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Enhancing Multi-Label ECG Classification via Task-Guided Lead Correlations in Internet of Medical Things

IEEE Internet of Things Journal 2025年第12期12卷 20544-20555页

作者： Yuan, Xiaoyan Wang, Wei Chen, Junxin Fang, Kai Bashir, Ali Kashif Mondal, Tapas Hu, Xiping Deen, M. Jamal Beijing Institute of Technology School of Medical Technology Beijing100081 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Guangdong Shenzhen518172 China Dalian University of Technology School of Software Dalian116621 China Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Chitkara University Institute of Engineering and Technology Centre for Research Impact and Outcome Chitkara University Punjab Rajpura140401 India McMaster University Faculty of Health Science and the Department of Pediatrics Hamilton Canada McMaster University McMaster School of Biomedical Engineering Hamilton Canada McMaster University Electrical and Computer Engineering Department Hamilton Canada

With the rise of the Internet of Things (IoT), wearable devices have enabled real-time health monitoring, particularly through physiological signals like electrocardiograms (ECG). The standard 12-lead ECG records the electrical activity of the heart from multiple perspectives, providing valuable insights into cardiac health. However, existing 12-lead ECG analysis methods often treat leads as channel-level arrangements or rely on spatial adjacency to predefine lead connections, limiting their ability to capture the complex spatial and functional relationships between leads fully. To address this limitation, we propose TGLLNet, a task-driven model that automatically learns inter-lead relationships to improve multi-label ECG classification. TGLLNet adaptively learns lead connectivity patterns and relational strengths, enhancing ECG representation and improving model generalizability across tasks. Specifically, TGLLNet employs a Temporal Graph Construction (ETGC) module to convert ECG signals into temporal graphs and uses a Residual Pyramid Graph Convolution (RPG) module for multi-level graph embeddings, utilizing a Graph Convolutional Network (GCN) with independently learnable adjacency matrices. Combined with a Temporal Context Convolution (TCC) module, TGLLNet captures spatio-temporal dependencies, significantly improving ECG representation. Experimental results on 7 tasks from PTB-XL and CPSC2018 datasets demonstrate that TGLLNet outperforms existing methods, showing superior generalizability across different tasks. Our code is available at https://***/rosemary333/TGLLnet. © 2014 IEEE.

关键词： Graph embeddings

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Faceswap Finder: A Fusion-Based Deepfake Detection Technique

Faceswap Finder: A Fusion-Based Deepfake Detection Technique

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Nimra Basit Fatima Khalid Qurat Ul Ain Maria Andleeb Department of Software Engineering University of Engineering and Technology Taxila Pakistan Faculty of Computer Sciences and Engineeing GIKI Topi Pakistan Department of Computing Shifa Tameer-e-Millat University PRC Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

The significant improvement in deepfake generation algorithms has made possible the manipulation of visual data. The advancement of GAN technology and tools has made it relatively simple to use source and target photos and build realistic deepfakes. The challenges associated with deepfakes include political defamation, impersonation, misinformation, and cyberstalking. The generation of deepfakes through faceswap approach hinders the detection oftentimes as it spans numerous artifacts – illumination, facial skin tones, and other conditions. Existing techniques do not employ robust mechanisms to detect such deepfakes either due to computational inefficiency or focusing on a few parameters for decision-making. To counteract these dangers, a robust deepfake detection model must be developed and implemented. This paper proposes Efficient-Fused Net (EF-Net) - a transfer learning approach to two EfficientNet-B5 models with partial freezing between its layers that can differentiate between real and fake images generated from faceswap implementation. We evaluated our model’s performance using the challenging deepfake subset of the extensive and varied FaceForensics++ and DFDC-Preview datasets. The proposed method outperforms state of the art methods in the detection of faceswap deepfakes, surpassing the performance by $89.19 \%$ accuracy on DFDC-Preview and $\mathbf{9 7 . 7 0 \%}$ of Deepfakes subset of FaceForensics++.

关键词： Deepfakes Visualization Computational modeling Transfer learning Decision making Lighting Focusing Skin Robustness Resilience

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Identifying COVID-19 through X-ray and CT scan images using Machine Learning 18

Identifying COVID-19 through X-ray and CT scan images using ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Shehzadi, Maham Arshad, Usama Abideen, Zainul Ali, Raja Hashim Khan, Ammad Aslam Ijaz, Ali Zeeshan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences Rawalpindi Pakistan National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COVID-19 effectively. This involves the application of a variety of AI algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COVID-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COVID-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management. © 2023 IEEE.

