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Computer Science, Artificial Intelligence
Qiao Jin
Summary: With the rapid development of computer technology, text data has increased exponentially, posing challenges due to complexity. Developing effective text classification methods has become a popular research topic. This study proposes a combination of GA-SVM and GA-FCM models to improve the efficiency and accuracy of text classification. Experimental results show significant improvements and the potential to filter irrelevant and harmful information in English text data, benefiting various industries and fields.
Article
Multidisciplinary Sciences
Oyeniyi Akeem Alimi, Khmaies Ouahada, Adnan M. Abu-Mahfouz, Suvendi Rimer
Summary: Efficient classification of unwanted events in power systems is crucial for preventing large-scale blackouts. The paper proposes a hybrid classification technique using SVM and GA models, applying principal component analysis and genetic algorithm for feature selection and weight optimization to achieve accurate classification results.
Article
Cardiac & Cardiovascular Systems
Javad Hassannataj Joloudari, Faezeh Azizi, Mohammad Ali Nematollahi, Roohallah Alizadehsani, Edris Hassannatajjeloudari, Issa Nodehi, Amir Mosavi
Summary: This study proposed a hybrid machine learning model called genetic support vector machine and analysis of variance (GSVMA) for diagnosing coronary artery disease (CAD). Through testing on a dataset, it was found that this model achieved the highest accuracy and outperformed other methods. The results demonstrated that support vector machine combined with genetic optimization algorithm could improve the accuracy of CAD diagnosis.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2022)
Article
Quantum Science & Technology
N. Innan, M. A. Z. Khan, B. Panda, M. Bennai
Summary: We propose a new model, quantum variational kernel SVM (QVK-SVM), in quantum machine learning (QML), which combines the quantum kernel and quantum variational algorithms to improve accuracy. extensive experiments on the Iris dataset demonstrate that QVK-SVM outperforms existing models regarding accuracy, loss, and confusion matrix indicators.
QUANTUM INFORMATION PROCESSING
(2023)
Article
Health Care Sciences & Services
Ana G. Sanchez-Reyna, Jose M. Celaya-Padilla, Carlos E. Galvan-Tejada, Huizilopoztli Luna-Garcia, Hamurabi Gamboa-Rosales, Andres Ramirez-Morales, Jorge Galvan-Tejada
Summary: In this research, a novel methodology utilizing machine learning techniques and genetic algorithms was proposed to develop a multivariate model for the detection of Alzheimer's disease. The model achieved an AUC of 100% in an independent blind test, showcasing its robustness in AD diagnosis.
Article
Mathematics, Applied
Pelin Akin
Summary: The crucial problem in applying classification algorithms is imbalanced classes. The SMOTE algorithm is developed to address the classification of imbalanced datasets. It increases the number of samples by interpolating between the minority samples. However, finding the best parameter values for the SMOTE algorithm is complicated.
Article
Environmental Sciences
Guangxin Liu, Liguo Wang, Danfeng Liu, Lei Fei, Jinghui Yang
Summary: This article proposes a non-parallel SVM model, which improves the classification effect and generalization performance for hyperspectral images by adding an additional empirical risk minimization term and bias constraint.
Article
Computer Science, Information Systems
Guoquan Li, Linxi Yang, Zhiyou Wu, Changzhi Wu
Summary: Proximal support vector machine (PSVM) is a variant of support vector machine (SVM) which aims to generate a pair of non-parallel hyperplanes for classification. Introducing l(0)-norm regularization in PSVM enables simultaneous selection of important features and removal of redundant features for classification. The proposed method utilizes a continuous nonconvex function and difference of convex functions algorithms (DCA) to solve the optimization problem efficiently.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Liuyuan Chen, Kanglei Zhou, Junchang Jing, Haiju Fan, Juntao Li
Summary: This work proposes a fast regularization parameter tuning algorithm for the twin multi-class support vector machine. By adopting a novel sample data set partition strategy and utilizing linear equations and block matrix theory, the regularization parameters are continuously updated, and the relationship between the Lagrangian multipliers and the regularization parameters is proven. Finally, different events are defined to seek for the starting event for the next iteration, and the effectiveness of the proposed method is validated through experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Biology
Shahadat Hussain, Zahid Raza, Giorgio Giacomini, Nandu Goswami
Summary: Syncope is a medical condition where consciousness is lost due to temporary lack of blood flow to the brain. This study demonstrates the effectiveness of Support Vector Machine (SVM) based classification for neuro-mediated syncope, using patient physiological data collected in clinical settings. The experimental results validate the proactive diagnosis of syncope using SVM-based techniques.
