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Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss

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Mathematical Biosciences and engineering 2024年第4期21卷 5521-5535页

作者： Ran, Zhongnan Jiang, Mingfeng Li, Yang Wang, Zhefeng Wu, Yongquan Ke, Wei Xia, Ling School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China Department of Cardiology Beijing Anzhen Hospital Capital Medical University No. 2 Anzhen Road Chaoyang District Beijing100029 China School of Applied Sciences Macao Polytechnic Institute China Department of Biomedical Engineering Zhejiang University Hangzhou310027 China

Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imbalance of ECG categories, arrhythmia classification cannot achieve satisfactory classification results under the inter-patient assessment paradigm. In this paper, a multi-path parallel deep convolutional neural network was proposed for arrhythmia classification. Furthermore, a global average RR interval was introduced to address the issue of similarities between N vs. S categories, and a weighted loss function was developed to solve the imbalance problem using the dynamically adjusted weights based on the proportion of each class in the input batch. The MIT-BIH arrhythmia dataset was used to validate the classification performances of the proposed method. Experimental results under the intra-patient evaluation paradigm and inter-patient evaluation paradigm showed that the proposed method could achieve better classification results than other methods. Among them, the accuracy, average sensitivity, average precision, and average specificity under the intra-patient paradigm were 98.73%, 94.89%, 89.38%, and 98.24%, respectively. The accuracy, average sensitivity, average precision, and average specificity under the inter-patient paradigm were 91.22%, 89.91%, 68.23%, and 95.23%, respectively. © 2024 the Author(s).

关键词： Diseases

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Baggage Threat Detection Under Extreme Class Imbalance 2

Baggage Threat Detection Under Extreme Class Imbalance

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Ahmed, Abdelfatah Velayudhan, Divya Hassan, Taimur Hassan, Bilal Dias, Jorge Werghi, Naoufel Khalifa University C2PS and Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665498197

Automatic detection of prohibited items is a critical but difficult task during aviation security. Manual detection of such items is a time-consuming process that is also limited by the examination capacity of the security inspector. To overcome these constraints, several researchers have proposed deep learning solutions to identify contraband data contained within baggage X-ray imagery. However, when trained on the imbalanced data that is frequently encountered in real-world aviation screening, the performance of these models suffers significantly. Towards this end, this paper proposes the coupling of various imbalanced learning strategies that can be used to augment traditional threat detection models and enable them to effectively learn the extremely imbalanced distribution of normal and threat object categories. The proposed approach is validated on three public datasets, namely SIXray, OPIXray, and COMPASS-XP, where it achieved the performance improvement of 9.52%, 11.32%, and 10.98%, respectively, on all three datasets in terms of mean intersection-over-union as compared to the state-of-the-art threat detection frameworks. © 2022 IEEE.

关键词： Deep learning

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A Method to Classify Emotions Based on BERT 4

A Method to Classify Emotions Based on BERT

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4th International Signal Processing, Communications and engineering Management Conference, ISPCEM 2024

作者： Chen, Zeli Lin, Jian Jiang, Jie Jing, Di Department of Computer Science Lafayette College EastonPA United States School of Information Technology and Artificial Intelligence Zhejiang University of Finance and Economics Zhejiang Hangzhou China Department of Mathematical and Statistical Sciences University of Alberta EdmontonABT6G 2G1 Canada School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

ISBN: (纸本)9798331528676

Nowadays, as science and technology continue to advance, addressing people's emotional issues is of particular significance, and how to accurately and efficiently recognize emotions has become increasingly prominent. We explores sentiment recognition and classification methods, including rule-based approaches, traditional machine learning models, deep learning models, and multi-modal sentiment analysis. Through comparative analysis, we will evaluate the effectiveness of these methods in handling social media texts. Additionally, we has developed a BERT-based two-stage model that first determines sentiment polarity (positive or negative) and then performs detailed classification of negative emotions. This two-stage model significantly improves accuracy and detail recognition compared to traditional one-stage models and other baseline methods. The results show that our approach not only enhances sentiment analysis accuracy but also provides deeper insights into the emotional subtleties of social media texts. This advancement holds significant practical implications for real-time sentiment monitoring and more effective crisis management strategies. ©2024 IEEE.

