Utilizing Big Data in Identification and Correction of OCR Errors

In this thesis, we report on our experiments for detection and correction of OCR errors with web data. More specifically, we utilize Google search to access the big data resources available to identify possible candidates for correction. We then use a combination of the Longest Common Subsequences (LCS) and Bayesian estimates to automatically pick the proper candidate. Our experimental results on a small set of historical newspaper data show a recall and precision of 51% and 100%, respectively. The work in this thesis further provides a detailed classification and analysis of all errors. In particular, we point out the shortcomings of our approach in its ability to suggest proper candidates to correct the remaining errors

Myocardial Infarction: A Comprehensive Review

Myocardial infarction (MI), commonly known as a heart attack, is a critical medical condition resulting from the blockage of one or more coronary arteries. MI has been classified differently over time, with the most recent classification proposed by the European Society of Cardiology and the American College of Cardiology. This new classification considers various types of MI based on clinical presentations and underlying mechanisms. MI is a significant public health issue globally, with a high prevalence and a substantial impact on healthcare resources and the economy. The pathophysiology of MI is multifactorial, with factors such as atherosclerosis, thrombosis, and inflammation playing crucial roles. Complications of MI can include heart failure, cardiogenic shock, and arrhythmias. Diagnosis of MI involves clinical evaluation, imaging studies, and biomarker testing. Treatment of MI includes reperfusion therapy, medical management, and cardiac rehabilitation. Reperfusion therapy, including thrombolytic therapy and primary percutaneous coronary intervention, is the cornerstone of treatment for ST-segment elevation MI. Medical management involves antiplatelet and anticoagulation therapy, as well as beta-blockers, while cardiac rehabilitation can help improve cardiovascular function and reduce the risk of further cardiac events. Prompt diagnosis and appropriate treatment are essential for improving outcomes and reducing morbidity and mortality associated with MI

Law enforcement spillover effects in the financial sector.

Recipient firms but also comparable peer firms exhibit asizeable negative capital market reaction to UnitedKingdom's regulatory enforcement actions. This result isinvariant to the identification of peer firms as belongingto the same industry classification or as having com-parable propensity scores to attract a sanction. Indis-criminate regulatory contagion, however, is ruled out.As per expectation, enforcement actions which piercethe‘corporate veil’, that is, target an individual within afirm, are related to no significant firm‐level market re-actions. These findings, in the financial sector, indicatethat sanctions are associated with a material spillovereffect consistent with informed regulatory contagion

An Unusual Association of Cemento-ossifying Fibroma with an Odontoma in Mandible: A case report

Cemento-ossifying fibroma is a fibro-osseous lesion of jaws, commonly present as a progressively growing lesion, if left untreated can attain an enormous size with resultant deformity. They commonly affect adult females between third and fourth decade of life, predominantly occurring in premolar/molar region of mandible. Odontomas are benign tumors of odontogenic origin characterized by their slow growth. They consist of enamel, dentine, cementum and pulpal tissue and constitute 22% of all odontogenic tumors. We have discussed a case of cemento-ossifying fibroma (COF) involving right mandibular region together with an odontoma present in left mandibular posterior region in a 35year old female patient with its clinical, radiographical, histological and surgical findings

Knowledge and attitude among Indian medical students towards thalassemia: a study in Delhi NCR

Background: Thalassemia can easily be prevented by awareness, education, screening, premarital genetic counselling and prenatal diagnosis. There are only a handful of articles on knowledge and awareness about thalassemia among general population or parents of thalassaemic children. Aims and objectives was to evaluate the level of awareness, knowledge and attitudes of medical students towards thalassemia as well as to analyse the differences if any between the first year and second year MBBS students and their correlation with various socio-demographic parameters.Methods: This was an institutional based cross sectional observational descriptive study regarding knowledge and attitude of first and second year MBBS students about thalassemia using a pre-designed, structured, self-administered questionnaire. Data was analyzed using SPSS software version 17. Values of p<0.05 were considered significant.Results: Mean knowledge scores of second year MBBS students compared to first year MBBS were 11.73±1.78 versus 10.8±1.92, the difference being statistically significant, however, the difference between mean attitude scores was not found to be significant. There was no effect of age, gender, region or Kuppuswamy’s socio-economic class on the knowledge or attitude of MBBS students towards thalassemia.Conclusions: Majority of the MBBS students had good knowledge and positive attitude towards thalassemia. To confirm the observations, large scale studies need to be conducted comprising of different study populations. Screening for thalassemia should be made mandatory, as part of medical examination, at entry to a medical college so that the medical college students are sensitized and can spread awareness among general population

Countering racial discrimination in algorithmic lending: A case for model-agnostic interpretation methods

In respect to racial discrimination in lending, we introduce global Shapley value and Shapley–Lorenz explainable AI methods to attain algorithmic justice. Using 157,269 loan applications during 2017 in New York, we confirm that these methods, consistent with the parameters of a logistic regression model, reveal prima facie evidence of racial discrimination. We show, critically, that these explainable AI methods can enable a financial institution to select an opaque creditworthiness model which blends out-of-sample performance with ethical considerations