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A Study of Neural Collapse for Text Classification 1

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4th international conference on Deep learning theory and Applications (DeLTA)

作者： Feng, Jia Hui Lai, Edmund M-K Li, Weihua Auckland Univ Technol Sch Engn Comp & Math Sci Auckland New Zealand

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

In this study, we explore the phenomenon of neural collapse (NC) in text classification using convolutional neural networks (CNNs) applied to the AG News dataset [23]. Initially, our findings indicate the occurrence of NC, which initially underperforms compared to a non-collapsed CNN. However, upon closer examination, we uncover an intriguing insight: certain data points converge towards an unknown cluster during NC. Further analysis reveals that this additional cluster represents an additional topic within the dataset, challenging the initial assumption of four distinct classes in AG News. this significant discovery suggests a promising research direction, where NC can serve as a tool for cluster discovery in semi-supervised learning scenarios.

关键词： Neural networks Natural language processing Deep learning Semi-supervised learning Neural collapse Text classification

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AI Empowers data Mining Models for Financial Fraud Detection and Prevention Systems 4

AI Empowers Data Mining Models for Financial Fraud Detection...

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4th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Wang, Ziyue Shen, Qinyan Bi, Shuochen Fu, Chengqian New York NY10012 United States Jersey City NJ07302 United States Boston MA02110 United States Baltimore MD21211 United States

With the rapid development of Internet finance, the problem of financial fraud has become increasingly prominent, which has brought severe challenges to the security and stability of the financial industry. this paper aims to use artificial intelligence (AI) technology to empower data mining models to build an efficient financial fraud detection and prevention system. First of all, through the analysis of the development trend of Internet finance and the current situation of financial fraud, the limitations of traditional prevention and control measures in the face of big data and complex fraud modes are revealed. Secondly, the paper introduces the application prospect of AI technology in the financial field, especially the advantages and potential in data mining and fraud detection. then, the data mining techniques adopted are discussed in detail, including the application of machine learning-based model recognition and artificial intelligence algorithms, as well as data preprocessing and feature engineering in big data environment. Further, the paper describes the system architecture design and key technology implementation, including model training and optimization, real-time monitoring and early warning solutions. Finally, through the in-depth analysis and evaluation of the experimental results, the paper verifies the remarkable effect and feasibility of the system in financial fraud detection and prevention, and provides a new technical guarantee and risk management strategy for the financial industry. the experimental results show that the model has achieved good results in the actual data in the financial field, and provides effective technical guarantee and support for financial security. © 2024 the Authors.

关键词： Decentralized finance

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X-Ray Insights: A Siamese with CNN and Spatial Attention Network for Innovative Person Identification 4th

X-Ray Insights: A Siamese with CNN and Spatial Attention Net...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Hazem, Farah Akram, Bennour Mekhaznia, Tahar Al-Sarem, Mohammed Shukla, Piyush Kumar Khalaf, Osamah Ibrahim Echahid Cheikh Larbi Tebessi Univ LAMIS Lab Tebessa Algeria Taibah Univ Coll Comp Sci & Engn Medina Saudi Arabia Technol Univ Madhya Pradesh Bhopal Madhya Pradesh India Al Nahrain Univ Dept Solar Al Nahrain Res Ctr Renewable Energy Baghdad Iraq

ISBN: (纸本)9783031821554;9783031821561

In the security and healthcare domains, using data from medical imaging for precise individual identification and verification has become increasingly vital. this necessity is particularly pronounced in scenarios where old and traditional identification techniqhes prove inadequate, such as during natural disasters or instances of physical impairment. Chest X-ray radiographs offer a promising avenue for reliable identification owing to their ability to capture distinctive anatomical features of the rib cage, lungs, and heart. Our research introduces a new approach to person identification utilizing chest X-ray radiographs, employing ResNet50 with spatial attention within a Siamese and Triplet network architecture. In contrast to conventional convolutional neural networks (CNNs), the inclusion of ResNet50 with spatial attention enhances the model's capability to discern global dependencies within images, potentially facilitating the extraction of discriminative characteristics for identification purposes. this research contributes to the existing by presenting a tailored methodology explicitly crafted for processing chest X-ray images, thereby presenting a viable solution for reliable identification in security and medical contexts, particularly in emergency scenarios. To validate the efficacy of the proposed method and explore its practical applications, rigorous validation and testing procedures are imperative.

关键词： Siamese network Triplet loss Spatial Attention person identification Chest X-rays deep learning ResNet50

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4th international conference on Soft Computing and its Engineering Applications, icSoftComp 2022

4th International Conference on Soft Computing and its Engin...

