Background: Anterior uveitis is the inflammation of the anterior uveal tissue that is iris and pars plicata of the ciliary body. Due to varied etiology, detailed history and systematic investigations are needed for accurate diagnosis and management. Aims and Objectives: The aims of this study were to describe the clinical features, causes, and management of patients with acute anterior uveitis. Materials and Methods: A prospective study of clinical features, complication, and prognosis was done on anterior uveitis patients for a period of 10 months. Detailed history, complete ocular examination, systemic evaluation, and relevant investigations were done in 64 patients; appropriate systemic and ocular management were done. Results: Of 64 patients, 65.6% were male and 34.3% female. Mean age of presentation was 42.6 years. About 71.8% unilateral and 28.1% bilateral eye involvement were seen. Visual acuity was between 6/18 and 3/60 at the time of presentation. About 21.8% had tuberculosis, 9.3% had trauma, 4.6% had UTI, 3.1% had ankylosing spondylitis (HLA B27 associated) and 3.1% had rheumatoid arthritis, 3.1% had sarcoidosis, and 1.5% had toxoplasmosis. About 53.1% were idiopathic. Complications were noted in 50% of cases, 25% had secondary glaucoma, 21.8% had posterior subcapsular cataract, and 3.1% has cystoid macular edema. Appropriate medical management was started in patients. Conclusion: Idiopathic uveitis was higher in our study. Trauma was the most common non-infectious entity. Tuberculosis was most common infectious cause in our study, all patients responded well with medication.

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Major income of banks and any financial organization is generated by loans. Banks can issue loans only to specific authentic people or organizations due to restricted resources or credits. Those who actually can able to repay the taken loan amount along with interest are safe people to whom loan can be sanctioned, but finding eligible (safe) people is a monotonous process. The problem is addressed by various researchers in the literature, however, accuracy level of their models proposed is utmost of 80%. Hence in our work, we proposed a model in which various machine learning algorithms are aggregated with ensemble algorithms like bagging and voting classifiers. The pre-eminent objective of our work is to predict whether a particular person is eligible for the loan or not. Our proposed model reduces human efforts and processing time as well and produces more accurate results than existing models. Experimental results show that our model improves the performance of the existing model from 80% to 94%.