AI-Driven Crisis Intervention Sysytem For Perintal Mental Health

Combining advanced artificial intelligence with satellite technologies and vessel tracking has transformed oil spill detection in maritime environments. Our research introduces a robust automated system that integrates data from Automatic Identification Systems (AIS) and Synthetic Aperture Radar (SAR) satellites. By leveraging machine learning algorithms, our approach analyzes vessel behaviors, detects anomalies, and verifies spills using high-resolution SAR images. This multi-source method ensures real-time monitoring and rapid response capabilities, significantly reducing detection times while maintaining high accuracy. Through careful dataset augmentation and training with diverse environmental conditions, our system achieves over 97% accuracy, demonstrating its effectiveness in mitigating ecological and economic impacts. This innovative solution not only enhances environmental monitoring but also underscores our commitment to sustainable practices for safeguarding marine ecosystems.

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Publication Details

Journal
International jounal of information technology and computer engineering.
Published
2025-06-12
DOI
https://doi.org/10.62647/ijitce2025v13i2spp280-284
Primary Topic
Resilience and Mental Health
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Driven Crisis Intervention Sysytem For Perintal Mental Health

Mohd ibrahim, Gubbala chakradhar, Dr surya Mukhi
International jounal of information technology and computer engineering.
Resilience and Mental Health
article

AI-Driven Crisis Intervention Sysytem For Perintal Mental Health

Mohd ibrahim, Gubbala chakradhar, Dr surya Mukhi
article en

Abstract

Combining advanced artificial intelligence with satellite technologies and vessel tracking has transformed oil spill detection in maritime environments. Our research introduces a robust automated system that integrates data from Automatic Identification Systems (AIS) and Synthetic Aperture Radar (SAR) satellites. By leveraging machine learning algorithms, our approach analyzes vessel behaviors, detects anomalies, and verifies spills using high-resolution SAR images. This multi-source method ensures real-time monitoring and rapid response capabilities, significantly reducing detection times while maintaining high accuracy. Through careful dataset augmentation and training with diverse environmental conditions, our system achieves over 97% accuracy, demonstrating its effectiveness in mitigating ecological and economic impacts. This innovative solution not only enhances environmental monitoring but also underscores our commitment to sustainable practices for safeguarding marine ecosystems.

International jounal of information technology and computer engineering.Vol. 13(2s)
English and Foreign Languages University (IN)
Openalex Percentile: Top 81%
Resilience and Mental Health
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