Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning paradigm leveraging high-dimensional spaces for efficient tasks like pattern recognition and medical diagnostics. As a lightweight altern...
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In this paper, design and modeling of an all-optical 2×1 multiplexer based on 2D photonic crystals and artificial neural networks (ANNs) are presented. The proposed structure aims to maximize the difference betwe...
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Emotion recognition from speech is a significant research area in human–computer interaction and psychological assessments. This study proposes a novel three-stage process for emotion recognition from speech signals....
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The feasibility of using passive radiometric detection of chaotic electromagnetic signals emanating from low density plasma plumes of the jet exhaust gases to detect low radar cross section aircrafts is analyzed for t...
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Hidden Markov models (HMMs) are a powerful class of dynamical models for representing complex systems that are partially observed through sensory data. Existing data collection methods for HMMs, typically based on act...
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The field of computer vision is predominantly driven by supervised models, which, despite their efficacy, are computationally expensive and often intractable for many applications. Recently, research has expedited alt...
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This paper provides a finite-sample analysis of a passive stochastic gradient Langevin dynamics (PSGLD) algorithm. This algorithm is designed to achieve adaptive inverse reinforcement learning (IRL). Adaptive IRL aims...
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Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural net...
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Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.
In this article the legend of Fig. 6 was presented without a reference. The legend of Fig. 6 has been changed from "The general framework for knowledge distillation involving a teacher-student relationship&q...
In thicker polymer active layers charge collection efficiency suffers due to low carrier mobility and increased recombination losses. In thin absorber polymer solar cell to increase absorption, light-trapping techniqu...
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In thicker polymer active layers charge collection efficiency suffers due to low carrier mobility and increased recombination losses. In thin absorber polymer solar cell to increase absorption, light-trapping techniques and plasmonic structures are essential. This study investigates the effect of shell thickness on the photocurrent density of a poly(3-hexylthiophene): phenyl-C61- butyric acid methyl ester (P3HT:PCBM) polymer based solar cell incorporating core–shell nanoparticles with configurations of Au–Ag and Ag-Au core–shell nanoparticles. Through a series of simulation, the photocurrent density was calculated as a function of shell thickness. The results demonstrate that, for both nanoparticle configurations, the photocurrent density generally increases with shell thickness, reaching an optimal point before stabilizing or slightly decreasing. Additionally, the effects of dielectric shells made of SiO₂ and Al₂O₃ on its performance parameters were analyzed. The study also found that the photocurrent decreases with increasing shell thickness for both SiO₂ and Al₂O₃ shells, with a more pronounced decrease for SiO₂ due to its smaller refractive index and greater change in shorter wavelengths. The photocurrent density of 13.74 mA/cm2 is achieved for a cell with a thickness of 80 nm without nanoparticles. This value increases to 16.62 mA/cm2 for a cell incorporating Ag nanoparticles and reaches 19.3 mA/cm2 for a cell with Au–Ag core–shell nanoparticles at the optimal shell thickness. The power conversion efficiency of the polymer solar cell increases from 7.02% without nanoparticles to 8.67% with Ag, 8.45% with Au, and reaches the highest value of 10.26% with Au–Ag core–shell nanoparticles, highlighting the superior performance of the core–shell configuration. This superior performance is attributed to the enhanced plasmonic effects of the Au–Ag combination, which facilitates better light trapping and absorption. These findings underscore the importance of optimizing
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