Identifying and Blocking of Non-productive Calls in Emergency Call System Using Machine Learning and IVRS Integration

Year : 2024 | Volume : 14 | Issue : 03 | Page : 20 28
    By

    Ishika Jain,

  • Anjali Singh,

  1. Student, Department of Computer Science and Engineering, Poornima College of Engineering, Jaipur, Rajasthan, India
  2. Assistant Professor, Department of Computer Science and Engineering, Poornima College of Engineering Jaipur, Rajasthan, India

Abstract

The Dial 100 emergency reaction gadget handles over 1,000,000 calls every day, with over 95% being unproductive, such as blank, machine-generated, and spoofed calls. These futile calls waste resources and put off responses to actual emergencies. This look presents a comprehensive solution integrating advanced name evaluation, system learning, and an interactive voice response system (IVRS) to filter out and prevent these calls. Our technique starts with studying incoming calls to pick out patterns typical of unproductive ones. Audio processing detects blank calls, even as device-generated ones are recognized via sample recognition. Caller ID monitoring and dynamic thresholds for call frequency and period make certain adaptive filtering. Machines getting-to-know fashions, consisting of Random Forest and Support Vector Machines, use historic call data to classify calls based on various standards, constantly enhancing through new information. IVRS integration similarly reduces the weight of emergency operators by intercepting unproductive calls. The device also keeps blacklists to block repeat offenders. A feedback loop allows non-stop machine optimization with the aid of incorporating operator entries and periodic opinions.

Keywords: Interactive voice response system (IVRS), caller ID, random forest, support vector machines

[This article belongs to Journal of Communication Engineering & Systems ]

How to cite this article:
Ishika Jain, Anjali Singh. Identifying and Blocking of Non-productive Calls in Emergency Call System Using Machine Learning and IVRS Integration. Journal of Communication Engineering & Systems. 2024; 14(03):20-28.
How to cite this URL:
Ishika Jain, Anjali Singh. Identifying and Blocking of Non-productive Calls in Emergency Call System Using Machine Learning and IVRS Integration. Journal of Communication Engineering & Systems. 2024; 14(03):20-28. Available from: https://journals.stmjournals.com/joces/article=2024/view=177262


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Regular Issue Subscription Review Article
Volume 14
Issue 03
Received 16/09/2024
Accepted 24/09/2024
Published 07/10/2024
259


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