Large language models (LLMs) with retrieval augmented-generation (RAG) have been the optimal choice for scalable generative AI solutions in the recent past. Although RAG implemented with AI agents (agentic-RAG) has been recently popularized, its suffers from unstable cost and unreliable performances for Enterprise-level data-practices. Most existing use-cases that incorporate RAG with LLMs have been either generic or extremely domain specific, thereby questioning the scalability and generalizability of RAG-LLM approaches. In this work, we propose a unique LLM-based system where multiple LLMs can be invoked to enable data authentication, user-query routing, data-retrieval and custom prompting for question-answering capabilities from Enterprise-level data tables on sustainability. The source tables here are highly fluctuating and large in size storing carbon footprint, energy and water usage at buildings in regional levels globally and the proposed framework enables structured responses in under 10 seconds per query. Additionally, we propose a five metric scoring module that detects and reports hallucinations in the LLM responses. Our proposed system and scoring metrics achieve >90% confidence scores across hundreds of user queries in the sustainability, financial health and social media domains. Extensions to the proposed extreme RAG architectures can enable heterogeneous source querying using LLMs.

Generative AI has significantly reduced the entry barrier to the domain of AI owing to the ease of use and core capabilities of automation, translation, and intelligent actions in our day to day lives. Currently, Large language models (LLMs) that power such chatbots are being utilized primarily for their automation capabilities on a limited scope. One major limitation of the currently evolving family of LLMs is hallucinations, wherein inaccurate responses are reported as factual. Hallucinations are primarily caused by biased training data, ambiguous prompts and inaccurate LLM parameters, and they majorly occur while combining mathematical facts with language-based context. In this work we present the three major stages in the journey of designing hallucination-minimized LLM-based solutions that are specialized for the decision makers of the financial domain, namely: prototyping, scaling and LLM evolution using human feedback. These three stages and the novel data to answer generation modules presented in this work are necessary to ensure that the Generative AI products are reliable and high-quality to aid key decision-making processes.

Large Language Models (LLMs) have been applied to build several automation and personalized question-answering prototypes so far. However, scaling such prototypes to robust products with minimized hallucinations or fake responses still remains an open challenge, especially in niche data-table heavy domains such as financial decision making. In this work, we present a novel Langchain-based framework that transforms data tables into hierarchical textual "data chunks" to enable a wide variety of actionable question answering. First, the user-queries are classified by intention followed by automated retrieval of the most relevant data chunks to generate customized LLM prompts per query. Next, the custom prompts and their responses undergo multi-metric scoring to assess for hallucinations and response confidence. The proposed system is optimized with user-query intention classification, advanced prompting, data scaling capabilities and it achieves over $ 90\%$ confidence scores for a variety of user-queries responses ranging from {What, Where, Why, How, predict, trend, anomalies, exceptions} that are crucial for financial decision making applications. The proposed data to answers framework can be extended to other analytical domains such as sales and payroll to ensure optimal hallucination control guardrails.

Big data mining and unsupervised pattern recognition from large corpus of text-based documents has been an active research topic over the past decade. This paper presents a novel sequence of network-based models for identifying high-dimensional clustering patterns between topics for quantitative and predictive modeling of trends in Management Science (an INFORMS Journal) papers over the past 54 years. The proposed methods extrapolate a new spatial dimension from publication records to identify and assess topic inter-dependence and clustering trends over time. First, the optimal number of topics for trend analysis is identified based on spatial clustering patterns using Self-Organizing-Maps (SOM). Next, topic models are used to construct weighted and unweighted complex networks. Based on spatio-temporal clustering trends in the complex networks, the influence, importance and uniqueness of topics are quantified. Finally, the dynamic trends in topic influence are modeled for predictive purposes using Hidden Markov Models (HMM). The proposed methods provide insights into topic type co-existence patterns, topic type rankings, identify ~40% topics as unique and predict topic importance with average accuracy per topic in the range of 79-84%. Thus, the proposed methods provide the apparatus to translate time-series text-intensive data sets to spatio-temporal models that can provide additional insights on data interdependencies and inter-data influences.

