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- 저자 : Bsoul, Nazzal (검색결과 5 건)
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Bsoul, Qusay,Abdul Salam, Rosalina,Atwan, Jaffar,Jawarneh, Malik Korea Institute of Science and Technology Informat 2021 Journal of Information Science Theory and Practice Vol.9 No.4
Text clustering is one of the most commonly used methods for detecting themes or types of documents. Text clustering is used in many fields, but its effectiveness is still not sufficient to be used for the understanding of Arabic text, especially with respect to terms extraction, unsupervised feature selection, and clustering algorithms. In most cases, terms extraction focuses on nouns. Clustering simplifies the understanding of an Arabic text like the text of the Quran; it is important not only for Muslims but for all people who want to know more about Islam. This paper discusses the complexity and limitations of Arabic text clustering in the Quran based on their themes. Unsupervised feature selection does not consider the relationships between the selected features. One weakness of clustering algorithms is that the selection of the optimal initial centroid still depends on chances and manual settings. Consequently, this paper reviews literature about the three major stages of Arabic clustering: terms extraction, unsupervised feature selection, and clustering. Six experiments were conducted to demonstrate previously un-discussed problems related to the metrics used for feature selection and clustering. Suggestions to improve clustering of the Quran based on themes are presented and discussed.
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Structural and magnetic properties of Ga-substituted Co2−W hexaferrites
Sami H. Mahmood,Qusai Al Sheyab,Ibrahim Bsoul,Osama Mohsen,Ahmad Awadallah 한국물리학회 2018 Current Applied Physics Vol.18 No.5
Precursor powders of BaCo2Fe16-xGaxO27 with 0.0 ≤ x ≤ 0.8 were prepared using high-energy ball milling, and the effects of chemical composition on the structural and magnetic properties of the powders sintered at 1300 °C were investigated using x-ray diffractometer (XRD), scanning electron microscopy (SEM), and vibrating sample magnetometry (VSM). XRD patterns of all samples indicated crystallization of pure BaCo2−W (BaCo2Fe16O27) hexaferrite phase. SEM measurements revealed large step-like formations with hexagonal crystallites. The magnetic data revealed small fluctuations of the saturation magnetization below the value 72.56 emu/g corresponding to the unsubstituted sample. The coercive field Hc of all samples ranged between 70 Oe and 130 Oe, indicating soft magnetic phase. Curie temperature determined from the thermomagnetic curves of the samples decreased from 485 °C at x = 0.0 down to 451 °C at x = 0.6. Also, the thermomagnetic curves revealed the presence of a minority magnetic phase with enhanced superexchange interaction, and the occurrence of complex magnetic phase transitions associated with spin reorientation transitions above room temperature.
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Ayed Ahmad Hamdan Al-Radaideh,Mohd Shafry bin Mohd Rahim,Wad Ghaban,Majdi Bsoul,Shahid Kamal,Naveed Abbas 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.7
Innovation and rapid increased functionality in user friendly smartphones has encouraged shutterbugs to have picturesque image macros while in work environment or during travel. Formal signboards are placed with marketing objectives and are enriched with text for attracting people. Extracting and recognition of the text from natural images is an emerging research issue and needs consideration. When compared to conventional optical character recognition (OCR), the complex background, implicit noise, lighting, and orientation of these scenic text photos make this problem more difficult. Arabic language text scene extraction and recognition adds a number of complications and difficulties. The method described in this paper uses a two-phase methodology to extract Arabic text and word boundaries awareness from scenic images with varying text orientations. The first stage uses a convolution auto-encoder, and the second uses Arabic Character Segmentation (ACS), which is followed by traditional two-layer neural networks for recognition. This study presents the way that how can an Arabic training and synthetic dataset be created for exemplify the superimposed text in different scene images. For this purpose a dataset of size 10K of cropped images has been created in the detection phase wherein Arabic text was found and 127k Arabic character dataset for the recognition phase. The phase-1 labels were generated from an Arabic corpus of quotes and sentences, which consists of 15kquotes and sentences. This study ensures that Arabic Word Awareness Region Detection (AWARD) approach with high flexibility in identifying complex Arabic text scene images, such as texts that are arbitrarily oriented, curved, or deformed, is used to detect these texts. Our research after experimentations shows that the system has a 91.8% word segmentation accuracy and a 94.2% character recognition accuracy. We believe in the future that the researchers will excel in the field of image processing while treating text images to improve or reduce noise by processing scene images in any language by enhancing the functionality of VGG-16 based model using Neural Networks.
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Bodoor, Khaldon,Haddad, Yazan,Alkhateeb, Asem,Al-Abbadi, Abdullah,Dowairi, Mohammad,Magableh, Ahmad,Bsoul, Nazzal,Ghabkari, Abdulhameed Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.1
Aberrant DNA methylation of tumor suppressor genes has been reported in all major types of leukemia with potential involvement in the inactivation of regulatory cell cycle and apoptosis genes. However, most of the previous reports did not show the extent of concurrent methylation of multiple genes in the four leukemia types. Here, we analyzed six key genes (p14, p15, p16, p53, DAPK and TMS1) for DNA methylation using methylation specific PCR to analyze peripheral blood of 78 leukemia patients (24 CML, 25 CLL, 12 AML, and 17 ALL) and 24 healthy volunteers. In CML, methylation was detected for p15 (11%), p16 (9%), p53 (23%) and DAPK (23%), in CLL, p14 (25%), p15 (19%), p16 (12%), p53 (17%) and DAPK (36%), in AML, p14 (8%), p15 (45%), p53 (9%) and DAPK (17%) and in ALL, p15 (14%), p16 (8%), and p53 (8%). This study highlighted an essential role of DAPK methylation in chronic leukemia in contrast to p15 methylation in the acute cases, whereas TMS1 hypermethylation was absent in all cases. Furthermore, hypermethylation of multiple genes per patient was observed, with obvious selectiveness in the 9p21 chromosomal region genes (p14, p15 and p16). Interestingly, methylation of p15 increased the risk of methylation in p53, and vice versa, by five folds (p=0.03) indicating possible synergistic epigenetic disruption of different phases of the cell cycle or between the cell cycle and apoptosis. The investigation of multiple relationships between methylated genes might shed light on tumor specific inactivation of the cell cycle and apoptotic pathways.
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