; 61:1, s. 7-26
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>We consider a new Sobolev type function space called the space with multiweighted derivatives W-p(n),(alpha) over bar, where (alpha) over bar = (alpha(0), alpha(1), ......, alpha(n)), alpha(i) is an element of R, i = 0, 1,......,n, and parallel to f parallel to W-p(n),((alpha) over bar) = parallel to D((alpha) over bar)(n)f parallel to(p) + Sigma(n-1) (i=0) vertical bar D((alpha) over bar)(i)f(1)vertical bar, D((alpha) over bar)(0)f(t) = t(alpha 0) f(t), d((alpha) over bar)(i)f(t) = t(alpha i) d/dt D-(alpha) over bar(i-1) f(t), i = 1, 2, ....., n. We establish necessary and sufficient conditions for the boundedness and compactness of the embedding W-p,(alpha) over bar(n) -> W-q,(beta) over bar,(m) when 1 <= q < p < infinity, 0 <= m < n</p>
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