Article
Chemistry, Analytical
Amin Ullah, Sadaqat ur Rehman, Shanshan Tu, Raja Majid Mehmood, Fawad, Muhammad Ehatisham-ul-haq
Summary: The study aims to develop a robust algorithm for accurately classifying ECG signals even in the presence of environmental noise. They proposed a 1D CNN with two convolutional layers, two down-sampling layers, and a fully connected layer, followed by transforming the data into 2D images to improve classification accuracy. The classification accuracy achieved with the proposed 1D and 2D model on the MIT-BIH arrhythmia database outperformed the state-of-the-art algorithms for the same dataset, demonstrating the effectiveness of the proposed models.
Article
Computer Science, Information Systems
Ayub Othman Abdulrahman, Karwan Mahdi Hama Rawf, Aree Ali Mohammed
Summary: A model based on the 1D-CNN algorithm is proposed for the binary classification of ECG signals to detect and separate regular and irregular heartbeat signals. Test results on the MIT-BIH dataset showed significant improvements in accuracy, sensitivity, and specificity compared to other related works.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Biomedical
Youngshin Kang, Geunbo Yang, Heesang Eom, Seungwoo Han, Suwhan Baek, Seungil Noh, Youngjoo Shin, Cheolsoo Park
Summary: Biometric technologies such as face, irises, and fingerprints are used in personal authentication systems to identify individuals. This study demonstrates a multi-task neural network model that simultaneously identifies an individual's ECG and arrhythmia patterns using a small network. The multi-task model shows similar performance to the single-task model with fewer parameters.
BIOMEDICAL ENGINEERING LETTERS
(2023)
Article
Computer Science, Cybernetics
Kursat Cakal, Mehmet Onder Efe
Summary: The proposed study introduces a novel classification and detection ability for stochastic environmental conditions by combining WSS functionality and a Lightweight CNN. It improves accuracy by about 40% compared to existing literature. The study synthesizes coherent ECG signals and adapts them to wearable devices, using operations such as geneticization, segment shifting, time contraction, and expansion, magnification, projection, permutation, upscaling, downscaling, noising, and denoising to ensure robust anomaly detection.
CYBERNETICS AND SYSTEMS
(2023)
Article
Health Care Sciences & Services
Wei Yan, Zhen Zhang
Summary: This paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data to enhance the diagnostic efficiency of arrhythmias.
JOURNAL OF HEALTHCARE ENGINEERING
(2021)
Article
Biology
Sanjay Kumar, Abhishek Mallik, Akshi Kumar, Javier Del Ser, Guang Yang
Summary: Electrocardiogram (ECG) is a widely used non-invasive technique to diagnose cardiovascular diseases. In this work, a deep learning and fuzzy clustering based approach (Fuzz-ClustNet) is proposed for Arrhythmia detection from ECG signals. The collected ECG signals are denoised and segmented, followed by data augmentation and feature extraction using a CNN. Fuzzy clustering algorithm is then used to classify the ECG signals for their respective cardio diseases. The proposed approach shows better performance compared to other contemporary algorithms.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Physiology
Chenchen Zhou, Xiangkui Li, Fan Feng, Jian Zhang, He Lyu, Weixuan Wu, Xuezhi Tang, Bin Luo, Dong Li, Wei Xiang, Dengju Yao
Summary: This research aims to address the problem of sample imbalance in arrhythmia classification. The proposed method combines MLP, weight capsule network, and Seq2seq network, and achieves good performance on the MIT-BIH arrhythmia database.
