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Deep learning framework for automatic detection and classification of sleep apnea severity from polysomnography signals

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Neural Computing and Applications 2024年第28期36卷 17483-17493页

作者： Raja Brundha, A. Lakshmi Sangeetha, A. Balajiganesh, A. Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India Department of Computer Science Engineering Velammal Engineering College Chennai India

Sleep apnea (SA) is a sleep-related breathing disorder characterized by breathing pauses during sleep. A person’s sleep schedule is significantly influenced by that person’s hectic lifestyle, which may include unhealthy eating habits and their line of work. Polysomnographic (PSG) sleep studies examine sleep-related disorders by recording various biosignals from the human body. However, SA classification methods could be more robust in terms of performance because they rely on feature-engineering strategies or employ a particular signal from PSG recording for diagnosis. This study aims to classify the severity of SA according to the apnea–hypopnea index (AHI) into normal, mild, and moderate-to-severe groups using oxygen saturation (SpO2), electroencephalogram (EEG), and electrocardiogram (ECG) signals. The proposed deep neural network (DNN)-bidirectional long short-term memory (DNN-BiLSTM) framework addresses the issue of low detection accuracy in analysis. The DNN-BiLSTM approach employs features extracted from a multiscale dilation attention 1D convolutional neural network (MSDA-1DCNN) as input for detection and classification purposes. The MSDA-1DCNN network extracts deep features from processed SpO2, EEG, and ECG signals. The developed firefly combined electric fish optimization (FCEFO) algorithm improves performance by optimizing the hidden neuron count of the DNN and the learning rate of the BiLSTM framework. The performance measures proved the effectiveness of the model over conventional machine and deep learning approaches. With the integration of deep features, the proposed DNN-BiLSTM model provides enhanced performance in terms of accuracy and precision. Thus, the proposed approach is progressive and can be used for medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electrocardiograms

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Secure container Orchestration: A framework for detecting and mitigating Orchestrator - level vulnerabilities

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Multimedia Tools and Applications 2025年第17期84卷 18351-18371页

作者： Mahavaishnavi, V. Saminathan, R. Prithviraj, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Chidambaram India

This paper presents Secure Orchestration, a novel framework meticulously planned to uphold rigorous security measures over the profound security concerns that lie within the container orchestration platforms, especially in Kubernetes. The widespread adoption of containerized environments has raised concerns about the vulnerabilities at the level of the orchestrator. Secure Orchestration delivers a comprehensive approach by leveraging cutting-edge methods to detect and prevent the exposure of vulnerabilities from orchestrator misconfigurations, privilege escalation exploits that can thwart the orchestration security layer, and unauthorized access attempts targeting the orchestration system itself. In addition, it is further strengthened by deploying an Intrusion Detection and Prevention System (IDPS), which is responsible for actively monitoring the orchestration infrastructure. The IDPS utilizes sophisticated algorithms and mechanisms for detecting anomalies to continuously assess the orchestration environment for indications of intrusion, vulnerabilities, and anomalous activities. By using this proactive approach, the framework is able to rapidly identify potential threats and respond through countermeasures in a timely manner, thus greatly reducing the risks associated with orchestrator level vulnerabilities. Conducting a set of real-world experiments that closely simulate a variety of threat scenarios is imperative for empirically validating the efficiency of this framework. The obtained empirical results yield insightful revelations on the efficacy of the framework in accurately identifying and mitigating vulnerabilities, thereby ensuring the privacy and integrity of container orchestration systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Containers

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Unraveling the intricacies of EEG seizure detection: A comprehensive exploration of machine learning model performance, interpretability, and clinical insights

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Multimedia Tools and Applications 2024年第41期83卷 89217-89247页

作者： Mridha, Krishna Priyok, Masrur Ahsan Shukla, Madhu Department of Computer Engineering Marwadi University Rajkot India Department of Software Engineering Tianjin University Tianjin China Department of Computer Engineering Artificial Intelligence and Big Data Analytics Marwadi University Gujarat Rajkot India

In neurology, it is critical to promptly and precisely identify epileptic episodes using EEG data. Interpretability and thorough model evaluation are still crucial to guarantee reliability, even though machine learning provides sophisticated tools for automated EEG analysis. We undertook a thorough investigation of EEG seizure classification in this work, with equal emphasis on statistical validation, interpretability, and model performance. Following thorough preprocessing, we used statistical significance tests in conjunction with Chernoff and Bhattacharyya Bounds to select features. Top performers were found to be the Random Forest which had accuracies of 96%. A paired t-test and bootstrapped 95% confidence intervals were used to statistically validate the performance robustness, demonstrating the large variation in model performances. Going beyond simple performance measures, we set out to decipher the nuances of model choices by utilizing methods devoted to model interpretability and shedding light on the importance of distinct aspects. This helped neurologists grasp the primary EEG indicators predictive of seizures and opened the door for future clinical insights. It also provided transparency in diagnostic predictions. Our efforts highlight the mutually reinforcing importance of predictability and model interpretability in the context of medical machine-learning applications, hence intensifying the demand for the creation and use of trustworthy and transparent neurology diagnostic instruments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neurology

