Handwritten Character Recognition (HCR) is a critical area of research within pattern recognition and artificial intelligence, with applications spanning from document digitization, optical character recognition (OCR)...
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This paper investigates the use of Convolutional Neural Networks (CNNs) for plant disease diagnosis, utilizing the notion of transfer learning to develop a deep CNN network at a low cost. The dataset utilized includes...
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The study of leaf diseases, as well as their detection and diagnosis, has been the subject of an increasing amount of research and attention as intelligent agricultural systems have become increasingly common and wide...
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The quick rise in smart home technologies calls for the development of advanced energy forecasting models that can accurately predict consumption patterns while maintaining user privacy. Existing energy forecasting me...
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The proposed models can design the airfoil by Cuckoo search with Levenberg-Marquardt. The Neural Network framework has impediments due to over-fitting. This paper proposed a modified cuckoo search. here the aerodynami...
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The installation of water distribution networks (WDNs) on farmland is a key facet of agricultural practices in India, particularly for ensuring adequate irrigation of crops, especially in the context of facility agric...
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Personal safety, especially for women during nighttime, remains a pressing concern. The "Guardian Pi"is an innovative solution that leveragesthe Raspberry Pi 4 Model B+ to provide real-time surveillance and ...
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Robots are the solutions for replacing humans from dangerous and manual labour. Repetitive, non-creative manual tasks can be automated by using robots. One of the principal approaches to automate robots is by line sen...
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Bangla is the sixth most widely spoken language worldwide, making the recognition of handwritten characters in this language an important task. However, recognizing Bangla characters is challenging due to the large nu...
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Ensuring the safe navigation of autonomous vehicles in intelligent transportation system depends on their ability to detect pedestrians and vehicles. While transformer-based models for object detection have shown rema...
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Ensuring the safe navigation of autonomous vehicles in intelligent transportation system depends on their ability to detect pedestrians and vehicles. While transformer-based models for object detection have shown remarkable advancements, accurately identifying pedestrians and vehicles in adverse weather conditions remains a challenging task. Adverse weather introduces image quality degradation, leading to issues such as low contrast, reduced visibility, blurred edges, false detection, misdetection of tiny objects, and other impediments that further complicate the accuracy of detection. This paper introduces a novel Pedestrian and Vehicle Detection Model under adverse weather conditions, denoted as PVDM-YOLOv8l. In our proposed model, we first incorporate the Swin-Transformer method, which is designed for global extraction of feature of small objects to identify in poor visibility, into the YOLOv8l backbone structure. To enhance detection accuracy and address the impact of inaccurate features on recognition performance, CBAM is integrated between the neck and head networks of YOLOv8l, aiming to gather crucial information and obtain essential data. Finally, we adopted the loss function Wise-IOU v3. This function was implemented to mitigate the adverse effects of low-quality instances by minimizing negative gradients. Additionally, we enhanced and augmented the DAWN dataset and created a custom dataset, named DAWN2024, to cater to the specific requirements of our study. To verify the superiority of PVDM-YOLOV8l, its performance was compared against several commonly used object detectors, including YOLOv3, YOLOv3-tiny, YOLOv3-spp, YOLOv5, YOLOv6, and all the versions of YOLOv8 (n, m, s, l, and x) and some traditional models. The experimental results demonstrate that our proposed model achieved a 6.6%, 5.4%, 6%, and 5.1% improvement in precision, recall, F1-score and mean Average Precision (mAP) on the custom DAWN2024 dataset. This substantial improvement in accuracy ind
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