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6 articles for “generative adversarial networks (GANs)”
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Enhancing Image Quality with Deep Learning Techniques for Restoration and Denoising
Abstract: Abstract: Clear and high-quality images have become more and more necessary in recent years due to the rapid development of computer vision-based autonomous systems, including autonomous driving, underwater robots, video surveillance, and medical imaging. However, real-world circumstances, such as inclement weather, underwater scenes, and moving cameras, can lead to poorer-quality photographs. These degradations, which include color distortions, noise, and blurring, hurt how well visual systems accomplish tasks like target tracking, …
Published in International Journal of Broadband Cellular Communication · Vol. 10, Issue 2, 2024 · pp. 1–6 Read article
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Customer preferences in adopting AI in their building designs: A Perspective case study from Saudi Arabian architects
Abstract: Recent AI advances have improved Saudi building design inventiveness, efficiency, and cultural sensitivity. Automating tedious activities like image resizing and layout optimization with AI helps designers focus on novel concepts. AI may assess client preferences and society conventions to assist designers adapt their ideas to local cultural values, making architectural development projects more appealing. Even though it has many advantages, artificial intelligence in architecture is confronted with several challenges. The …
Published in International Journal of Architecture and Infrastructure Planning · Vol. 10, Issue 2, 2024 · pp. 25–40 Read article
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SYNTHETIC MEDICAL DATA GENERATIONUSING GAN MODEL
Abstract: The healthcare industry has witnessed growing interest in synthetic data generation, driven by increasing demands for large and diverse datasets. Real medical data is often inaccessible due to stringent privacy laws, data scarcity, and ethical considerations. Sophisticated models, such as Generative Adversarial Networks (GANs), have emerged as powerful tools to generate synthetic data that closely resembles real-world data. This approach is particularly effective for addressing the challenges of researching rare …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 11, Issue 2, 2025 Read article
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Advanced Classification of Diabetic Retinopathy Using GAN and Deep Residual CNN Model
Abstract: Diabetic retinopathy (DR) is a leading cause of blindness worldwide, impacting the largest number of individuals who have diabetes. Early identification of diabetic retinopathy may help to prevent severe effects; however, the very limited availability of labeled datasets, and the possibility of overfitting of the model, presents obstacles to obtaining an accurate diagnosis. In this research, a hybrid deep learning strategy is introduced, where Generative Adversarial Networks (GANs) are used …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 8–16 Read article
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Combating Deepfakes and AI-Generated Misinformation: Towards a Digital Likeness and Integrity Framework for India
Abstract: The real struggle is to distinguish what is real and what is fake in the communication of the hyper-digitalized age, in which AI and generative technologies significantly impact the field of communication. The change in the creation and consumption of information has come with the deepfake AI-generated fake media that can imitate a human in voice, image, and video. However, on the one hand, generative models such as Generative Adversarial …
Published in International Journal of Digital Communication and Analog Signals · Vol. 12, Issue 1, 2026 · pp. 1–20 Read article
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Intelligent Trajectories: Harnessing Artificial Intelligence for Next Generation Missile and Propellant Design
Abstract: The rapid evolution of artificial intelligence (AI) has unlocked transformative pathways for high-performance aerospace engineering, yet its integration into missile and propellant design remains nascent. This study investigates a hybrid AI-driven framework that unites physics-based modeling, data-centric learning, and multi-objective optimization to accelerate the conception, validation, and refinement of missile airframes and solid-propellant formulations. First, a physics-informed neural network (PINN) is trained on a curated database of historic missile geometries, …
Published in International Journal of Energetic Materials · Vol. 12, Issue 1, 2026 · pp. 20–26 Read article