This study explores the effectiveness of various machine learning algorithms in forecasting hair health using a comprehensive dataset incorporating individual traits and lifestyle elements. Logistic regression, random...
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The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of re...
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The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some
Maritime Intelligence is essential for safeguarding maritime interests of coastal nations. Traditionally, vessel movements are tracked using self-reported positions via Automatic Identification System (AIS) transponde...
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The importance of predictive modelling for green selection-making in a variety of fields has expanded because of the fast development of information-driven technologies. The basic and primary goal of this research is ...
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Worldwide Approximate count of 130 million babies were born per annum. Maintaining newborn babies is a great difficulty, mainly for first-time parents. However, intimations from experienced parents, books, and videos ...
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Software-defined vehicles (SDVs) are an emerging paradigm in the automotive industry where vehicles' functionality, performance, and safety can be enhanced and updated through software, even after production. Unli...
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The majority of current deepfake detection methods are constrained to identifying one or two specific types of counterfeit images,which limits their ability to keep pace with the rapid advancements in deepfake ***,in ...
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The majority of current deepfake detection methods are constrained to identifying one or two specific types of counterfeit images,which limits their ability to keep pace with the rapid advancements in deepfake ***,in this study,we propose a novel algorithm,StereoMixture Density Network(SMNDNet),which can detect multiple types of deepfake face manipulations using a single network *** is an end-to-end CNNbased network specially designed for detecting various manipulation types of deepfake face ***,we design a Subtle Distinguishable Feature Enhancement Module to emphasize the differentiation between authentic and forged ***,we introduce aMulti-Scale Forged Region AdaptiveModule that dynamically adapts to extract forged features from images of varying synthesis ***,we integrate a Nonlinear Expression Capability Enhancement Module to augment the model’s capacity for capturing intricate nonlinear patterns across various types of ***,these modules empower our model to efficiently extract forgery features fromdiverse manipulation types,ensuring a more satisfactory performance in multiple-types deepfake *** show that the proposed method outperforms alternative approaches in detection accuracy and AUC across all four types of deepfake *** also demonstrates strong generalization on cross-dataset and cross-type detection,along with robust performance against post-processing manipulations.
Yoga practice offers numerous health benefits, but incorrect poses can lead to injuries and hinder progress. This project leverages the power of deep learning, specifically Convolutional Neural Networks (CNNs) and Ten...
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The transferability of adversarial examples is of central importance to transfer-based black-box adversarial attacks. Previous works for generating transferable adversarial examples focus on attacking given pretrained...
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This research work explores the effects of dry, liquid N2-based cryogenic cooling and cryogenic plus MQL hybrid strategy on surface roughness, rake surface temperature, principal cutting-edge temperature, auxiliary cu...
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