Dr. Rinku Jacob

@rajagiritech.ac.in

Assistant Professor, Department of Basic Sciences and Humanities
Rajagiri School of Engineering and Technology (RSET),

Dr. Rinku Jacob


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https://researchid.co/rinkujacob84

RESEARCH INTERESTS

Nonlinear time series analysis, nonlinear dynamics, and chaos, complex networks, multiplex networks
12

Scopus Publications

226

Scholar Citations

6

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • Topological Methods for Flight Trajectory Analysis and Planned Path Optimization
    C. Jeeva Jose, Salviyas Sojan, Geo Mathew Gregory, Arjun Krishnan, Christom Joseph, Jithin Mathews, Rinku Jacob, G. Sreekumar, Joel George, P. B. Vinod Kumar
    New Mathematics and Natural Computation, 2026
    Flight trajectories are typically analyzed using a single reference path, although aircraft rarely follow the same route across different days or seasons due to meteorological and operational factors. This limits the ability of conventional methods to capture systematic and season-dependent variations in flight behavior. To address this limitation, this study applies Topological Data Analysis (TDA) to the trajectories of flight KL1613 between Amsterdam and Istanbul over the period from February 2023 to January 2024. The proposed framework integrates geometric measures, including the Haversine distance, with topological descriptors such as persistence diagrams and persistence landscapes to characterize structural deviations in flight paths. The analysis identifies recurrent one-dimensional topological features corresponding to alternative routing patterns and seasonal variations. Monthly trajectory data are clustered into two dominant groups based on combined geometric and topological similarity, from which representative reference paths are extracted for each month. Cost analysis shows that the proposed monthly reference trajectories consistently reduce fuel consumption and travel time compared to both the planned annual reference route and the actual flown trajectories. These results demonstrate the effectiveness of TDA for capturing seasonal structure in flight trajectories and its potential applicability to large-scale air traffic analysis.
  • Physiologically interpretable ECG classification using recurrence network topology and amplitude-preserving normalization
    Sruthi S L, Rinku Jacob, Suresh Davis
    Aip Advances, 2025
    This study proposes a novel normalization method that retains signal-specific amplitude characteristics, improving the differentiation between healthy and unhealthy electrocardiogram (ECG) signals. We present a nonlinear network-based approach to analyze ECG dynamics using a recurrence network (RN), described by characteristic path length, link density, and the clustering coefficient as key network measures. To quantify signal complexity, we introduce weighted Shannon entropy measures based on the distributions of shortest path lengths and clustering coefficients in the RN. Degree heterogeneity is further investigated to examine network-level local node variability between cardiac conditions. A comprehensive analysis using the Physikalisch-Technische Bundesanstalt (PTB) Diagnostic ECG Database of Physionet shows that the proposed approach effectively differentiates between normal and abnormal signals, including bundle branch block, myocardial infarction, dysrhythmias, hypertrophy, and cardiomyopathy even when only the V1 lead is used. The method achieves a classification accuracy of 93.5% for Random Forest and 91.7% with XGBoost, confirming the robustness of recurrence-based features in short-duration ECG analysis. The recurrence-based topological and complexity measures, integrated with the proposed amplitude-preserving normalization, quantitatively agree with physiological mechanisms such as conduction delay, repolarization instability, and morphological irregularity, offering a reliable framework for real-time and clinically useful cardiac diagnosis.
  • Integrating Quantum Mechanics and Machine Learning for Enhanced Molecular Simulation and Drug Discovery
    Kuppani Sathish, Rinku Jacob, Anbarasu. M, Dedeepya Sai Gondi, Eric Howard, Divya Haridas
    2025 2nd International Conference on Multidisciplinary Research and Innovations in Engineering Mrie 2025, 2025
  • Analyzing Electrocardiogram Signal Complexity with Weighted Entropy
    S. L. Sruthi, Rinku Jacob
    Springer Proceedings in Physics, 2024
  • Weighted recurrence networks for the analysis of time-series data
    Rinku Jacob, K. P. Harikrishnan, R. Misra, G. Ambika
    Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences, 2019