关键词： Contrastive Learning

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Incremental Instance Segmentation for the Gleason Tissues Driven Prostate Cancer Prognosis 2

Incremental Instance Segmentation for the Gleason Tissues Dr...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Hassan, Taimur Ahmed, Abdelfatah Hassan, Bilal Shafay, Muhammad Elbaz, Ayman Dias, Jorge Werghi, Naoufel Khalifa University KUCARS Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University C2PS and Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665498197

Prostate cancer (PCa) is the second most commonly diagnosed cancer in men and the fifth-highest cause of death globally. Early-stage prostate cancer is frequently asymptomatic and has an indolent course, requiring active observation. Early detection and recognition of Gleason tissue can help handle the PCa spread. Therefore, many deep learning-based systems have been proposed by researchers in order to screen the PCa. Moreover, acquiring such large-scale, well-annotated data can improve the performance of screening and detecting PCa. However, this process is typically challenging and impractical. This paper addresses this issue by proposing a novel knowledge distillation-driven instance segmentation framework. This approach is fused with incremental few-shot training and allows the traditional semantic segmentation models to grade the PCa utilizing instance-aware segmentation, along with the extraction of correlated samples of the Gleason tissue patterns. Furthermore, the proposed approach has been validated on a dataset that contains around 71.7M whole slide image patches. Our approach has outperformed the state-of-the-art models by 2.01% in terms of mean IoU and 9.69% in terms of F1 score for the extraction of Gleason tissue instances and grading PCa, respectively. © 2022 IEEE.

关键词： Tissue

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How End Users Develop Point-and-Click Games 9th

How End Users Develop Point-and-Click Games

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9th International Symposium on End-User Development, IS-EUD 2023

作者： Artizzu, Valentino Blečić, Ivan Frau, Vittoria Spano, Lucio Davide Department of Mathematics and Computer Science University of Cagliari Via Ospedale 72 Cagliari09124 Italy Department of Civil Engineering and Architecture University of Cagliari Via Marengo 2 Cagliari09123 Italy

ISBN: (纸本)9783031344329

Rule-based approaches have proven effective in automating smart homes, managing IoT sensors and devices, or expressing simple web-based workflows. More recently, rules have been used to define the behaviour of point-and-click games and virtual reality experiences. In this paper, we propose an analysis of the first dataset of point-and-click games developed by end users. It includes 143 games, some created for cultural heritage promotion and others created in dedicated events. The dataset shows peculiar characteristics in the rules defining the game navigation and more general patterns in managing enumerations, integers and booleans. The dataset is publicly available for further research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Automation

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Fortifying Information Security: Security Implications of Microservice and Monolithic Architectures 16

Fortifying Information Security: Security Implications of Mi...

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16th International Conference on Security of Information and Networks, SIN 2023

作者： Nemmini, Sanghamitra Abhishek, S. Anjali, T. Raj, Sincy Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Amritapuri India Vrije Universiteit Brussel Macs Pleinlaan 2 BrusselsB-1050 Belgium

ISBN: (纸本)9798350343212

Monolithic architecture is a traditional and primary method for creating software programs. On the other hand, monolithic architecture's shortcomings were addressed by developing the unique evolutionary technique known as microservice architecture, which led to businesses' widespread acceptance of this design. However, the microservice design raises several security concerns, particularly web application security, a significant business problem. The purpose of this study is to analyse and contrast the security and safety features of monolithic and microservice architectures, emphasizing the security issues that each presents and the solutions that have been proposed to address them. Examining both architectures security risks and investigating viable mitigation strategies are covered in the paper. Microservices improve security and flexibility but also need a more complex and dynamic security strategy. © 2023 IEEE.

关键词： Architecture Attacks Microservices Monolithic Remote Security Web Application

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Development of 5G Cyber-Physical Production System

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International Journal of Networked and Distributed Computing 2023年第1期11卷 9-19页

作者： Huang, Hsuan-Chao Tsai, Chun-Hsien Lin, Hsiung-Cheng Department of Computer Science and Information Engineering National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan PhD program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan Department of Electronic Engineering National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan

The cyber-physical production system (CPPS) was developed for the interconnection between operational technology (OT) and information and communication technology (ICT) among the machines and decentralized production control. However, OT and ICT are now getting more complex in the modern industry. To enhance industrial management and production process, the CPPS based on the 5G architecture under production environments can provide a more comprehensive solution in the context of Industry 4.0. For this purpose, a 5G CPPS model to improve the interoperability of CPPS in ICT and OT is proposed to build a virtualized Industry 4.0 asset management shell system. First, real-time task artifacts and 5G communication modules are bound in a certain form of virtualization. Second, task artifacts are released to the production line in a real-time scheduling. Third, an order is placed on a production line and split into multiple tasks for production process. Single-machine single-workpiece and multi-machine and multi-workpiece processing cases are used for testing in the production line. Experimental results show that the proposed 5G CPPS can efficiently achieve multi-machine work piece processing. © 2022, The Author(s).

