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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Journal of Computing Theories and Applications
Sarker, Md Kamruzzaman
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Journal : Journal of Computing Theories and Applications

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Image Encryption using Half-Inverted Cascading Chaos Cipheration Setiadi, De Rosal Ignatius Moses; Robet, Robet; Pribadi, Octara; Widiono, Suyud; Sarker, Md Kamruzzaman
Journal of Computing Theories and Applications Vol. 1 No. 2 (2023): JCTA 1(2) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9388

Abstract

This research introduces an image encryption scheme combining several permutations and substitution-based chaotic techniques, such as Arnold Chaotic Map, 2D-SLMM, 2D-LICM, and 1D-MLM. The proposed method is called Half-Inverted Cascading Chaos Cipheration (HIC3), designed to increase digital image security and confidentiality. The main problem solved is the image's degree of confusion and diffusion. Extensive testing included chi-square analysis, information entropy, NCPCR, UACI, adjacent pixel correlation, key sensitivity and space analysis, NIST randomness testing, robustness testing, and visual analysis. The results show that HIC3 effectively protects digital images from various attacks and maintains their integrity. Thus, this method successfully achieves its goal of increasing security in digital image encryption
Butterflies Recognition using Enhanced Transfer Learning and Data Augmentation Adityawan, Harish Trio; Farroq, Omar; Santosa, Stefanus; Islam, Hussain Md Mehedul; Sarker, Md Kamruzzaman; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications Vol. 1 No. 2 (2023): JCTA 1(2) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9443

Abstract

Butterflies’ recognition serves a crucial role as an environmental indicator and a key factor in plant pollination. The automation of this recognition process, facilitated by Convolutional Neural Networks (CNNs), can expedite this task. Several pre-trained CNN models, such as VGG, ResNet, and Inception, have been widely used for this purpose. However, the scope of previous research has been somewhat constrained, focusing only on a maximum of 15 classes. This study proposes to modify the CNN InceptionV3 model and combine it with three data augmentations to recognize up to 100 butterfly species. To curb overfitting, this study employs a series of data augmentation techniques. In parallel, we refine the InceptionV3 model by reducing the number of layers and integrating four new layers. The test results demonstrate that our proposed model achieves an impressive accuracy of 99.43% for 15 classes with only 10 epochs, exceeding prior models by approximately 5%. When extended to 100 classes, the model maintains a high accuracy rate of 98.49% with 50 epochs. The proposed model surpasses the performance of standard pre-trained models, including VGG16, ResNet50, and InceptionV3, illustrating its potential for broader application.
Co-Authors Adityawan, Harish Trio Ajib Susanto Christy Atika Sari De Rosal Ignatius Moses Setiadi Didik Hermanto Eko Hari Rachmawanto Farroq, Omar Islam, Hussain Md Mehedul Mulyono, Ibnu Utomo Wahyu Octara Pribadi Robet Robet Setiarso, Ichwan Stefanus Santosa Suyud Widiono