Disaster management calls for as quick a detection of critical infrastructure-like airports and runways. Most of the traditional object detection models, which include CNNs, R-CNN s, SSDs, and earlier versions of YOLO...
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This research tackles the rising significance of Facial Emotion Recognition (FER) for personalized user experiences. Employing transfer learning, the proposed system pinpoints user emotions from. facial expressions wi...
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In the rapidly evolving landscape of cybersecurity, the classification of malware has become increasingly critical due to the exponential growth in the volume of malware, which poses significant security threats to fi...
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The internet, social media, and other cutting-edge technologies are all contributing to the exponential growth of digital information. This makes interpreting and applying this enormous amount of data both possible an...
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Identifying antenna structures from satellite images is essential for managing telecommunications infrastructure. Traditional object detection methods, including edge detection, template matching, and machine learning...
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The research aims to optimize agriculture using data science techniques. Agriculture is a critical sector for sustaining life on earth, and optimizing it can enhance food security and increase the profitability of far...
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Voice assistant applications have become integral parts of modern technology ecosystems, offering users convenient and efficient ways to interact with their devices. This paper introduces Voice Ai, a versatile voice a...
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Locating suspicious military tents or encampments is crucial for national security. Recently, deep learning methods have become increasingly popular for detecting military objects at country borders. Traditional metho...
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In an era marked by rapid advancements in science and technology, the potential for growth and efficiency across all sectors is immense. However, the healthcare industry is still in the midst of its digitization journ...
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Traffic congestion poses a significant challenge in urban areas, and deep reinforcement learning (DRL) offers an encouraging method for traffic signal control. We evaluate the DQN algorithm, with PQAS (Pressure, Queue...
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