Amrita Virtual Labs

24/09/2026

18/09/2026

How can digital technologies make sustainability assessments in food systems more transparent, adaptive, and accountable?

A new study from Parvathy S U, Dr. Vysakh Kani Kolil, Prof. Raghu Raman, Prof. Nripendra P. Rana, PhD, SFHEA (Queen's University Belfast, UK), Prof. Sasangan Ramanathan, and Prof. Krishnashree Achuthan explores how Artificial Intelligence, Blockchain, IoT, and Remote Sensing can be integrated with Life Cycle Thinking (LCT) to improve sustainability decision-making across food supply chains.

Published in Scientific Reports (Nature Portfolio), the research proposes a digitally enabled LCT framework that goes beyond conventional sustainability assessment by introducing two novel constructs:.
• Digital Trust Index (DTI): A measure of stakeholder trust based on both technological capability and governance mechanisms.
• Proof of Climate Impact (PCI): A framework for verifying whether digital interventions result in measurable environmental outcomes.

The findings suggest that integrated digital and governance systems can achieve up to 34% climate impact reduction, while simulation results indicate an average reduction of around 20% under uncertainty. The study also highlights the strong alignment of digital research with 12, SDG 9, SDG 2, and SDG 13, while identifying opportunities for greater focus on biodiversity-related goals.

📖 Read the paper: https://lnkd.in/g93iy7UU
RAISE Labs: https://lnkd.in/gB754kh3

15/09/2026

Can an IoT intrusion detection system remain trustworthy when both device behavior and attack patterns constantly evolve?

Our latest work from RAISE Labs, a research group under the Amrita Center for Cybersecurity Systems And Networks, Amrita Vishwa Vidyapeetham, explores this challenge through DA-DCC-IDS, recently published in IEEE Access.

The framework is designed for real-world IoT gateways where security systems must handle:
• Diverse device behaviors across smart homes and industrial environments
• Previously unseen (open-set) attack families
• Uncertainty in model predictions and confidence estimates

Rather than relying solely on classification accuracy, DA-DCC-IDS combines device-aware anomaly detection, uncertainty-aware decision-making, and open-set recognition to identify both known and unknown threats. The study demonstrates strong open-set detection performance across TON_IoT, IoT-23, and N-BaIoT datasets, while providing fast inference suitable for edge deployments.

A key finding is that while closed-set attack classification depends heavily on temporal traffic dynamics, the proposed device-manifold-aware reject layer remains robust even when temporal features are removed, highlighting the importance of trustworthy uncertainty-aware security mechanisms.

Congratulations to Gayathri Dinesh (S3 M.Tech Cybersecurity), Dr. Vysakh Kani Kolil (Assistant Professor), Dr. Devi Rajeev (Assistant Professor), and Dr. Krishnashree Achuthan (Dean, PG Programs) on this contribution to trustworthy AI-driven cybersecurity research.

To know more about RAISE Labs, please visit https://www.linkedin.com/company/raise-labs-amrita/

23/06/2026

We are pleased to share our latest publication from RAISE Labs:
"Large Language Model Adoption: Systematic Review, Theoretical Frameworks, and Meta-Analytic Evidence"
Published in Information (MDPI)

This study provides one of the most comprehensive syntheses of Large Language Model (LLM) adoption research to date, combining a systematic review and meta-analysis of:
🔹 758 journal articles analyzed
🔹 383 studies included in the systematic review
🔹 154 studies included in the meta-analysis
🔹 88,886 participants synthesized
🔹 59 pairwise relationships examined

Key findings reveal that:
✅ Education is the most extensively studied domain for LLM adoption
✅ TAM and UTAUT+ remain the dominant theoretical frameworks
✅ Trust, performance expectancy, social influence, and personal innovativeness are key drivers of adoption
✅ Behavioral intention is the strongest predictor of actual LLM use
This work contributes to a deeper understanding of how individuals and organizations adopt generative AI technologies across education, healthcare, commerce, banking, and service sectors.

👨‍🔬 Authors:
Dr. Krishnashree Achuthan (Dean, PG Programs, Amrita Vishwa Vidyapeetham) | Dr. Vysakh Kani Kolil, (Assistant Professor, Amrita Vishwa Vidyapeetham) | Dr. Kai-Yu Tang (Associate Professor, National Chung Hsing University, Taiwan) | Dr. Raghu Raman (Dean, School of Business, Amrita Vishwa Vidyapeetham)

📄 Read the full article: https://lnkd.in/gtWQZn3s

For more details about RAISE Labs, please visit: https://www.amrita.edu/center/amrita-center-for-cybersecurity-systems-and-networks/raise-labs/