Dysgraphia, which is known as a writing disorder, is a specific disorder of writing regarding the reproduction of alphabetical and numerical signs. Dysgraphia may be related to dyspraxia, which is secondary to incomplete lateralization and characterized by a difficulty to reproduce alphabetical and numerical signs. Since the causes of dysgraphia are unknown, the rapid detection of symptoms is very important. In academic and clinical uses, the most common tool for detecting dysgraphia is an evaluation of the quality of writing on paper sheets. A writing analysis is based on rules for scoring the writing quality. In this paper, we discuss TestGraphia, which is a software system that can support doctors in making diagnoses and monitoring patients with dysgraphia in an objective manner. The system is based on known document analysis algorithms and modified or specially designed algorithms. Based on this software, a forms analysis requires considerably less time than that needed by traditional methods, enabling large screening activities and reducing time and cost. Potential dynamic changes in dysgraphia screening can be assessed by monitoring the quality of writing in a non-invasive way with reduced costs, both in the laboratory and the patient's home, and the appropriate frequency. In the system that we will describe, the mean time to execute a diagnosis is nearly ten times faster with trustworthy results. INDEX TERMS Dysgraphia, document analysis system, BHK test, handwriting analysis.
Rhinology studies the anatomy, physiology, and diseases affecting the nasal region—one of the most modern techniques to diagnose these diseases is nasal cytology, which involves microscopic analysis of the cells contained in the nasal mucosa. The standard clinical protocol regulates the compilation of the rhino-cytogram by observing, for each slide, at least 50 fields under an optical microscope to evaluate the cell population and search for cells important for diagnosis. The time and effort required for the specialist to analyze a slide are significant. In this paper, we present a smartphones-based system to support cell segmentation on images acquired directly from the microscope. Then, the specialist can analyze the cells and the other elements extracted directly or, alternatively, he can send them to Rhino-cyt, a server system recently presented in the literature, that also performs the automatic cell classification, giving back the final rhinocytogram. This way he significantly reduces the time for diagnosing. The system crops cells with sensitivity = 0.96, which is satisfactory because it shows that cells are not overlooked as false negatives are few, and therefore largely sufficient to support the specialist effectively. The use of traditional image processing techniques to preprocess the images also makes the process sustainable from the computational point of view for medium–low end architectures and is battery-efficient on a mobile phone.
A significant part of the current research in the field of Artificial Intelligence is devoted to knowledge bases. New techniques and methodologies are emerging every day for the storage, maintenance and reasoning over knowledge bases. Recently, the most common way of representing knowledge bases is by means of graph structures. More specifically, according to the Semantic Web perspective, many knowledge sources are in the form of a graph adopting the Resource Description Framework model. At the same time, graphs have also started to gain momentum as a model for databases. Graph DBMSs, such as Neo4j, adopt the Labeled Property Graph model. Many works tried to merge these two perspectives. In this paper, we will overview different proposals aimed at combining these two aspects, especially focusing on possibility for them to add reasoning capabilities. In doing this, we will show current trends, issues and possible solutions. In this context, we will describe our proposal and its novelties with respect to the current state of the art, highlighting its current status, potential, the methodology, and our prospect.
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