    Recurrence networks (RNs) have become very popular tools for the nonlinear analysis of time-series data. They are unweighted and undirected complex networks constructed with specific criteria from time series. In this work, we propose a method to construct a ‘weighted recurrence network’ from a time series and show that it can reveal useful information regarding the structure of a chaotic attractor which the usual unweighted RN cannot provide. Especially, a network measure, the node strength distribution, from every chaotic attractor follows a power law (with exponential cut off at the tail) with an index characteristic to the fractal structure of the attractor. This provides a new class among complex networks to which networks from all standard chaotic attractors are found to belong. Two other prominent network measures, clustering coefficient and characteristic path length, are generalized and their utility in discriminating chaotic dynamics from noise is highlighted. As an application of the proposed measure, we present an analysis of variable star light curves whose behaviour has been reported to be strange non-chaotic in a recent study. Our numerical results indicate that the weighted recurrence network and the associated measures can become potentially important tools for the analysis of short and noisy time series from the real world.
  • Recurrence network measures for hypothesis testing using surrogate data: Application to black hole light curves
    Rinku Jacob, K.P. Harikrishnan, R. Misra, G. Ambika
    Communications in Nonlinear Science and Numerical Simulation, 2018
  • Cross over of recurrence networks to random graphs and random geometric graphs
    RINKU JACOB, K P HARIKRISHNAN, R MISRA, G AMBIKA
    Pramana Journal of Physics, 2017
    Recurrence networks are complex networks constructed from the time series of chaotic dynamical systems where the connection between two nodes is limited by the recurrence threshold. This condition makes the topology of every recurrence network unique with the degree distribution determined by the probability density variations of the representative attractor from which it is constructed. Here we numerically investigate the properties of recurrence networks from standard low-dimensional chaotic attractors using some basic network measures and show how the recurrence networks are different from random and scale-free networks. In particular, we show that all recurrence networks can cross over to random geometric graphs by adding sufficient amount of noise to the time series and into the classical random graphs by increasing the range of interaction to the system size. We also highlight the effectiveness of a combined plot of characteristic path length and clustering coefficient in capturing the small changes in the network characteristics.
  • Weighted recurrence networks from chaotic time series
    Chaos 2017 Proceedings 10th Chaotic Modeling and Simulation International Conference, 2017
  • Measure for degree heterogeneity in complex networks and its application to recurrence network analysis
    Rinku Jacob, K. P. Harikrishnan, R. Misra, G. Ambika
    Royal Society Open Science, 2017
    We propose a novel measure of degree heterogeneity, for unweighted and undirected complex networks, which requires only the degree distribution of the network for its computation. We show that the proposed measure can be applied to all types of network topology with ease and increases with the diversity of node degrees in the network. The measure is applied to compute the heterogeneity of synthetic (both random and scale free (SF)) and real-world networks with its value normalized in the interval [ 0 , 1 ] . To define the measure, we introduce a limiting network whose heterogeneity can be expressed analytically with the value tending to 1 as the size of the network N tends to infinity. We numerically study the variation of heterogeneity for random graphs (as a function of p and N ) and for SF networks with γ and N as variables. Finally, as a specific application, we show that the proposed measure can be used to compare the heterogeneity of recurrence networks constructed from the time series of several low-dimensional chaotic attractors, thereby providing a single index to compare the structural complexity of chaotic attractors.
  • Characterization of chaotic attractors under noise: A recurrence network perspective
    Rinku Jacob, K.P. Harikrishnan, R. Misra, G. Ambika
    Communications in Nonlinear Science and Numerical Simulation, 2016
  • Can recurrence networks show small-world property?
    Rinku Jacob, K.P. Harikrishnan, R. Misra, G. Ambika
    Physics Letters Section A General Atomic and Solid State Physics, 2016
  • Uniform framework for the recurrence-network analysis of chaotic time series
    Rinku Jacob, K. P. Harikrishnan, R. Misra, G. Ambika
    Physical Review E, 2016

