Article
Automation & Control Systems
Ziqian Zheng, Wei Zhao, Brock Hable, Yutao Gong, Xuan Wang, Robert W. Shannon, Kaibo Liu
Summary: This paper proposes a transfer learning-based independent component analysis (ICA) method to address the issue of degraded component extraction accuracy with limited available data. By transferring component distribution from a source domain, accurate component extraction results can be achieved in the target domain. Numerical simulations and a case study demonstrate the effectiveness of the proposed method in transferring knowledge and reducing negative transfer.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Rahul Singh, Aditya Goel, D. K. Raghuvanshi
Summary: In this study, a revolutionary magnetic resonance brain tumor detection and segmentation approach was presented. The method utilizes wavelet transform for tumor extraction and K-SVM for classification, followed by region growing technique for segmentation of infected area. Experimental results show that the approach performs efficiently and robustly for almost entire dataset, outperforming existing methodologies both qualitatively and quantitatively.
SIGNAL IMAGE AND VIDEO PROCESSING
(2021)
Article
Neurosciences
Shile Qi, Rogers F. Silva, Daoqiang Zhang, Sergey M. Plis, Robyn Miller, Victor M. Vergara, Rongtao Jiang, Dongmei Zhi, Jing Sui, Vince D. Calhoun
Summary: This study introduces a novel three-way parallel group independent component analysis (pGICA) fusion method that effectively incorporates temporal information in multimodal data fusion, demonstrating high accuracy and comparability in estimating cross-modality links. Experimental results suggest the potential of this method in investigating brain disorders.
HUMAN BRAIN MAPPING
(2022)
Article
Computer Science, Artificial Intelligence
Shili Peng, Wenwu Wang, Yinli Chen, Xueling Zhong, Qinghua Hu
Summary: This article presents a new idea for addressing the challenge of unifying classification and regression in machine learning. It proposes converting the classification problem into a regression problem and using regression methods to solve key problems in classification. Experimental results demonstrate that the proposed method outperforms existing algorithms in terms of prediction accuracy and model uncertainty.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Review
Chemistry, Analytical
Ruiming Guo, Zhongqiang Luo, Mingchun Li
Summary: With the advent of the era of big data information, artificial intelligence (AI) methods have become extremely promising and attractive, especially in the field of blind source separation (BSS). Independent component analysis (ICA) and independent vector analysis (IVA) are powerful techniques that have been applied to multichannel audio processing. Researchers have made significant contributions to optimize and improve IVA algorithms for different application scenarios.
Article
Psychiatry
Bowen Geng, Ming Gao, Ruiqing Piao, Chengxiang Liu, Ke Xu, Shuming Zhang, Xiao Zeng, Peng Liu, Yanzhu Wang
Summary: This study developed an SVM classifier based on multi-modal data to detect the main brain networks involved in group separation of premature ejaculation. The majority of the brain abnormalities for the classification were located within or across several networks. These findings contribute to the understanding of the neural mechanisms of premature ejaculation and provide new insights into the pathophysiology of patients with lifelong premature ejaculation.
FRONTIERS IN PSYCHIATRY
(2022)
Article
Engineering, Civil
Anahita Bolourani, Maryam Bitaraf, Ala Nekouvaght Tak
Summary: The study aims to develop a structural health monitoring system for detecting damage in harbor caissons effectively. By conducting dynamic analysis and signal processing, damage-sensitive features in the structure can be accurately identified. Through the use of PCA and SVM, the damage features are classified and reduced in dimensionality, enabling the assessment of the structure’s state.
