🚨 ML for Gas Sensors: Anomaly Detection & Domain-Aware Modeling | Live Workshop

🚨 ML for Gas Sensors: Anomaly Detection & Domain-Aware Modeling

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Home >Courses >🚨 ML for Gas Sensors: Anomaly Detection & Domain-Aware Modeling

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Workshop Registration End Date :2025-06-28

Mentor Based

Limited SeatsClosing Soon

🚨 ML for Gas Sensors: Anomaly Detection & Domain-Aware Modeling

Professional Certification Program

🎥

FormatLive + Recorded

📅

Duration8 Weeks

📜

CertificationVerified

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Program Syllabus

Module 1

About

“Graphene-Based Sensor Data Analytics” is a first-of-its-kind international workshop that combines the nanomaterial science of graphene with the data-centric world of AI and IoT analytics. Graphene’s ultra-sensitivity makes it ideal for sensors in biomedicine, environmental monitoring, chemical detection, and flexible electronics—but leveraging these sensors at scale requires sophisticated data processing pipelines.

Participants will explore the fundamentals of graphene-based sensor mechanisms, and build practical workflows using tools like Python, pandas, scikit-learn, TensorFlow, MATLAB, and real-time signal analysis libraries to process, classify, and predict sensor responses.

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Module 2

Aim

To equip participants with the knowledge and practical skills to analyze, interpret, and model data generated by graphene-based sensors using modern data analytics and machine learning frameworks for real-world applications.

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Module 3

Workshop Objectives

Bridge the gap between sensor hardware innovation and intelligent data use
Promote cross-disciplinary learning between nanoscience and AI
Empower participants to contribute to next-gen sensor networks and smart systems
Foster innovation in sustainable, scalable, and real-time sensing platforms
Prepare researchers to publish or commercialize sensor-based data solutions

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Module 4

Workshop Structure

Module 5

Day 1: Signal Preprocessing for SAW Gas Sensors

Introduction to SAW Gas Sensors
- Working principles and applications
- Common signal characteristics and noise sources
Signal Preprocessing Techniques
- Filtering: Low-pass, band-pass, median filters
- Baseline correction and normalization
Feature Extraction & Dimensionality Reduction
- Time-domain and frequency-domain features
- PCA, FFT, and wavelet transforms for SAW data

Module 6

Day 2: Anomaly Detection Using Autoencoders

Basics of Autoencoders
- Architecture: Encoder, bottleneck, decoder
- Training for reconstruction accuracy
Autoencoders for Anomaly Detection
- Loss-based detection of anomalous gas responses
- Performance evaluation metrics (AUC, precision-recall)
Implementation & Case Study
- Building an autoencoder model in Python (Keras/PyTorch)
- Real SAW data analysis with labeled anomalies

Module 7

Day 3: Transfer Learning for New Analytes

Introduction to Transfer Learning
- What is transfer learning and why it matters
- Types: Feature-based, fine-tuning, domain adaptation
Applying Transfer Learning to Sensor Data
- Transferring models across sensor types or analyte classes
- Handling distribution shift and domain generalization
Advanced Techniques & Future Directions
- Meta-learning, few-shot learning, and continual learning
- Preparing your model for deployment in dynamic environments

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Module 8

Intended For

Nanotechnology researchers and material scientists
Sensor engineers and IoT hardware developers
AI/ML professionals working in biomedical or environmental sensing
Researchers in wearable and flexible electronics
UG/PG/PhD students in physics, electronics, materials, or data science

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Module 9

Important Dates

Module 10

Registration Ends

2025-06-25
Indian Standard Timing 4 PM

Module 11

Workshop Dates

2025-06-28 to 2025-06-30
Indian Standard Timing 6 PM

Module 12

Workshop Outcomes

Understand the properties and sensing behavior of graphene-based systems
Clean, normalize, and visualize sensor data for real-world use
Apply supervised and unsupervised machine learning to sensor datasets
Integrate graphene sensors with AI pipelines for diagnostics or alerts
Receive an international certification and take home a complete analytics workflow

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Module 13

Mentor Profile

Mr. Indra Neel Pulidindi

Scientific consultant

Module 14

Fee Structure

Module 15

Student Fee

INR. 1999
USD. 50

Module 16

Ph.D. Scholar / Researcher Fee

INR. 2999
USD. 60

Module 17

Academician / Faculty Fee

INR. 3999
USD. 70

Module 18

Industry Professional Fee

INR. 5999
USD. 90

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Module 19