RECENT SCHOLAR PUBLICATIONS

  • Topological Methods for Flight Trajectory Analysis and Planned Path Optimization
    C Jeeva Jose, S Sojan, GM Gregory, A Krishnan, C Joseph, J Mathews, ...
    New Mathematics and Natural Computation, 1-21 , 2026
    2026
  • Physiologically interpretable ECG classification using recurrence network topology and amplitude-preserving normalization
    S SL, R Jacob, S Davis
    AIP Advances 15 (12) , 2025
    2025
  • Tracking Dynamical Transitions using Link Density of Recurrence Networks
    R Jacob, R Misra, KP Harikrishnan, G Ambika
    arXiv preprint arXiv:2405.19357 , 2024
    2024
  • Analyzing Electrocardiogram Signal Complexity with Weighted Entropy
    SL Sruthi, R Jacob
    International Conference on Nonlinear Dynamics and Applications, 152-163 , 2024
    2024
  • Weighted recurrence networks for the analysis of time-series data
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Proceedings of the Royal Society A: Mathematical, Physical and Engineering … , 2019
    2019
    Citations: 11
  • Recurrence network measures for hypothesis testing using surrogate data: Application to black hole light curves
    KP Harikrishnan, R Jacob, R Misra, G AMBIKA
    Elsevier BV , 2018
    2018
  • Recurrence network measures for hypothesis testing using surrogate data: Application to black hole light curves
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Communications in Nonlinear Science and Numerical Simulation 54, 84-99 , 2018
    2018
    Citations: 15
  • Degree weighted recurrence networks for the analysis of time series data
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    arXiv preprint arXiv:1709.05042 , 2017
    2017
  • Determining the minimum embedding dimension for state space reconstruction through recurrence networks
    KP Harikrishnan, R Jacob, R Misra, G Ambika
    Indian Academy of Sciences ‘Conference Series 1 (1), 43-49 , 2017
    2017
    Citations: 3
  • Cross over of recurrence networks to random graphs and random geometric graphs
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Pramana 88 (2), 37 , 2017
    2017
    Citations: 6
  • Measure for degree heterogeneity in complex networks and its application to recurrence network analysis
    KP Harikrishnan, R Jacob, R Misra, G Ambika
    Royal Society of Chemistry , 2017
    2017
    Citations: 1
  • Weighted recurrence networks from chaotic time series
    KP Harikrishnan, R Jacob, R Misra, G Ambika
    Chaotic Model. Simul 4, 433-440 , 2017
    2017
    Citations: 2
  • Cross over of recurrence networks to random graphs and random geometric graphs
    KP Harikrishnan, R Jacob, R Misra, G AMBIKA
    Springer Nature , 2017
    2017
  • Measure for degree heterogeneity in complex networks and its application to recurrence network analysis
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Royal Society open science 4 (1) , 2017
    2017
    Citations: 92
  • Characterization of chaotic attractors under noise: A recurrence network perspective
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Communications in Nonlinear Science and Numerical Simulation 41, 32-47 , 2016
    2016
    Citations: 26
  • Can recurrence networks show small-world property?
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Physics Letters A 380 (35), 2718-2723 , 2016
    2016
    Citations: 12
  • A new measure of heterogeneity for complex networks.
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    arXiv preprint arXiv:1605.06607 , 2016
    2016
  • Characterization of chaotic attractors under noise: A recurrence network perspective
    KP Harikrishnan, R Jacob, R Misra, G AMBIKA
    Elsevier BV , 2016
    2016
  • Uniform framework for the recurrence-network analysis of chaotic time series
    KP Harikrishnan, R Jacob, R Misra, G AMBIKA
    American Physical Society , 2016
    2016
  • Uniform framework for the recurrence-network analysis of chaotic time series
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Physical review E 93 (1), 012202 , 2016
    2016
    Citations: 55