FRONTIERS IN PHYSIOLOGY
(2023)
Article
Computer Science, Information Systems
Sumanta Kuila, Namrata Dhanda, Subhankar Joardar
Summary: In this study, a novel classification algorithm combining ELM and RNN with morphological filtering was proposed for arrhythmia detection. Experimental results on the MIT-BIH arrhythmia database demonstrated the high accuracy, sensitivity and specificity of the proposed algorithm. Compared to similar models, the algorithm showed faster performance.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Review
Computer Science, Information Systems
Mohamed Hammad, Rajesh N. V. P. S. Kandala, Amira Abdelatey, Moloud Abdar, Mariam Zomorodi-Moghadam, Ru San Tan, U. Rajendra Acharya, Joanna Plawiak, Ryszard Tadeusiewicz, Vladimir Makarenkov, Nizal Sarrafzadegan, Abbas Khosravi, Saeid Nahavandi, Ahmed A. Abd EL-Latif, Pawel Plawiak
Summary: This paper reviews the state-of-the-art machine and deep learning based CAAC expert systems for shockable ECG signal recognition, discussing their strengths, advantages, and drawbacks. Moreover, unique bispectrum and recurrence plots are proposed to represent shockable and non-shockable ECG signals. Deep learning methods are usually more robust and accurate than standard machine learning methods but require big data of good quality for training.
INFORMATION SCIENCES
(2021)
Article
Engineering, Biomedical
M. Ramkumar, A. Lakshmi, M. Pallikonda Rajasekaran, A. Manjunathan
Summary: This manuscript proposes a method combining Multiscale Laplacian graph kernel features with Tree Deep Convolutional Neural Network for the detection of Electrocardiogram arrhythmia. Experimental results demonstrate the superior accuracy of the proposed approach compared to existing methods on two datasets.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Information Systems
Shengnan Hao, Hang Xu, Hongyu Ji, Zhiwu Wang, Li Zhao, Zhanlin Ji, Ivan Ganchev
Summary: Electrocardiograms (ECG) are crucial for diagnosing cardiovascular diseases, but manual diagnosis is time-consuming due to the large volume of patient data. Therefore, intelligent automatic ECG signal classification is important for addressing the shortage of medical resources. This study proposes a novel model called G2-ResNeXt for inter-patient heartbeat classification, which enhances feature extraction and classification of ECG signals. Experimental results on the MIT-BIH arrhythmia database show that the proposed model outperforms state-of-the-art models, achieving an overall accuracy of 96.16% and high sensitivity and precision for different types of heartbeat abnormalities.
Article
Computer Science, Artificial Intelligence
Jianfeng Cui, Lixin Wang, Xiangmin He, Victor Hugo C. De Albuquerque, Salman A. AlQahtani, Mohammad Mehedi Hassan
Summary: Feature extraction plays a crucial role in arrhythmia classification. This paper presents a feature extraction method that combines traditional approaches and 1D-CNN to improve the accuracy of arrhythmia classification. Experimental results show that the proposed method achieves an average classification accuracy of 98.35%, surpassing the latest methods.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
M. Ramkumar, Manjunathan Alagarsamy, A. Balakumar, S. Pradeep
Summary: Electrocardiogram (ECG) is a non-invasive medical tool used to reveal the rhythm and function of the human heart, and is widely employed in heart disease detection. Automatic ECG analysis helps cardiologists diagnose ECG signals and is used in cardiac patient monitoring systems.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2023)
Article
Engineering, Biomedical
P. Varalakshmi, Atshaya P. Sankaran
Summary: An arrhythmia is a condition characterized by irregular heartbeats, and its prevalence has increased in recent years. This paper proposes a hybrid model for feature extraction and classification, utilizing deep learning and machine learning algorithms. The results demonstrate that the hybrid model achieves high accuracy and reduces training time effectively.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Biology
Rui Hu, Jie Chen, Li Zhou
Summary: This paper proposes a novel transformer-based deep learning neural network, ECG DETR, for arrhythmia detection on continuous single-lead ECG segments. The model simultaneously predicts the positions and categories of all heartbeats within an ECG segment, eliminating the need for explicit heartbeat segmentation. The proposed method shows comparable performance to previous works, achieving high overall accuracy on different arrhythmia detection tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)