Research Roadmap of Service Ecosystems: A Crowd Intelligence Perspective

Xue, Xiao; Li, Guanding; Zhou, Deyu; Zhang, Yepeng; Zhang, Lu; Zhao, Yang; Feng, Zhiyong; Cui, Lizhen; Zhou, Zhangbing; Sun, Xiao; Lü, Xudong; Chen, Shizhan

doi:10.26599/ijcs.2022.9100026

Cited by 39 publications

(14 citation statements)

References 137 publications

(155 reference statements)

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“…As a result, the bSCBA-KELM model is anticipated to be a reliable and effective tool for classifying and predicting toxicology. The SCBA-KELM model will be used in further research to address problems with disease diagnosis, 56,57 image segmentation, 58,59 image reconstruction, 60,61 optimization of machine learning models, service ecosystem, 62 power distribution network, 19 computational experiments, 63 and so on.…”

Section: Discussionmentioning

confidence: 99%

Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats

Su,

Zhao,

Heidari

et al. 2023

Basic Clin Pharma Tox

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Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high‐performance optimization method (SCBA) and added the Sobol sequence, vertical crossover, and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA‐KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA‐KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2, and CYP1A1/2 have the most impact on rats. The SCBA‐KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment.

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Section: Discussionmentioning

confidence: 99%

Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats

Su,

Zhao,

Heidari

et al. 2023

Basic Clin Pharma Tox

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“…Through the synergy of the service ecosystem, more accurate diagnosis and treatment can be provided to patients, leading to improved healthcare outcomes. 1 However, compared to computerassisted techniques, the long-term assessment of ECG records by qualified cardiologists is labor-intensive and ineffective.…”

Section: Introductionmentioning

confidence: 99%

“…In ECG testing, medical institutions and doctors play a crucial role by providing electrocardiogram equipment, while patients receive monitoring and evaluation of their heart health. Through the synergy of the service ecosystem, more accurate diagnosis and treatment can be provided to patients, leading to improved healthcare outcomes 1 . However, compared to computer‐assisted techniques, the long‐term assessment of ECG records by qualified cardiologists is labor‐intensive and ineffective.…”

Section: Introductionmentioning

confidence: 99%

Using normalized echo state network to detect abnormal ECG patterns

Wang

Ding

Wang

et al. 2023

Int J Imaging Syst Tech

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Cardiovascular disease (CVD) stands as a prominent contributor to human mortality. Electrocardiogram (ECG) represents a widely adopted noninvasive method employed by clinicians to detect and diagnose CVDs. Nonetheless, conventional ECG‐based detection approaches for cardiac disorders tend to be time‐consuming and inefficient, necessitating the need for more effective solutions. Recent studies have highlighted the effectiveness of the echo state network (ESN) in detecting abnormal ECG patterns. However, traditional ESN models often face challenges such as unstable training and convergence difficulties due to variations in the range of reservoir state values. To address this issue, this study introduces a novel approach called the normalized echo state network (NESN). The NESN method normalizes the states of all neurons within the reservoir before applying the nonlinear activation function. In our study, we conducted performance evaluations of the proposed model using the MIT‐BIH arrhythmia database. We performed a synergistical analysis to investigate the impact of reservoir parameters on the network performance. The experimental results demonstrated promising outcomes, with an accuracy of 99.1% and an F1‐score of 96.4%. Specifically, for detecting abnormal ECG patterns, our model achieved a sensitivity of 90.2%, a positive predictive value of 96.6%, and a specificity of 99.8%. These results highlight the superior performance of our classifier compared to most traditional mainstream heartbeat detection methods and ring topology ESN model.

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“…As deep learning techniques have proven effective in numerous detection and prediction tasks in recent years, an increasing number of studies are beginning to use deep learning models to automate ECG signals detection. [13][14][15][16][17][18][19] Convolutional Neural Networks (CNNs) have been widely adopted in previous studies. [20][21][22][23][24][25][26][27][28][29] Gao et al 30 applied CNN to the abnormal detection of atrial fibrillation and proposed a residual-based temporal attention CNN (RTA-CNN).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel transformer‐based ECG dimensionality reduction stacked auto‐encoders for arrhythmia beat detection

et al. 2023

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BackgroundElectrocardiogram (ECG) is a powerful tool for studying cardiac activity and diagnosing various cardiovascular diseases, including arrhythmia. While machine learning and deep learning algorithms have been applied to ECG interpretation, there is still room for improvement. For instance, the commonly used Recurrent Neural Networks (RNNs), reply on its previous state to update and is therefore ineffective for parallel computing. RNN also struggles to efficiently address the issue of long‐distance reliance.PurposeTo reduce computational complexity by dimensionality reduction of ECG signals we constructed a Stacked Auto‐encoders model using Transformer for ECG‐based arrhythmia detection. And overcome the challenges of long‐term dependencies and limited parallelizability in traditional RNNs when applied to ECG signal processing.MethodsIn this paper, a Transformer‐Based ECG Dimensionality Reduction Stacked Auto‐encoders model is proposed for ECG‐based arrhythmia detection. The transformer is used to encode ECG signals into a feature matrix, which is then dimensionally reduced using unsupervised greedy training through the four linear layers. This resulted in a low‐dimensional representation of ECG features, which are subsequently classified using support vector machines (SVM) to minimize overfitting.ResultsThe proposed method is benchmarked on the MIT‐BIH Arrhythmia database. In the 10‐fold cross validation of beat‐based arrhythmia detection, the average accuracy, sensitivity, specificity and F1 score of the proposed method are 99.83%, 98.84%, 99.84% and 99.13%, respectively, for the record‐based arrhythmia detection which refers to the approach where the training and testing sets use ECG data from independent recorded patients are 88.10%, 49.79%, 91.56% and 39.95%, respectively.ConclusionsCompared to other existing ECG‐based arrhythmia detection methods, our proposed approach exhibits improved detection accuracy and stronger generalization for arrhythmia beats. Additionally, the use of the record‐based data division method makes our approach more suitable for clinical practice.

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Research Roadmap of Service Ecosystems: A Crowd Intelligence Perspective

Cited by 39 publications

References 137 publications

Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats

Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats

Using normalized echo state network to detect abnormal ECG patterns

A novel transformer‐based ECG dimensionality reduction stacked auto‐encoders for arrhythmia beat detection

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