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26 August 2026 Supriya Kar

A Proof-of-Concept for a Decentralised Fraud Defence System Using Federated Learning and Graph Neural Networks in Indian Banking

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Geopolitical Risk Index for Commodity Volatility Prediction

25 August 2026 • Supriya Kar

A Daily, Commodity-Specific, Exposure-Weighted Geopolitical Risk Signal Built on GDELT, Validated Against Commodity Markets (2018–2026)

The Honest Number Was 83%: Leakage, Abstention, and a Complaint Router You Can Actually Deploy

18 August 2026 • Samay Rajput

The same complaint-routing model scores 96.3% or 83.2% depending on which three columns you leave in the training data. The high number is the intake form being read back to you. This is what the leakage audit found before a single model was trained, why 83.2% is the honest figure, and how the same model — given permission to say "I don't know" — becomes deployable at 90.7% accuracy on 79.5% of traffic.

EdgeGuard: AI-Driven Predictive Maintenance for Power Transformers

29 July 2026 • Admin

Power transformers are among the most critical assets in electrical distribution infrastructure. Their unexpected failure can result in power outages, safety hazards, equipment damage, expensive repairs, and long service interruptions. Traditional transformer maintenance practices often rely on periodic manual inspection, offline testing, or run-to-failure maintenance. These methods are expensive, slow, labor-intensive, and unable to detect rapidly developing faults in real time. EdgeGuard is an AI-driven, edge-computing predictive maintenance system designed to continuously monitor transformer health and forecast failures before catastrophic damage occurs. The system acts as a retrofittable “Digital Doctor” for distribution transformers by combining low-cost industrial sensors, an ESP32 microcontroller, local intelligence, machine learning-based risk prediction, autonomous relay control, and a real-time web dashboard. The proposed system monitors six major transformer health indicators: temperature, humidity, vibration, oil level, current, and voltage. These signals are normalized and processed through a Multi-Layer Perceptron neural network to classify transformer condition and estimate failure risk. If the predicted risk crosses a critical threshold of 80%, EdgeGuard automatically triggers a relay through GPIO 26 to isolate the transformer from the electrical network. The system also supports secure remote control, dashboard monitoring, API-key-based hardware authentication, JWT-based user access, WebSocket live updates, and automatic live-hardware detection. With an estimated deployment cost of approximately ₹3,850, EdgeGuard offers a low-cost alternative to conventional transformer monitoring systems. Its cloud-independent operation and edge-based decision-making make it especially useful for rural and semi-urban distribution grids where connectivity and maintenance resources are limited.

Predictive Analytics for Hotel Booking Cancellations: A Data-Driven Framework for Revenue Optimization and Operational Resilience

28 July 2026 • Yash Mhatre

Analyzing 119,390 hotel bookings, this white paper shows a tuned Random Forest model beats Logistic Regression and XGBoost at predicting cancellations, powering segment-specific deposit policies and model-driven overbooking strategy.

FairClaim: Medicare Fraud Detection

28 July 2026 • Dr. Rishov Mukhopadhyay | Ph.D. (Medicine) (Netherlands) | MRSC (U.K.)

A data-driven fraud detection framework to identify anomalous insurance claims and improve healthcare payment integrity through predictive analytics.

Hallucination Risk and Collaboration Efficiency in Human–AI Interaction: A Proxy-Based Framework for Large-Scale Evaluation

28 July 2026 • Anum Ahmed, MSc Data Analytics | Business Analyst | UAE

Analyzing 207,862 human–AI interactions, this study introduces a proxy-based framework (HRS + CEP) showing that hallucination risk and response verbosity together drive a moderate, statistically significant increase in human collaboration effort.

Predicting Smart Grid Stability Using Terno Agentic AI

28 July 2026 • Manoj Pawar

Analyzing 60,000 smart grid records with Terno AI, XGBoost hits 98% accuracy and a 0.998 ROC-AUC in classifying grid stability — with price elasticity and reaction-time features doing the heavy lifting.

ANALYZING TOXIC USER BEHAVIOR AND RISK PATTERNS IN ONLINE GAMING PLATFORMS

28 July 2026 • Anum Ahmed | MSc Data Analytics | Business Analyst, UAE

This study shows that behavioral data alone can't reliably predict gaming toxicity — but a risk-based model combining behavioral and engineered features does a much better job of flagging the small segment of high-risk users driving disproportionate harm.

Predicting Delivery Time for Logistics Optimization Using Terno AI

27 July 2026 • Mudrika Rojiwadiya

Using Terno AI's prompt-driven pipeline, an XGBoost model predicts Porter delivery times with 2.37-minute RMSE and 93.6% explained variance — turning driving duration and order congestion into a real-time ETA engine.

Predictive Modeling of Patient Attrition in Clinical Trials Using Terno AI

27 July 2026 • Dr. Rishov Mukhopadhyay | Ph.D. (Medicine) (Netherlands) | MRSC (U.K.)

A machine learning framework using Terno AI benchmarks Logistic Regression, Random Forest, and XGBoost to predict clinical trial patient dropout — and shows why standardized preprocessing, not just model choice, is the key to real-world generalizability.

Predictive Analysis of Supply Chain Delivery Delays

27 July 2026 • Yash Mhatre

Scheduling Inefficiency vs. Logistics Route Failures: A Data-Driven Investigation

Predicting Dielectron Invariant Mass

27 July 2026 • Manoj Pawar

Analyzing 100,000 CMS collision events shows XGBoost predicts dielectron invariant mass with 0.98 R² by learning the non-linear physics that transverse momenta and electron energies encode, far outperforming linear regression's 0.41.

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