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Quality Assessment of Electrocardiogram Signals Using Contrastive learning 13

Quality Assessment of Electrocardiogram Signals Using Contra...

引用

13th international conference on Information Technology in Medicine and Education, ITME 2023

作者： Zhong, Meiping Li, Zuoyong Wang, ChuanSheng Huang, Jiayan Zhao, WanQi Fan, Haoyi College of Computer and Big Data Fuzhou University Fuzhou350108 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China Polytechnic University of Catalonia Department of Automatic Control Technical Barcelona08034 Spain New Engineering Industry College Putian University Putian351100 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9798350319156

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods. © 2023 IEEE.

关键词： Electrocardiograms

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Multi-threaded Approach in Associative Classification 11th

Multi-threaded Approach in Associative Classification

引用

11th international conference on Future data and Security engineering, FDSE 2024

作者： Nguyen Quoc, Huy Tran Anh, Tuan Tai, Do Nhu Tu Lang, Phieu Saigon University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819604364

Associative classification is one interesting approach of supervised learning that are easily explainable. this approach is often constructed based on both classification and association rule mining techniques, to find out a set of rules called the classification association rules (CAR), to classify target attribute. there are many methods to improve the model following the feature selection approach and achieve quite good results. this article focuses on improving running time in multi-threaded approach. there are many experiments performed on many different kinds of dataset, and there are very reasonable results when we could explain which types of datasets are suitable for multi-threaded implementation as well as parallelization. Based on that, we can choose the appropriate number of threads for each type of dataset. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Self-supervised learning

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QG-SKI: Question Classification and MCQ Question Generation Using Sequential Knowledge Induction 22nd

QG-SKI: Question Classification and MCQ Question Generation ...

引用

22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Dhanvardini, R. Deepak, Gerard Santhanavijayan, A. Health Care Informatics Domain Optum-United Health Groups Hyderabad India Department of Computer Science Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031274084

E-learning has emerged as the most effective way of getting information in a range of sectors in the contemporary age. the utilisation of electronic content to provide education and development is referred to as e-learning. While the broadening internet has a plethora of e-learning tools, knowledge acquisition is not just an aspect that adds to an individual's enrichment. Assessment and evaluation are crucial parts of every learning system. Due to more complex assessments and quicker inspection, multiple choice questions are becoming extremely prevalent in current evaluations. However, establishing a diversified pool of MCQs relevant to a certain subject matter presents a hurdle. Manually creating high-quality MCQ exams is a time-consuming and arduous procedure that demands skill. As a result, research has concentrated on the automated construction of well-structured MCQ-based tests. this paper presents a paradigm using natural language processing based on semantic similarity and dynamic ontology. the proposed QG-SKI model uses LOD Cloud and Wiki data to generate Ontologies dynamically and Knowledge reservoir is performed. the dataset is analysed using TF-IDF algorithm and the Semantic Similarity and Semantic Dissimilarity are computed using Shannon’s entropy, Jaccard Similarity and Normalised Google Distance. these algorithms are executed for a multitude of degrees and levels to generate a similar of similar instances. the suggested model has a 98.15% accuracy and outperforms previous baseline models by dampening resilience. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： E-learning

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Real-time Tiredness Detection System using Nvidia Jetson Nano and OpenCV 11

Real-time Tiredness Detection System using Nvidia Jetson Nan...

引用

11th international conference on Information Technology and Quantitative Management, ITQM 2024

作者： Florian, Nicolae Popescu, Dumitru Hossu, Andrei Department of Automatic Control and Systems Engineering University Politehnica Bucuresti Bucharest060042 Romania Academy of Romanian Scientists Romania

In this paper, we explore how to use a Nvidia Jetson Nano and Python to create a system that detects weariness in a person's face using computer vision and machine learning techniques. the system captures the person's face in real time, preprocesses the picture to cut noise, then detects the face using Haar cascades. Next, using computer vision algorithms, characteristics associated with weariness, such as the eyes, are retrieved. these characteristics are then used to build a machine learning model that can predict weariness in the live stream feed. Lastly, using a graphical user interface, the findings are shown, and the system may be fine-tuned to increase accuracy. this device might be used in applications such as driver monitoring and traffic safety. © 2024 the Authors.

关键词： Graphical user interfaces

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A Comprehensive Machine learning Approach in Detecting Coronary Heart Disease 11th

A Comprehensive Machine Learning Approach in Detecting Coron...