关键词： COVID-19

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Reinforcement Learning based MIMO Controller for Virtual Inertia Control in Isolated Microgrids

Reinforcement Learning based MIMO Controller for Virtual Ine...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Skiparev, Vjatseslav Belikov, Juri Petlenkov, Eduard Levron, Yoash Tallinn University of Technology Department of Software Science Tallinn Estonia Tallinn University of Technology Department of Computer Systems Tallinn Estonia Technion Faculty of Electrical Engineering Haifa Israel

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

In this paper, we propose a multi-input multi-output controller for optimal control of nonlinear energy storage, using deep reinforcement learning (DRL) algorithm. This controller provides the frequency support in an isolated microgrid with high penetration of variable renewable energy sources and varying system inertia. To achieve an optimal control we redesigned neural network of actor and critic, simplified deep deterministic policy gradient (DDPG) rules, and reorganized the reward/punishment system. Simulation results show the efficiency of the proposed virtual inertia control architecture in several scenarios. © 2022 IEEE.

关键词： Reinforcement learning

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Intelligent photonic crystal-based optical sensor for accurate glycerol-water mixture measurement using artificial neural networks

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Optical Materials Express 2025年第7期15卷 1710-1729页

作者： Saeed Roshani Salah I Yahya Pouya Karami Muhammad Akmal Chaudhary Maher Assaad Fariborz Parin Fawwaz Hazzazi Fawnizu Azmadi Hussin Sobhan Roshani Department of Electrical Engineering Ker.C. Islamic Azad University Kermanshah Iran Department of Software Engineering Faculty of Engineering Koya University Koya KOY45 Iraq Department of Computer Technology Engineering College of Technical Engineering Al-Hadba University Mosul Iraq Department of Electrical and Computer Engineering College of Engineering and Information Technology Ajman University Ajman 346 United Arab Emirates Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11492 Saudi Arabia Department of Electrical and Electronic Engineering Universiti Teknologi PETRONAS Seri Iskandar 32610 Malaysia

This paper proposes an intelligent photonic crystal-based optical sensor designed for the first time to accurately measure glycerol-water concentration and temperature. The proposed sensor features a novel two-dimensional (2D) photonic crystal structure with an optimized waveguide configuration to enhance refractive index sensitivity. The sensor structure does not include defect rods, which simplifies fabrication and enhances stability. By using the unique optical properties of photonic crystals and the artificial neural network (ANN), the proposed design ensures high precision and stability in detecting changes in the glycerol concentration. The performance of the sensor was evaluated based on sensitivity, detection limit (DL), figure of merit (FOM), and quality factor (Q-F) across different temperatures and glycerol concentrations. The optical response of the sensor was numerically analyzed and simulated using the finite-difference time-domain (FDTD) method. Then, a feedforward ANN model was developed and trained to predict glycerol concentration and temperature from the output spectral data, enabling intelligent and real-time analysis. The results demonstrate that the proposed sensor achieves high sensitivity (up to 89.9 nm/RIU), a low detection limit (0.0003–0.0010 RIU −1 ), and an excellent Q-factor (5233), making it a highly effective solution for refractive index sensing. Overall, the findings confirm that the proposed photonic crystal sensor, enhanced with ANN-based intelligent analysis, offers high accuracy, stability, and fast response, making it suitable for biomedical, pharmaceutical, and industrial applications where precise glycerol concentration measurements are required.

关键词： Effective refractive index Nematic liquid crystals Neural networks Photonic crystal sensors Photonic crystals Sensor performance

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Coordinated PI-based frequency deviation control of isolated hybrid microgrid: An online multi-agent tuning approach via reinforcement learning

Coordinated PI-based frequency deviation control of isolated...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Nosrati, K. Tepljakov, A. Petlenkov, E. Levron, Y. Skiparev, V. Belikov, J. Tallinn University of Technology Department of Computer Systems Tallinn Estonia Technion Faculty of Electrical Engineering Haifa Israel Tallinn University of Technology Department of Software Science Tallinn Estonia

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

Numerous remote area applications welcome standalone renewable energy power generation systems or isolated microgrids (MGs). Due to the nature of solar and wind energy, the frequency deviation control (FDC) in hybrid MGs has become more complicated and critical than the conventional grid for power quality purposes. By using a coordination control strategy between a double-layered capacitor and a fuel cell, our mission here is to design a FDC system based on the PI controller which is tuned by an artificial neural network (ANN) in a multi-agent structure. To achieve this aim, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed FDC system has been verified under different conditions by using real data to demonstrate the stability and robustness of the proposed controller. © 2022 IEEE.

关键词： Reinforcement learning

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A comprehensive study of the Design and Security of the IoT layer Attacks 6

A comprehensive study of the Design and Security of the IoT ...

