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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Robotics and Control (JRC)
Nanjundegowda, Raghu
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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A Comprehensive Review of AI and Deep Learning Applications in Dentistry: From Image Segmentation to Treatment Planning Nambiar, Rajashree; Nanjundegowda, Raghu
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23056

Abstract

Deep learning leverages multi-layered neural networks to analyze intricate data patterns, offering advancements beyond traditional methods. This review paper explores the significant impact of deep learning on diagnostic and treatment processes across various dental specialties. In restorative dentistry, deep learning algorithms enhance the detection of dental caries and optimize the design of restorations. Orthodontics benefits from automated cephalometric analysis and personalized treatment planning. Periodontics utilizes deep learning for accurate diagnosis and classification of periodontal diseases, as well as monitoring disease progression. In endodontics, these technologies improve root canal detection and treatment outcome predictions. Prosthodontics and oral surgery leverage deep learning for precise prosthesis design and surgical planning, enhancing patient-specific care. Despite the promising advancements, challenges such as data quality, model interpretability, and regulatory issues persist. To solve these problems and get the most out of deep learning in dentistry, the review stresses the need for ongoing research and collaboration between different fields. In our review, we discuss significant deep learning models such as Convolutional Neural Networks (CNNs) and their applications in dentistry, including tooth segmentation, lesion detection, and orthodontic treatment planning. We also examine the use of Generative Adversarial Networks (GANs) for generating synthetic data to enhance training datasets. This paper reviews recent research to provide a comprehensive overview of how deep learning is transforming dentistry, leading to improved patient outcomes, diagnostic accuracy, and treatment efficiency. The advancements in AI and 3D imaging herald a future of automated, high-quality dental diagnostics and treatments.
Comparative Analysis of CryptoGAN: Evaluating Quality Metrics and Security in GAN-based Image Encryption Bhat, Ranjith; Nanjundegowda, Raghu
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.23096

Abstract

Balancing security with image quality is a critical challenge in image encryption, particularly for applications like medical imaging that require high visual fidelity. Traditional encryption methods often fail to preserve image integrity and are vulnerable to advanced attacks. This paper introduces CryptoGAN, a novel GAN-based model designed for image encryption. CryptoGAN employs an architecture to effectively encrypt a dataset of 2000 butterfly images with a resolution of 256x256 pixels, integrating Generative Adversarial Networks (GANs) with symmetric key encryption. Using a U-Net Generator and a PatchGAN Discriminator, CryptoGAN optimizes for key metrics including Structural Similarity Index (SSIM), entropy, and correlation measures. CryptoGAN's performance is comprehensively compared against state-of-the-art models such as Cycle GAN-based Image Steganography, EncryptGAN, and DeepEDN. Our evaluation, based on metrics like SSIM, entropy, and PSNR, demonstrates CryptoGAN's superior ability to enhance encryption robustness while maintaining high image quality. Extensive experimental results confirm that CryptoGAN effectively balances security and visual fidelity, making it a promising solution for secure image transmission and storage. This study is supported by a literature survey and detailed analysis of the model architecture, underscoring CryptoGAN's significant contributions to the field of image encryption.
CryptoGAN: a new frontier in generative adversarial network-driven image encryption Bhat, Ranjith; Nanjundegowda, Raghu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4813-4821

Abstract

There is a growing need for an image encryption scheme, for huge amount of social media data or even the medical data to secure the privacy of the patients or the user. This study introduces a ground-breaking deep learning architecture named crypto generative adversarial networks (CryptoGAN), a novel architecture for generating cipher images. This architecture has the ability to generate both encrypted and decrypted images. The CryptoGAN system consists of an initial encryption network, a generative network that verifies the output against the desired domain, and a subsequent decryption phase. The generative adversarial networks (GAN) are utilised as the learning network to generate cipher images. This is achieved by training the neural network using images encrypted from a conventional image encryption scheme such as advanced encryption standards (AES), and learning from the resulting losses. This enhances security measures when dealing with a large dataset of photos. The assessment of the performance metrics of the encrypted image, including entropy, histogram, correlation plot, and vulnerability to assaults, demonstrates that the suggested generative network may get a higher level of security.
A Comprehensive Review of AI and Deep Learning Applications in Dentistry: From Image Segmentation to Treatment Planning Nambiar, Rajashree; Nanjundegowda, Raghu
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23056