引用

11th international conference on Frontiers of intelligent Computing: theory and Applications, FICTA 2023

作者： Prasanna, ElaproluSai Anuradha, T. Swetha, Vara Pragathi, Jangam Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India

ISBN: (纸本)9789819967018

Heart disease is on the rise, among addition to affecting the elderly heart disease is becoming more prevalent in young people. Not only India, but the entire world is impacted by this disease. A recent study found that at least 500,000 open heart surgeries are carried out annually and that the main causes of heart disease include high blood pressure, smoking, inactivity, etc. Diagnosing the heart disease early on leads to potentially avoid a serious issue. Many researchers are working on machine learning techniques for early diagnosis of disease. Non-invasive diagnose methods are used for diagnosing heart disease without opening the skin. this paper proposes a machine learning methods to accurately predict whether a person having heart disease or not. Supervised machine learning techniques were used for prediction and Django Framework for designing web page. An easy to use user interface was designed by using Django Framework as front end, where we can check the status of having coronary heart disease. At the back end, machine learning algorithms will predict based on the data entered by the user. Our approach attained a maximum of 98% accuracy. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Design and Implementation of an intelligent Monitoring System for Internet of things Aquaculture Based on ZigBee Technology 11

Design and Implementation of an Intelligent Monitoring Syste...

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11th Joint international Information Technology and Artificial Intelligence conference, ITAIC 2023

作者： Fu, Yuanfang Liu, Yingying Wang, Danping Liu, Kaiwen Li, Xinyu Xu, Jiaxin Wu, Zilong School of Information Engineering Shenyang University Shenyang China School of Mechanical Engineering Shenyang University Shenyang China

ISBN: (纸本)9798350333664

According to the impact of marine pollution on mariculture, an intelligent monitoring system for mariculture water quality based on ZigBee is designed in this study. the system includes a data acquisition part, a data transmission part and a user-side early warning part. It is composed of data terminals and multiple data acquisition points, which detect the temperature, PH value, oxygen solubility and other related parameters of water quality. the data of each collection point is transmitted to the ZigBee coordinator node through the ZigBee network. the coordinator node monitors the collected data value and makes a risk warning according to the early warning value. GPRS is used to upload the data to the server in real time, so that the staff can find and handle the abnormalities in time, and an intelligent monitoring system based on the Internet of things is utilized. © 2023 IEEE.

关键词： intelligent monitoring system Internet of things mariculture ZigBee

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Promoting Security and Trust on Social Networks: Explainable Cyberbullying Detection Using Large Language Models in a Stream-Based Machine learning Framework 11

Promoting Security and Trust on Social Networks: Explainable...

引用

11th IEEE international conference on Social Networks Analysis, Management and Security, SNAMS 2024

作者： Garcia-Mendez, Silvia De Arriba-Perez, Francisco Information Technologies Group-atlanTTic University of Vigo Vigo Spain

ISBN: (纸本)9798331518349

Social media platforms enable instant and ubiquitous connectivity and are essential to social interaction and communication in our technological society. Apart from its advantages, these platforms have given rise to negative behaviors in the online community, the so-called cyberbullying. Despite the many works involving generative Artificial Intelligence (AI) in the literature lately, there remain opportunities to study its performance apart from zero/few-shot learning strategies. Accordingly, we propose an innovative and real-Time solution for cyberbullying detection that leverages stream-based Machine learning (ML) models able to process the incoming samples incrementally and Large Language Models (LLMS) for feature engineering to address the evolving nature of abusive and hate speech online. An explainability dashboard is provided to promote the system's trustworthiness, reliability, and accountability. Results on experimental data report promising performance close to 90 % in all evaluation metrics and surpassing those obtained by competing works in the literature. Ultimately, our proposal contributes to the safety of online communities by timely detecting abusive behavior to prevent long-lasting harassment and reduce the negative consequences in society. © 2024 IEEE.

关键词： Adversarial machine learning

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A Multi-input Deep learning Model to Classify COVID-19 Pneumonia Severity from Imaging and Clinical data 11th

A Multi-input Deep Learning Model to Classify COVID-19 Pneum...

