Handwritten Arabic Character Recognition for Children Writ-ing Using Convolutional Neural Network and Stroke Identification

Automatic Arabic handwritten recognition is one of the recently studied problems in the field of Machine Learning. Unlike Latin languages, Arabic is a Semitic language that forms a harder challenge, especially with variability of patterns caused by factors such as writer age. Most of the studies focused on adults, with only one recent study on children. Moreover, much of the recent Machine Learning methods focused on using Convolutional Neural Networks, a powerful class of neural networks that can extract complex features from images. In this paper we propose a convolutional neural network (CNN) model that recognizes children handwriting with an accuracy of 91% on the Hijja dataset, a recent dataset built by collecting images of the Arabic characters written by children, and 97% on Arabic Handwritten Character Dataset. The results showed a good improvement over the proposed model from the Hijja dataset authors, yet it reveals a bigger challenge to solve for children Arabic handwritten character recognition. Moreover, we proposed a new approach using multi models instead of single model based on the number of strokes in a character, and merged Hijja with AHCD which reached an averaged prediction accuracy of 96%.Comment: 1

The problems of novice mathematics teachers in Kingdom of Saudi Arabia from their perspective and their educational supervisors

This study aimed to determine the problems that face the novice mathematics teacher from their perspective and their educational supervisors. The study followed a survey descriptive methodology and its sample consisted of (310) of novice teacher and (115) educational supervisors. The Study used the questionnaire. The results showed that the most prominent curriculums-related problems of novice teachers are: the weakness of the novice teacher on each of the use of modern teaching strategies, dealing with exploration and expansion lesson and the formulation of objectives that measure the levels and dealing with the issues of higher-order thinking skills. While the physical environment-related problems are: lack of mathematics lab, lack of availability of school references of teaching mathematics , a large number of students in one class, lack of teaching aids. Also lack of maintenance for equipment , and lack of facilities used to teach mathematics in LRC and school. Meanwhile; the most prominent moral environment-related problems of teachers was the teaching overload for the novice teacher. While the most prominent students and their parents-related problems of teachers are: lack of follow-up children's learning by parents and lack of knowledge of the teacher about the social background of students. The most prominent problems related with their personality are: the poor knowledge of teachers about rules and regulations, the lower job degree which the teachers deserve better systematically, the long distance between the teacher's home and work and feeling bored of monotony and routine of teacher's job

SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word Embeddings

Automatic synonym extraction plays an important role in many natural language processing systems, such as those involving information retrieval and question answering. Recently, research has focused on extracting semantic relations from word embeddings since they capture relatedness and similarity between words. However, using word embeddings alone poses problems for synonym extraction because it cannot determine whether the relation between words is synonymy or some other semantic relation. In this paper, we present a novel solution for this problem by proposing the SynoExtractor pipeline, which can be used to filter similar word embeddings to retain synonyms based on specified linguistic rules. Our experiments were conducted using KSUCCA and Gigaword embeddings and trained with CBOW and SG models. We evaluated automatically extracted synonyms by comparing them with Alma’any Arabic synonym thesauri. We also arranged for a manual evaluation by two Arabic linguists. The results of experiments we conducted show that using the SynoExtractor pipeline enhances the precision of synonym extraction compared to using the cosine similarity measure alone. SynoExtractor obtained a 0.605 mean average precision (MAP) for the King Saud University Corpus of Classical Arabic with 21% improvement over the baseline and a 0.748 MAP for the Gigaword corpus with 25% improvement. SynoExtractor outperformed the Sketch Engine thesaurus for synonym extraction by 32% in terms of MAP. Our work shows promising results for synonym extraction suggesting that our method can also be used with other languages

Establishment of an Accelerated Doctor of Family Medicine Program at Unaizah College of Medicine, Qassim University, Kingdom of Saudi Arabia

