RESUMO
OBJECTIVE: To analyse the frequency of different types of amblyopia and its association with gender. METHODS: The cross-sectional study was conducted from March 2014 to October 2014 at the Department of Ophthalmology of a tertiary care hospital inKarachi. The patients were classified into different types of amblyopia like anisometropic, strabismic, sensory deprivation and meridional amblyopia. Stratification was done according to age, gender and type of amblyopia by using SPSS version 21. RESULTS: Out of 130 patients, 71 were male and 59 were female. The amblyopia rate determined was anisometropic 69.23%, strabismic amblyopia 21.51% and others 9.23%. The mean age of subjects was 18.02±9 years. Type of amblyopia was independent of gender, and anisometropic amblyopia was the most common type of amblyopia found in both genders. CONCLUSIONS: This study found no significant association between gender and types of amblyopia and anisometropic amblyopia was the most common type of amblyopia in the data collected.
Assuntos
Ambliopia/classificação , Adolescente , Adulto , Ambliopia/epidemiologia , Criança , Estudos Transversais , Feminino , Humanos , Masculino , Paquistão/epidemiologia , Fatores Sexuais , Centros de Atenção Terciária , Adulto JovemRESUMO
Advances in machine and language translation immerge new fields and research opportunities for researchers, whereas Natural Language Processing and Computational Linguistics deal with communication between natural languages and their interaction. The objective of this research is to develop and test a novel tactic to solve the issue of translation from Roman Urdu to the English language. The approach used to construct this practical model is divided into three stages; each stage works out to achieve its desired task. Self-maintained corpus alongwith its corresponding tag-set is used for tokenization. The syntactical structure is covered by writing Urdu POS tagger based on grammatical rules. We prepared the grammatical structures of different sentences for Roman Urdu to English translation. Since Roman script can be expressed in numerous ways, our grammatical structures fulfill the maximum possible needs of writing and produce the best possible English translation. We entered a sentence in Roman Urdu which gave the best possible translation in the English language. In comparison with Google Translator, Transtech worked better and gives more accurate results.