检索结果-内蒙古大学图书馆

Precursors-driven machine learning prediction of chaotic extreme pulses in Kerr resonators

CHAOS SOLITONS & FRACTALS 2022年 160卷

作者： Coulibaly, Saliya Bessin, Florent Clerc, Marcel G. Mussot, Arnaud Univ Lille CNRS UMR 8523 PhLAM Phys Lasers Atomes & Mol F-59000 Lille France Aston Univ Aston Inst Photon Technol Birmingham B4 7ET England Univ Chile Dept Fis FCFM Casilla 487-3 Santiago Chile Univ Chile Millennium Inst Res Opt FCFM Casilla 487-3 Santiago Chile

Machine learning algorithms have opened a breach in the prediction's fortress of high-dimensional chaotic systems. Their ability to find hidden correlations in data can be exploited to perform model-free forecasting of spatiotemporal chaos and extreme events. However, the extensive feature of these evolutions makes up a critical limitation for full-size forecasting processes. Hence, the main challenge for forecasting relevant events is to establish the set of pertinent information. Here, we identify precursors from the transfer entropy of the system and a deep Long Short-Term Memory network to forecast the complex dynamics of a system evolving in a high dimensional spatiotemporal chaotic regime. Performances of this triggerable model-free prediction protocol based on the information flowing map are tested from experimental data originating from a passive resonator operating in such a complex nonlinear regime. We have been able to predict the occurrence of extreme events up to 9 round trips after the detection of precursor, i.e., 3 times the horizon provided by Lyapunov exponents, with 92% of true positive predictions leading to 60% of *** have implemented a process to forecast extreme events in a fully developed turbulent flow. The novelty of our strategy lies in the information's use theory method to detect precursors and use them as the input of a neural network to infer the incoming extreme events. Our process is suitable for all the extended dissipative systems that can only be partially observed or real-world data.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Spatiotemporal Chaos Kerr frequency combs Forecasting extreme events Machine learning Long Short-Term Memory (LSTM) encoder-decoder Transfer entropy

来源：评论

学校读者我要写书评

暂无评论

MobileXNet: An Efficient Convolutional Neural Network for Monocular Depth Estimation

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第11期23卷 20134-20147页

作者： Dong, Xingshuai Garratt, Matthew A. Anavatti, Sreenatha G. Abbass, Hussein A. Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2612 Australia

Depth estimation from a single RGB image has attracted great interest in autonomous driving and robotics. State-of-the-art methods are usually designed on top of complex and extremely deep network architectures, which require more computational resources. Moreover, the inherent characteristic of the backbone used by the existing approaches results in severe spatial information loss in the produced feature maps, which impairs the accuracy of depth estimation on small sized images. In this study, we aimed to design a novel and efficient Convolutional Neural Network (CNN) to address these problems. Specifically, we stacked two shallow encoder-decoder style subnetworks successively in a unified network. Extensive experiments have been conducted on the NYU depth v2, KITTI, Make3D and Unreal data sets. Experimental results show that the proposed network achieves comparable accuracy to state-of-the-art methods that have extremely deep architectures but runs at a much faster speed on a single, less powerful GPU.

关键词： Estimation Convolution Convolutional neural networks Task analysis Decoding Computer architecture Semantics Monocular depth estimation depth prediction convolutional neural networks encoder-decoder autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

VAE and AAE Networks Segment Brain Tumors from MRI 30

VAE and AAE Networks Segment Brain Tumors from MRI

引用

30th National and 8th International Iranian Conference on Biomedical Engineering (ICBME)

作者： Mirani, Mehri Soltani-Gol, Mohammad Soltanian-Zadeh, Hamid Univ Tehran Sch Elect & Comp Engn CICPE Tehran Iran

ISBN: (纸本)9798350359732;9798350359749

Brain tumor segmentation is essential in medical imaging for accurate diagnosis and effective treatment planning. Brain tumors are formed during the uncontrolled growth of cells and are divided into two primary and secondary categories. The most common type of primary brain tumors is gliomas, which are examined in this research. In recent years, deep learning models have shown promising results in automating tumor segmentation from medical imaging data. This study compares the performance of Variational Autoencoder (VAE) and Adversarial Autoencoder (AAE) networks for brain tumor segmentation based on an encoder-decoder architecture. The encoder part extracts image features, while the decoder part reconstructs segmentation labels. These two networks are powerful generative models that capture the underlying distribution of the data. This capability makes them suitable for extracting meaningful tumor features. In the VAE network, a variational autoencoder branch is added to reconstruct the input images. In the adversarial network, the discriminator part is added to train the encoder-decode network and helps the decoder to generate outputs that closely match the tumor labels. The results indicate that both VAE and AAE achieve satisfactory segmentation accuracy. However, AAE achieves higher Dice scores compared to VAE, indicating that it is more effective at segmenting tumor regions.

