This paper introduces an artificialneural Network model that integrates advanced deep learning techniques from computer vision and natural language processing domains. The model focuses on automating the captioning p...
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imageprocessing has become a central topic in the era of big data, particularly within computer vision, due to the growing volume and diverse resolutions of images. Low-resolution images introduce uncertainty, unders...
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This study presents a novel approach to phishing email detection, leveraging artificialneuralnetworks (ANN) with soft attention in natural language processing (NLP) through the integration of BERT encoders. Addressi...
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ISBN:
(纸本)9783031837890;9783031837906
This study presents a novel approach to phishing email detection, leveraging artificialneuralnetworks (ANN) with soft attention in natural language processing (NLP) through the integration of BERT encoders. Addressing the critical need for effective aritifical intelligence (AI)-driven cybersecurity solutions, this research combines BERT's NLP capabilities with a modified crayfish optimization algorithm (COA) to fine-tune the hyperparameters of deep neural network models, enhancing classification accuracy. Experimental results show that our optimized model achieves a phishing detection accuracy of 92.5%, outperforming several high-performing optimizers. Comparative analysis demonstrates that this approach offers superior detection capabilities, underscoring its potential for real-world applications. This work advances the field by refining BERT's application with optimization algorithms and provides a valuable framework for future cybersecurity research.
artificial intelligence and machine learning, including convolutional neuralnetworks are increasingly entering the field of healthcare and medicine. The aim of the study is to optimize the learning process of convolu...
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ISBN:
(纸本)9783031349591;9783031349607
artificial intelligence and machine learning, including convolutional neuralnetworks are increasingly entering the field of healthcare and medicine. The aim of the study is to optimize the learning process of convolutional neuralnetworks through X-ray images pre-processing. A model for optimizing the overall architecture of a classifying convolutional neural network of chest X-rays by reducing the total number of convolutional operations is presented. The experimental results prove the successful application of the optimization process on the training of classification convolutional networks. There is a significant reduction in the training time of each epoch in the optimized convolutional networks. The optimization is of the order of 25% for the network with an input layer size of 124 x 124 and about 27% for the network with an input layer size of 122 x 122. The method can be applied in any field of image classification in which the informative image regions are grouped and subject to segmentation.
In the field of EEG-based gaze prediction, the application of deep learning to interpret complex neural data poses significant challenges. This study evaluates the effectiveness of pre-processing techniques and the ef...
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ISBN:
(纸本)9783031615719;9783031615726
In the field of EEG-based gaze prediction, the application of deep learning to interpret complex neural data poses significant challenges. This study evaluates the effectiveness of pre-processing techniques and the effect of additional depthwise separable convolution on EEG vision transformers (ViTs) in a pretrained model architecture. We introduce a novel method, the EEG Deeper Clustered Vision Transformer (EEG-DCViT), which combines depthwise separable convolutional neuralnetworks (CNNs) with vision transformers, enriched by a pre-processing strategy involving data clustering. The new approach demonstrates superior performance, establishing a new benchmark with a Root Mean Square Error (RMSE) of 51.6 mm. This achievement underscores the impact of pre-processing and model refinement in enhancing EEG-based applications.
In the paper, a new machine-learning technique is proposed to recognize movement patterns. The efficient system designed for this purpose uses an artificialneural network (ANN) model implemented on a microcontroller ...
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ISBN:
(纸本)9783031530357;9783031530364
In the paper, a new machine-learning technique is proposed to recognize movement patterns. The efficient system designed for this purpose uses an artificialneural network (ANN) model implemented on a microcontroller to classify boxing punches. artificial intelligence (AI) enables the processing of sophisticated and complex patterns, and the X-CUBE-AI package allows the use of these possibilities in portable microprocessor systems. The input data to the network are linear accelerations and angular velocities read from the sensor mounted on the boxer's wrist. By using simple time-domain measurements without extracting signal features, the classification is performed in real-time. An extensive experiment was carried out for two groups with different levels of boxing skills. The developed model demonstrated high efficiency in the identification of individual types of blows.
The proceedings contain 9 papers. The special focus in this conference is on Design and Architecture for Signal and imageprocessing. The topics include: sEMG-Based Gesture Recognition with Spiking neural Network...
ISBN:
(纸本)9783031628733
The proceedings contain 9 papers. The special focus in this conference is on Design and Architecture for Signal and imageprocessing. The topics include: sEMG-Based Gesture Recognition with Spiking neuralnetworks on Low-Power FPGA;A Highly Configurable Platform for Advanced PPG Analysis;preface;Standalone Nested Loop Acceleration on CGRAs for Signal processingapplications;optimising Graph Representation for Hardware Implementation of Graph Convolutional networks for Event-Based Vision;Improving the Energy Efficiency of CNN Inference on FPGA Using Partial Reconfiguration;scratchy: A Class of Adaptable Architectures with Software-Managed Communication for Edge Streaming applications.
In this paper, we propose HFDA-Net, a novel approach for image manipulation detection and localization (IMDL) tasks. Unlike existing methods that only extract high-frequency features from the input image, HFDA-Net fur...
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ISBN:
(纸本)9783031723346;9783031723353
In this paper, we propose HFDA-Net, a novel approach for image manipulation detection and localization (IMDL) tasks. Unlike existing methods that only extract high-frequency features from the input image, HFDA-Net further extracts high-frequency features from RGB feature maps, capturing richer manipulation traces. In addition, HFDA-Net introduces a new module that efficiently calculates and combines position and channel attention, improving the accuracy and efficiency of manipulated region localization. Moreover, HFDA-Net supports feature extraction and aggregation at multiple scales and employs a coarse-to-fine pattern to predict manipulated regions, demonstrating remarkable generalizability. Thanks to its lightweight architecture, HFDA-Net achieves a processing speed of 65+ FPS when handling 1080P images. Extensive experiments on four image forensics benchmarks demonstrate that HFDA-Net generally outperforms existing advanced methods in manipulation detection by 1% to 15% and in manipulation localization by 1.5% to 5.4% under AUC. Furthermore, HFDA-Net exhibits good robustness compared to existing methods.
This work analyzes the impact of the hyper-parameters of a weightless neural network based on Multi-valued Probabilistic Logic Neurons (MPLN) in order to design an efficient and concise network topology. The study is ...
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ISBN:
(纸本)9789819746767;9789819746774
This work analyzes the impact of the hyper-parameters of a weightless neural network based on Multi-valued Probabilistic Logic Neurons (MPLN) in order to design an efficient and concise network topology. The study is done based on the implementation of several MPLN architectures for the handwritten digit identification application. The analysis is performed by varying one given parameter while the others are kept unchanged. This allows the impact evaluation of such parameter on the classification accuracy, necessary epoch number to train the network and required processing time. The present work further proposes a modification in the MPLN network for multi-class problems, termed the Mod-MPLN network. The Mod-MPLN network is defined by a change in the network training algorithm and by the inclusion of a specific discriminator at the network output, without changing the intrinsic characteristics of the MPLN-based topology.
Over the past few years, artificial Intelligence has achieved significant performance in many fields. In artificial intelligence techniques, deep neuralnetworks have experienced rapid development recently. They have ...
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