Publications

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Intrusion Detection with Black Hole Feature Selection

Published in Congress on Smart Computing Technologies, 2022

Network intrusion detection datasets are high-dimensional, containing many redundant and noisy features that degrade classifier performance. This paper applies Binary and Real-Coded variants of the Black Hole algorithm to select the most informative feature subsets, evaluated with a Random Forest classifier on three standard intrusion detection benchmarks — NSL-KDD, CIC-IDS2017, and the Aegean Wi-Fi dataset. The Real-Coded variant encodes feature relevance as continuous values rather than hard binary flags, offering finer-grained selection. Both variants outperform conventional feature selection methods, achieving higher detection accuracy with fewer features.

Recommended citation: Kulkarni, S., Ovhal, P., Valadi, J.K. (2023). Intrusion Detection with Black Hole Feature Selection. In: Bansal, J.C., Sharma, H., Chakravorty, A. (eds) Congress on Smart Computing Technologies. CSCT 2022. Smart Innovation, Systems and Technologies, vol 351. Springer, Singapore. https://doi.org/10.1007/978-981-99-2468-4_9 https://link.springer.com/chapter/10.1007/978-981-99-2468-4_9

Improving Black Hole Algorithm Performance by Coupling with Genetic Algorithm for Feature Selection

Published in Congress on Intelligent Systems: Proceedings of CIS 2021, Volume 1, 2022

The Black Hole algorithm converges efficiently but can get trapped in local optima; Genetic Algorithms diversify the search well through crossover and mutation but converge slowly. This paper couples both into a single hybrid: a switching probability parameter controls when the algorithm follows Black Hole update rules versus Genetic Algorithm operators, combining the strengths of both. Tuning the switching probability is shown to be key — the optimally configured hybrid yields considerably better feature subsets than either algorithm run independently.

Recommended citation: Bhosale, H., Ovhal, P., Sane, A., Valadi, J.K. (2022). Improving Black Hole Algorithm Performance by Coupling with Genetic Algorithm for Feature Selection. In: Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 114. Springer, Singapore. https://doi.org/10.1007/978-981-16-9416-5_26 https://link.springer.com/chapter/10.1007/978-981-16-9416-5_26

Improved filter ranking incorporated binary black hole algorithm for feature selection

Published in SN Computer Science, 2022

Pure wrapper-based feature selection methods like Black Hole are computationally effective but ignore the statistical properties of individual features. This paper embeds filter-based rankings — Pearson correlation combined with Gini importance, and Pearson correlation combined with mutual information — directly into the Black Hole algorithm’s fitness function, switching between the filter-guided and standard criteria probabilistically during the search. The hybrid strategy produces smaller feature subsets with higher accuracy than the standalone Black Hole algorithm, and performs competitively against a filter-enhanced Ant Colony Optimization baseline across diverse benchmark datasets from science and engineering.

Recommended citation: Ovhal, P., Kulkarni, S. & Valadi, J.K. Improved Filter Ranking Incorporated Binary Black Hole Algorithm for Feature Selection. SN COMPUT. SCI. 3, 51 (2022). https://doi.org/10.1007/s42979-021-00933-w https://link.springer.com/article/10.1007/s42979-021-00933-w

Black Hole—White Hole algorithm for dynamic optimization of chemically reacting systems

Published in Congress on Intelligent Systems: Proceedings of CIS 2020, Volume 2, 2021

Dynamic optimization of chemical reactors — finding optimal control profiles over time to maximize yield or minimize cost — is a difficult problem that conventional solvers struggle with due to non-convexity and sensitivity to initial conditions. This paper extends the Black Hole algorithm by introducing a White Hole component that counterbalances the algorithm’s exploitation tendency with active exploration, preventing the search from stagnating in local optima. Tested on benchmark chemical reaction systems using both piecewise linear and piecewise constant control profiles, the Black Hole–White Hole algorithm matches or outperforms existing methods while remaining simple to implement.

Recommended citation: Ovhal, P., Valadi, J.K. (2021). Black Hole—White Hole Algorithm for Dynamic Optimization of Chemically Reacting Systems. In: Sharma, H., Saraswat, M., Yadav, A., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. CIS 2020. Advances in Intelligent Systems and Computing, vol 1335. Springer, Singapore. https://doi.org/10.1007/978-981-33-6984-9_43 https://link.springer.com/chapter/10.1007/978-981-33-6984-9_43

Random forest and autoencoder data-driven models for prediction of dispersed-phase holdup and drop size in rotating disc contactors

Published in Industrial & Engineering Chemistry Research, 2020

Accurate prediction of dispersed-phase holdup and drop size is essential for designing and scaling rotating disc contactors (RDCs) used in chemical extraction processes — yet the underlying relationships are highly nonlinear and poorly captured by classical regression. This paper applies Random Forest (RF) and an autoencoder-augmented RF to these prediction tasks. The standalone RF generalizes well across both targets; the autoencoder combination improves drop size prediction but offers limited benefit for holdup. The work demonstrates that data-driven ML models are a viable replacement for physics-based correlations in chemical engineering design.

Recommended citation: Swetha Saraswathi K., Hrushikesh Bhosale, Prasad Ovhal, Naren Parlikkad Rajan, and Jayaraman Krishnamoorthy Valadi Industrial & Engineering Chemistry Research 2021 60 (1), 425-435 DOI: 10.1021/acs.iecr.0c04149 https://pubs.acs.org/doi/abs/10.1021/acs.iecr.0c04149

Twin and multiple black holes algorithm for feature selection

Published in 2020 IEEE-HYDCON., 2020

The standard Black Hole algorithm uses a single attractor to guide the search, which can limit diversity and cause premature convergence. This paper introduces Twin and Multiple Black Hole variants that maintain several competing attractors simultaneously, improving exploration of the feature space. Paired with an SVM classifier, the new variants outperform the original algorithm on feature subset quality and classification accuracy.

Recommended citation: P. T. Ovhal, J. K. Valadi and A. Sane, "Twin and Multiple Black Holes Algorithm for Feature Selection," 2020 IEEE-HYDCON, Hyderabad, India, 2020, pp. 1-6, doi: 10.1109/HYDCON48903.2020.9242882. https://ieeexplore.ieee.org/abstract/document/9242882

A Simple Method of Solution For Multi-label Feature Selection

Published in IEEE International Conference on Electrical, Computer and Communication Technologies, 2019

Multi-label classification problems suffer from an exponentially large label space, making feature selection computationally expensive. This paper proposes a two-step approach: first compress the label space into a lower-dimensional representation, then run feature selection within that reduced space. The method significantly cuts computational cost while retaining predictive accuracy on high-dimensional benchmark datasets.

Recommended citation: Valadi, Jayaraman K., Prasad T. Ovhal, and Kunal J. Rathore. "A simple method of solution for multi-label feature selection." 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2019. https://ieeexplore.ieee.org/document/8869493