Article
Ergonomics
Dezso V. Silagyi II, Dahai Liu
Summary: The study aims to predict the severity of aircraft damage and personal injury during aircraft approach and landing accidents using SVM models. Three new factors related to inattentional blindness were introduced. SVM models using the RBF kernel achieved the highest accuracy for predicting the severity of aircraft damage and personal injury. The top predictors included flight hours, accident time, pilot's age, crosswind component, landing runway number, single-engine land certificate, and obstacle penetration.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Biochemistry & Molecular Biology
Chia-Tzu Ho, Yu-Wei Huang, Teng-Ruei Chen, Chia-Hua Lo, Wei-Cheng Lo
Summary: Secondary structure prediction (SSP) of proteins is an important technique in structural biology with many applications. In the past seven decades, around 300 algorithms have been published, and the estimated limit of three-state SSP accuracy has been re-evaluated to be approximately 92%, while the limit for eight-state SSP is estimated to be in the range of 84-87%. This shows that SSP remains challenging and there is room for improvement in the field.
Article
Computer Science, Artificial Intelligence
M. Tanveer, A. Tiwari, R. Choudhary, M. A. Ganaie
Summary: This study proposes a novel large scale pinball twin support vector machine (LPTWSVM) to address the limitations of the twin support vector machines (TWSVMs), using a unique pinball loss function and improving model performance by eliminating matrix inversion calculation and minimizing structural risk.
Article
Green & Sustainable Science & Technology
Xuesong Zhang, Biao He, Mohanad Muayad Sabri Sabri, Mohammed Al-Bahrani, Dmitrii Vladimirovich Ulrikh
Summary: This study accurately predicted soil liquefaction potential using support vector machines (SVMs) and Bayesian optimization (BO). The evolutionary random forest (ERF) model was first used for input selection, which identified six important variables out of nine candidates. The results showed that the BOSVM model outperformed other models and achieved high accuracy and AUC values. The findings suggest that BOSVM is a viable alternative to conventional soil liquefaction prediction methods, and the BO method is successful in training the SVM model.
Article
Computer Science, Artificial Intelligence
Chun-Na Li, Yuan-Hai Shao, Huajun Wang, Yu-Ting Zhao, Naihua Xiu, Nai-Yang Deng
Summary: This paper investigates the general forms and characteristics of nonparallel support vector machines (NSVMs) and categorizes them into two types. It reveals the advantages and defects of different types and points out the inconsistency problems. Based on this observation, a novel max-min distance-based NSVM is proposed with desired consistency. The proposed NSVM has the consistency of training and test and the consistency of metric, and it assigns each sample an ascertained loss.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoming Wang, Shitong Wang, Zengxi Huang, Yajun Du
Summary: This paper introduces a novel method called sparse support vector machine guided by radius-margin bound (RMB-SSVM) to efficiently condense the basis vectors in support vector machines. By selecting basis vectors and learning corresponding coefficients with a criterion related to SVM's generalization ability, the RMB-SSVM model can yield better performance.
APPLIED SOFT COMPUTING
(2021)
Article
Multidisciplinary Sciences
Jia-Jun Liu, Chin-Sheng Yu, Hsiao-Wei Wu, Yu-Jen Chang, Chih-Peng Lin, Chih-Hao Lu
Summary: The study developed a prediction system based on protein sequences and structures to evaluate the relationship between SAVs and cancer with an accuracy of 89.73%. By converting SAV characteristics into feature vectors using a support vector machine and genetic algorithm, the system was able to predict changes in protein stability caused by SAVs.
SCIENTIFIC REPORTS
(2021)
Article
Computer Science, Artificial Intelligence
Sambhav Jain, Reshma Rastogi
Summary: This paper proposes Parametric non-parallel support vector machines for binary pattern classification. The model brings noise resilience and sparsity by intelligently redesigning the Support vector machine optimization. The experimental results validate its scalability for large scale problems.
Article
Automation & Control Systems
Ning Chu, Weimin Kang, Xinhua Yao, Jianzhong Fu
Summary: This paper proposes an online prediction method for the roundness of grinding workpieces based on vibration signals. Vibration sensors are used to collect vibration signals during grinding, and wavelet packet denoising is used to preprocess original signals to obtain effective vibration signals. Then use time domain analysis and frequency domain analysis to extract features and normalize them to form feature vectors. The roundness of the finished workpiece is measured using a shape-measuring instrument and integrated with the feature vectors to generate a usable data set. The support vector machine (SVM) algorithm is implemented using A Library for Support Vector Machines (LIBSVM), and a prediction model is constructed. Use the data set to train the model and evaluate the accuracy of the model to verify the effectiveness of the model. The results show that the prediction accuracy of the prediction method can reach 92.86%, and it can better predict whether the roundness is qualified.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Sebastian Maldonado, Julio Lopez, Carla Vairetti
Summary: The predictive performance of classification methods relies heavily on the nature of the environment and dataset shift issue. A novel Fuzzy Support Vector Machine strategy is proposed in this paper to improve performance by redefining the loss function and applying aggregation operators to deal with dataset shift. Our methods outperform traditional classifiers in terms of out-of-time prediction using simulated and real-world dataset for credit scoring.
INFORMATION SCIENCES
(2021)
Article
Oncology
Tao Duan, Zhufang Kuang, Lei Deng
Summary: miRNA is a potential therapeutic target due to its complex gene regulation mechanism, and its abnormal expression can cause drug resistance, affecting the therapeutic effect of diseases. Developing computational methods to predict miRNA-drug resistance associations is of practical value for designing effective drugs or combinations.
