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Author: search.filters.author.Kolukuluri, V.Has files: No

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    FT-MSIS: A Fault-Tolerant (n, n) Multi-Secret Image Sharing Scheme Without All-or-Nothing Recovery
    (Springer Science and Business Media B.V., 2025) Kolukuluri, V.; B R, P.
    A fault-tolerant multi-secret image sharing scheme is proposed under an (n, n) threshold framework to enhance secure visual data distribution. Unlike traditional approaches, the interleaving strategy embeds fragments of each secret image across all shares, enabling partial reconstruction with gracefully degrading quality when fewer than n shares are available. To strengthen security, a pre-shared key is incorporated using XOR encryption with minimal computational overhead. The scheme supports efficient share generation and recovery, making it well-suited for resource-constrained environments. Experimental evaluations using Structural Similarity Index, Peak Signal-to-Noise Ratio, and Mean Square Error demonstrate the scheme’s effectiveness in balancing security and reconstruction quality. The method is fault-tolerant, resilient to share loss, and ensures flexible multi-secret image sharing. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.
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    Can News Sentiment Improve Deep Learning Models for Nifty 50 Index Forecasting?
    (Institute of Electrical and Electronics Engineers Inc., 2025) Kotekar, C.S.; Mohan, R.; Kolukuluri, V.
    A stock index, such as the Nifty 50, offers diversified exposure and reduces the risk of investing in individual companies. Index price movements are influenced by internal and external factors, including political, economic, and environmental developments, as well as historical trends. The relationship between news sentiment and the Nifty50 return has not been thoroughly studied. This study examines whether financial news sentiment affects index movements and how sentiment can enhance the prediction of next-day returns. Polarity and subjectivity are extracted from financial news using pre-trained transformer models. Deep learning models, including LSTM, GRU, SimpleRNN, and temporal Kolmogorov-Arnold network (TKAN), are trained on return sign, polarity, and subjectivity using a five-day rolling window to forecast the next-day index return sign. Experimental results demonstrate that the proposed approach outperforms baseline methods, achieving a 5.2% improvement in average accuracy. Incorporating polarity and historical return signs enhances performance across all models. By employing a focused feature set, domain-specific sentiment analysis, and a streamlined architecture, the model achieves superior predictive accuracy. Causal analysis and Shapley Additive Explanations (SHAP) reveal that polarity exhibits a causal effect on returns, while subjectivity does not. The study has practical significance, offering day traders and short-term investors timely, data-driven insights to manage risk and make informed investment choices. © 2013 IEEE.