Article
Engineering, Biomedical
B. Indira Priyadarshini, D. Krishna Reddy
Summary: An optimized Adaptive Neuro Fuzzy Inference System (OANFIS) classifier is proposed in this paper to automatically detect seizures, aiming to increase classifier accuracy while reducing computational complexity. By selecting optimal features using the Binary Particle Swarm Optimization (BPSO) algorithm, the proposed system achieves a classification accuracy of 99.25% and consumes only 2.018 mu W power.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Cong Wang, Witold Pedrycz, ZhiWu Li, MengChu Zhou, Jun Zhao
Summary: This article introduces a residual-sparse Fuzzy C-Means (FCM) algorithm for image segmentation, which improves FCM's robustness by introducing an l(0) regularization term and utilizing morphological reconstruction and tight wavelet frame decomposition for segmentation and reconstruction.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Cong Wang, Witold Pedrycz, ZhiWu Li, MengChu Zhou, Shuzhi Sam Ge
Summary: This article elaborates on a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation, which introduces a Kullback-Leibler divergence term and considers the spatial information of image pixels to enhance robustness. The proposed FCM is performed in wavelet space for high robustness, demonstrating superior performance compared to state-of-the-art segmentation algorithms while requiring less computation.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Biochemistry & Molecular Biology
Ramy Hussein, Soojin Lee, Rabab Ward
Summary: In this study, a Transformer-based approach called MViT is introduced for automated learning of spatio-temporal-spectral features in multi-channel EEG data. Extensive experiments demonstrate the superiority of MViT algorithm in seizure prediction.
Article
Computer Science, Artificial Intelligence
Ramy Hussein, Soojin Lee, Rabab Ward, Martin J. McKeown
Summary: This study introduces a novel semi-dilated convolutional neural network architecture that outperforms previous methods in predicting epileptic seizures, achieving an average prediction sensitivity of 98.90% for scalp EEG.
Article
Chemistry, Analytical
Kunpeng Song, Jiajia Fang, Lei Zhang, Fangni Chen, Jian Wan, Neal Xiong
Summary: In this paper, an intelligent epileptic prediction system based on Synchrosqueezed Wavelet Transform (SWT) and Multi-Level Feature Convolutional Neural Network (MLF-CNN) was proposed for smart healthcare IoT network. The system achieved high accuracy and sensitivity on both public and private datasets.
Article
Computer Science, Information Systems
Jie Wu, Xiaoqian Zhang
Summary: This paper proposes a target recognition method based on fuzzy C-means incremental update and neural network, which can achieve higher target recognition rate under conditions of low signal-to-noise ratio and high threshold signal-to-noise ratio.
Article
Computer Science, Artificial Intelligence
Rong Ma, Wenyi Zeng, Guangcheng Song, Qian Yin, Zeshui Xu
Summary: This paper introduces the Pythagorean fuzzy set (PFS) to handle uncertainty in image segmentation, proposing the Pythagorean fuzzy C-means (PFCM) algorithm. Experimental results on different images and datasets demonstrate the effectiveness and applicability of the proposed algorithm.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Chemistry, Analytical
Nabeel Ali Khan, Sadiq Ali, Kwonhue Choi
Summary: In this study, an automated method for detecting seizures in newborns using multi-channel electroencephalogram (EEG) recording was proposed. A set of novel time-frequency marginal features were defined to detect seizure activity in newborns. The proposed method achieved better accuracy by combining modified time-domain marginal and frequency-domain marginal features with TF statistical and frequency-related features.
Article
Behavioral Sciences
Nazanin Nemati, Saeed Meshgini
Summary: This study proposes an effective classification strategy using discrete wavelet transform and deep convolutional neural network for diagnosing epileptic seizures. The results show that the method achieves proper classification results under various conditions.
BRAIN AND BEHAVIOR
(2022)
Article
Engineering, Multidisciplinary
Mohammed Diykh, Firas Sabar Miften, Shahab Abdulla, Ravinesh C. Deo, Siuly Siuly, Jonathan H. Green, Atheer Y. Oudahb
Summary: This paper proposes a new framework for the automated detection of neonatal seizures based on the Morse Wavelet approach, local binary pattern algorithm, and graph-based community detection algorithm. The experimental results demonstrate that the proposed model is more accurate in detecting seizures compared to traditional approaches.
Article
Materials Science, Multidisciplinary
Asif Afzal, Zahid Ansari, Saad Alshahrani, Arun K. Raj, Mohamed Saheer Kuruniyan, C. Ahamed Saleel, Kottakkaran Sooppy Nisar
Summary: In this study, partitioning clustering of COVID-19 data was conducted using c-Means and Fuzy c-Means algorithms, revealing five major centroids where the pandemic is concentrated. The three main COVID-19 clusters were identified in the US, Brazil, and India, with Fc-M technique showing better clustering performance compared to the c-M algorithm.
RESULTS IN PHYSICS
(2021)
Article
Computer Science, Artificial Intelligence
Cong Wang, Witold Pedrycz, MengChu Zhou, ZhiWu Li
Summary: The study introduces an improved fuzzy C-means (FCM) model with sparse regularization, which achieves fast and accurate segmentation of real images through MGR operation and feature clustering.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Engineering, Mechanical
E. Carrera-Avendano, G. Urquiza-Beltran, Eduardo Trutie-Carrero, Jose M. Nieto-Jalil, C. Carrillo-Pereyra, D. Seuret-Jimenez
Summary: A new method for extracting features from signals immersed in background noise was proposed in this study, using the dyadic Wavelet transform and the Welch-Bartlett classic periodogram to obtain a modified periodogram. Validation results showed that this method had superior performance in detecting engine faults compared to existing procedures in the literature.
ENGINEERING FAILURE ANALYSIS
(2022)
Article
Chemistry, Analytical
Khondoker Mirazul Mumenin, Prapti Biswas, Md. Al-Masrur Khan, Ali Saleh Alammary, Abdullah-Al Nahid
Summary: Electroencephalography (EEG) is increasingly used in pediatric neurology for more accurate diagnosis, especially in identifying newborn seizures. However, EEG interpretation is time-consuming and requires specialists. The advancements in Machine Learning (ML) have enabled rapid and automated diagnosis of newborn seizures.