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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Srivastava, Smriti
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Computer Science & IT Engineering

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Performance analysis of congestion-aware Q-routing algorithm for network on chip Srivastava, Smriti; Moharir, Minal; Gunisetty, Shivaneetha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp798-806

Abstract

A network on chip’s performance is greatly impacted by network congestion due to the substantial increase in latency and energy utilized. Designing routing strategies that keep the network informed of the status of traffic is made easier by machine learning techniques. In this work, a reinforcement-based congestion-aware Q-routing (CAQR) technique has been presented. The proposed algorithm performed better in comparison to the conventional XY routing method tested against the SPEC CPU2006 benchmark suite in the gem5 NoC simulator tool. The suite used has 4 benchmarks, namely, namd, lbm, leslie3d and bzip2 which can be used for the cores in the network in any combination. The tests were run with 16 cores on a 44 network with the maximum instruction count supported by the system (here 5,000). The proposed Q-routing algorithm showed an average of 19% reduction for benchmark simulation as compared to the Dimension-ordered (X-Y) routing for readings of average packet latency which is a crucial factor in determining a network’s efficiency. The analysis also shows an average reduction of 24%, 10%, 23% and 47% in terms of average packet network latency, average flit latency, average flit network latency and average energy consumption across various benchmarks.
Automated legal content management system for multi-country integration Pawar, Hardik; Prakash, Nidhi; Srivastava, Smriti; M., Sneha; Syedabdulkader, Shaik Mohideen; D., Pratiba; S., Sandhya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4511-4519

Abstract

This paper presents an automated legal content management system (CMS) designed for multi-country integration, addressing the complex challenges of legal document migration across more than 180 countries while ensuring regulatory compliance and accessibility standards. The system implements a hierarchical four-level architecture, migrating more than 2,740 legal documents with zero data loss incidents through fault-tolerant processing pipelines. The automated portable document format (PDF) migration component demonstrates exceptional efficiency, processing documents 36 times faster compared to manual approaches, while article migration achieves 230 times faster processing speeds. The integrated artificial intelligence (AI)-powered accessibility enhancement system generates contextually appropriate alt text descriptions, allowing organizations to process 10,000 images annually with savings of $14,990. The complete country migration process, covering both PDF and article processing, executes in 30 seconds compared to 56 minutes for manual processing, representing a 112-fold improvement in performance. System scalability demonstrates linear performance characteristics up to more than 5,000 documents with consistent processing metrics while maintaining compliance across diverse regulatory frameworks. These quantitative improvements establish a new paradigm for automated legal content management, providing a scalable foundation for global enterprises managing multi jurisdictional legal documentation requirements.
Co-Authors D., Pratiba Gunisetty, Shivaneetha M., Sneha Moharir, Minal Pawar, Hardik Prakash, Nidhi S., Sandhya Syedabdulkader, Shaik Mohideen