Crowd counting has raised a major issue in computer vision with its extensive range of applications, like crowd management, public protection, and city development. The proposed model involves developing a method that...
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With the integration of smartphones, VR head-mounted displays that are affordable (HMDs) have made VR more accessible to the general public and increased its widespread use. However, due to their lack of interaction, ...
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Skin cancer is a serious worldwide health issue, precise and early detection is essential for better patient outcomes and effective treatment. In this research, we use modern deep learning methods and explainable arti...
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The Internet of Vehicles (IoV) has become one challenging communication technology in the current internet world. IoV enables real-time data exchange between vehicles, road infrastructures, and mobile communication de...
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Robust watermarking tries to conceal information within a cover imag e/video imperceptibly that is resistant to various distortions. Recently, deep learning-based approaches for image watermarking have made significan...
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MANETs, as self-configuring networks lacking a fixed infrastructure, are exceptionally vulnerable to a multitude of security threats. One such severe threat is the Wormhole Attack, where malicious nodes create a virtu...
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When people observe pictures, different pictures will generate different emotions, and the painters often convey emotional energy to the audience through the media. Through the effect of this emotional transfer, peopl...
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During corn's exploration and manufacturing phases, farmers have a complicated issue in accurately diagnosing corn crop infections. To solve this issue, this work provides a method for specific position three prev...
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As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around...
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As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors
Speech analysis has emerged as a crucial tool in bridging the gap between the real and virtual worlds as the amount of human contact with machines increases. One subfield that has long been investigated in both psychi...
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