The area of brain-computer interface research is widely spreading as it has a diverse array of potential applications. Motor imagery classification is a boon to several people with motor impairment. Low accuracy and d...
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In this article, we present the first rigorous theoretical analysis of the generalisation performance of a Geometric Semantic Genetic Programming (GSGP) system. More specifically, we consider a hill-climber using the ...
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A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic *** advancements in deep neural networks have enabled the development of models to represent traffic flow ***,accurat...
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A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic *** advancements in deep neural networks have enabled the development of models to represent traffic flow ***,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal *** paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention *** studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast *** addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast *** experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.
Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and...
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To reduce system complexity and bridge the interface between electronic and photonic circuits,there is a high demand for a non-volatile memory that can be accessed both electrically and ***,practical solutions are sti...
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To reduce system complexity and bridge the interface between electronic and photonic circuits,there is a high demand for a non-volatile memory that can be accessed both electrically and ***,practical solutions are still lacking when considering the potential for large-scale complementary metal-oxide semiconductor compatible ***,we present an experimental demonstration of a non-volatile photonic-electronic memory based on a 3-dimensional monolithic integrated ferroelectric-silicon ring *** successfully demonstrate programming and erasing the memory using both electrical and optical methods,assisted by optical-to-electrical-to-optical *** memory cell exhibits a high optical extinction ratio of 6.6 dB at a low working voltage of 5 V and an endurance of 4×10^(4) ***,the multi-level storage capability is analyzed in detail,revealing stable performance with a raw bit-error-rate smaller than 5.9×10^(−2).This ground-breaking work could be a key technology enabler for future hybrid electronic-photonic systems,targeting a wide range of applications such as photonic interconnect,high-speed data communication,and neuromorphic computing.
The online social platforms witnessed enormous growth in its networked structure as users continue to connect and interact through e-social dialogues. This eventually causes the transformational emergence of online so...
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Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but th...
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Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.
With the development of the smart shipping industry, unmanned vessel technology is rapidly evolving, accompanied by a demand for robust Internet of Things (IoT) communication security practices. Key communication tech...
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In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh env...
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In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.
作者：
Gabr, MohamedKorayem, YousefChen, Yen-LinYee, Por LipKu, Chin SoonAlexan, Wassim
Faculty of Media Engineering and Technology Computer Science Department Cairo11835 Egypt National Taipei University of Technology
Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya
Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman
Department of Computer Science Kampar31900 Malaysia
Faculty of Information Engineering and Technology Communications Department Cairo11835 Egypt
New Administrative Capital Mathematics Department Cairo13507 Egypt
This work proposes a novel image encryption algorithm that integrates unique image transformation techniques with the principles of chaotic and hyper-chaotic systems. By harnessing the unpredictable behavior of the Ch...
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