关键词： Brain tumor segmentation deep learning encoder-decoder Variational Autoencoder (VAE) Adversarial Autoencoder (AAE)

来源：评论

学校读者我要写书评

暂无评论

Multi-task Learning Based Skin Segmentation 1

引用

16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Tan, Taizhe Shan, Zhenghao Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China Heyuan Bay Area Digital Econ Technol Innovat Ctr Heyuan 517001 Peoples R China

ISBN: (数字)9783031402890

ISBN: (纸本)9783031402883;9783031402890

Skin segmentation is a critical task in computer vision that has diverse applications in several fields such as biometrics, medical imaging, and video surveillance. Despite its importance, the acquisition of high-quality data remains a significant challenge in skin segmentation research. In this paper, we propose a novel skin segmentation algorithm for single-person images by utilizing a dual-task neural network built on the multi-task learning framework. Specifically, the algorithm employs an encoder-decoder architecture consisting of a shared backbone, two dynamic encoders, and a decoder. The dynamic encoders use dynamic convolution to extract more spatial location information, while the decoder utilizes a query-based dual-task approach that allows each task to utilize the information generated by the other one efficiently. The experimental results indicate that the proposed skin segmentation algorithm outperforms or matches the current state-of-the-art techniques on the benchmark test set.

关键词： Skin segmentation query-based multi-task learning encoder-decoder deep learning

来源：评论

学校读者我要写书评

暂无评论

The Application of Generating API Call Sequence Code for Android Driven by Neural Network

The Application of Generating API Call Sequence Code for And...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yang, Jingbo Wu, Wenjun Ren, Jian Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781665488679

API, namely application programming interface, can help developers implement their functions conveniently. To implement a function like dialing in Android, developers sometimes need to call many APIs organised in a special pattern, called API call sequence. However, existing methods rarely focus on code generation for API call sequence. In this paper, we introduce neural network into the application field of generating API call sequence code for Android. The purpose is realising Android code automatically generation by inputting function description. To reach this goal, we first design an API call sequence code graph which is named ACSCG to well represent the Android function code structure and then we convert the ACSCG to API call sequence. Besides, we devise an AI model based on encoder-decoder neural network to study the corresponding relation feature of function description and API call sequence. When finishing training the model, one can give a function description to it and generate corresponding API call sequence. After all above has been done, an algorithm is implemented to successfully convert the API call sequence into target code. To verify the efficiency of our method, we collect high quality code from Github and Gitee to build a dataset including 1000 items associated with essential functions in Android such as taking photo, file management, android browser and so on. The experiment shows that our model has a better performance in generating API call sequence code than state-of-the-art technologies.

关键词： API call sequence neural network encoder-decoder AI code generation

来源：评论

学校读者我要写书评

暂无评论

Bridging the Language Gap: Enhancing English-to-Telugu Translation using NMT and Encoding Decoding Techniques 15

Bridging the Language Gap: Enhancing English-to-Telugu Trans...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Asmitha, M. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Language translation, particularly in the context of natural language processing, involves converting text or speech from one language into another while preserving its meaning, context, and cultural nuances. This task is critical for enabling communication across different languages and is a significant area of research and development in artificial intelligence. Machine translation has a wide range of real-time applications that have significantly impacted various fields by bridging language barriers. Some of the real time application includes Google Translate, Microsoft Translator, Skype Translator. NMT has revolutionized language translation with models like Google's Transformer, OpenAI's GPT, Facebook's BART, and Google's T5. Development of a machine translation system to translate English sentences into Telugu. The system utilizes a neural network model based on an encoder-decoder architecture with attention mechanism. Preprocessing techniques are applied to the dataset, including lowercasing, removal of special characters, and tokenization. The sparse categorical cross-entropy loss function and Adam optimizer are used to train the model. The actual vs. projected output is used to evaluate performance. Pre-trained GloVe word embeddings are used in the implementation to improve the model's capacity to extract semantic information. © 2024 IEEE.

关键词： Attention mechanism encoder-decoder GloVe word embeddings Machine translation Neural network Pre-processing

来源：评论

学校读者我要写书评

暂无评论

Transformer-Based Multivariate Time Series Forecasting

Transformer-Based Multivariate Time Series Forecasting

引用

2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Bharti, Mukesh Kumar Wadhvani, Rajesh Gyanchandani, Manasi Gupta, Muktesh Maulana Azad National Institute of Technology Department of Cse Bhopal India

ISBN: (纸本)9798350348460

This study explores multivariate time series forecasting, centering on the transformer model. It examines the shortcomings of other predictive models like Recurrent Neural Networks (RNN) and Temporal Convolutional Networks (TCN), particularly their inadequacies in handling autocorrelation. The transformer model stands out for its accuracy, thanks to its attention mechanism that focuses on essential parts of the input. The research introduces a novel approach that employs the transformer's architecture for effective feature selection in time series data. A vital aspect of this approach is the use of unsupervised pre-training, which shows superior results compared to traditional fully supervised methods. This advancement underscores the effectiveness of unsupervised learning in time series regression, offering significant benefits for diverse scientific and industrial fields. © 2024 IEEE.

