This research study aims to create a full time winner predictor program for Counter-Strike Global Offensive tournaments that can predict the round winner of the game being spectated. The algorithm creates accurate pre...
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Due to the negligence of driver or to some exterior factors may cause many people lost their lives in road accidents. So, there is a vital requirement to develop an efficient and effective accident detection system wh...
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In recent years, there has been a persistent focus on developing systems that can automatically identify the hate speech content circulating on diverse social media platforms. This paper describes the team "Trans...
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The advancement in technology has enabled people to give reviews in their native language. Urdu is spoken by millions of people worldwide and a substantial amount of textual data is generated in the Urdu language. The...
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The current growth in e-content is attributed to, information exchanged through social media, e-news, etc. Several researchers have proposed an encoder-decoder model with impressive accuracy. This paper exploits featu...
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This paper proposes an image forgery detection technique combining the use of Convolutional Neural Networks (CNNs) with Error Level Analysis (ELA) for efficiently distinguishing tampered images from those that are ori...
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Intention detection is a crucial aspect of natural language understanding (NLU), focusing on identifying the primary objective underlying user input. In this work, we present a transformer-based method that excels in ...
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Over the past years, researchers across the globe have made significant efforts to develop systems capable of identifying the presence of hate speech in different languages. This paper describes the team Transformers&...
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This Multi-Server Management aims to offer a web-based management system with its different capabilities that support SSH by Paramiko library at the same time, it provides the users an interface to communicate with th...
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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
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