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Performance Evaluation of Machine Learning Classifiers for Sudden Cardiac Arrest Detection

Journal of The Institution of Engineers (India): Series B 2022年 1-7页

作者： Banerjee, Prakash Bhattacherjee, Saptak Dasgupta, Kousik Sen, Shibaprasad Department of Electrical and Electronics Engineering University of Engineering and Management Kolkata India School of Mechatronics and Robotics Indian Institute of Engineering Science and Technology Shibpur Howrah India Department of Computer Science and Engineering Kalyani Government Engineering College Kalyani India Department of Computer Science and Engineering University of Engineering and Management Kolkata India

Sudden cardiac arrest (SCA) is a type of cardiovascular disease which attacks a person so promptly that it gives minimum time for hospitalization and may also lead to the patient death. In order to save the patient from death, rapid detection of SCA just after the onset of symptoms with a high accuracy is very critical. Although numerous approaches of SCA detection based on electrocardiogram have been carried out, there is still need of standard method for SCA detection with high precession and fast detection. Hence, the present work proposes the use of four machine learning classifiers for detection of SCA based on temporal features extracted from ECG. Performance analysis of the classifiers used in this study on 19 SCA and 20 normal, i.e., healthy subjects, yields classification accuracies of 97.4%, 94.8%, 97.4% and 97.4% for SVM, decision tree, Naïve Bayes and KNN, respectively. Experimental results thus obtained demonstrate the potential of the proposed method as a promising alternative for SCA detection. © 2022, The Institution of Engineers (India).

关键词： Electrocardiograms

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MambaMOS: LiDAR-based 3D Moving Object Segmentation with Motion-aware State Space Model 24

MambaMOS: LiDAR-based 3D Moving Object Segmentation with Mot...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zeng, Kang Shi, Hao Lin, Jiacheng Li, Siyu Cheng, Jintao Wang, Kaiwei Li, Zhiyong Yang, Kailun College of Computer Science and Electronic Engineering Hunan University Changsha China College of Optical Science and Engineering Zhejiang University Hangzhou China School of Robotics Hunan University Changsha China School of Electronics and Information Engineering South China Normal University Foshan China

ISBN: (纸本)9798400706868

LiDAR-based Moving Object Segmentation (MOS) aims to locate and segment moving objects in point clouds of the current scan using motion information from previous scans. Despite the promising results achieved by previous MOS methods, several key issues, such as the weak coupling of temporal and spatial information, still need further study. In this paper, we propose a novel LiDAR-based 3D Moving Object Segmentation with Motion-aware State Space Model, termed MambaMOS. Firstly, we develop a novel embedding module, the Time Clue Bootstrapping Embedding (TCBE), to enhance the coupling of temporal and spatial information in point clouds and alleviate the issue of overlooked temporal clues. Secondly, we introduce the Motion-aware State Space Model (MSSM) to endow the model with the capacity to understand the temporal correlations of the same object across different time steps. Specifically, MSSM emphasizes the motion states of the same object at different time steps through two distinct temporal modeling and correlation steps. We utilize an improved state space model to represent these motion differences, significantly modeling the motion states. Finally, extensive experiments on the SemanticKITTI-MOS and KITTI-Road benchmarks demonstrate that the proposed MambaMOS achieves state-of-the-art performance. The source code is publicly available at https://***/Terminal-K/MambaMOS © 2024 ACM.

关键词： Embeddings

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Energy-Efficient Shop Scheduling Using Space-Cooperation Multi-Objective Optimization

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 601-615页

作者： Ding, Jiepin Xia, Jun Yang, Yaning Zhou, Junlong Chen, Mingsong Li, Keqin East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai200062 China Ningxia Normal University School of Physics and Electronic Information Engineering Guyuan756500 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China State University of New York Department of Computer Science New PaltzNY12561 United States

Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521s to 475s. © 2016 IEEE.