关键词： 5G Cyber-physical production system (CPPS) Digital twin (DT) Industry 4.0 Information technology Operational technology

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Benchmarking Neural Capacity Estimation: Viability and Reliability

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IEEE Transactions on Communications 2023年第5期71卷 2654-2669页

作者： Mirkarimi, Farhad Rini, Stefano Farsad, Nariman Sharif University of Technology Department of Electrical Engineering Tehran11365-11155 Iran MontrealQCH3A 0G4 Canada Hsinchu30010 Taiwan Department of Computer Science TorontoONM5B 2K3 Canada

Recently, several methods have been proposed for estimating the mutual information from sample data using deep neural networks. This approach is referred to as (). s differ from other approaches in the literature as they are data-driven estimators. As such, they have the potential to perform well on a large class of capacity problems. To test the performance across various s, it is desirable to establish a benchmark encompassing the different challenges of capacity estimation. This is the objective of this paper. We consider three scenarios for benchmarking: (i) the classic AWGN channel, (ii) channels continuous inputs- the optical intensity and peak-power constrained AWGN channel (iii) channels with a discrete output- i.e., the Poisson channel. We also consider the extension to the multi-terminal case with (iv) the AWGN and optical MAC models. We argue that benchmarking a certain across these four scenarios provides a substantive test of performance. We study the performance of mutual information neural estimator (MINE), smoothed mutual information lower-bound estimator (SMILE), and directed information neural estimator (DINE) and provide insights into the performance of other methods as well. To summarize our benchmarking results, MINE provides the most reliable performance. © 1972-2012 IEEE.

关键词： Benchmarking

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Recognition and evaluation of cutaneous condition through assorted artificial intelligence reliant algorithms

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International Journal of Information Technology (Singapore) 2025年 1-13页

作者： Mishra, Manmohan Yadav, Ajay Kumar Mazumdar, Bireshwar Dass Gupta, Prashant K. Panwar, Arvind Bharadwaj, Shivam Department of Computer Application United Institute of Management Prayagraj India School of Computer Science & Engineering Technology Bennett University Plot Nos 8-11 TechZone II Uttar Pradesh Greater Noida 201310 India Galgotias University Plot No. 2 Yamuna Expy opposite Buddha International Circuit Sector 17A Uttar Pradesh Prayagraj 203201 India

Our skin is the hefty organ that envelops and shields body. It prevents us from numerous fatal and non fatal diseases. It is observed that due to bacteria or other causes of infection, skin faces certain minor or life threatening diseases. The most prioritized step toward restoring health is early illness signs identification. Identifying Cutaneous Condition from clinical images is one of the foremost challenges in medical image investigation. In the presented study we will enlighten the various Artificial Intelligence techniques falling under the categories of supervised machine learning including Probabilistic classifier (Naïve Bayes), Statistical algorithm (Logistic Regression), Ensemble learning (Random Decision Trees), Data analysis technique (Convolutional Neural Network) and Kernel approach (Support Vector Machine) to identify and classify the cutaneous condition appropriately so that corrective measures of skin treatment can be endow with. The proposed approach entails collecting images as input, preprocessing, segmenting, feature extraction and lastly applying the classification algorithms to derive the Cutaneous Condition categories. Additional trials are conducted using the different approaches as indicated and it was discovered from the suggested tests that the Convolutional Neural Network strategy yields the best results overall. The proposed model is trained, tested, and evaluated using the International Skin Imaging Collaboration (ISIC) 2019 challenge dataset and Human Against Machine with 10,000 training images (HAM10000) for the detection of manifold Cutaneous Condition. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Artificial intelligence Confusion matrix Convolutional neural networks Cutaneous condition Image processing Machine learning

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AGENT-BASED MODELING AND SIMULATION OF DEHYDRATION PROCESS 37

AGENT-BASED MODELING AND SIMULATION OF DEHYDRATION PROCESS

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37th ECMS International Conference on Modelling and Simulation, ECMS 2023

作者： Seddari, Noureddine Hamioud, Sohaib Bouras, Abdelghani Kerraoui, Sara Krim, Fouzia LIRE Laboratory Abdelhamid Mehri-Constantine 2 University Constantine25000 Algeria LICUS Laboratory Department of Computer Science Université 20 Août 1955-Skikda Skikda21000 Algeria LISCO Laboratory Computer Science Department Badji-Mokhtar University Annaba23000 Algeria Department of Industrial Engineering College of Engineering Alfaisal University Riyadh11533 Saudi Arabia

ISBN: (纸本)9783937436807

Agent-based modeling and simulation (ABMS) is a new approach to modeling autonomous systems and interacting agents. This method is becoming more and more popular for its efficiency and simplicity. It constitutes an approach in the field of modeling complex systems. Indeed, ABMS offers, contrary to other types of simulations, the possibility of directly representing the simulated entities, their behaviors, and their interactions without having to recourse to mathematical equations. This work is a contribution in this sense, the goal is to propose an agent-based model to simulate an industrial system. The latter presents the problem of complexity, which can be mastered with the Multi-Agent Systems (MAS) approach. Our model is validated by a case study of the natural gas dehydration process. The latter is consolidated by a simulation made in the multiagent platform JADE Java Agent DEvelopment Framework. © ECMS Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni (Editors) 2023.

关键词： Multi agent systems

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