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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Rahul Chiranjeevi, V. Kumar, P. Rubesh, S. Murali Krishna, G. Rajalakshmi Engineering College Department of Cse Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The projected market size for optical Character Recognition (OCR) is in-creasing rapidly. The rise is propelled by the swift digitalization of company processes through the utilization of OCR technology, which aims to decrease labor expenses and conserve valuable work hours. While OCR is generally regarded as a resolved issue, one crucial aspect of it, recognizing hand writ-ten texts remain a tough subject. Different handwriting styles across various people and the low quality of text in comparison to printed text provide substantial challenges in the process of transforming it into machine-readable text. This research study presents different methods for offline handwriting character identification using a graph convolutional network. The process of character segmentation and normalization is expensive, particularly when dealing with big datasets, especially in the context of offline handwriting detection using a structural method. The primary objective of this research is to create a model that converts a handwritten character in-to a string graph representation. The goal of these models is to enhance recognition accuracy without depending on normalization techniques. The graph is composed of multiple edges that represent the inter-connected vertices. The vertices represent the curves that define the character's shape. The curve is derived from the analysis of the character's chain code, and its string characteristic is generated based on specific regulations. Proposed method achieved significant results compared to state of art methods in various benchmark datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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2024 International Conference on Computer Application and Information Security, ICCAIS 2024

作者： Yu, Bing Zheng, Qilong School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230026 China

ISBN: (纸本)9781510689312

Deep convolutional neural networks have demonstrated significant performance beyond traditional methods in more and more fields. However, with the increasing network size and the emergence of various complex structures, the increasing demand for computing and storage resources limits their deployment and application on resource-constrained devices. The mainstream structured pruning methods mostly focus on channel or filter pruning, and such coarse-grained pruning also leads to the loss of too many important parameters, which causes unnecessary performance reduction of the compressed network. In this paper, we propose a kernel norm-similarity based group pruning method to prune at a finer granularity while maintaining a regular structure of parameters. Specifically, we first adopt a K-PCA-based K-Means++ algorithm to realize the reasonable grouping of the filters. Afterward, we comprehensively evaluate the importance of the grouped kernels by combining their norm and similarity and then pruning those unimportant ones. Finally, the pruned convolutional layers are reconstructed into a dense group convolution structure. We conduct comprehensive experiments and evaluation of our method on various different models and datasets to prove its effectiveness. The experimental results indicate that our method enables CNNs to maintain better performance while achieving a higher compression ratio, which outperforms most current network pruning methods. © The Authors.

关键词： Convolutional neural networks

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Emotion recognition research of eye-movement feature extraction classification network in online video learning environment 3

Emotion recognition research of eye-movement feature extract...

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3rd International Seminar on Artificial Intelligence, networking, and Information Technology, AINIT 2022

作者： Liu, Shengxi Li, Ze Tao, Xiaomei College of Computer Engineering Henan Institute of Economy and Trade Henan Zhengzhou China School of information science and engineering Guilin University of Technology Guangxi Guilin China School of Computer Science and Engineering School of Software Guangxi Normal University Guangxi Guilin China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

With the rapid development of artificial intelligence technology, emotion recognition has been applied in all aspects of life, using eye movement tracking technology for emotion recognition has become an important branch of emotion computing. In order to explore the relationship between eye movement signals and learners' emotional states in the online video learning environment, we used machine learning and convolutional neural network methods to recognize eye movement signals, and classify learners' emotional states into two categories, positive and negative. The study of eye movement data under different time windows mainly includes four stages: data collection, data preprocessing, classifier modeling, training and testing. In this paper, a Eye-movement Feature Extraction Classification network(EFECN) based on convolutional neural network is proposed for small sample data and the classification of emotion state based on eye movement. The eye movement data were transformed into images through cross-modal conversion as input of multiple different deep convolutional neural networks, and the emotional states were classified in positive and negative directions. The accuracy was used as the evaluation index to evaluate and compare the different models. The accuracy of the eye movement emotion recognition model reached 72% in the SVM model and 91.62% in the EFECN model. Experimental results show that the convolutional neural network based on deep learning has a significant improvement in recognition accuracy compared with traditional machine learning methods. © 2023 SPIE.

关键词： Emotion Recognition

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Transforming Traditional neural networks into Neuromorphic Quantum-Cognitive models: A Tutorial with Applications

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arXiv 2025年

作者： Maksimovic, Milan Maksymov, Ivan S. Artificial Intelligence and Cyber Futures Institute Charles Sturt University BathurstNSW2795 Australia

Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to quantum technologies in computation remains largely limited to professionals in research organisations and high-tech industries. This paper demonstrates how traditional neural networks can be transformed into neuromorphic quantum models, enabling anyone with a basic understanding of undergraduate-level machine learning to create quantum-inspired models that mimic the functioning of the human brain—all using a standard laptop. We present several examples of these quantum machine learning transformations and explore their potential applications, aiming to make quantum technology more accessible and practical for broader use. The examples discussed in this paper include quantum-inspired analogues of feedforward neural networks, recurrent neural networks, Echo State network reservoir computing and Bayesian neural networks, demonstrating that a quantum approach can both optimise the training process and equip the models with certain human-like cognitive characteristics. © 2025, CC BY.

