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15 articles for “Generative Adversarial Networks (GANs)”
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A Comparison of Different Generative AI Models
Abstract: Generative models have significantly advanced the field of artificial intelligence by allowing machines to produce complex and realistic outputs such as images, text, and other forms of data. Among the leading frameworks in this domain are generative adversarial networks (GANs), variational autoencoders (VAEs), and architectures based on Transformers. Each model offers specific benefits and drawbacks concerning design structure, training demands, and range of applications. This paper provides a detailed comparison …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 · pp. 16–22 Read article
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Leveraging Generative AI for Test Case Creation in Complex Systems
Abstract: Modern software systems exhibit increasing complexity, demanding sophisticated testing methodologies to ensure reliability and functionality. Traditional manual testing approaches often struggle to keep pace with this complexity, leading to inadequate test coverage and increased risk of unforeseen issues. This study explores the potential of Generative AI (GAI) in revolutionizing test case creation for complex systems. We delve into the practical application of GAI techniques, such as Variational Autoencoders (VAEs) and …
Published in Recent Trends in Programming languages · Vol. 12, Issue 3, 2025 · pp. 16–22 Read article
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Deep Learning Algorithms for Medical Image Encryption to Ensure Secure Data Transfer
Abstract: Deep learning has significantly impacted various fields, including medical imaging, by offering new ways to encrypt medical images for secure data transfer. This research work examines how deep learning algorithms are used to enhance medical image security during transmission. Given the high sensitivity and privacy requirements of medical data, it’s crucial to maintain its confidentiality. Traditional encryption techniques, while reliable, often struggle with issues like scalability, computational efficiency, and the …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 3, 2024 · pp. 28–36 Read article
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Transfer Learning in Deep Learning Models for Medical Imaging: Utilizing Pretrained Models to Improve Performance in Medical Image Analysis
Abstract: Transfer learning is now a trending technique in deep learning, especially in medical imaging. This technique solves landmark problems by utilizing the pre-trained models, including the limited availability of the annotated medical data and the time-consuming computational costs of training deep learning models from scratch. The generalizability of deep models could increase diagnostic precision for specific medical tasks, require fewer samples to train, and take less time to train due …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 1, 2025 · pp. 67–85 Read article
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A Study of DCGAN-Based Generative Models for Anime Character Face Generation
Abstract: Artificial intelligence, or AI, has in recent years moved from simple rule-based systems to models that are now fully capable of creative content generation and are referred to as generative AI. One such approach for content generation, introduced in the year 2014, is called generative adversarial networks (GANs), which consists of training a generator to create fake content that tries to mimic real content as closely as possible and a …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 1, 2026 · pp. 31–41 Read article
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Autonomous Calibration of Medical Devices Using Synthetic Biosignals and Adaptive Learning
Abstract: The accuracy and reliability of modern biomedical diagnostic devices are critically dependent on effective calibration mechanisms capable of handling dynamic physiological and environmental variations. Conventional calibration approaches, which rely on static reference signals and manual adjustments, are inadequate in addressing challenges such as sensor drift, noise interference, motion artifacts, and long-term performance degradation. To overcome these limitations, this research proposes an innovative AI-driven adaptive biosignal simulation and calibration architecture for …
Published in Journal of Instrumentation Technology & Innovations · Vol. 16, Issue 2, 2026 Read article
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State of the Art: A Pandemic Big HealthCare Analytics Solution: Image Data Classification Using Quantum MAML
Abstract: The modern age is facing many pandemic healthcare problems, e.g., covid 19, infections, inflammations, and many more, leading to critical, deadly situations. Survival rate can be increased with proper diagnosis of such data. We have proposed one of the implementations based on a medical image dataset for classification using deep reinforcement learning (RL) with quantum computing. Deep RL is the combination of DL (deep learning), generative adversarial network (GAN), and …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 2, 2024 · pp. 1–9 Read article
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Surveillance Car using ESP32 Cam Module by using GAN Model
Abstract: This paper shows the Surveillance Car system which leverages the ESP32 Cam module and incorporates advanced image processing through a Generative Adversarial Network (GAN) model to redefine the landscape of mobile surveillance systems. The ESP32 Cam serves as the core hardware platform, offering compact design and wireless capabilities for real-time image capture and remote monitoring. The system’s innovation lies in the integration of a GAN model for image processing, enhancing …