关键词： Job shop scheduling

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Drawing Order Diagrams Through Two-Dimension Extension

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Journal of Graph Algorithms and Applications 2023年第9期27卷 783-802页

作者： Dürrschnabel, Dominik Hanika, Tom Stumme, Gerd Knowledge & Data Engineering Group Research Center for Information System Design & Department of Electrical Engineering and Computer Science University of Kassel Germany Institute of Computer Science University of Hildesheim Germany Berlin School of Library and Information Science Humboldt-Universität zu Berlin Germany

Ordinal real-world data such as concept hierarchies, ontologies, genealogies, or task dependencies in scheduling often has the property to not only contain pairwise comparable, but also incomparable elements. Order diagrams provide an important tool for understanding and thus discovering knowledge in such data. Easily readable drawings of such order diagrams are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work, we present the novel algorithm DimDraw to decompose an ordered set (e.g., a concept hierarchy) in linear orders and to produce a corresponding order diagram. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of its transitive incompatibility graph. To evaluate the quality of the algorithm, a user study was conducted where generated drawings were compared with ones from state-of-the-art drawing algorithms. © 2023 Brown University. All rights reserved.

关键词： Set theory

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A novel coarse-grained knowledge graph embedding framework for platform risk identification from relational data

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Neural Computing and Applications 2025年 1-21页

作者： Zhang, Qi Wang, Shicheng Wang, Lihong Sheng, Jiawei Guo, Shu Li, Chen He, Min Zhang, Renqiang School of Economics and Management Beihang University Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China School of Computer Science and Engineering Beihang University Beijing China

Platform risk identification from relational data aims to determine whether the target platforms are risky or not. However, in such a scenario, the relation data involve massive types of relations and numerous numerical-aware relations composed of attributes and infinite continuous values are challenges in this field. It is paramount to capture semantic information of relations and precise control power, drastically affecting the power of learning and the performance of identifying risky platforms. Recent financial risk studies often employed graph neural networks (GNNs) to model relational data, which fall short of capturing the intricacies of such complex relationships resulting in incorrect identification of platforms. To solve this problem, we propose a novel coarse-grained knowledge graph embedding (CKGE) framework that integrates coarse-grained knowledge graph construction and adopts five well-known knowledge graph embedding methods and ensemble learning. The extensive experimental results based on a real-world peer-to-peer lending platform dataset demonstrate that the CKGE framework achieves state of the art over GNNs for platform risk identification. Our study highlights the importance of utilizing the relational data of multi-type and multi-numerical relations for revealing platform risk situations and provides an alternative solution for identifying platform risk. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Knowledge graph

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Learning Robust Global-Local Representation From EEG for Neural Epilepsy Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5720-5732页

作者： Zhou, Xinliang Liu, Chenyu Yang, Ruizhi Zhang, Liangwei Zhai, Liming Jia, Ziyu Liu, Yang Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Carnegie Mellon University School of Computer Science PittsburghPA15213 United States Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Wuhan430079 China Central China Normal University School of Computer Science Wuhan430079 China Chinese Academy of Sciences Brainnetome Center Institute of Automation Beijing100190 China

Epilepsy is a life-threatening and challenging neurological disorder, and applying an electroencephalogram (EEG) is a commonly used clinical approach for its detection. Neuropsychological research indicates that epilepsy seizures are highly associated with distinct ranges of temporal EEG patterns. Although previous attempts to automatically detect epilepsy have achieved high classification performance, one crucial challenge still remains: how to effectively learn the robust global-local representation associated with epilepsy in the signals? To address the above challenge, we propose global-local neural epilepsy detection network (GlepNet), a novel architecture for automatic EEG epilepsy detection. We interleave the temporal convolution model together with the multihead attention mechanism within the GlepNet's encoder blocks to jointly capture the interlaced epilepsy seizure local and global features in EEG signals. Meanwhile, the interpretable method, gradient-weighted class activation mapping (Grad-CAM), is applied to visually confirm that the GlepNet acquires the ability to accord significant weight to EEG segments containing epileptiform abnormalities such as spike-wave complexes. Specifically, the Grad-CAM heatmaps are generated by backpropagating the gradients from the encoder blocks to highlight the epilepsy seizure-related parts. Extensive experiments show the superiority of the GlepNet over state-of-the-art methods on multiple EEG epilepsy datasets. The code will soon be open-sourced on GitHub. © 2024 IEEE.

