Noopur Zambare

Noopur Zambare

This study presents AIOptimizer, a prototype for a cost-reduction-based software performance optimisation tool. The study focuses on the design elements of AIOptimizer, including user-friendliness, scalability, accuracy, and adaptability. To deliver efficient and user-focused performance optimisation solutions, it promotes the use of robust integration, continuous learning, modular design, and data collection methods. The paper also looks into AIOptimizer features including collaboration, efficiency prediction, cost optimisation suggestions, and fault diagnosis. Additionally, it introduces AIOptimizer, a recommendation engine for cost optimisation based on reinforcement learning, and examines several software development life cycle models. The goal of this research study is to showcase AIOptimizer as a prototype that continuously improves software performance and reduces costs by utilising sophisticated optimisation techniques and intelligent recommendation systems. Numerous software development life cycle models, including the Big Bang, V-, Waterfall, Iterative, and Agile models are the subject of the study. Every model has benefits and drawbacks, and the features and requirements of the project will decide how useful each is.

Noopur Zambare, Ravindranath Sawane

In this study, we explore how to improve the functionality of multiclass classification algorithms. We used a benchmark dataset from Kaggle to create a framework. They have been used in a number of fields, including image recognition, natural language processing, and bioinformatics. This study is focused on the prediction of financial distress in companies in addition to the wider application in multiclass classification. Identifying businesses that are likely to experience financial distress is a crucial task in the fields of finance and risk management. Whenever a business experiences serious challenges keeping its operations going and meeting its financial responsibilities, it is said to be in financial distress. It commonly happens when a company has a sharp and sustained recession in profitability, cash flow issues, or an unsustainable level of debt.

Kiana Aghakasiri, Noopur Zambare, JoAnn Thai et al.

De-identification in the healthcare setting is an application of NLP where automated algorithms are used to remove personally identifying information of patients (and, sometimes, providers). With the recent rise of generative large language models (LLMs), there has been a corresponding rise in the number of papers that apply LLMs to de-identification. Although these approaches often report near-perfect results, significant challenges concerning reproducibility and utility of the research papers persist. This paper identifies three key limitations in the current literature: inconsistent reporting metrics hindering direct comparisons, the inadequacy of traditional classification metrics in capturing errors which LLMs may be more prone to (i.e., altering clinically relevant information), and lack of manual validation of automated metrics which aim to quantify these errors. To address these issues, we first present a survey of LLM-based de-identification research, highlighting the heterogeneity in reporting standards. Second, we evaluated a diverse set of models to quantify the extent of inappropriate removal of clinical information. Next, we conduct a manual validation of an existing evaluation metric to measure the removal of clinical information, employing clinical experts to assess their efficacy. We highlight poor performance and describe the inherent limitations of such metrics in identifying clinically significant changes. Lastly, we propose a novel methodology for the detection of clinically relevant information removal.