关键词： Quantum computers

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MetaScene: A Novel Metaverse large-scale 3D Scene Rendering Framework 17

MetaScene: A Novel Metaverse large-scale 3D Scene Rendering ...

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17th IEEE International Conference on Social computing and networking, SocialCom 2024

作者： Chang, Qingling Meng, Xiaofei Hong, Zhiyong Xu, Shiting Wuyi University Institute of China-Germany Artificial Intelligence Jiangmen China Wuyi University School of Electronics and Information Engineering Jiangmen China Zhuhai 4Dage Network Technology Zhuhai China

ISBN: (纸本)9798331521097

We are excited to introduce MetaScene, a ground-breaking framework for rendering Metaverse's large-scale virtual 3D scenes that is versatile and lightweight. Previous construction solutions relied heavily on direct loading and rendering of 3D detailed models. However, our approach is simply to load a 3D simplified model and a live video stream projected onto the model. The 3D simplified model is derived from the 3D fine model, contains only the necessary bite, and the amount of data is much smaller. Real-time video streams are encoded with images rendered by a 3D fine model, and then the video stream is decoded and projected onto a 3D simplified model. Our MetaScene framework is so efficient that it can handle scenes of infinite scale, rendering quality on web pages is comparable to Unreal Engine (e.g. UE5), and can deliver photo-grade images. MetaScene supports cross-platform, such as web browsers, PC apps, mobile apps, and more. It also allows multiple users to interact seamlessly in virtual 3D scenes. And can be extended to any virtual 3D scene application. We have verified the data usage of multi-client parallel access through a large number of experiments, and our model can match or even exceed the UE5 framework in terms of FPS and bandwidth usage. © 2024 IEEE.

关键词： Rendering (computer graphics)

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Exploring the Tractability of Data Fusion models for Detecting Anomalies in IoT Based Dataset

Exploring the Tractability of Data Fusion Models for Detecti...

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Big Data V: Learning, Analytics, and Applications 2023

作者： Devagopal, A.M. Menon, Vishal Ezekiel, Soundararajan Chaudhary, Pankaj Amrita School of Computing Amritapuri Campus Kerala Kollam India Indiana University of Pennsylvania IN United States North Carolina A&T State University United States

ISBN: (数字)9781510661592

ISBN: (纸本)9781510661585

In recent years Internet of Things (IoT) devices have made their way into many different i ndustries. Deep learning and machine learning methodologies have been applied to many IoT-related tasks123 such as Intrusion Detection Systems or Anomaly Detection. The efficiency of IoT systems is often hindered by anomalies in data present within the system, often leading to undesirable behavior or possibly a full system shutdown. Due to this, the detection of these anomalies is of the utmost importance. Over the years, various traditional and neural network-based machine learning models have emerged for anomaly detection and classification of corrupted IoT data. However, many of these models fail to capture important features in the data which can lead to false anomaly detection or none at all. In this paper we investigate the applicability of using data fusion to improve the detection of data anomalies. This method uses many different models, such as VGG16, Inception, Xception, and ResNet, to extract features from the data. These extracted features are then fused together, to see if the use of multiple models is better than relying on a single model. This paper also provides a detailed analysis of the efficacy of th is fu sion- ba sed cl assification met hod com pared to sim pler cla ssification meth ods. This work investigates the applicability of various machine learning and deep learning models, for anomaly detection in various IoT data sets4,5 © 2023 SPIE.

关键词： Internet of things

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RESEARCH on medical named entity recognition based on DB-MA-BiLSTM-CRF

RESEARCH on medical named entity recognition based on DB-MA-...

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2022 International Conference on Cloud computing, Performance computing, and Deep Learning, CCPCDL 2022

作者： Wei, Ru Ding, GuoHui School of Computer Science Shenyang Aerospace University Liaoning Shenyang110136 China

ISBN: (纸本)9781510655867

At present, the uneven distribution of entities and the low frequency of some entities in medical text data leads to the low accuracy of medical named entity recognition. To solve the above problems, a neural network model based on dictionary and mutual attention (DB-MA-BiLSTM-CRF) is proposed. The model includes Bert embedding layer, BiLSTM-CNN layer, mutual attention layer and CRF layer. The medical dictionary is fused in the Bert embedding layer to inject medical vocabulary information;Then it is input into the BiLSTM-CNN network layer to extract the global and local features of the text respectively;The features extracted from BiLSTM-CNN network layer are spliced into the mutual attention layer for weighted extraction of important features. Compared with the two benchmark models, the experimental results show that the model proposed in this paper has strong advantages. © 2022 SPIE.