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6th IEEE International Conference on Communication and Electronics Systems, ICCES 2021

作者： Rajan, M.Sundar Arunkumar, J.R. Ramasamy, Anusuya Sisay, Berhanu Arbaminch Institute of Technology Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Arbaminch Institute of Technology Arbaminch University Faculty of Computing and Software Engineering Ethiopia

ISBN: (纸本)9781665435871

IoT is the network of thousands of individuals, resources and expertise interconnecting and sharing. IoT implementations are strongly validated by increased usability, reliability and automation. Authentication and recovery are important in order to set up an automated IoT environment for the protection and privacy of attacks. This research work presents a survey on the design and security of the IoT layer attack. In this thesis, security and privacy concerns will be detailed. This paper also performs a comprehensive overview on the security threats and sources of threat in IoT applications addressing current IoT security principles and IoT protection and privacy potential proposals. © 2021 IEEE.

关键词： Radio frequency identification (RFID)

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Classification and Evaluation Framework of Automated testing tools for agile software: Technical Review

Classification and Evaluation Framework of Automated testing...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Mogeeb A.A. Moseh Nashwan Ameen Al-Khulaidi Abdu H. Gumaei Ayman Alsabry Ali A. A. Musleh Software Engineering Dept Faculty of Engineering & Information Technology Taiz University Taiz Yemen Industrial Dept. Engineering Dep Faculty of Engineering & Information Technology Taiz Univerity Taiz Yemen Computer Science Dept. College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Science International University of Technology Twintech Sana’a Yemen Faculty of Computer Science & Information Technology International University of Technology Twintech Sana’a Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Test automation is crucial for agile software projects to enable frequent delivery of working software with cost and time and minimal bugs. However, selecting the right automated testing tool is considered challenging due to the wide range of such existing tools. Additionally, the challenges occur clearly due to several issues such as the programming code language, the categorization of the developed system, and the tester’s knowledge and skills. This paper aims to address this gap by proposing an evaluation framework for comparing and classifying the existing automated testing tools used in agile projects. The framework is developed based on an extensive literature review of existing agile testing methodologies and common commercial automation testing techniques. The key criteria for tool evaluation are identified to cover the main testing objective aspects such as test design support, testing interfaces, reporting capabilities, etc. These criteria are considered the core methodology for this study used to analyze and compare the popular open-source and commercial tools. The proposed evaluation framework provides agile practitioners with guidelines to assist in selecting the appropriate tools based on their specific project needs such as budget, timelines, and technical expertise. This study is considered a comparative evaluation of existing agile testing tools to highlight their key strengths and limitations. The findings of this study categorized the testing tools based on the interface, code, design, and report features. This research contributes to assisting the project developer and tester in selecting suitable tools for the adoption of automated testing tools in their agile software projects. It also identifies the direction for future work, such as integrations with modern development methodologies and technologies.

关键词： Codes Automation Reviews Agile software development Programming software software measurement

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A Systemic Big Data Framework for the Charging Pile Business 22

A Systemic Big Data Framework for the Charging Pile Business

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Proceedings of the 7th International Conference on Information Systems engineering

作者： Licheng Xu Shuai Xie Department of Software Engineering Universiti Malaya Malaysia Faculty of Computer Science and Information Technology Universiti Malaya Malaysia

ISBN: (纸本)9781450397889

There are some problems in the electric vehicle (EV) charging pile industry, such as the unreasonable location of charging station construction, low utilization rate of charging piles, and imprecise marketing strategies, which may elicit a negative response from EV users, cause colossal waste of resources and hinder the development of EVs. Based on the relevant charging pile data to be analyzed, this paper combines big data-related technologies to propose a big data framework for analyzing the charging pile data to solve the common charging service problems from a systemic perspective. The purpose of the framework is to provide decision-making reference information for charging pile operators, charging pile application operators and charging service marketers to improve the charging pile business more effectively. The framework is demonstrated by functional structure and technical structure.

关键词： Charging pile data Big Data framework Decision-making reference Charging business improvement

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Triple Key Security Algorithm Against Single Key Attack on Multiple Rounds

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computers, Materials & Continua 2022年第9期72卷 6061-6077页

作者： Muhammad Akram Muhammad Waseem Iqbal Syed Ashraf Ali Muhammad Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Department of Computer Science Superior University Lahore54000Pakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science The Institute of Management Sciences(PAK-AIMS)Lahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia

In cipher algorithms,the encryption and decryption are based on the same *** are some limitations in cipher algorithms,for example in polyalphabetic substitution cipher the key size must be equal to plaintext otherwise it will be repeated and if the key is known then encryption becomes *** paper aims to improve the said limitations by designing of Triple key security algorithm(TKS)in which the key is modified on polyalphabetic substitution cipher to maintain the size of the key and *** plaintext character is substituted by an alternative *** mode of substitution is transformed cyclically which depends on the current position of the modified *** keys are used in the encryption and decryption process on 8 or 16 rounds with the Exclusively-OR(XOR)of the 1st *** study also identifies a single-key attack on multiple rounds block cipher in mobile communications and applied the proposed technique to prevent the *** utilization of the TKS algorithm,the decryption is illustrated,and security is analyzed in detail with mathematical examples.

关键词： Encryption decryption cipher symmetric key cryptography single key attack

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