关键词： Attention Mechanism Autocorrelation Deep Learning encoder-decoder Heteroskedasticity Quantile Regression Time Series Forecasting Transformers

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of Generative Neural Attention-based Service Chatbot

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第8期13卷 742-751页

作者： Suhaili, Sinarwati Mohamad Salim, Naomie Jambli, Mohamad Nazim Pre Univ Kota Samarahan Sarawak Malaysia Univ Teknol Malaysia Fac Comp Skudai 81310 Johor Malaysia Univ Teknol Malaysia Ibnu Sina Inst Sci & Ind Res UTM Big Data Ctr Skudai 81310 Johor Malaysia Univ Malaysia Sarawak Fac Comp Sci & Informat Technol Kota Samarahan Sarawak Malaysia

Companies constantly rely on customer support to deliver pre-and post-sale services to their clients through websites, mobile devices or social media platforms such as Twitter. In assisting customers, companies employ virtual service agents (chatbots) to provide support via communication devices. The primary focus is to automate the generation of conversational chat between a computer and a human by constructing vir-tual service agents that can predict appropriate and automatic responses to customers' queries. This paper aims to present and implement a seq2seq-based learning task model based on encoder-decoder architectural solutions by training generative chatbots on customer support Twitter datasets. The model is based on deep Recurrent Neural Networks (RNNs) structures which are uni-directional and bi-directional encoder types of Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). The RNNs are augmented with an attention layer to focus on important information between input and output sequences. Word level embedding such as Word2Vec, GloVe, and FastText are employed as input to the model. Incorporating the base architecture, a comparative analysis is applied where baseline models are compared with and without the use of attention as well as different types of input embedding for each experi-ment. Bilingual Evaluation Understudy (BLEU) was employed to evaluate the model's performance. Results revealed that while biLSTM performs better with Glove, biGRU operates better with FastText. Thus, the finding significantly indicated that the attention-based, bi-directional RNNs (LSTM or GRU) model significantly outperformed baseline approaches in their BLEU score as a promising use in future works.

关键词： Sequence-to-sequence encoder-decoder service chatbot attention-based encoder-decoder Recurrent Neural Network (RNN) Long Short-Term Memory (LSTM) Gated Recurrent Unit (GRU) word embedding

来源：评论

学校读者我要写书评

暂无评论

Multi-Path U-Net Architecture for Cell and Colony-Forming Unit Image Segmentation

引用

SENSORS 2022年第3期22卷 990-990页

作者： Jumutc, Vilen Bliznuks, Dmitrijs Lihachev, Alexey Riga Tech Univ Inst Smart Comp Technol LV-1658 Riga Latvia Univ Latvia Inst Atom Phys & Spect LV-1586 Riga Latvia

U-Net is the most cited and widely-used deep learning model for biomedical image segmentation. In this paper, we propose a new enhanced version of a ubiquitous U-Net architecture, which improves upon the original one in terms of generalization capabilities, while addressing several immanent shortcomings, such as constrained resolution and non-resilient receptive fields of the main pathway. Our novel multi-path architecture introduces a notion of an individual receptive field pathway, which is merged with other pathways at the bottom-most layer by concatenation and subsequent application of Layer Normalization and Spatial Dropout, which can improve generalization performance for small datasets. In general, our experiments show that the proposed multi-path architecture outperforms other state-of-the-art approaches that embark on similar ideas of pyramid structures, skip-connections, and encoder-decoder pathways. A significant improvement of the Dice similarity coefficient is attained at our proprietary colony-forming unit dataset, where a score of 0.809 was achieved for the foreground class.

关键词： U-Net skip-connections neural network encoder-decoder Layer Normalization

来源：评论

学校读者我要写书评

暂无评论

Dense spatially-weighted attentive residual-haze network for image dehazing

引用

APPLIED INTELLIGENCE 2022年第12期52卷 13855-13869页

作者： Singh, Mohit Laxmi, Vijay Faruki, Parvez MNIT Jaipur Rajasthan India AVPTI Rajkot Gujarat India

Haze severely affects computer vision algorithms by degrading the quality of captured images and results in image data loss. With several available approaches for dehazing, single image dehazing is most preferred and challenging. We proposed a Dense Spatially-weighted Attentive Residual-haze Network (DSA Net), a novel end-to-end encoder-decoder architecture to learn the residual haze layer between the hazy and haze-free image. We use encoder-decoder blocks with multiple skip connections to improve feature propagation. Feature Learning block uses a novel Residual Inception fused with Attention (RIA) block to learn the complex non-linearity from features extracted from the encoder part. Learning residual image is more straightforward than the whole haze-free image, and it improves the ability of the network to estimate the haze thickness accurately. DSA Net learns this less complex residual-map from the hazy input image and subtracts it from the input to obtain the dehazed images. Detail ablation study shows the effectiveness of different modules used in our architecture. Experiment results on different haze conditions demonstrate that our method shows significant improvement over other state-of-the-art methods.

关键词： Dehazing Attention mechanism encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：