Publications by authors named "Usman Qamar"

Objective: To assess the results of double-face buccal mucosal graft urethroplasty (BMG) for treating anterior urethral stricture in adult males.

Study Design: An observational study. Place and Duration of the Study: Department of Urology, Sindh Institute of Urology and Transplantation, Karachi, Pakistan, from 2021 to 2022.

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Introduction: New Delhi Metallo--lactamase producing (NDM-1-KP) sequence type (ST) 147 poses a significant threat in clinical settings due to its evolution into two distinct directions: hypervirulence and carbapenem resistance. Hypervirulence results from a range of virulence factors, while carbapenem resistance stems from complex biological mechanisms. The NDM-1-KP ST147 clone has emerged as a recent addition to the family of successful clones within the species.

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Coronary Artery Diseases (CADs) are a dominant cause of worldwide fatalities. The development of accurate and timely diagnosis routines is imperative to reduce these risks and mortalities. Coronary angiography, an invasive and expensive technique, is currently used as a diagnostic tool for the detection of CAD but it has some procedural hazards, i.

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The natural history of benign enlargement of the prostate is variable and ranges from mild symptoms to chronic retention and renal failure. In this study, the outcomes of patients with urinary retention alone were compared with those of chronic retention and renal failure caused by an enlarged prostate. The first group had 79, while the second group had 20 patients included.

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Objective: To evaluate surgical outcomes and renal functions after cystectomy + MAINZ Pouch II and epispadias repair as a staged procedure in adult patients with exstrophy epispadias complex (EEC).

Study Design: Descriptive study.

Place And Duration Of Study: Department of Urology, Sindh Institute of Urology and Transplantation (SIUT), Karachi, from January 2004 to December 2020.

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A Recommender System (RS) is an intelligent system that assists users in finding the items of their interest (e.g. books, movies, music) by preventing them to go through huge piles of data available online.

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Accuracy plays a vital role in the medical field as it concerns with the life of an individual. Extensive research has been conducted on disease classification and prediction using machine learning techniques. However, there is no agreement on which classifier produces the best results.

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The information extraction from unstructured text segments is a complex task. Although manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually because of the exponential increase in data size. Thus, there is a need for automatic tools and techniques for information extraction in biomedical text mining.

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DNA bar-coding is a taxonomic method that uses small genetic markers in organisms' mitochondrial DNA (mt DNA) for identification of particular species. It uses sequence diversity in a 658-base pair fragment near the 5' end of the mitochondrial cytochrome c oxidase subunit 1 (CO1) gene as a tool for species identification. DNA barcoding is more accurate and reliable method as compared with the morphological identification.

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Conventional clinical decision support systems are based on individual classifiers or simple combination of these classifiers which tend to show moderate performance. This research paper presents a novel classifier ensemble framework based on enhanced bagging approach with multi-objective weighted voting scheme for prediction and analysis of heart disease. The proposed model overcomes the limitations of conventional performance by utilizing an ensemble of five heterogeneous classifiers: Naïve Bayes, linear regression, quadratic discriminant analysis, instance based learner and support vector machines.

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Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space.

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