Primary health care is well known to be the cornerstone for the health of the society. Furthermore, efficient health care at the secondary and tertiary levels is entirely dependent on effective primary health care. The Kingdom of Saudi Arabia (KSA) is currently building up a rigorous primary health care system with a large number of well-equipped primary health care centers. However, there is an acute shortage of Saudi family physicians throughout the country; both in urban and rural areas. There is no evidence in the literature supporting the relatively long 7 years’ traditional duration of medical programs in the KSA. Rather, several US and Canadian medical schools have established accelerated programs in Internal Medicine and Family Medicine with graduates comparable with those of the traditional curricula in terms of standardized tests, initial resident characteristics, and performance outcomes. In response to the challenges the KSA is facing in primary health care, Unaizah College of Medicine at Qassim University is proposing to establish an accelerated Doctor of Family Medicine Program that would run for total duration of 6 years. Herein, we describe a concise outline of this program

Cold agglutinin disease in fibrolamellar hepatocellular carcinoma: a rare association with a rare cancer variant

Cold agglutinin disease (CAD) is a rare autoimmune hemolytic anemia. Although it can occur secondary to lymphoproliferative disorders and autoimmune or infectious diseases, CAD is rarely reported as secondary to solid tumors. We report a case of a woman aged 18 years diagnosed with a well-differentiated hepatocellular carcinoma of the fibrolamellar subtype, who was shown to have CAD also. Her general condition, including CAD, improved after targeted therapy with sorafenib for the hepatocellular carcinoma and only conservative measures for the CAD that consisted of avoidance of cold. In summary, although it is an extremely rare association and less common than lymphoproliferative disorders, CAD can be associated with solid tumors

A comparative analysis of in vitro expansion of natural killer cells of a patient with autoimmune haemolytic anaemia and ovarian cancer with patients with other solid tumours

The functional profile of natural killer (NK) cells has been reported to be lower in auto-immune haemolytic anaemia (AIHA). In this study, we report a comparative analysis of peripheral blood mononuclear cells (PBMNCs) and the in vitro expansion of NK cells in a patient with AIHA and cancer, with that of other cancer patients without AIHA. PBMNCs and in vitro NK-cell expansion of a 64-year old female patient with ovarian cancer and AIHA was compared with that of four other patients with cancer without AIHA who underwent autologous immune enhancement therapy (AIET). The NK cells were cultured using autologous plasma without feeder layers. The quantities of PBMNCs, NK cells and CD3−CD56+ cells were compared. The average quantity of PBMNCs per ml in Cases I to V were 10.71, 39.2, 49.26, 65.16 and 49.33×104, respectively, and the average maximum count of NK cells was 3.9, 1730.03, 1824.16, 1058.61 and 761×106, respectively. The average percentage of CD3−CD56+ cells in Cases I to V following in vitro expansion was 1.2, 65.7, 28.63, 65.9 and 40%, respectively. In the present study, probably the first in the literature, the in vitro expansion of NK cells was found to be significantly lower in the AIHA patient. Previously, only a lower NK-cell functional profile was reported. Further studies are required to establish the association between AIHA and NK-cell profile and in vitro expansion, and to find common antibodies between red blood cells (RBCs) and NK cells

Body-monitoring and health supervision by means of optical fiber-based sensing systems in medical textiles

Long-term monitoring with optical fibers has moved into the focus of attention due to the applicability for medical measurements. Within this Review, setups of flexible, unobtrusive body-monitoring systems based on optical fibers and the respective measured vital parameters are in focus. Optical principles are discussed as well as the interaction of light with tissue. Optical fiber-based sensors that are already used in first trials are primarily selected for the section on possible applications. These medical textiles include the supervision of respiration, cardiac output, blood pressure, blood flow and its saturation with hemoglobin as well as oxygen, pressure, shear stress, mobility, gait, temperature, and electrolyte balance. The implementation of these sensor concepts prompts the development of wearable smart textiles. Thus, current sensing techniques and possibilities within photonic textiles are reviewed leading to multiparameter designs. Evaluation of these designs should show the great potential of optical fibers for the introduction into textiles especially due to the benefit of immunity to electromagnetic radiation. Still, further improvement of the signal-to-noise ratio is often necessary to develop a commercial monitoring system