Abstract

Deep learning leverages multi-layered neural networks to analyze intricate data patterns, offering advancements beyond traditional methods. This review paper explores the significant impact of deep learning on diagnostic and treatment processes across various dental specialties. In restorative dentistry, deep learning algorithms enhance the detection of dental caries and optimize the design of restorations. Orthodontics benefits from automated cephalometric analysis and personalized treatment planning. Periodontics utilizes deep learning for accurate diagnosis and classification of periodontal diseases, as well as monitoring disease progression. In endodontics, these technologies improve root canal detection and treatment outcome predictions. Prosthodontics and oral surgery leverage deep learning for precise prosthesis design and surgical planning, enhancing patient-specific care. Despite the promising advancements, challenges such as data quality, model interpretability, and regulatory issues persist. To solve these problems and get the most out of deep learning in dentistry, the review stresses the need for ongoing research and collaboration between different fields. In our review, we discuss significant deep learning models such as Convolutional Neural Networks (CNNs) and their applications in dentistry, including tooth segmentation, lesion detection, and orthodontic treatment planning. We also examine the use of Generative Adversarial Networks (GANs) for generating synthetic data to enhance training datasets. This paper reviews recent research to provide a comprehensive overview of how deep learning is transforming dentistry, leading to improved patient outcomes, diagnostic accuracy, and treatment efficiency. The advancements in AI and 3D imaging herald a future of automated, high-quality dental diagnostics and treatments.
Comparative Analysis of CryptoGAN: Evaluating Quality Metrics and Security in GAN-based Image Encryption Bhat, Ranjith; Nanjundegowda, Raghu
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.23096

Abstract

Balancing security with image quality is a critical challenge in image encryption, particularly for applications like medical imaging that require high visual fidelity. Traditional encryption methods often fail to preserve image integrity and are vulnerable to advanced attacks. This paper introduces CryptoGAN, a novel GAN-based model designed for image encryption. CryptoGAN employs an architecture to effectively encrypt a dataset of 2000 butterfly images with a resolution of 256x256 pixels, integrating Generative Adversarial Networks (GANs) with symmetric key encryption. Using a U-Net Generator and a PatchGAN Discriminator, CryptoGAN optimizes for key metrics including Structural Similarity Index (SSIM), entropy, and correlation measures. CryptoGAN's performance is comprehensively compared against state-of-the-art models such as Cycle GAN-based Image Steganography, EncryptGAN, and DeepEDN. Our evaluation, based on metrics like SSIM, entropy, and PSNR, demonstrates CryptoGAN's superior ability to enhance encryption robustness while maintaining high image quality. Extensive experimental results confirm that CryptoGAN effectively balances security and visual fidelity, making it a promising solution for secure image transmission and storage. This study is supported by a literature survey and detailed analysis of the model architecture, underscoring CryptoGAN's significant contributions to the field of image encryption.
A Review on Comparative Analysis of Generative Adversarial Networks’ Architectures and Applications Bhat, Ranjith; Nanjundegowda, Raghu
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.24160

Abstract

Generative Adversarial Networks (GANs) are a major advancement in generative modeling, surpassing traditional machine learning models in tasks such as image generation, super-resolution, and image-to-text translation. A GAN consists of two neural networks: a Generator (G), which creates data from noise or a latent vector, and a Discriminator (D), which determines whether the data is real or generated. These networks train competitively, improving each other iteratively to produce increasingly realistic outputs. However, GANs face challenges like mode collapse, unstable training, and convergence issues, leading to the adoption of strategies such as instance normalization and enhanced loss functions. Future research can focus on improving stability, developing novel loss functions, and applying GANs in unsupervised learning. Performance metrics like Inception Score, Fréchet Inception Distance (FID), and Structural Similarity Index (SSIM) are essential for evaluating and comparing GAN architectures. Additionally, ethical concerns, including the misuse of GANs for deepfakes and synthetic data, underscore the importance of transparency, accountability, and ethical standards in research and deployment. This review provides an accessible introduction to GANs for novice researchers, along with a detailed analysis of their limitations, applications, and future prospects, offering valuable insights and direction for advancing this field.
Co-Authors Bhat, Ranjith Nambiar, Rajashree