1 citations · 1 across the 10 of their papers we have counts for
10 papers
Bi-Level Chaotic Fusion Based Graph Convolutional Network for Stock Market Prediction Interval
Eshwar Sai Kandimalla, Sravan Chowdary Kankanala, Sumana Bhimineni +2
Financial market forecasting is inherently uncertain, yet most deep learning approaches rely on point predictions that provide only single-value estimates without quantifying uncer…
Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems
Aadarsh Senapati, Neha Kujur, Vivek Yelleti
Recommender systems are essential components of modern online platforms which presents personalized content in various domain. The traditional collaborative filtering methods depen…
FeedbackLLM: Metadata driven Multi-Agentic Language Agnostic Test Case Generator with Evolving prompt and Coverage Feedback
Kushal Jasti, Tejamani Prashanth Sahu, Rishitha Pentyala +2
Traditional approaches to test case generation often involve manual effort and incur significant computational overhead. Additionally, these approaches are not scalable, and hence,…
PPO guided Agentic Pipeline for Adaptive Prompt Selection and Test Case Generation
Gourisetty Venkata Sai Koushik, Dama Aditya, Mahankali Harish Sai +3
Developing effective test cases capable of thoroughly exercising large-scale software systems is inherently difficult, especially if such systems have voluminous, complex, and deep…
FGDM: Reasoning Aware Multi-Agentic Framework for Software Bug Detection using Chain of Thought and Tree of Thought Prompting
Srita Padmanabhuni, Bhargavi Karuturi, Jerusha Karen Indupalli +2
Deep Learning methods are becoming prominent in automated software bug detection; however, they lack the global understanding of the given code. Consequently, their performance ten…
LLMDR: Large language model driven framework for missing data recovery in mixed data under low resource regime
Durga Keshav, GVD Praneeth, Chetan Kumar Patruni +2
The missing data problem is one of the important issues to address for achieving data quality. While imputation-based methods are designed to achieve data completeness, their effic…
Log anomaly detection via Meta Learning and Prototypical Networks for Cross domain generalization
Krishna Sharma, Vivek Yelleti
Log anomaly detection is essential for system reliability, but it is extremely challenging to do considering it involves class imbalance. Additionally, the models trained in one do…
Transformer based Multi-task Fusion Network for Food Spoilage Detection and Shelf life Forecasting
Mounika Kanulla, Rajasree Dadigi, Sailaja Thota +1
Food wastage is one of the critical challenges in the agricultural supply chain, and accurate and effective spoilage detection can help to reduce it. Further, it is highly importan…
ROSFD: Robust Online Streaming Fraud Detection with Resilience to Concept Drift in Data Streams
Vivek Yelleti
Continuous generation of streaming data from diverse sources, such as online transactions and digital interactions, necessitates timely fraud detection. Traditional batch processin…
Incremental Outlier Detection Modelling Using Streaming Analytics in Finance & Health Care
Vivek Yelleti, Ch Priyanka
In the era of real-time data, traditional methods often struggle to keep pace with the dynamic nature of streaming environments. In this paper, we proposed a hybrid framework where…