Article
Computer Science, Artificial Intelligence
Xiaochen Zhou, Xudong Wang
Summary: Fed-KSVM is a federated learning scheme designed for training low-memory-consumption kernel SVM models. By decomposing the training process into subproblems and using an incremental learning algorithm, it achieves reduced memory consumption on edge devices. Additionally, by constructing a global model after training the local models, the scheme reduces communication costs while maintaining high accuracy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Optics
Yinshan Yu, Mingzhen Shao, Lingjie Jiang, Yongbin Ke, Dandan Wei, Dongyang Zhang, Mingxin Jiang, Yudong Yang
Summary: In this study, support vector machine (SVM) was used for spectral detection of multiple components in the water environment system. The results showed that the proposed method has high accuracy and robustness, making it suitable for quantitative analysis of multiple components.
Article
Computer Science, Information Systems
Tiep M. Hoang, Trung Q. Duong, Hoang Duong Tuan, Sangarapillai Lambotharan, Lajos Hanzo
Summary: This article presents a framework for converting wireless signals into structured datasets for detecting active eavesdropping attacks at the physical layer using machine learning algorithms.
Review
Operations Research & Management Science
M. Tanveer, T. Rajani, R. Rastogi, Y. H. Shao, M. A. Ganaie
Summary: TWSVM and TSVR are emerging machine learning techniques for classification and regression challenges. TWSVM classifies data points using two nonparallel hyperplanes, while TSVR is based on TWSVM and solves two SVM-type problems. Although there has been progress in research on these techniques, there is limited literature on the comparison of different variants of TSVR.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Mathematics
Meliz Yuvali, Belma Yaman, Oezguer Tosun
Summary: This study tested two coronary artery disease datasets from different countries and analyzed their classification efficiency using various machine learning algorithms. The Random Forest algorithm showed the most successful classification performance, while kNN underperformed in all stages. Other methods, such as logistic regression, had varying classification performances at each step.
Article
Computer Science, Artificial Intelligence
Chen Ding, Tian-Yi Bao, He-Liang Huang
Summary: The study proposes a quantum-inspired classical algorithm for LS-SVM, utilizing an improved sampling technique for classification. The theoretical analysis indicates that the algorithm can achieve classification with logarithmic runtime for low-rank, low-condition number, and high-dimensional data matrices.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Management
Haimonti Dutta
Summary: In the era of big data, a scalable support vector machine (SVM) algorithm is an important tool for machine learning researchers. This paper presents a distributed algorithm, called the gossip-based subgradient (GADGET) SVM, for learning linear SVMs in the primal form. The algorithm can be executed locally on sites of a distributed system, and it has fast convergence speed and low message complexity. Empirical results show that the algorithm performs comparably to other state-of-the-art solvers.
MANAGEMENT SCIENCE
(2022)
Article
Mathematics
Akash Saxena, Ahmad M. Alshamrani, Adel Fahad Alrasheedi, Khalid Abdulaziz Alnowibet, Ali Wagdy Mohamed
Summary: This paper presents the application of Support Vector Machine (SVM) in classifying power quality events using well-known signal processing techniques. The results show that SVM with attributes from both signal-processing techniques gives satisfactory results.
Article
Computer Science, Information Systems
Yi-Ju Chiang, Yen-Chieh Ouyang, Ching-Hsien (Robert) Hsu
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2015)
Article
Computer Science, Information Systems
Yi-Ju Chiang, Yen-Chieh Ouyang, Ching-Hsien (Robert) Hsu
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2016)
Article
Radiology, Nuclear Medicine & Medical Imaging
Hsian-Min Chen, Clayton Chi-Chang Chen, Hsin Che Wang, Yung-Chieh Chang, Kuan-Jung Pan, Wen-Hsien Chen, Hung-Chieh Chen, Yi-Ying Wu, Jyh-Wen Chai, Yen-Chieh Ouyang, San-Kan Lee
CURRENT MEDICAL IMAGING
(2020)
Article
Biotechnology & Applied Microbiology
Si-Wa Chan, Yung-Chieh Chang, Po-Wen Huang, Yen-Chieh Ouyang, Yu-Tzu Chang, Ruey-Feng Chang, Jyh-Wen Chai, Clayton Chi-Chang Chen, Hsian-Min Chen, Chein- Chang, Chin-Yao Lin
BIOMED RESEARCH INTERNATIONAL
(2019)
Article
Health Care Sciences & Services
Si-Wa Chan, Wei-Hsuan Hu, Yen-Chieh Ouyang, Hsien-Chi Su, Chin-Yao Lin, Yung-Chieh Chang, Chia-Chun Hsu, Kuan-Wen Chen, Chia-Chen Liu, Sou-Hsin Chien
Summary: This study explored automatic tumor detection by IVIM-DWI for breast cancer, with CEM methods showing better performance in tumor detection compared to clustering methods. All four methods successfully detected tumors in all patients studied.
