In medical images, image segmentation is a very important method, which can accurately locate and analyze the lesions and tissues. However, due to the complexity of medical images and noise, accurate and robust segmen...
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The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and...
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Today, educational tools have become necessary for learners to learn well and improve their knowledge. These tools complement knowledge as they can make it more vivid and add some simulation or game into the learning ...
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The urgency for precise diagnostics during the COVID-19 pandemic has driven advancements in imaging and deep learning tools. However, progress is impeded by limited access to medical imaging data. This study employs c...
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Putting text documents into predetermined genres or categories is a critical operation in natural language processing (NLP) known as genre categorization. While a significant amount of research has been done on genre ...
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Breast cancer is a prevalent tumor across women and is associated with a high mortality rate. Prompt diagnosis is one of the biggest challenges that needs to be addressed globally, as it can considerably improve survi...
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Recently, social media platforms have become very popular as they offer unbelievable opportunities to their users. Twitter is one of the social media platforms on which a huge number of people exchange their messages ...
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Recently, social media platforms have become very popular as they offer unbelievable opportunities to their users. Twitter is one of the social media platforms on which a huge number of people exchange their messages by posting tweets. However, this platform is usually used by automated accounts called bots. Such bots are used to spread fake news, fake ideas, and products. Hence, it is essential to detect the presence of spam bots on Twitter. In order to detect spam bots on Twitter, an effective feature selection technique using a novel hybrid deep learning model is introduced in this paper. This paper proposes a novel spam bot detection system for the Twitter social network that combines profile and tweet-based features. Initially, the Twitter data are pre-processed to improve the accuracy of classification. The pre-processing stage involves various steps such as stopping word removal, tokenization, stemming, n-gram identification, user mention, and vocabulary density and richness. After pre-processing, the tweets are given to the next stage for feature extraction. In this stage, the user profile-based features such as name, screen name, location, and time, as well as the tweet-based features such as hashtags, retweeting of tweets, etc., are extracted from the tweets. The extracted features are then subjected to feature selection, where a meta-heuristic-based optimization algorithm called the Binary Golden Search Optimization algorithm (BGSO) is used. This method helps to reduce the feature dimensionality and overfitting issues. In order to improve the optimization algorithm's searching ability, an X-shaped transfer function is used. Finally, the selected features are provided to the novel Hybrid Hopfield Dilated Depthwise Separable Convolutional Neural Network (HHD2SCNN) based classification model, where the output layer classifies the given tweets as spam bots or legitimate. The proposed method is experimentally verified, and the performance metrics are evaluated
Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be ***,approaches using normalizing flows can accurately evaluate sample di...
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Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be ***,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside ***,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature ***,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly *** two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection *** experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior *** the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 ***,it achieves 100%optimal detection performance in five *** the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.
The global Total Electron Content (TEC) is a critical parameter to present ionosphere morphology, at the same time it significantly affects the propagation of trans-ionosphere radio waves, especially the global naviga...
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ISBN:
(纸本)9780936406404
The global Total Electron Content (TEC) is a critical parameter to present ionosphere morphology, at the same time it significantly affects the propagation of trans-ionosphere radio waves, especially the global navigation satellite signals. The variations in ionosphere can cause satellite signal delays. To address the problem, it is necessary to make accurate prediction of ionosphere TEC so as to strengthen navigation performance. With the rapid development of deep learning techniques, time-series prediction models based on deep learning networks have demonstrated great potential in TEC prediction. Compared to traditional methods, deep learning models have many advantages in capturing complex spatiotemporal dependencies in TEC series. To address the problem, this work compares the performance of five deep learning models in application of ionosphere TEC prediction, focusing particularly on the usage of attention mechanisms on improvement for prediction accuracy. In the experiments, a number of leading deep learning models are evaluated and tested for short-term ionosphere TEC prediction, including the long-short term memory (LSTM), those attention-based networks like CNN-BiLSTM-Attention, Transformer, PatchTST, the TimesNet based on convolutional kernel was also considered. Two years of TEC series are selected for testing, the year 2014 as the solar maximum in solar cycle 24, and the year 2017 in the solar descending phase of solar cycle 24. The advantage of attention-mechanism are analyzed and compared. The results show that the PatchTST model has significant improvement in prediction accuracy, effectively capturing spatiotemporal variations in TEC. Models with attention mechanisms show better performance in prediction accuracy and reliability. By dynamically adjusting the hyper-parameters in different parts of the input sequence, the attention mechanism helps to capture variation features that have greater impact on prediction in time series, thus effectively impr
The rapid adoption of smart vehicles and their interconnection through the Internet of Vehicles (IoV) has increased the use of Electronic Control Units (ECUs) in cars. These ECUs, while enabling advanced features, als...
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