This study proposes a hybrid deep learning approach to address the complexity and dynamic characteristics of modern network environments. The research integrates Graph Neural Networks (GNNs) and Convolutional Long and...
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In crop production, diseases are the most important concern regarding food security. The most popular crop in the world is the tomato which is found in different verity apart from the meal. It is the most cultivated c...
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ISBN:
(数字)9798331512248
ISBN:
(纸本)9798331512255
In crop production, diseases are the most important concern regarding food security. The most popular crop in the world is the tomato which is found in different verity apart from the meal. It is the most cultivated crop after the potato in the world but due to the various types of diseases, the quality and quantity of the tomato has been decreasing. In recent trends, the combination of the Internet of Things and Machine Learning has played a vital role in the agriculture industry and providing support to the farmers to keep track of crop growth, monitor the temperature, humidity, and water level, and also use disease detection systems. The disease detection system is used but farmers are unaware of the disease at the initial level in the crop. This paper aims to propose a system based on machine learning and the Internet of Things to identify the plant root disease at early stage and provide a smart farming solution.
Dynamic handover decisions are among the most important in such a way that the transmission should be seamless with the preservation of quality of service (QoS) in wireless networks. Traditional handover mechanisms ha...
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This research study analyzes six key factors in the education and teaching of IoT embedded direction: training objectives (which direction to teach), curriculum system (what to teach), teaching organization (how to te...
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Real-time sports analysis provides valuable insights to teams, allowing them to change their tactics and increase their winning probability through strategising. In India, the globalisation of sports, specifically Cri...
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Real-time sports analysis provides valuable insights to teams, allowing them to change their tactics and increase their winning probability through strategising. In India, the globalisation of sports, specifically Cricket, is at its peak, due to the Indian Premier League (IPL). In a match, a team’s performance heavily relies on the individual performances of its players. This research paper proposes a model that internally utilises Detectron2, an AI tool, for pose detection. This model generates key points that are then used to recognize various batting shots and utilize the eXtreme Gradient Boosting (XGBoost) Classifier, achieving an accuracy of 92.13%. Furthermore, comparisons between the methods proposed and other researched methods show outperforming them. The proposed method has been tested on a dataset containing four (4) types of shots: pull shot, sweep, drive, and leg glance flick.
In recent years, advances in deep learning have made a major impact in fields such as computer vision and image classification, and convolutional neural networks (CNN) have played an important role in visual recogniti...
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In recent years, advances in deep learning have made a major impact in fields such as computer vision and image classification, and convolutional neural networks (CNN) have played an important role in visual recognition such as yoga pose classification. However, this model is difficult to implement on edge devices with limited resources due to the large number of calculations and operations. Memory intensive needs. Quantitative methods offer solutions by reducing sample size and increasing inference speed while maintaining accuracy. In this work, we investigate how to apply quantization techniques to the MobileNetV3Large model for yoga pose classification. The main goals include using post-training quantization (PTQ) and quantization-aware training (QAT) to improve the model’s performance, compare the performance of PTQ and QAT models, and send the best models to the edge. Kaggle’s yoga poses dataset contains 3700 images of 43 poses, which were pre-processed and used to fine-tune the MobileNetV3Large model. Using the PTQ, the sample size increased slightly from 12.5 MB to 3.43 MB. clear. In comparison, the QAT model has a higher accuracy of 84.71% and a model size of 11.2 MB. These results demonstrate the effectiveness of the quantization method in optimizing Yoga’s MobileNetV3Large model. Simplify deployment of edge devices by sharing beacons. Future research should focus on further refinement and experimental validation of these models to improve their use and support their development. Interactive and easy-to-use yoga practice tools for mobile and embedded platforms.
Music plays a vital role in human life, serving as a universal medium that connects people across the world. The process of classifying music into genre labels by analyzing its sounds, rhythms, and lyrics is known as ...
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This study addresses the challenge of class imbalance in the CIFAR-10 dataset, where certain classes are significantly underrepresented. To tackle this issue, data augmentation and resampling techniques, particularly ...
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In this experiment, high-temperature polyethylene terephthalate (PT) was mixed with epoxy resin (ER) that had been thinned with acetone. Sisal fibers were coated with the resulting product. Composites of Coated treate...
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