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Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images

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Journal of Bionic engineering 2023年第3期20卷 1198-1262页

作者： Yan Han Weibin Chen Ali Asghar Heidari Huiling Chen Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran

Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the *** quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the ***,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray *** we all know,image segmentation is a critical stage in image processing and *** achieve better image segmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named *** utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image *** image segmentation scheme is called *** ran two sets of experiments to test the performance of RDMVO and ***,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark ***,the image segmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as *** test image dataset includes Berkeley images and COVID-19 Chest X-ray *** experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

关键词： COVID-19 Multilevel threshold image segmentation Kapur’s entropy Multi-verse optimizer Meta-heuristic algorithm Bionic algorithm

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Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction

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Digital Communications and Networks 2025年第2期11卷 537-546页

作者： Feng Zeng Zheng Zhang Jinsong Wu School of Computer Science and Engineering Central South UniversityChangsha410083China School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin541000China The Department of Computer Science Universidad de ChileSantiagoChile

In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service *** this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking ***,a Bi-LSTM-based model is proposed to predict the trajectories of *** service area is divided into several equal-sized *** the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory ***,we propose a scheduling strategy for delay optimization based on the vehicle trajectory *** the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task *** results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.

关键词： Vehicular edge computing Task offloading Vehicle trajectory prediction Delay minimization Bi-LSTM model

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Efficient machine learning based techniques for fault detection and identification in spacecraft reaction wheel

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Aerospace Systems 2024年第4期7卷 815-828页

作者： Abdel Aziz, T.S. Salama, G.I. Mohamed, M.S. Hussein, S. Sector of Assembly Integration and Test Space Technology Center Cairo Egypt Department of Computer Engineering and Artificial Intelligence MTC Cairo Egypt

Space exploration demands robust spacecraft(SC) subsystems to endure the harsh conditions of space and ensure mission success. Attitude determination and control subsystems (ADCS), as a significant subsystem within SC, are essential for providing the necessary pointing accuracy and stability, and failures in the ADCS can lead to mission failure. Therefore, robust design, thorough testing, and Fault Detection, Isolation and Identification(FDII) techniques are crucial for spacecraft operations. This paper focuses on developing advanced FDII techniques for reaction wheels(RW) within ADCS, evaluating the Prony-based FDII technique for RW, considering its accuracy, time complexity, and memory usage, and Additionally, it introduces new machine learning-based FDII techniques, including enhancements to the Prony-based FDII technique, to manage single faults more effectively. The new proposed techniques used to explore the novel area of multiple faults within the same subsystem. Results indicate that the proposed FDII techniques significantly improve fault detection accuracy, isolation time, and memory efficiency compared to traditional techniques. These advancements enhance the reliability and longevity of spacecraft missions, ensuring that critical subsystems like ADCS operate effectively in the challenging conditions of space. The contributions presented in the paper are introducing three different FDII machine learning-based techniques that support identifying five types of single faults in spacecraft ADCS RW, outperform the Prony-based FDII technique for spacecraft ADCS RW in terms of time and memory complexity, and Finally, improves the fault tolerance of the spacecraft system by detecting Multiple fault combinations that may occur at the same time in one system. © Shanghai Jiao Tong University 2024.

关键词： Space research

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A new design of parity-preserving reversible multipliers based on multiple-control toffoli synthesis targeting emerging quantum circuits

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Frontiers of computer Science 2024年第6期18卷 229-244页

作者： Mojtaba NOORALLAHZADEH Mohammad MOSLEH Kamalika DATTA Department of Computer Engineering Dezful BranchIslamic Azad UniversityDezfulIran German Research Centre for Artificial Intelligence(DFKI) Bremen 28359Germany Institute of Computer Science University of BremenBremen 28359Germany

With the recent demonstration of quantum computers,interests in the field of reversible logic synthesis and optimization have taken a different *** every quantum operation is inherently reversible,there is an immense motivation for exploring reversible circuit design and *** it comes to faults in circuits,the parity-preserving feature donates to the detection of permanent and temporary *** the context of reversible circuits,the parity-preserving property ensures that the input and output parities are *** this paper we suggest six parity-preserving reversible blocks(ZFATSL)with improved quantum *** reversible blocks are synthesized using an existing synthesis method that generates a netlist of multiple-control Toffoli(MCT)*** optimization rules are applied at the reversible circuit level,followed by transformation into a netlist of elementary quantum gates from the NCV *** designs of full-adder and unsigned and signed multipliers are proposed using the functional blocks that possess parity-preserving *** proposed designs are compared with state-of-the-art methods and found to be better in terms of cost of *** savings of 25.04%,20.89%,21.17%,and 51.03%,and 18.59%,13.82%,13.82%,and 27.65% respectively,are observed for 4-bit unsigned and 5-bit signed multipliers in terms of quantum cost,garbage output,constant input,and gate count as compared to recent works.

