Traffic volume surveying is a crucial activity to get traffic statistics for road management and traffic congestion control. In recent years, the target environment of traffic volume surveying has become more complex,...
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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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In digital identification technologies, fingerprint-based biometric systems are extensively utilized for authentication. Fingerprint authentication systems usually store the original minutiae points as user templates ...
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Emotion detection stands as a pivotal approach for extracting unbiased insights from textual documents. This process empowers business experts to identify areas of improvement and bolster key facets of their operation...
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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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Human activity detection plays a vital role in applications such as healthcare monitoring, smart environments, and security surveillance. However, traditional methods often rely on computationally intensive models, wh...
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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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