关键词： Feature extraction

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A 4-channel acoustic sensor system for sound source detection of the service robot

A 4-channel acoustic sensor system for sound source detectio...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Hu, Jiaxin Lu, Bo Liu, Jinlin Zhou, Tiancheng Fan, Xinjian Liu, Xiaoang Dong, Dianbiao Chen, Long Northwestern Polytechnical University School of Mechanical Engineering Xi'an China Soochow University Robotics and Microsystems Center School of Mechanical and Electric Engineering Suzhou China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China Hebei University of Technology Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles School of Mechanical Engineering Tianjin China

ISBN: (纸本)9798350372601

Constrained by the physical geometry of microphone arrays on the service robot, there exist limitations on both the minimum and maximum frequency bounds along with the coverage area. While the mobility of a microphone array carried by a robot offers the potential for a broader operational frequency range and a more comprehensive perspective, achieving this necessitates a practically more extensive and densely packed array configuration. This is often accomplished by employing non-synchronous measurements in beamforming. Here, we propose a novel approach for locating broadband sound sources using an asynchronous measurement technique that employs Laplacian-enhanced low-rank tensor completion. Our technique analyzes the tensor data structure within broadband signals and incorporates an alternating-direction approach based on multiplier optimization. Through the simulation study using only four physical channels, our Laplacian-enhanced algorithm has demonstrated the ability to generate a highly accurate sound map capturing distinct speech signal sources, thus providing a superior global view compared to the alternative convex surrogate. This advancement holds substantial promise for applications in which precise localization of broadband sound sources is paramount. © 2024 IEEE.

关键词： Tensors

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QFSM: A Novel Quantum Federated Learning Algorithm for Speech Emotion Recognition With Minimal Gated Unit in 5G IoV

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Qu, Zhiguo Chen, Zhixiao Dehdashti, Shahram Tiwari, Prayag Jiangsu Collaborative Innovation Center of Atmospheric Environment the Equipment Technology and the Engineering Research Center of Digital Forensics Ministry of Education and the School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Computer Science Nanjing University of Information Science and Technology Nanjing China Emmy-Noether Gruppe Theoretisches Quantensystemdesign Lehrstuhl Für Theoretische Informationstechnik Technische Universität München Munich Germany School of Information Technology Halmstad University Halmstad Sweden

The technology of speech emotion recognition (SER) has been widely applied in the field of human-computer interaction within the Internet of Vehicles (IoV). The incorporation of emerging technologies such as artificial intelligence and big data has accelerated the advancement of SER technology. However, this reveals challenges such as limited computational resources, data processing inefficiency, and security and privacy concerns. In recent years, quantum machine learning has been applied to the field of intelligent transportation, which has demonstrated its various advantages, including high prediction accuracy, robust noise resistance, and strong security. This study first integrates quantum federated learning (QFL) into 5G IoV using a quantum minimal gated unit (QMGU) recurrent neural network for local training. Then, it proposes a novel quantum federated learning algorithm, QFSM, to further enhance computational efficiency and privacy protection. Experimental results demonstrate that compared to existing algorithms using quantum long short-term memory network or quantum gated recurrent unit models, the QFSM algorithm has a higher recognition accuracy and faster training convergence rate. It also performs better in terms of privacy protection and noise robustness, enhancing its applicability and practicality. IEEE

关键词： Speech recognition

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A Reduced State-Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia University College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model-checking method traverses the full state space, which suffers from the state-space explosion problem. A reduced state-space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state-space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model-checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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Intelligent Personality Assessment and Verification from Handwriting using Machine Learning

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4541-4546页

作者： Mishra, Pradeep Kumar Mishra, Gouri Sankar Abidi, Ali Imam Kumar, Rajiv Sahoo, Ashok Kumar Maini, Tarun Department of Computer Science and Application SSET Sharda University Department of Computer Science and Engineering SSET Sharda University NVIDIA AI Learning & amp Research Center Department of Applied Computational Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida Graphic Era Hill University School of Computer Science and Engineering Galgotias University

It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as a person. There has been a lot of study on the possibility of deducing aspects of a person’s character from their handwriting. In this piece, we’ll look at the different personality qualities and how they might be utilized to make an evaluation. The report includes a dataset showing a correlation between several traits of personality. The Big Five Personality Traits are mapped using handwritten personality data. It was suggested that a comparison table should be used to spot distinctive character quirks. The article includes a review of previous studies and an assessment of the current status of the topic. Handwriting embodies have both creativity and expressiveness. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Agreeableness Conscientiousness Extraversion Neuroticism Openness

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