关键词： reversible circuits parity-preserving NCV library multiple-control Toffoli gates quantum circuits quantum cost

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BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

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computers, Materials & Continua 2024年第6期79卷 4621-4639页

作者： Xuejie Wang Jianxun Zhang Ye Tao Xiaoli Yuan Yifan Guo Department of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Liangjiang Institute of Artificial Intelligence Chongqing University of TechnologyChongqing400054China

While single-modal visible light images or infrared images provide limited information,infrared light captures significant thermal radiation data,whereas visible light excels in presenting detailed texture ***-bining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations,resulting in high-quality images with enhanced contrast and rich texture *** capabilities hold promising applications in advanced visual tasks including target detection,instance segmentation,military surveillance,pedestrian detection,among *** paper introduces a novel approach,a dual-branch decomposition fusion network based on AutoEncoder(AE),which decomposes multi-modal features into intensity and texture information for enhanced *** contrast enhancement module(CEM)and texture detail enhancement module(DEM)are devised to process the decomposed images,followed by image fusion through the *** proposed loss function ensures effective retention of key information from the source images of both *** comparisons and generalization experiments demonstrate the superior performance of our network in preserving pixel intensity distribution and retaining texture *** the qualitative results,we can see the advantages of fusion details and local *** the quantitative experiments,entropy(EN),mutual information(MI),structural similarity(SSIM)and other results have improved and exceeded the SOTA(State of the Art)model as a whole.

关键词： Deep learning feature enhancement computer vision

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Image enhancement with intensity transformation on embedding space

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CAAI Transactions on intelligence Technology 2024年第1期9卷 101-115页

作者： Hanul Kim Yeji Jeon Yeong Jun Koh Department of Applied Artificial Intelligence Seoul National University of Science and TechnologySeoulSouth Korea Department of Computer Science and Engineering Chungnam National UniversityDaejeonSouth Korea

In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained ***,many existing methods based on this approach have a limitation:their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality *** order to address this limitation,a simple yet effective approach for image enhancement is *** proposed algorithm based on the channel-wise intensity transformation is ***,this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to *** this end,the authors define the continuous intensity transformation(CIT)to describe the mapping between input and output intensities on the embedding ***,the enhancement network is developed,which produces multi-scale feature maps from input images,derives the set of transformation functions,and performs the CIT to obtain enhanced *** experiments on the MIT-Adobe 5K dataset demonstrate that the authors’approach improves the performance of conventional intensity transforms on colour space ***,the authors achieved a 3.8%improvement in peak signal-to-noise ratio,a 1.8%improvement in structual similarity index measure,and a 27.5%improvement in learned perceptual image patch ***,the authors’algorithm outperforms state-of-the-art alternatives on three image enhancement datasets:MIT-Adobe 5K,Low-Light,and Google HDRþ.

关键词： computer vision deep learning image enhancement image processing

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ConHGNN-SUM: A Contextualized Heterogeneous Graph Neural Network for Extractive Text Summarization 20

ConHGNN-SUM: A Contextualized Heterogeneous Graph Neural Net...

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20th CSI International Symposium on artificial intelligence and Signal Processing, AISP 2024

作者： Nourbakhsh, Seyed Erfan Kashani, Hamidreza Baradaran University of Isfahan Faculty of Computer Engineering Artificial Intelligence Department Isfahan Iran

ISBN: (纸本)9798350383942

Text summarization is a valuable method for extracting important details from large volumes of text data, facilitating tasks like text data analysis. Various text summarization techniques have been developed over time, with some focusing on selecting and summarizing short sentences, while others overlook the semantic relationship between sentences. Extractive document summarization involves learning cross-sentence relations, a critical aspect that has been extensively explored using various approaches. One effective method is to employ neural networks based on graphs, which offer an intricate structure capable of obtaining relations among sentences. In this paper, we present a contextualized heterogeneous graph neural network for extractive text summarization (ConHGNN-SUM), incorporating semantic nodes that extend beyond individual sentences, and emphasizes the importance of capturing the relationship between selected sentences as a final step in the summarization process. These extra nodes function as intermediaries connecting sentences and enhancing the interrelationships between them. Our model enhances conventional graph-based extractive methods and delivers comparable performance to other advanced systems for extractive summarization. © 2024 IEEE.

关键词： Graph neural networks

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