Publications

This study documents and interprets different modes of a salutation, namely 'poothara (also puttara) thozhal / vandanam', across various styles of kalarippayattu, a traditional martial art and healing system, popular in the southern states of India.

The right stepping generates a restraining bend in the dominantly left-lateral shear zone. Such fault stepovers can localise strain and act as loci for seismic ruptures.

This article charts out the evolution of conservation social sciences alongside the evolution of The Social Science Working Group under Society of Conservation Biology (SCB).

This study explores the realm of chaotic dynamics, Neurochaos Learning (a brain-inspired machine learning paradigm) and Normal numbers, focusing on the introduction of a novel chaotic trajectory termed the Universal Orbit. The study investigates the characteristics and generation of universal orbits within two prominent chaotic maps: the Decimal Shift Map and the Gauss Map. It explores the set of points capable of forming such orbits, revealing connections with normal numbers and continued fractions. Points within the interval (0, 1) can produce universal orbits under specific conditions, highlighting the intricate relationship between machine learning, chaotic dynamics and number theory. While not all points forming universal orbits are normal numbers, the trajectory of a normal number may represent a universal orbit (under certain conditions). When employing the universal orbit for feature extraction in Neurochaos Learning, the firing time feature can be interpreted by establishing an upper bound and examining its trend. Future research aims to identify sets of points producing universal orbits under various chaotic maps, intending to enhance the performance of algorithms like the Neurochaos Learning algorithm. This study contributes to advancing our understanding of chaotic systems and their applications in artificial intelligence.

Inspired by the human brain's structure and function, Artificial Neural Networks (ANN) were developed for data classification. However, existing Neural Networks, including Deep Neural Networks, do not mimic the brain's rich structure. They lack key features such as randomness and neuron heterogeneity, which are inherently chaotic in their firing behavior. Neurochaos Learning (NL), a chaos-based neural network, recently employed one-dimensional chaotic maps like Generalized Lüroth Series (GLS) and Logistic map as neurons. For the first time, we propose a random heterogeneous extension of NL, where various chaotic neurons are randomly placed in the input layer, mimicking the randomness and heterogeneous nature of human brain networks. We evaluated the performance of the newly proposed Random Heterogeneous Neurochaos Learning (RHNL) architectures combined with traditional Machine Learning (ML) methods. On public datasets, RHNL outperformed both homogeneous NL and fixed heterogeneous NL architectures in nearly all classification tasks. RHNL achieved high F1 scores on the Wine dataset (1.0), Bank Note Authentication dataset (0.99), Breast Cancer Wisconsin dataset (0.99), and Free Spoken Digit Dataset (FSDD) (0.98). These RHNL results are among the best in the literature for these datasets. We investigated RHNL performance on image datasets, where it outperformed stand-alone ML classifiers. In low training sample regimes, RHNL was the best among stand-alone ML. Our architecture bridges the gap between existing ANN architectures and the human brain's chaotic, random, and heterogeneous properties. We foresee the development of several novel learning algorithms centered around Random Heterogeneous Neurochaos Learning in the coming days.