End-Shape Analysis for Automatic Segmentation of Arabic Handwritten Texts

Word segmentation is an important task for many methods that are related to document understanding especially word spotting and word recognition. Several approaches of word segmentation have been proposed for Latin-based languages while a few of them have been introduced for Arabic texts. The fact that Arabic writing is cursive by nature and unconstrained with no clear boundaries between the words makes the processing of Arabic handwritten text a more challenging problem. In this thesis, the design and implementation of an End-Shape Letter (ESL) based segmentation system for Arabic handwritten text is presented. This incorporates four novel aspects: (i) removal of secondary components, (ii) baseline estimation, (iii) ESL recognition, and (iv) the creation of a new off-line CENPARMI ESL database. Arabic texts include small connected components, also called secondary components. Removing these components can improve the performance of several systems such as baseline estimation. Thus, a robust method to remove secondary components that takes into consideration the challenges in the Arabic handwriting is introduced. The methods reconstruct the image based on some criteria. The results of this method were subsequently compared with those of two other methods that used the same database. The results show that the proposed method is effective. Baseline estimation is a challenging task for Arabic texts since it includes ligature, overlapping, and secondary components. Therefore, we propose a learning-based approach that addresses these challenges. Our method analyzes the image and extracts baseline dependent features. Then, the baseline is estimated using a classifier. Algorithms dealing with text segmentation usually analyze the gaps between connected components. These algorithms are based on metric calculation, finding threshold, and/or gap classification. We use two well-known metrics: bounding box and convex hull to test metric-based method on Arabic handwritten texts, and to include this technique in our approach. To determine the threshold, an unsupervised learning approach, known as the Gaussian Mixture Model, is used. Our ESL-based segmentation approach extracts the final letter of a word using rule-based technique and recognizes these letters using the implemented ESL classifier. To demonstrate the benefit of text segmentation, a holistic word spotting system is implemented. For this system, a word recognition system is implemented. A series of experiments with different sets of features are conducted. The system shows promising results

A UML framework for OLAP conceptual modeling

Data warehouses are used by organizations around the world to store huge volumes of historical data. Ultimately, the purpose of the warehouse is to allow decision makers to assess both the history and, more importantly, the future of the organization. In practice, the capacity to make meaningful decisions is further supported through the use of Online Analytical Processing (OLAP) applications that provide more sophisticated representations of the warehouse data. In order to do this, OLAP systems rely on a multidimensional conceptual data model that represents the core elements of the data warehouse, as well as the relationships between them. Currently, there is no definitive conceptual model for this kind of environment. It is therefore quite difficult for data warehouse designers to express the kinds of complex analytical requirements which arise in real-world situations. In this thesis, we propose a robust and flexible conceptual model that can be used to represent multi-dimensional OLAP domains. Specifically, we present a profile extension of the Unified Modeling Language (UML) that consists of a set of stereotypes, constraints and tagged values that elegantly represent multi-dimensional properties at the conceptual level. We also make use of the Object Constraint Language (OCL) to ensure the correctness and completeness of the specification, thereby avoiding an arbitrary use of the basic components. Furthermore, we demonstrate how the new OLAP profile is utilized in MagicDraw, one of the leading UML development tools. The end result is an OLAP Modeling Environment (OME) that should significantly reduce development time, as well as improving the quality of the analytical interface for the end user

Digital Information Needs for Understanding Cell Divisions in the Human Body

Information needs for understanding cell divisions in the human body is important in the learning process. Although sketches, images and blocks of 3D puzzles were used for teaching and learning, unfortunately those tools are static and incapable of being manipulated. Hence, digital information is the best tool for the teaching and learning of cell divisions in the human body via software applications. A cell motion is a digital information application developed using leap motion to demonstrate cell movement in the human body. However, the factors that influence students towards adopting this application are not obvious and often ignored. The method for evaluating the factors influencing its user's acceptance is the Technology Acceptance Model (TAM) via a questionnaire distributed among medical students to gain statistically valid quantitative results through hypothesis-testing. The result indicates that digital information needs for the understanding of cell divisions in the human body are influenced by the user's Perceived Ease of Use (PEOU) and Perceived Usefulness (PU). However, the Attitude (AT) towards use did have a significant effect on PU and PEOU. Moreover, PEOU had a strong and significant influence on PU, while AT positively influenced users' behavioural intention (BI) of using digital information needs for the understanding of cell divisions in the human body