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4th international conference on Soft Computing and its Engineering Applications, icSoftComp 2022

ISBN: (纸本)9783031276088

the proceedings contain 36 papers. the special focus in this conference is on Soft Computing and its Engineering Applications. the topics include: Explainable AI for Predictive Analytics on Employee Attrition;graph Convolutional Neural Networks for Nuclei Segmentation from Histopathology Images;Performance Analysis of Cache Memory in CPU;extraction of Single Chemical Structure from Handwritten Complex Chemical Structure with Junction Point Based Segmentation;automatic Mapping of Deciduous and Evergreen Forest by Using machine learning and Satellite Imagery;RIN: Towards a Semantic Rigorous Interpretable Artificial Immune System for Intrusion Detection;SVRCI: An Approach for Semantically Driven Video Recommendation Incorporating Collective Intelligence;deep learning Based Model for Fundus Retinal Image Classification;one True Pairing: Evaluating Effective Language Pairings for Fake News Detection Employing Zero-Shot Cross-Lingual Transfer;reliable Network-Packet Binary Classification;semKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization;extraction and Analysis of Speech Emotion Features Using Hybrid Punjabi Audio dataset;human Activity recognition in Videos Using Deep learning;Brain Tumor Classification Using VGG-16 and MobileNetV2 Deep learning Techniques on Magnetic Resonance Images (MRI);five-Year Life Expectancy Prediction of Prostate Cancer Patients Using machine learning Algorithms;an Ensemble MultiLabel Classifier for Intra-Cranial Haemorrhage Detection from Large, Heterogeneous and Imbalanced database;A Method for Workflow Segmentation and Action Prediction from Video data - AR Content;convolutional Neural Network Approach for Iris Segmentation;reinforcement learning Algorithms for Effective Resource Management in Cloud Computing;FedCLUS: Federated Clustering from Distributed Homogeneous data;corpus Building for Hate Speech Detection of Gujarati Language;a Software

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Neural Network Based GIS Local Discharge Spectrogram recognition Study

Neural Network Based GIS Local Discharge Spectrogram Recogni...

引用

Energy, Power and Electrical Engineering (EPEE), international conference on

作者： Honghao Luo Yifa Sheng School of Electrical Engineering University of South China Hengyang China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

With the wide application of the deep learning algorithm in the field of PD pattern recognition, it makes up for the disadvantages of the traditional method, at the same time, it can solve the problems of traditional machine learning methods such as poor performance and under-fitting when the training sample size is *** training the convolutional neural network to recognize the PRPD maps, not only can we avoid the complex feature extraction of statistical data, but also can greatly improve the work efficiency and the accuracy of localization type recognition. For this reason, this paper is designed based on the comparative analysis of convolutional neural network and support vector machine. First, an experimental model of four typical partial discharge defects in GIS was designed and constructed, and experimental data were collected. the PRPD spectrogram training set is then augmented with data using conditional generative adversarial networks. Finally, the PRPD spectra of each type of defects are identified and classified using convolutional networks and conventional machine learning support vector machines, respectively, and analyzed for comparison.

关键词： Training Support vector machines Partial discharges Location awareness machine learning algorithms Neural networks Vectors pattern recognition Convolutional neural networks Spectrogram

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Text Line Segmentation on Ancient Egyptian Papyri: Layout Analysis with Object Detection Networks and Connected Components 18th

Text Line Segmentation on Ancient Egyptian Papyri: Layout An...

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18th international conference on Document Analysis and recognition (ICDAR)

作者： Unter, Stephan M. Univ Basel Basel Switzerland

ISBN: (纸本)9783031705427;9783031705434

the automatic localization of text lines is an important step in the analysis of handwritten historical documents. It is a valuable tool for further analysis, such as studying handwriting styles and typefaces or realigning fragments based on a continuation of text lines. this paper tests and compares various architectures, originally designed for object detection tasks, for text line segmentation in ancient Egyptian hieratic papyri. the corpus used in this study presents a significant challenge due to noisy text carriers, complex scripts and layouts, and a highly fragmentary condition. the experiments conducted include tests on transfer learning, data augmentation, and various input resolutions. the results suggest that most object detection networks, particularly the Faster R-CNN and Mask R-CNN architectures, are well-suited to identify lines of text even in very complex or fragmented textual layouts. In addition, we present and evaluate an alternative 'bottom-up' approach that utilises binarised documents. this method derives text lines from connected components, allowing for greater control over parameters and improved identification of small ink remnants. Although the numerical evaluation metrics may be lower, this technique offers increased control by the user.

关键词： Text Line Segmentation Historical Document Analysis Ancient Egyptian Papyri machine learning Object Detection

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Material Named Entity recognition (MNER) for Knowledge-Driven Materials Using Deep learning Approach 4th

Material Named Entity Recognition (MNER) for Knowledge-Drive...