With the increasing applications of Big Data analytics in medical image processing systems, there has been a growing need for quantitative medical image quality assessment techniques. Specifically for computed tomography (CT) images, quantitative image assessment can allow for benchmarking image processing methods and optimization of image acquisition parameters. In this work, large volumes of CT images from phantoms and patients are analyzed using 3 data models that vary in their implementation time complexities. The goal here is to identify the optimal method that scales across data set variabilities for predictive modeling of CT image quality (CTIQ). The first two models rely on spatial segmentation of regions-of-interest (ROIs) and estimate CTIQs in terms of segmented pixel variabilities. The third, convolutional neural network (CNN) model relies on error back-propagation from the training set of images to learn the regions indicative of CTIQ. We observe that for 70/30 data split, the average multi-class classification accuracies for CTIQ prediction using the 3 data models range from 73.6-100% and 50-100% for the phantom and patient CT images, respectively. Using variance of pixels within the segmented ROIs as a CTIQ classification parameter, the spatial segmentation data models are found to be more generalizable that the CNN model. However, the CNN model is found to be more suitable for CT image texture classification in the absence of structural variabilities. Our analysis demonstrates that spatial ROI segmentation data models are consistent CTIQ estimators while the CNN models are consistent identifiers of structural similarities for CT image data sets.

Convolutional Neural Networks (CNN) are useful methods for identification of previously unknown embedded patterns in images. Several object and facial recognition along with image segmentation tasks have benefited from the non-linear abstraction of hybrid features using CNN. This work presents a novel CNN model parametrization work-flow developed on the cloud-computing platform of Microsoft Azure Machine Learning Studio (MAMLS) that is capable of learning from the feature maps and classifying multi-modal images with different variabilities using one common flow. This two-step work-flow trains CNN models using 70/30 data split. First, the CNN layers are fixed and the optimal kernel and normalization parameters are identified that maximize classification accuracy on the test data. Next, using the optimal kernel and normalization parameters, the best CNN architecture that maximizes classification accuracy is detected. Finally, the activated feature maps (AFMs) from the optimally parameterized CNN model are analyzed to learn new features that can enhance image-based classification accuracies. The proposed flow achieves classification accuracies in the range of 92.5-99.2% that can be further enhanced by doubling the samples based on the features learned from the AFMs. The proposed non-deep CNN models in the MAMLS platform are capable of processing image data sets with 400-4 million samples using a common flow without exponential increase in the computation time. Thus, optimally parametrized non-deep CNN models are capable of identifying novel features that may enhance image-based classification accuracies.

The constant growth in the present day real-world databases pose computational challenges for a single computer. Cloud-based platforms, on the other hand, are capable of handling large volumes of information manipulation tasks, thereby necessitating their use for large real-world data set computations. This work focuses on creating a novel Generalized Flow within the cloud-based computing platform: Microsoft Azure Machine Learning Studio (MAMLS) that accepts multi-class and binary classification data sets alike and processes them to maximize the overall classification accuracy. First, each data set is split into training and testing data sets, respectively. Then, linear and nonlinear classification model parameters are estimated using the training data set. Data dimensionality reduction is then performed to maximize classification accuracy. For multi-class data sets, data-centric information is used to further improve overall classification accuracy by reducing the multi-class classification to a series of hierarchical binary classification tasks. Finally, the performance of optimized classification model thus achieved is evaluated and scored on the testing data set. The classification characteristics of the proposed flow are comparatively evaluated on 3 public data sets and a local data set with respect to existing state-of-the-art methods. On the 3 public data sets, the proposed flow achieves 78-97.5% classification accuracy. Also, the local data set, created using the information regarding presence of Diabetic Retinopathy lesions in fundus images, results in 85.3-95.7% average classification accuracy, which is higher than the existing methods. Thus, the proposed generalized flow can be useful for a wide range of application-oriented "big data sets".