引用

11th international Work-conference on Bioinformatics and Biomedical engineering (IWBBIO)

作者： Lizzi, Francesca Brero, Francesca Fantacci, Maria Evelina Lascialfari, Alessandro Paterno, Gianfranco Postuma, Ian Oliva, Piernicola Scapicchio, Camilla Retico, Alessandra Natl Inst Nucl Phys Pisa Italy Univ Pavia Dept Phys Pavia Italy Univ Pisa Dept Phys Pisa Italy Natl Inst Nucl Phys Pavia Italy Natl Inst Nucl Phys Ferrara Italy Univ Sassari Dept Chem Phys Math & Nat Sci Sassari Italy Natl Inst Nucl Phys Cagliari Italy

ISBN: (纸本)9783031646355;9783031646362

the study of multi-input models that can process heterogeneous data is a challenge at the frontier of machine learning. We implemented a multi-modal approach aiming at exploiting both imaging and clinical information of patients to predict the severity of their outcome. As a specific use case, we developed a fully automated algorithm to predict the outcome of COVID-19 patients based on chest X-Ray images and clinical data, provided by the AI4COVID Challenge (1589 subjects). the system can distinguish between severe cases, those who needed intensive care or died, and mild ones. the system is composed of three Convolutional Neural Networks (CNN) for pre-processing, lung segmentation (U-Net architecture), and outcome classification. the first CNN is devoted to recognize the gray-level encoding needed to standardize the images. the U-Net for lung segmentation has been trained using two datasets collected for Tuberculosis screening. We achieved a Dice Similarity Coefficient (DSC) equal to 0.96 +/- 0.03. this was needed to focus the final classifier on evaluating features within the lung. Without the careful selection of the lung, in fact, the prediction strongly depended on features outside the lung district (e.g. ECG cables, respiratory masks). the outcome classifier is a multi-input CNN made of two branches joined at the bottom. the first branch is a ResNet that takes the segmented images as input, while the second branch is a Multi-Layer Perceptron (MLP) that takes in the preprocessed clinical parameters. We obtained an AUC equal to 0.84 and an accuracy equal to 76%. We also computed the saliency maps with the gradCAM and the feature importance to obtain a reasonable explanation of the classifier. this method based on data aggregation and on merging clinical and imaging information can be applied also to domains different from COVID-19 patients.

关键词： Multi-input models AI Chest X-Ray COVID-19

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A Deep learning-Based Approach for Generating 3D Models of Fluid Arts 11th

A Deep Learning-Based Approach for Generating 3D Models of F...

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11th international conference on Arts and Technologies Interactivity and Game Creation

作者： Cong, Hung Mai Trang, Mai Xuan Yamada, Akihiro Takashi, Suzuki Tosa, Naoko Nakatsu, Ryohei Osaka Univ Osaka Japan Phenikaa Univ Fac Comp Sci Hanoi Vietnam Toppan Inc Tokyo Japan Kyoto Univ Disaster Prevent Res Inst Kyoto Japan

ISBN: (纸本)9783031289927;9783031289934

this paper explores a method for creating 3D models of video artwork based on a fluid phenomenon called the Sound of Ikebana artwork. A process of using multiple generative adversarial networks (GANs) to reconstruct and predict the shape of the fluid artworks from two-dimensional reference photos was proposed. this is an extension of our previous efforts with Wasserstein GAN enhancements to predict the shape of the unmapped part and correct the texture. the experiment's results show that our process can reconstruct 3D arts without having large amount of 3D training data.

关键词： 3D fluid art Sound of Ikebana DIB-R network GAN variants

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DELVING INTO SEMANTIC SCALE IMBALANCE

DELVING INTO SEMANTIC SCALE IMBALANCE

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11th international conference on learning Representations, ICLR 2023

作者： Ma, Yanbiao Jiao, Licheng Liu, Fang Li, Yuxin Yang, Shuyuan Liu, Xu Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education School of Artificial Intelligence Xidian University Xi'an710071 China

Model bias triggered by long-tailed data has been widely studied. However, measure based on the number of samples cannot explicate three phenomena simultaneously: (1) Given enough data, the classification performance gain is marginal with additional samples. (2) Classification performance decays precipitously as the number of training samples decreases when there is insufficient data. (3) Model trained on sample-balanced datasets still has different biases for different classes. In this work, we define and quantify the semantic scale of classes, which is used to measure the feature diversity of classes. It is exciting to find experimentally that there is a marginal effect of semantic scale, which perfectly describes the first two phenomena. Further, the quantitative measurement of semantic scale imbalance is proposed, which can accurately reflect model bias on multiple datasets, even on sample-balanced data, revealing a novel perspective for the study of class imbalance. Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations. Comprehensive experiments show that dynamic semantic-scale-balanced learning consistently enables the model to perform superiorly on large-scale long-tailed and non-long-tailed natural and medical datasets, which is a good starting point for mitigating the prevalent but unnoticed model bias. Furthermore, we look ahead to future challenges in the field. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Semantics

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