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p-Index From 2021 - 2026
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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Murthy, Pavithra Narasimha
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Novel hybrid generative adversarial network for synthesizing image from sketch Murthy, Pavithra Narasimha; Yeliyur Hanumanthaiah, Sharath Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6293-6301

Abstract

In the area of sketch-based image retrieval process, there is a potential difference between retrieving the match images from defined dataset and constructing the synthesized image. The former process is quite easier while the latter process requires more faster, accurate, and intellectual decision making by the processor. After reviewing open-end research problems from existing approaches, the proposed scheme introduces a computational framework of hybrid generative adversarial network (GAN) as a solution to address the identified research problem. The model takes the input of query image which is processed by generator module running 3 different deep learning modes of ResNet, MobileNet, and U-Net. The discriminator module processes the input of real images as well as output from generator. With a novel interactive communication between generator and discriminator, the proposed model offers optimal retrieval performance along with an inclusion of optimizer. The study outcome shows significant performance improvement.
Smart contracts vulnerabilities detection using ensemble architecture of graphical attention model distillation and inference network Preethi, Preethi; Ulla, Mohammed Mujeer; Anni, Ashwitha; Murthy, Pavithra Narasimha; Renukaradhya, Sapna
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp724-736

Abstract

Smart contracts are automated agreements executed on a blockchain, offering reliability through their immutable and distributed nature. Yet, their unalterable deployment necessitates precise preemptive security checks, as vulnerabilities could lead to substantial financial damages henceforth testing for vulnerabilities is necessary prior to deployment. This paper presents the graphical attention model distillation and inference network (GAMDI-Net), a pioneering methodology that significantly enhances smart contract vulnerability detection. GAMDI-Net introduces a unique graphical learning module that employs attention mechanism networks to transform complex contract code into a smart graphical representation. In addition to this a dual-modality model distillation and mutual modality learning mechanism, GAMDI-Net excels in synthesizing semantic and control flow data to predict absent bytecode embeddings with high accuracy. This methodology not only improves the precision of vulnerability detection but also addresses scalability and efficiency challenges, reinforcing trust in the deployment of secure smart contracts within the blockchain ecosystem.
Co-Authors Anni, Ashwitha Preethi, Preethi Renukaradhya, Sapna Ulla, Mohammed Mujeer Yeliyur Hanumanthaiah, Sharath Kumar