Article
Computer Science, Artificial Intelligence
Tong Gao, Hao Chen
Summary: In this study, a multicycle disassembly-based decomposition algorithm (MCD-DA) is proposed to efficiently solve the training problem of multiclass support vector machine (SVM). MCD-DA constructs a graph model to re-express the constraints in multiclass SVM, partitions the complex feasible region into simple sub-feasible regions, and designs multiple cycle-based disassembly strategies to update the working variables analytically. Experimental results demonstrate that MCD-DA outperforms typical optimization algorithms for more sample cases.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Qi Si, Zhixia Yang, Junyou Ye
Summary: This research proposes a new TSVM method for robust classification, which improves its performance on data with outliers or noises by using the symmetric LINEX loss function and introducing a regularization term. Experimental results show that the proposed method is competitive on multiple datasets.
Article
Engineering, Electrical & Electronic
Mahdi Ajdani, Hamidreza Ghaffary
Summary: The study presented a method to design an analytical framework for detecting destructive data based on time, users' information, and scale factors, which can be applied to big data. The method divides time into subperiods to train data using users' review information, and storage methods are applied for scalability. The combination of hardware-software method is used to detect destructive data, along with a new modified vector machine algorithm, showing higher efficiency than traditional support vector machine methods. The proposed method achieved an accuracy of 0.97, making it more acceptable than previous methods.
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
(2021)
Article
Environmental Sciences
Wenge Zhang, Shengling Hou, Huijuan Yin, Lingqi Li, Kai Wu
Summary: The evaluation of regional water-saving level provides scientific theoretical support for the implementation of a national water-saving priority strategy. The study found that there are regional differences in water-saving level in China, indicating the need for effective water-saving measures and schemes tailored to different regions and industries.
Article
Quantum Science & Technology
Fei Wang, Kunlun Xie, Lin Han, Menghui Han, Zeshi Wang
Summary: This paper introduces support vector machine and its problems, proposes an improved quantum genetic algorithm, and applies it to SVM parameter optimization. Experimental results show that the improved algorithm has better performance compared to other algorithms.
QUANTUM INFORMATION PROCESSING
(2023)
Article
Biophysics
Jing Yu, Yue Zhang, Chunming Xia
JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY
(2020)
Article
Engineering, Biomedical
Yue Zhang, Chunming Xia
MEDICAL ENGINEERING & PHYSICS
(2020)
Article
Biophysics
Hanyang Zhang, Yanbiao Zhong, Yue Zhang, Ke Yang, Chunming Xia, Chunlei Shan
Summary: This study investigated the effects of transcranial magnetic stimulation (TMS) on target muscles by simultaneously recording mechanomyography (MMG) and electromyography (EMG), and identified the TMS-MMG system as a fourth-order model using the subspace method (N4SID) and transfer function. It was found that MMG signals can be used as diagnostic indicators of TMS.
JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY
(2021)
Article
Engineering, Biomedical
Hanyang Zhang, Xinping Wang, Yue Zhang, Gangsheng Cao, Chunming Xia
Summary: This paper presents a novel wireless MMG signal acquisition system, which includes modular nodes and a core board. Different algorithms are used for feature selection and their effects on the recognition of eight types of lower limb movements based on MMG signals are compared. The results demonstrate the advantages of swarm intelligence algorithms in feature selection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Engineering, Biomedical
Tongtong Zhao, Gangsheng Cao, Yue Zhang, Hanyang Zhang, Chunming Xia
Summary: This research proposes an incremental learning method for deep learning based on mechanomyography (MMG) to improve the recognition effect on upper limb rehabilitation action. The experimental results demonstrate that the incremental learning method can improve the recognition rates and the generalization performance of the model.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Chemistry, Physical
Maoxun Sun, Yanxun Xiang, Wei Shen, Hongye Liu, Biao Xiao, Yue Zhang, Mingxi Deng
Summary: Nonlinear guided elastic waves have high sensitivity to microstructural changes, but it is difficult to locate micro-defects using commonly used second and third harmonics. Nonlinear mixing of guided waves may solve this problem. Phenomena of phase mismatching affect energy transmission and sensitivity to micro-damage, and their investigation allows for more accurate assessment of microstructural changes. The cumulative effect of difference- or sum-frequency components is affected by phase mismatching, accompanied by the beat effect, and their spatial periodicity is inversely proportional to the wavenumber difference. The sensitivity to micro-damage is compared between two mode triplets, and the better one is used to assess accumulated plastic deformations in thin plates.
Article
Biophysics
Yue Zhang, Gangsheng Cao, Tongtong Zhao, Hanyang Zhang, Juntian Zhang, Chunming Xia
JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY
(2020)
