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All Journal JOIV : International Journal on Informatics Visualization
Jothi, Neesha
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Computer Science & IT

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Atomic Structure Simulation and Properties’ Prediction using Machine Learning on Neodymium Oxide Nanoparticles Zinc Tellurite Glasses Aided by FTIR and TEM Analysis Nazrin, S.N.; Zaman, Halimah Badioze; Jothi, Neesha; Jouay, Doha; Lahrach, Badreddine; Halimah, M.K.
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.3097

Abstract

The optical, structural, and physical characteristics of zinc tellurite glasses doped with neodymium oxide nanoparticles, which are produced by the melt-quenching method, were examined in this work. The amorphous character of the glasses was verified by XRD analysis. Using the Pair Distribution Function (PDF) and Monte Carlo simulations and visualisation for precise molecule distribution representation, an intuitive Python interface was created to emphasize these features. The density increased with increasing Nd2O3 concentrations, from 5346 to 5606 kg/cm2. Density data was used to infer the molar volume. The best projected density was achieved by the Gradient Boosting Regressor model, with a R2 of 0.9988 and an RMSE of 0.0032; the best predicted molar volume was achieved by linear regression, with a R2 of 1 and an RMSE of 2.67e-15. These models successfully represent the correlations between dopant concentration and glass properties, advancing our knowledge of the optical properties for further glass technology research.
DeepForgery Images Detection Using Deep Learning Approaches and Error Level Analysis Nazrin, S.N.; Burhanuddin, Liyana Adilla binti; Jothi, Neesha; Zaman, Halimah Badioze; Rosnan, Muhammad Fahmi Bin
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3504

Abstract

The increasing of manipulated images, often shared on social media platforms, poses significant challenges for distinguishing authentic content from forgeries. This study aims to enhance the detection of tampered images by integrating Error Level Analysis (ELA) with Convolutional Neural Networks (CNNs). Specifically, the objectives are to evaluate the performance of two CNN architectures, VGG16 and MesoNet, combined with ELA preprocessing, and to identify potential avenues for future improvements in forgery detection. The dataset used comprises 7,492 authentic and 5,124 tampered images, sourced from the CASIA database, and is complemented with images from the Milborrow University of Cape Town (MUCT) dataset. Images were preprocessed using ELA to amplify discrepancies caused by tampering before being analyzed by the CNN models. The results indicate that the proposed ELA-VGG16 model achieved an accuracy of 86.786%, while the ELA-MesoNet model demonstrated superior performance, with an accuracy of 92.7%. These findings highlight the potential of combining ELA preprocessing with CNN architectures for robust image forgery detection. Despite fluctuations in training curves and instances of overfitting, the model effectively detects subtle manipulations in the majority of cases. However, challenges such as false positives and generalization to diverse datasets persist. Future research should explore enhancements such as expanded data augmentation, the integration of multi-model architectures,such as Xception or capsule networks, and advanced preprocessing techniques, which could further refine the model’s applicability and accuracy. These efforts would advance both the practical detection of forgeries and theoretical developments in informatics visualization, addressing critical challenges in digital forensics and media integrity.
Co-Authors Burhanuddin, Liyana Adilla binti Halimah Badioze Zaman Halimah, M.K. Jouay, Doha Lahrach, Badreddine Nazrin, S.N. Rosnan, Muhammad Fahmi Bin