FRONTIERS IN ONCOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Wei Yang, Yang Liu, Chunjing Xiao
Summary: This study proposes a simple and effective deep centroid model based on deep metric learning for predicting the secondary structure of a protein from its amino acid sequence. By assigning secondary structure types based on the learned centroids, the need for a time-consuming k-nearest neighbor search is bypassed. The proposed model has a smaller model size and simpler architecture compared to existing models and achieves state-of-the-art performance on six test sets.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Environmental Sciences
Li Li, Zhongxu Zhang, Dongsheng Zhao, Yue Qiang, Bo Ni, Hengbin Wu, Shengchao Hu, Hanjie Lin
Summary: This study proposes an improved prediction model to estimate the scale of debris flows, and validates its effectiveness using data from Beichuan County. The results demonstrate strong predictive capabilities and improved accuracy in predicting the scale of debris flows. Additionally, the study identifies key factors that influence the scale of debris flows in Beichuan County.
Article
Computer Science, Artificial Intelligence
Wangyong Lv, Tingting Li, Huali Ren, Shijing Zeng, Jiao Zhou
Summary: The IDH-MSVM algorithm adjusts the distance between hyperplanes and classical margins to handle multiclassification problems more flexibly. Experimental results on UCI standard data sets show that this method achieves better classification accuracy for multiclass data compared to other algorithms.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Energy & Fuels
Qiaochu Wang, Dongxia Chen, Meijun Li, Sha Li, Fuwei Wang, Zijie Yang, Wanrong Zhang, Shumin Chen, Dongsheng Yao
Summary: This study introduces a novel machine learning method for predicting the potential of petroleum and natural gas resources using support vector machines (SVM). By combining support vector classification and support vector regression models, the method fully utilizes the professional knowledge of petroleum geology and the powerful data processing capabilities of machine learning algorithms, significantly improving its performance. The study demonstrates the accuracy and reliability of the method in petroleum exploration.
Article
Multidisciplinary Sciences
Liuyang Cai, Huidan Chang, Yaping Fang, Guoliang Li
SCIENTIFIC REPORTS
(2016)
Article
Multidisciplinary Sciences
Yaping Fang, Yunlong Wang, Qin Zhu, Jia Wang, Guoliang Li
SCIENTIFIC REPORTS
(2016)
Article
Evolutionary Biology
Cheng Zhang, Pan Ni, Hafiz Ishfaq Ahmad, M. Gemingguli, A. Baizilaitibei, D. Gulibaheti, Yaping Fang, Haiyang Wang, Akhtar Rasool Asif, Changyi Xiao, Jianhai Chen, Yunlong Ma, Xiangdong Liu, Xiaoyong Du, Shuhong Zhao
EVOLUTIONARY BIOINFORMATICS
(2018)
Article
Biology
Shan Gao, Shuo Xu, Yaping Fang, Jianwen Fang
JOURNAL OF THEORETICAL BIOLOGY
(2013)
Article
Biochemistry & Molecular Biology
Yaping Fang, Jianwen Fang
MOLECULAR BIOSYSTEMS
(2013)
Article
Chemistry, Multidisciplinary
Ann M. Thomas, Steve N. Hart, Guodong Li, Hong Lu, Yaping Fang, Jianwen Fang, Xiao-bo Zhong, Grace L. Guo
PHARMACEUTICAL RESEARCH
(2013)
Article
Multidisciplinary Sciences
Hui-Xin Liu, Yaping Fang, Ying Hu, Frank J. Gonzalez, Jianwen Fang, Yu-Jui Yvonne Wan
Article
Multidisciplinary Sciences
Le Zhan, Hui-Xin Liu, Yaping Fang, Bo Kong, Yuqi He, Xiao-bo Zhong, Jianwen Fang, Yu-Jui Yvonne Wan, Grace L. Guo
Article
Biochemical Research Methods
Shengsong Xie, Qin Zhu, Wubin Qu, Zhong Xu, Xiangdong Liu, Xinyun Li, Shijun Li, Wubin Ma, Yiliang Miao, Lisheng Zhang, Xiaoyong Du, Wuzi Dong, Haiwei Li, Changzhi Zhao, Yunlong Wang, Yaping Fang, Shuhong Zhao
Article
Biotechnology & Applied Microbiology
Ruimin Wang, Yunlong Wang, Xueying Zhang, Yaliang Zhang, Xiaoyong Du, Yaping Fang, Guoliang Li
Article
Biochemistry & Molecular Biology
Hui Zhang, Ruiqin Zheng, Yunlong Wang, Yu Zhang, Ping Hong, Yaping Fang, Guoliang Li, Yuda Fang
NUCLEIC ACIDS RESEARCH
(2019)
Article
Computer Science, Information Systems
Xiaomei Wei, Yaliang Zhang, Yu Huang, Yaping Fang
DATA TECHNOLOGIES AND APPLICATIONS
(2019)
Article
Biochemistry & Molecular Biology
Ting Zhang, PengPeng Guan, WenYe Liu, Guang Zhao, YaPing Fang, Hui Fu, Jian-Fang Gui, GuoLiang Li, Jing-Xia Liu
BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS
(2020)
Article
Biochemistry & Molecular Biology
Changzhi Zhao, Yunlong Wang, Xiongwei Nie, Xiaosong Han, Hailong Liu, Guanglei Li, Gaojuan Yang, Jinxue Ruan, Yunlong Ma, Xinyun Li, Huijun Cheng, Shuhong Zhao, Yaping Fang, Shengsong Xie
INTERNATIONAL JOURNAL OF BIOLOGICAL SCIENCES
(2019)
Article
Engineering, Biomedical
Haibo Cui, Xiaomei Wei, Yu Huang, Bin Hu, Yaping Fang, Jia Wang
BIO-MEDICAL MATERIALS AND ENGINEERING
(2014)