Article
Computer Science, Artificial Intelligence
Wojciech Dudzik, Jakub Nalepa, Michal Kawulok
Summary: This paper addresses the optimization problem of SVMs for binary classification of difficult datasets, introducing an evolutionary technique and a co-evolutionary scheme. Experimental results show that the proposed algorithm outperforms popular supervised learners and other techniques for optimizing SVMs.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Rongda Chen, Zhixia Yang, Junyou Ye
Summary: This article discusses the challenges of using support vector machine (SVM) models in multiview learning and proposes two multiview classifiers, C-MKNSVM and ?-MKNSVM, which overcome the difficulties by using kernel-free techniques. Experimental results show that these classifiers outperform traditional MVL classifiers.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Bin Gu, Ziran Xiong, Xiang Li, Zhou Zhai, Guansheng Zheng
Summary: This article proposes a new kernel path algorithm (KP nu SVC) to trace the solutions of nu-support vector classification (nu-SVC). It also introduces a new kernel error path (KEP) algorithm that ensures finding the global optimal kernel parameter. Experimental results demonstrate the effectiveness of KP nu SVC and the advantage of using KEP in selecting the optimal kernel parameter.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zhizheng Liang, Lei Zhang
Summary: In this paper, novel twin support vector machines (TSVMs) are proposed to handle uncertain data, where each uncertain sample is modeled as a random vector with Gaussian distributions. By deriving an important theorem to simplify the models and using a quasi-Newton optimization algorithm, the optimization problem becomes tractable. Experimental results show that the proposed models outperform some existing algorithms in terms of classification performance, especially for uncertain cross-plane problems.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Arin Chaudhuri, Carol Sadek, Deovrat Kakde, Haoyu Wang, Wenhao Hu, Hansi Jiang, Seunghyun Kong, Yuwei Liao, Sergiy Peredriy
Summary: Support vector data description (SVDD) is a popular anomaly detection technique that requires the use of a Gaussian kernel, with the bandwidth parameter being crucial for optimal performance. This paper introduces a new unsupervised method for selecting the Gaussian kernel bandwidth, utilizing a low-rank representation of the kernel matrix. The new technique is competitive with existing methods for low-dimensional data and excels in handling high-dimensional data.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
A. Ramirez-Morales, J. U. Salmon-Gamboa, Jin Li, A. G. Sanchez-Reyna, A. Palli-Valappil
Summary: This paper presents experimental studies on ensembles of binary classifiers based on individual support vector machines. The proposed GenBoost-SVM method uses an adaptive boosting algorithm to construct these ensembles. Genetic algorithms are used for pre-selections to reduce training times and address imbalanced data challenges. Diversity and early stopping are also considered in the ensembles to reduce generalization error. The study proposes 56 different types of ensembles that vary in support vector machine kernels, genetic selections, and diversity. The results show that the ensembles with genetic selections and diversity perform competitively compared to popular classifiers, and they outperform most of them for imbalanced data. The study also demonstrates that using different support vector machine kernels leads to enhanced performances. This is the first study to combine adaptive boosted ensembles, genetic selections, and support vector machines.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Theory & Methods
Sandamal Weerasinghe, Tansu Alpcan, Sarah M. Erfani, Christopher Leckie
Summary: The research shows that with a new defense algorithm and metric method, SVMs can improve resistance against targeted attacks and significantly reduce classification error rates.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2021)
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
Computer Science, Artificial Intelligence
F. Marchetti, E. Perracchione
Summary: This article proposes and studies a new efficient tool called Local-to-Global Support Vector Machine (LGSVM) for supervised classification tasks that involve a large number of instances. It constructs a global classifier by gluing together the local SVM contributions via compactly supported weights, which significantly reduces the complexity cost of SVMs for large-scale datasets.
PATTERN RECOGNITION
(2022)
Article
Telecommunications
Wanman Li, Xiaozhang Liu, Anli Yan, Jie Yang
Summary: This paper discusses evasion attacks against SVM classification in adversarial machine learning, proposing a defense strategy using vulnerability function and kernel optimization. The defense method proves to be effective on benchmark datasets, improving the robustness of SVM classifiers.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Article
Telecommunications
Wanman Li, Xiaozhang Liu, Anli Yan, Jie Yang
Summary: This paper introduces the evasion attack against SVM classification in the field of adversarial machine learning and proposes an effective defense strategy by optimizing the SVM kernel to enhance the robustness of the classifier.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
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
Automation & Control Systems
Scindhiya Laxmi, S. K. Gupta
Summary: The intuitionistic fuzzy twin support vector machine for multi-categorization is developed in this study, which combines the concepts of structural and empirical risk. Empirical findings show that this method outperforms existing methods on various datasets and has good generalization capacity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Mingzhang Pan, Qiye Yang, Tiecheng Su, Kuihua Geng, Ke Liang
Summary: A novel model is proposed in this study to eliminate hand tremor signals in teleoperation. The model utilizes an improved sparrow search algorithm and a multi-domain analysis layer to optimize the forecasting accuracy, and introduces a new wavelet kernel function. Compared with existing models, this model achieves higher forecasting accuracy.
APPLIED SOFT COMPUTING
(2022)
Article
Environmental Sciences
Barenya Bikash Hazarika, Deepak Gupta, Narayanan Natarajan
Summary: This study proposed wavelet kernel-based LSTSVR models for accurate wind speed prediction. The models were evaluated using data from four different stations in Tamil Nadu, India, and were found to outperform other models.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Clinical Neurology
Fariba Biyouki, Katri Laimi, Saeed Rahati, Reza Boostani, Ali Shoeibi
ACTA NEUROLOGICA BELGICA
(2016)
Article
Health Care Sciences & Services
Mahsa Raeiatibanadkooki, Saeed Rahati Quchani, MohammadMahdi KhalilZade, Kambiz Bahaadinbeigy
JOURNAL OF MEDICAL SYSTEMS
(2016)
Article
Engineering, Biomedical
Zeinab Mahmoudi, Saeed Rahati, Mohammad Mahdi Ghasemi, Vahid Asadpour, Hamid Tayarani, Mohsen Rajati
BIOMEDICAL ENGINEERING ONLINE
(2011)
Article
Engineering, Electrical & Electronic
Atefeh Goshvarpour, Ateke Goshvarpour, Saeed Rahati
DIGITAL SIGNAL PROCESSING
(2011)
Proceedings Paper
Engineering, Electrical & Electronic
Fariba Biyouki, Saeed Rahati, Katri Laimi, Reza Boostani, Ali Shoeibi
2013 21ST IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE)
(2013)
Article
Psychiatry
Ateke Goshvarpour, Atefeh Goshvarpour, Saeed Rahati, Vahid Saadatian
IRANIAN JOURNAL OF PSYCHIATRY AND BEHAVIORAL SCIENCES
(2012)
Proceedings Paper
Computer Science, Artificial Intelligence
Amir Azizi, Karim Faez, Amin Rezaeian Delui, Saeid Rahati
EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, PROCEEDINGS
(2009)
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)