In the rapidly evolving digital landscape, social media platforms play a critical role in fostering interactions between users and content creators. However, the sheer volume of user comments presents challenges in id...
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Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) sign...
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The effects of changing learning rates, data augmentation percentage and numbers of epochs on the performance of Wasserstein Generative Adversarial Networks with Gradient Penalties (WGAN-GP) are evaluated in this stud...
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In recent years, modernization, physical work scenarios technology-wise, lifestyle, culture, and personal environments contribute to the stressed state of individuals. However, the early evaluation of long-term mental...
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In recent years, modernization, physical work scenarios technology-wise, lifestyle, culture, and personal environments contribute to the stressed state of individuals. However, the early evaluation of long-term mental stress conditions is essential as it triggers several chronic disorders and affects the mental health of affected individuals. In traditional techniques, the multifaceted symptoms and comorbidities introduce difficulty in diagnosis, posing a risk of misdiagnosis. However, the existing techniques often failed to capture the relevant features and neglected to observe the notable shifts in various bio-signals caused by mental stress resulting in inaccurate detection. In addition, medical professionals are skeptical about the adoption of AI-assisted diagnosis due to their inability to be transparent in decision-making processes. In this regard, Explainable Artificial Intelligence (XAI) has surfaced to address the computational black box issue with AI systems by offering transparency and interpretability for model predictions. Consequently, this research proposes the Ensemble Optimization enabled Explainable Convolutional Neural Network (EO-ECNN) for mental stress detection by offering insights into its decision-making process which in turn enhances the system interpretability and transparency. The proposed model exploited the ECNN improves the effectiveness of the stress detection model in conjunction with Ensemble optimization, which combines the traits of the coyote’s and wolf’s individual and group huts, respectively. The high detection accuracy is made possible by the optimization that is being used, which increases the classifier’s slow convergence rate. The multimodal input data for the study still consist of text, images, and audio. The audio features are extracted with the help of the VGGish feature extractor, while the visual input is processed by Residual Network (ResNet). The experimental results demonstrate the superior performance of the multi
The pandemic of COVID-19 has affected worldwide population. Diagnosing this highly contagious disease at an initial stage is essential for controlling its spread. In this paper, we propose a novel lightweight hybrid c...
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Due to the high incidence and possibly fatal nature of skin cancer, early identification is crucial for enhancing patient results. This paper presents a unique deep learning network, EfficientNetB0 ViT, to accurately ...
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To learn and analyze graph-structured data, Graph Neural Networks (GNNs) have emerged as a powerful framework over traditional neural networks, which work well on grid-like or sequential structure data. GNNs are parti...
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Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dy...
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Grape farming is a globally significant agricultural practice, but grapevines frequently encounter viral, fungal, and bacterial infections that compromise crop quality and yield. Conventional disease detection methods...
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Medical Image Analysis (MIA) is integral to healthcare, demanding advanced computational techniques for precise diagnostics and treatment planning. The demand for accurate and interpretable models is imperative in the...
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Medical Image Analysis (MIA) is integral to healthcare, demanding advanced computational techniques for precise diagnostics and treatment planning. The demand for accurate and interpretable models is imperative in the ever-evolving healthcare landscape. This paper explores the potential of Self-Supervised Learning (SSL), transfer learning and domain adaptation methods in MIA. The study comprehensively reviews SSL-based computational techniques in the context of medical imaging, highlighting their merits and limitations. In an empirical investigation, this study examines the lack of interpretable and explainable component selection in existing SSL approaches for MIA. Unlike prior studies that randomly select SSL components based on their performance on natural images, this paper focuses on identifying components based on the quality of learned representations through various clustering evaluation metrics. Various SSL techniques and backbone combinations were rigorously assessed on diverse medical image datasets. The results of this experiment provided insights into the performance and behavior of SSL methods, paving the way for an explainable and interpretable component selection mechanism for artificial intelligence models in medical imaging. The empirical study reveals the superior performance of BYOL (Bootstrap Your Own Latent) with resnet as the backbone, as indicated by various clustering evaluation metrics such as Silhouette Coefficient (0.6), Davies-Bouldin Index (0.67), and Calinski-Harabasz Index (36.9). The study also emphasizes the benefits of transferring weights from a model trained on a similar dataset instead of a dataset from a different domain. Results indicate that the proposed mechanism expedited convergence, achieving 98.66% training accuracy and 92.48% testing accuracy in 23 epochs, requiring almost half the number of epochs for similar results with ImageNet weights. This research contributes to advancing the understanding of SSL in MIA, providin
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