JOURNAL OF PERSONALIZED MEDICINE
(2021)
Article
Environmental Sciences
Gui-Chou Liang, Yen-Chieh Ouyang, Shu-Mei Dai
Summary: To improve the accuracy of detecting rice leaf folder infestations, the study used hyperspectral sensors and deep neural networks to achieve a detection accuracy of 98%.
Proceedings Paper
Computer Science, Artificial Intelligence
Ju-Huei Chien, Siwa Chan, Shin Cheng, Yen-Chieh Ouyang
Summary: Blood smears have various medical applications, such as blood cell sorting and bone marrow examination, to help doctors diagnose and treat patients. This paper utilizes Faster R-CNN and CNN to detect and classify immature white blood cells, achieving an accuracy rate of nearly 90% through cross-validation.
2021 IEEE 3RD GLOBAL CONFERENCE ON LIFE SCIENCES AND TECHNOLOGIES (IEEE LIFETECH 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Bo-Han Chen, Yen-Chieh Ouyang, Mang Ou-Yang, Horng-Yuh Guo, Tsang-Sen Liu, Hsian-Min Chen, Chao-Cheng Wu, Chia-Hsien Wen, Chgein- Chang, Min-Shao Shih
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
(2020)
Proceedings Paper
Geosciences, Multidisciplinary
Yen-Chieh Ouyang, Bo-Han Chen, Meng-Chueh Lee, Tsang-Sen Liu, Mang Ou-Yang, Hsian-Min Chen, Chao-Cheng Wu, Chia-Hsien Wen, Min-Shao Shih, Chein- Chang, Yung-Jhe Yan
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)
(2019)
Proceedings Paper
Engineering, Electrical & Electronic
Meng-Chueh Lee, Kenneth-Yeonkong Ma, Yen-Chieh Ouyang, Mang Ou-Yang, Horng-Yuh Guo, Tsang-Sen Liu, Hsian-Min Chen, Chao-Cheng Wu, Chgein-I Chang
IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
(2018)
Proceedings Paper
Geosciences, Multidisciplinary
Kenneth-Yeonkong Ma, Yi-Mei Kuo, Yen-Chieh Ouyang, Chgein-I Chang
2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Yi-Ju Chiang, Yen-Chieh Ouyang, Armin B. Cremers, Liangyu Xu
2017 FIRST IEEE INTERNATIONAL CONFERENCE ON ROBOTIC COMPUTING (IRC)
(2017)
Proceedings Paper
Engineering, Electrical & Electronic
Li-Chien Lee, Yen-Chieh Ouyang, Shih-Yu Chen, Chein-I Chang
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
Clayton Chi-Chang Chen, Shih-Yu Chen, Hsian-Min Chen, Bor-Hung Lin, Yen-Chieh Ouyang, Jyh-Wen Chai, Ching-Wen Yang, San-Kan Lee, Chein-I Chang
INTELLIGENT SYSTEMS AND APPLICATIONS (ICS 2014)
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Hsian-Min Chen, Jyh-Wen Chai, Clayton Chi-Chang Chen, Yen-Chieh Ouyang, Ching-Wen Yang, San-Kan Lee, Chein-I Chang
INTELLIGENT SYSTEMS AND APPLICATIONS (ICS 2014)
(2015)