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                <text>Faculty Publications</text>
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    <name>Book Chapter</name>
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              <text>Reddy, N. Srikanth; Baruah, Anganabha; Raha, Shrinwantu; Babu, S. B. G. Tilak; Kumar, K. Keshav; Suganya, S.</text>
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          <name>Title</name>
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              <text>The Road Ahead: Charting Future Research Directions in Computational Intelligence</text>
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              <text>01-01-2025</text>
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              <text>Computational Intelligence Solutions for Real-Life Problems;pp.19-35</text>
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              <text>&lt;a href="https://doi.org/10.1201/9781998511013-2" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1201/9781998511013-2&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105024053455?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105024053455?origin=resultslist&lt;/a&gt;</text>
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              <text>Reddy N.S., School of Management, Presidency University, Karnataka, Bangalore, India; Baruah A., Department of Psychology, Christ University, NCR, Uttar Pradesh, Delhi, India; Raha S., Department of Geography, Bhairab Ganguly College, West Bengal, Kolkata, India; Babu S.B.G.T., Department of Electronics and Communication Engineering, Aditya Engineering College, Andhra Pradesh, Surampalem, India; Kumar K.K., Department of Humanities and Mathematics, G. Narayanamma Institute of Technology and Science (for Women), Telangana, Hyderabad, India; Suganya S., Department of Electrical and Electronics Engineering, Sethu Institute of Technology, Kariapatti, India</text>
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              <text>The synergy between neuroscience and computational intelligence fosters a dynamic exchange that significantly advances both fields. At its core, this convergence focuses on emulating the brains intricate mechanisms to inspire and enhance machine learning (ML) models. Neural networks (NNs), foundational to computational intelligence, are modeled after biological neurons and drive artificial neural networks (ANNs) that excel in tasks simulating human cognition. Neuromorphic computing furthers this concept by designing hardware and software with braininspired architectures, enabling energy-efficient AI systems. A notable breakthrough is brain-computer interfaces (BCIs), which translate neural signals into actionable commands, offering transformative solutions for individuals with paralysis. Additionally, cognitive computing leverages neuroscience insights to emulate higher-order mental processes, enabling the development of intelligent, context-aware systems. Conversely, ML algorithms, especially in pattern recognition, empower neuroscience by analyzing large-scale brain imaging data to uncover hidden patterns and correlations. This bidirectional interaction accelerates discoveries in neurology and psychology while deepening our understanding of brain function. Together, neuroscience and computational intelligence form a powerful alliance, shaping the future of intelligent technologies and brain science.  2026 by Apple Academic Press, Inc.</text>
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              <text>artificial neural networks; brain-computer interfaces; computational intelligence; machine learning; neural networks; neuromorphic computing; neuroscience</text>
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              <text>Apple Academic Press</text>
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              <text>ISBN: 978-104080568-8; 978-199851100-6;</text>
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              <text>Restricted Access; Hardcopy may be available in the library</text>
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