Prevalence of meropenem susceptibility among Gram-negative pathogens isolated from intensive care units in Jordan

Meropenem is a relatively new carbapenem in some Middle Eastcountries; our aim is to evaluate its susceptibility in gram-negative pathogens isolatedfrom ICU patients and to identify the prevalence of ICU bacterial isolates identifiedas pathogens based on CDC-NHSN definition for pathogens in the affected organs

Evaluation of the carotid artery using wavelet-based analysis of the pulse wave signal

The use of pulse wave analysis may assist cardiologists in diagnosing patients with vascular diseases. However, it is not common in clinical practice to interpret and analyze pulse wave data and utilize them to detect the abnormalities of the signal. This paper presents a novel approach to the clinical application of pulse waveform analysis using the wavelet technique by decomposing the normal and pathology signal into many levels. The discrete wavelet transform (DWT) decomposes the carotid arterial pulse wave (CAPW) signal, and the continuous wavelet transform (CWT) creates images of the decomposed signal. The wavelet analysis technique in this work aims to strengthen the medical benefits of the pulse wave. The obtained results show a clear difference between the signal and the images of the arterial pathologies in comparison with normal ones. The certain distinct that were achieved are promising but further improvement may be required in the future

Applying Machine Learning of Erythrocytes Dynamic Antigens Store in Medicine

Erythrocytes Dynamic Antigens Store (EDAS) is a new discovery. EDAS consists of self-antigens and foreign (non-self) antigens. In patients with infectious diseases or malignancies, antigens of infection microorganism or malignant tumor exist in EDAS. Storing EDAS of normal individuals and patients in a database has, at least, two benefits. First, EDAS can be mined to determine biomarkers representing diseases which can enable researchers to develop a new line of laboratory diagnostic tests and vaccines. Second, EDAS can be queried, directly, to reach a precise diagnosis without the need to do many laboratory tests. The target is to find the minimum set of proteins that can be used as biomarkers for a particular disease. A hypothetical EDAS is created. Hundred-thousand records are randomly generated. The mathematical model of hypothetical EDAS together with the proposed techniques for biomarker discovery and direct diagnosis are described. The different possibilities that may occur in reality are experimented. Biomarkers' proteins are identified for pathogens and malignancies, which can be used to diagnose conditions that are difficult to diagnose. The presented tool can be used in clinical laboratories to diagnose disease disorders

Prevalence of Clostridium diffiile infections among hospitalized patients in Amman, Jordan: A Multi-Center Study

Clostridium diffiile is a Gram-positive endospore-forming bacillus that was discovered as a bowel colonizer and pathogen in 1935. Healthy newborns and infants were found to be colonized with C. diffiil eat rates ranging from 45 to 70%. However, the prevalence of toxigenic strains was found to be as low as 13%. The colonization rates decline as individuals grow older, reaching rates lower than 5% in urban-dwelling healthy adults. However, colonization rates as high as 25-55% has been detected among adult hospitalized patientsand nursing home resident

Physicians Compliance with Antimicrobials’ De-escalation in Intensive Care Units in Jordan

Background To evaluate physicians’ behavior toward de-escalation of broad-spectrum antimicrobials to narrow-spectrum agents for patients when opportunities loom. Methods A prospective study held in three hospitals. Data were obtained prospectively for ICU patients with the diagnosis of severe infections i.e. severe systemic sepsis, multi-organ dysfunction syndrome and septic shock, and were started on BSA. Failure to de-escalate was considered if culture was available and was susceptible to a narrower antimicrobial agent; hitherto the treating physician did not de-escalate. Excluded from the study patients who were not started on BSA, were on antimicrobial prophylaxis or there was no clear indication for starting BSA, also patients whom their microbiological diagnoses were not available or the pathogen was only susceptible to the initially started BSA.   Results One hundred and nineteen patients’ charts were reviewed. There was 69 (58%) male and 50 (42%) female with mean ages of 59.3 and 68.6 years respectively.  Eight (6.7%) patients were de-escalated to narrower spectrum antimicrobials. None of: APACHE 2 score, comorbidities, patients’ outcome while on BSA, sepsis-predisposing clinical diagnosis and microbiological diagnosis significantly encourage physicians for de-escalation. The commonest initial antimicrobials used were Meropenem, Pipracillin/Tazobactam and Imipenem.   Conclusion The majority of physicians are not de-escalating when it ought to be done. The concept of de-escalation has to be stressed upon widely among treating physicians