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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 11 | Issue 3
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Impact Factor : 8.14
Issue per Year : 12
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Issue Published : 118
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| Paper Title: | AI-Powered Multi-Sensor Fusion Model for Non-Invasive Liquid Recognition Through Chemical Gas Profile Analysis |
| Authors Name: | Dnyaneshwari Landge , Samruddhi Pawar , Mohit Pachpande , Dr. R. V. Patil |
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IJRTI_207500
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| Published Paper Id: | IJRTI2511079 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | |
| Abstract: | The accurate, non-invasive identification of liquids based on their chemical gas emissions is a critical challenge with vast implications in medical diagnostics, environmental monitoring, and industrial quality control. This project presents an advanced AI-powered multi-sensor fusion model designed to analyze volatile chemical gas profiles emitted by various liquids without direct contact or sample destruction. By deploying an array of heterogeneous chemical gas sensors, the system captures diverse sensor modalities, which are preprocessed and fused at multiple levels to enhance signal reliability and information richness. Leveraging both low-level raw data fusion and mid-level detection fusion strategies, the model integrates complementary information from different sensors to mitigate individual sensor noise and environmental uncertainties. The fusion framework utilizes adaptive probabilistic algorithms that dynamically assign sensor reliability weights based on real-time measurement uncertainties, overcoming challenges posed by sensor degradation and fluctuating conditions. Deep learning techniques, including convolutional and recurrent neural networks, are employed for multi-modal feature extraction, capturing complex spatial and temporal patterns from fused sensor data. An attention-based mechanism further refines the fusion process by emphasizing the most informative sensor inputs, improving classification robustness across diverse liquid types. |
| Keywords: | AI, multi-sensor fusion, non-invasive liquid recognition, chemical gas profile analysis, adaptive probabilistic fusion, deep learning, volatile organic compounds |
| Cite Article: | "AI-Powered Multi-Sensor Fusion Model for Non-Invasive Liquid Recognition Through Chemical Gas Profile Analysis", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.a668-a673, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511079.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2511079
Registration ID:207500
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier):
Page No: a668-a673 Country: Pune, Maharashtra, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511079 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511079 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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