Published in Journal of VLSI Design Tools and Technology · Vol. 14, Issue 2, 2024 · pp. 28–37 Read article
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Physics-Informed Generative and Tensor-Based Framework for DNA Sequence Simulation and Genomic Structure Discovery
Abstract: In this paper, we explore the intersection of artificial intelligence (AI) and mathematical physics to propose advanced methods for DNA sequence generation and analysis. Specifically, we investigate how physics-informed Generative Adversarial Networks (GANs) and tensor network representations can be harnessed to restructure DNA for applications in genetic science. The proposed methodology offers a unique integration of concepts of thermodynamic modeling with innovative GAN architecture in order to allow the creation …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 2, 2026 Read article
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Role of Generative AI in Redefining Data Analytics
Abstract: The rapid evolution of data-driven technologies has introduced both significant challenges and promising opportunities within the field of data analytics. Among the most impactful advancements is Generative Artificial Intelligence (Generative AI), a groundbreaking subset of AI that is reshaping how data is interpreted, generated, and utilized. Unlike traditional analytical tools that rely solely on existing data patterns, generative AI possesses the capability to create synthetic data, simulate complex scenarios, and …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 2, 2025 · pp. 01–07 Read article
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Generative AI for VR: Creating Physically Realistic Models
Abstract: Virtual Reality has revolutionized the traditional learning system by creating and interactive and engaging environment. However, its ability to show precise real-world experiences is limited due to lack of physical realism. This study investigates the potential of Generative Adversarial Network (GAN) in creating physically realistic 3D models. Proposed system incorporates deep learning techniques along with physics-based constraints to enhance model’s accuracy and usability. To achieve this, experiments were conducted on …
Published in Journal of Advancements in Robotics · Vol. 12, Issue 3, 2025 · pp. 14–22 Read article
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Development of a Generative AI Model for Early Detection and Prevention of Electrical Faults in Thermal Power Plants
Abstract: Electrical faults in thermal power plants can lead to severe equipment damage, production downtime, and safety hazards if not detected in advance. This study presents the development of a Generative Artificial Intelligence (GenAI) model for the early detection and prevention of electrical faults using predictive analytics. The proposed framework integrates Generative Adversarial Networks (GANs) with deep learning (CNN) and machine learning algorithms (Random Forest, Logistic Regression) to enhance data diversity, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 1–10 Read article
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Forecasting Climate-Driven Healthcare Demand in Agricultural Regions: A Multi-Modal AI Approach
Abstract: The rapidly increasing instability of world climatic regimes has made past meteorological thresholds irrelevant, especially in the agricultural areas where monetary stability and well-being of humans are closely intertwined with an environmental situation. The more the frequency of 1 in every 1000-year events, i.e., heatwaves and catastrophic flooding increase, the greater the rural healthcare systems are in crisis, i.e., unable to predict a surge in demand because of data scarcity, …
Published in International Journal of Climate Conditions · Vol. 2, Issue 2, 2025 · pp. 28–38 Read article
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Comparative Analysis of Data Augmentation Techniques in CNN-based Classification of Atelectasis
Abstract: This research delves into the critical issue of atelectasis, its causes, and potential complications if left untreated. Leveraging deep learning algorithms, particularly convolutional neural networks (CNN), the paper explores their application in medical image analysis, focusing on the detection of atelectasis using the “chestX-ray8” database. The study compares various data augmentation techniques for improved accuracy, showcasing the importance of augmentation in enhancing model generalization. Through meticulous experimentation and evaluation, the …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 3, 2024 · pp. 1–8 Read article
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Generative Design of Bioactive Orthopedic Composites for Fracture Repair Using an Integrated Conditional GAN–Transformer Framework: A Multi-Objective Approach
Abstract: Orthopedic composite implants for fracture repair must simultaneously satisfy conflicting mechanical and biological demands: high fracture toughness, sufficient compressive stiffness, and bioactive surface chemistry enabling osteoblast adhesion and mineralization. Existing design approaches rely on trial-and-error experimentation, yielding sub-optimal trade-offs between these objectives. This paper presents an integrated conditional Generative Adversarial Network–Transformer (cGAN-T) framework for fully computational, multi-objective generative design of hydroxyapatite (HA)-reinforced polymer composite microstructures targeting Orthopedic fracture repair. A …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 21–35 Read article