This conference paper aims to create an innovative rental platform, acts as a bridge between users and providers. This platform transforms the rental landscape, offering a secure environment for seamless transactions ...
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The absorption and scattering of light in water result in low-quality photographs being captured underwater. This degradation hampers analysis in fields such as marine biology. To address this, we propose a novel deep...
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Next-word prediction is a fundamental task in natural language processing (NLP) with various applications in intelligent text completion, voice assistants, and human-computer interaction. Most statistical model-based ...
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Augmented reality is defined as a 3D computer generated imagery that is embedded into the real world. There are numerous areas in which augmented reality can be applied, including: education, medical care, entertainme...
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Speech-recognition technology is an essential component of many smart IoT devices. To make these systems secure and privacy-preserving, it is important to consider outsourcing speech-recognition functionality to a thi...
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It is quite challenging to monitor goods quality or security issues due to the intricacy of a tracking system, particularly for the fundamental farming food supply chains that contribute to making up everyday feeds of...
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This paper presents a concise methodology for the detection of partially reduplicated Multi-Word Expressions (MWEs) in Bengali texts. The entire process of identifying such reduplicated forms is carried out in two dis...
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Data mining applications use high-dimensional datasets, but still, a large number of extents causes the well-known ‘Curse of Dimensionality,' which leads to worse accuracy of machine learning classifiers due...
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Growing plants in nutrition solution with any growing medium or roots dipped in distilled water is Hydroponics. Hydroponics, the practice of growing plants in a nutrient-rich solution without soil, offers significant ...
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Underwater image enhancement and object detection has great potential for studying underwater environments. It has been utilized in various domains, including image-based underwater monitoring and Autonomous Underwate...
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Underwater image enhancement and object detection has great potential for studying underwater environments. It has been utilized in various domains, including image-based underwater monitoring and Autonomous Underwater Vehicle (AUV)-driven applications such as underwater terrain surveying. It has been observed that underwater images are not clear due to several factors such as low light, the presence of small particles, different levels of refraction of light, etc. Extracting high-quality features from these images to detect objects is a significant challenging task. To mitigate this challenge, MIRNet and the modified version of YOLOv3 namely Underwater-YOLOv3 (U-YOLOv3) is proposed. The MIRNet is a deep learning-based technology for enhancing underwater images. while using YOLOv3 for underwater object detection it lacks in detection of very small objects and huge-size objects. To address this problem proper anchor box size, quality feature aggregation technique, and during object classification image resizing is required. The proposed U-YOLOv3 has three unique features that help to work with the above specified issue like accurate anchor box determination using the K-means++ clustering algorithm, introduced Spatial Pyramid Pooling (SPP) layer during feature extraction which helps in feature aggregation, and added downsampling and upsampling to improve the detection rate of very large and very small size objects. The size of the anchor box is crucial in detecting objects of different sizes, SPP helps in aggregation of features, while down and upsampling changes sizes of objects during object detection. Precision, recall, F1-score and mAP are used as assessment metrics to assess proposed work. The proposed work compared with SSD, Tiny-YOLO, YOLOv2, YOLOv3, YOLOv4, YOLOv5, KPE-YOLOv5, YOLOv7, YOLOv8 and YOLOv9 single stage object detectors. The experiment on the Brackish and Trash ICRA19 datasets shows that our proposed method enhances the mean average precision for b
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