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4th international conference on Trends in Cognitive Computation Engineering, TCCE 2022

作者： Saef Ullah Miah, M. Sulaiman, Junaida Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Universiti Malaysia Pahang Pahang Pekan26600 Malaysia

ISBN: (纸本)9789811994821

the scientific literature contains an abundance of cutting-edge knowledge in the field of materials science, as well as useful data (e.g., numerical values from experimental results, properties, and structure of materials). To speed up the identification of new materials, these data are essential for data-driven machine learning (ML) and deep learning (DL) techniques. Due to the large and growing amount of publications, it is difficult for humans to manually retrieve and retain this knowledge. In this context, we investigate a deep neural network model based on Bi-LSTM to retrieve knowledge from published scientific articles. the proposed deep neural network-based model achieves an F1 score of 9 ~ 7 % for the Material Named Entity recognition (MNER) task. the study addresses motivation, relevant work, methodology, hyperparameters, and overall performance evaluation. the analysis provides insight into the results of the experiment and points to future directions for current research. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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PRNU-Net: a Deep learning Approach for Source Camera Model Identification based on Videos Taken with Smartphone 26

PRNU-Net: a Deep Learning Approach for Source Camera Model I...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Akbari, Younes Almaadeed, Noor Al-Maadeed, Somaya Khelifi, Fouad Bouridane, Ahmed Qatar Univ Dept Comp Sci & Engn Doha Qatar Northumbria Univ Dept Comp & Informat Sci London England Univ Sharjah Ctr Data Analyt & Cybernet Sharjah U Arab Emirates

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Recent advances in digital imaging have meant that every smartphone has a video camera that can record high-quality video for free and without restrictions. In addition, rapidly developing Internet technology has contributed significantly to the widespread distribution of digital video via web-based multimedia systems and mobile applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital video has become affordable nowadays, security issues have become threatening and have spread worldwide. One of the security issues is the identification of source cameras on videos. Generally, two common categories of methods are used in this area, namely Photo Response Non-Uniformity (PRNU) and machine learning approaches. To exploit the power of both approaches, this work adds a new PRNU-based layer to a convolutional neural network (CNN) called PRNU-Net. To explore the new layer, the main structure of the CNN is based on the MISLnet, which has been used in several studies to identify the source camera. the experimental results show that the PRNU-Net is more successful than the MISLnet and that the PRNU extracted by the layer from low features, namely edges or textures, is more useful than high and mid-level features, namely parts and objects, in classifying source camera models. On average, the network improves the results in a new database by about 4%.

关键词： Deep learning Video on demand Social networking (online) databases Feature extraction Cameras Propagation losses

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Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks 26

Automatic Foot Ulcer Segmentation Using an Ensemble of Convo...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Mahbod, Amirreza Schaefer, Gerald Ecker, Rupert Ellinger, Isabella Med Univ Vienna Inst Pathophysiol & Allergy Res Vienna Austria Loughborough Univ Dept Comp Sci Loughborough Leics England TissueGnost GmbH Dept Res & Dev Vienna Austria

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting accurate morphological features from foot wounds is crucial for appropriate treatment. Although visual inspection by a medical professional is the common approach for diagnosis, this is subjective and error-prone, and computer-aided approaches thus provide an interesting alternative. Deep learning-based methods, and in particular convolutional neural networks (CNNs), have shown excellent performance for various tasks in medical image analysis including medical image segmentation. In this paper, we propose an ensemble approach based on two encoder-decoder-based CNN models, namely LinkNet and U-Net, to perform foot ulcer segmentation. To deal with a limited number of available training samples, we use pretrained weights (EfficientNetB1 for the LinkNet model and EfficientNetB2 for the U-Net model) and perform further pre-training using the Medetec dataset while also applying a number of morphological-based and colour-based augmentation techniques. To boost the segmentation performance, we incorporate five-fold cross-validation, test time augmentation and result fusion. Applied on the publicly available chronic wound dataset and the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge, our method achieves state-of-the-art performance with data-based Dice scores of 92.07% and 88.80%, respectively, and is the top ranked method in the FUSeg challenge leaderboard. the Dockerised guidelines, inference codes and saved trained models are publicly available at https://***/masih4/Foot Ulcer Segmentation.

关键词： Foot ulcer medical image analysis image segmentation machine learning deep learning ensemble

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4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022 - Proceedings

4th International Conference on Pattern Analysis and Intelli...

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4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022

ISBN: (纸本)9781665461610

the proceedings contain 57 papers. the topics discussed include: a deep learning-based system for detection at-risk students;an explainable self-labeling grey-box model;development of a biometric authentication platform using voice recognition;binary Archimedes optimization algorithm based feature selection for regression problem;semantic segmentation of breast cancer histopathology images using deep learning;a biased random-key genetic algorithm for extractive single-document summarization;a virtual clustering for data dissemination in vehicular fog computing;a novel method of requirements analysis for big data projects;combination of closed-set and open-set biometric identification;a formal integrated approach for cyber physical systems;and testing cyber-physical production system: test methods categorization and dataset.

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