MOST CITED SCHOLAR PUBLICATIONS

  • Measure for degree heterogeneity in complex networks and its application to recurrence network analysis
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Royal Society open science 4 (1) , 2017
    2017
    Citations: 92
  • Uniform framework for the recurrence-network analysis of chaotic time series
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Physical review E 93 (1), 012202 , 2016
    2016
    Citations: 55
  • Characterization of chaotic attractors under noise: A recurrence network perspective
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Communications in Nonlinear Science and Numerical Simulation 41, 32-47 , 2016
    2016
    Citations: 26
  • Recurrence network measures for hypothesis testing using surrogate data: Application to black hole light curves
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Communications in Nonlinear Science and Numerical Simulation 54, 84-99 , 2018
    2018
    Citations: 15
  • Can recurrence networks show small-world property?
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Physics Letters A 380 (35), 2718-2723 , 2016
    2016
    Citations: 12
  • Weighted recurrence networks for the analysis of time-series data
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Proceedings of the Royal Society A: Mathematical, Physical and Engineering … , 2019
    2019
    Citations: 11
  • Cross over of recurrence networks to random graphs and random geometric graphs
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    Pramana 88 (2), 37 , 2017
    2017
    Citations: 6
  • Determining the minimum embedding dimension for state space reconstruction through recurrence networks
    KP Harikrishnan, R Jacob, R Misra, G Ambika
    Indian Academy of Sciences ‘Conference Series 1 (1), 43-49 , 2017
    2017
    Citations: 3
  • Fiber Bragg Grating based temperature and strain sensor simulation for biomedical applications
    G Gopalakrishnan, IM SERENE, R Jacob, G Amit, SK Sudheer, ZC Alex, ...
    Optoelectronics and Advanced Materials–Rapid Communications 2 (1), 10-14 , 2008
    2008
    Citations: 3
  • Weighted recurrence networks from chaotic time series
    KP Harikrishnan, R Jacob, R Misra, G Ambika
    Chaotic Model. Simul 4, 433-440 , 2017
    2017
    Citations: 2
  • Measure for degree heterogeneity in complex networks and its application to recurrence network analysis
    KP Harikrishnan, R Jacob, R Misra, G Ambika
    Royal Society of Chemistry , 2017
    2017
    Citations: 1
  • Topological Methods for Flight Trajectory Analysis and Planned Path Optimization
    C Jeeva Jose, S Sojan, GM Gregory, A Krishnan, C Joseph, J Mathews, ...
    New Mathematics and Natural Computation, 1-21 , 2026
    2026
  • Physiologically interpretable ECG classification using recurrence network topology and amplitude-preserving normalization
    S SL, R Jacob, S Davis
    AIP Advances 15 (12) , 2025
    2025
  • Tracking Dynamical Transitions using Link Density of Recurrence Networks
    R Jacob, R Misra, KP Harikrishnan, G Ambika
    arXiv preprint arXiv:2405.19357 , 2024
    2024
  • Analyzing Electrocardiogram Signal Complexity with Weighted Entropy
    SL Sruthi, R Jacob
    International Conference on Nonlinear Dynamics and Applications, 152-163 , 2024
    2024
  • Recurrence network measures for hypothesis testing using surrogate data: Application to black hole light curves
    KP Harikrishnan, R Jacob, R Misra, G AMBIKA
    Elsevier BV , 2018
    2018
  • Degree weighted recurrence networks for the analysis of time series data
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    arXiv preprint arXiv:1709.05042 , 2017
    2017
  • Cross over of recurrence networks to random graphs and random geometric graphs
    KP Harikrishnan, R Jacob, R Misra, G AMBIKA
    Springer Nature , 2017
    2017
  • A new measure of heterogeneity for complex networks.
    R Jacob, KP Harikrishnan, R Misra, G Ambika
    arXiv preprint arXiv:1605.06607 , 2016
    2016
  • Characterization of chaotic attractors under noise: A recurrence network perspective
    KP Harikrishnan, R Jacob, R Misra, G AMBIKA
    Elsevier BV , 2016
    2016