关键词： network layers

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Graph-Based Region and Boundary Aggregation for Biomedical Image Segmentation

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2022年第3期41卷 690-701页

作者： Meng, Yanda Zhang, Hongrun Zhao, Yitian Yang, Xiaoyun Qiao, Yihong MacCormick, Ian J. C. Huang, Xiaowei Zheng, Yalin Univ Liverpool Inst Life Course & Med Sci Liverpool L7 8TX Merseyside England Chinese Acad Sci Cixi Inst Biomed Engn Ningbo Inst Mat Technol & Engn Ningbo 315201 Peoples R China Remark AI UK Ltd London SE1 9PD England China Sci IntelliCloud Technol Co Ltd Shanghai 200333 Peoples R China Univ Edinburgh Ctr Inflammat Res Queens Med Res Inst Edinburgh EH16 4TJ Midlothian Scotland Univ Liverpool Dept Comp Sci Liverpool L69 3BX Merseyside England

Segmentation is a fundamental task in biomedical image analysis. Unlike the existing region-based dense pixel classification methods or boundary-based polygon regression methods, we build a novel graph neural network (GNN) based deep learning framework with multiple graph reasoning modules to explicitly leverage both region and boundary features in an end-to-end manner. The mechanism extracts discriminative region and boundary features, referred to as initialized region and boundary node embeddings, using a proposed Attention Enhancement Module (AEM). The weighted links between cross-domain nodes (region and boundary feature domains) in each graph are defined in a data-dependent way, which retains both global and local cross-node relationships. The iterative message aggregation and node update mechanism can enhance the interaction between each graph reasoning module's global semantic information and local spatial characteristics. Our model, in particular, is capable of concurrently addressing region and boundary feature reasoning and aggregation at several different feature levels due to the proposed multi-level feature node embeddings in different parallel graph reasoning modules. Experiments on two types of challenging datasets demonstrate that our method outperforms state-of-the-art approaches for segmentation of polyps in colonoscopy images and of the optic disc and optic cup in colour fundus images. The trained models will be made available at: https://***/smallmax00/Graph_Region_Boudnary

关键词： Image segmentation Feature extraction Cognition Task analysis Semantics optical imaging optical computing Region-boundary graph neural network segmentation

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Cloud Resource Load Prediction Based on EEMD-LSTM 4

Cloud Resource Load Prediction Based on EEMD-LSTM

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4th International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2024

作者： Cheng, Xuemei Ye, Xiaojiang Hou, Zhijian Zhang, Yunhui School of Optical Information and Energy Engineering Wuhan Institute of Technology Wuhan430200 China School of Mechanical and Electrical Engineering Shenzhen Polytechnic University Shenzhen518055 China

ISBN: (纸本)9798350378542

With the rapid advancement of cloud computing, accurate load prediction has become essential for optimizing system performance. The nonlinear and non-stationary characteristics of cloud resource load data have increased the complexity of prediction tasks. To address this challenge, this paper proposes a multi-input multi-output load prediction model based on Ensemble Empirical Mode Decomposition (EEMD) and Long Short-Term Memory (LSTM) networks. The model reduces data complexity through EEMD decomposition while effectively capturing the temporal dependencies in the sequence data using LSTM. Experiments conducted on publicly available cluster datasets from Alibaba demonstrate that the EEMD-LSTM model significantly outperforms traditional models, such as LSTM, Recurrent neural network (RNN), and Multi-Layer Perceptron (MLP), in predicting CPU and memory usage, as evaluated by metrics including Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R2). © 2024 IEEE.

关键词： Empirical mode decomposition

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Fault diagnosis method for embedded electromechanical systems based on 1DCNN-BiGRU 3

Fault diagnosis method for embedded electromechanical system...

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3rd International Conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Yan, Tanbao Wu, Jiayu Liu, Zehua Zhao, Yixuan Aeronautics Computing Technology Research Institute Xi'an China

ISBN: (纸本)9781510680449

Due to the nonlinear and uncertain characteristics of aerospace electromechanical systems, the temporal information is challenging to fully exploit during the fault diagnosis process. In order to address this issue and further enhance the accuracy of fault diagnosis models, this paper proposes a fault diagnosis approach that integrates a One-Dimensional Convolutional neural network (1D-CNN) with Bidirectional Gated Recurrent Unit (Bi-GRU). Initially, a sliding time window method is employed to prepare samples from the raw data as input for the 1D-CNN. Subsequently, the features extracted from the convolutional and pooling layers are fed into the Bi-GRU, and the diagnostic results are output through a fully connected layer. Finally, the algorithm model is embedded and deployed in the Xilinx Zynq 7Z100 environment. The proposed algorithm is validated, and the algorithm model undergoes optimization using the High Level Synthesis (HLS) tool to demonstrate the feasibility and necessity of model optimization. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Failure analysis

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