Article
Computer Science, Interdisciplinary Applications
Maliheh Abbaszadeh, Saeed Soltani-Mohammadi, Ali Najah Ahmed
Summary: This article introduces the application of the support vector classifier in geological modeling and proposes an improved method based on particle swarm optimization to select the best model parameters. Through the application in the modeling process of the Iju porphyry copper deposit, the effectiveness and superiority of this method are demonstrated.
COMPUTERS & GEOSCIENCES
(2022)
Article
Computer Science, Software Engineering
Uttam Singh Bist, Nanhay Singh
Summary: This article primarily focuses on the fundamentals and optimization techniques of support vector machines (SVMs) and its variants. It discusses the major issues and challenges in different variations of SVMs, as well as the advancements and optimizations made in SVM models and their kernels.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Green & Sustainable Science & Technology
Mei-Li Shen, Cheng-Feng Lee, Hsiou-Hsiang Liu, Po-Yin Chang, Cheng-Hong Yang
Summary: This study introduces a new forecasting approach, FSPSOSVR, which combines particle swarm optimization, random forest feature selection, and support vector regression for accurately predicting exchange rates. Empirical results show that the FSPSOSVR algorithm consistently outperforms competing models and has practical relevance for foreign exchange carry trades.
Article
Computer Science, Hardware & Architecture
Ruizhong Du, Yun Li, Xiaoyan Liang, Junfeng Tian
Summary: This paper proposes a lightweight support vector machine intrusion detection model based on Cloud-Fog Collaboration(CFC-SVM), which addresses the issues of fog nodes being closer to user equipment, having heterogeneous nodes, limited storage capacity resources, and greater vulnerability to intrusion. The model utilizes Principal Component Analysis (PCA) to reduce dimensionality, eliminates attribute correlation, and reduces training time. Experimental results using the KDD CUP 99 dataset demonstrate that the proposed model outperforms other similar algorithms in terms of detection time, detection rate, and accuracy, effectively solving the problem of intrusion detection in the fog environment.
MOBILE NETWORKS & APPLICATIONS
(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)
Article
Environmental Sciences
Chunfang Kong, Yiping Tian, Xiaogang Ma, Zhengping Weng, Zhiting Zhang, Kai Xu
Summary: This study used different models to evaluate landslide susceptibility in Zhaoping County, and found that the PSO-RF model had the highest accuracy. The PSO algorithm had a good effect on the SVM and RF models, and all four models performed well for landslide susceptibility evaluation.
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
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
Automation & Control Systems
Zhenchao Ma, Laurence Tianruo Yang, Qingchen Zhang
Summary: This study proposes a Support Multimode Tensor Machine (SMTM) algorithm that generalizes the formulation of traditional Support Tensor Machine (STM) by applying multimode product. Experiments conducted on various datasets validate the superior performance of SMTM in multiple classification tasks and suggest the potential of the proposed model for multiple classification in industrial big data.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Indu Singh, Narendra Kumar, K. G. Srinivasa, Shivam Maini, Umang Ahuja, Siddhant Jain
Summary: Credit scoring is a statistical technique used by financial institutions to make informed decisions about extending loans to customers to reduce operational costs and risks. Feature selection helps improve classification accuracy with large datasets, while heterogeneous ensemble-based models have been proven to outperform other mathematical and AI-based techniques for this issue.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Mustafa Acikkar, Yunus Altunkol
Summary: This study introduces a new hybrid optimization algorithm, PSOGS, which combines PSO and GS, and proves that it is a fast, stable, efficient, and reliable algorithm for optimizing hyperparameters of SVR. The results show that PSOGS-SVR performs better in terms of prediction accuracy and execution time compared to GS-SVR and PSO-SVR, and it also outperforms PSOGSA-SVR.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Yuxuan Shi, Jihong Pang, Yuanzhong Chen, Jinkun Dai, Yong Li
Summary: This study proposes an intelligent predictive quality control model for the assembly process of electromagnetic (EM) brakes based on digital twin technology and a data-driven algorithm. The results show the effectiveness and accuracy of the model in predicting assembly quality.
Article
Computer Science, Interdisciplinary Applications
M. J. Abinash, V. Vasudevan
Summary: The study focuses on detecting cancer-causing genes in microarray data analysis, utilizing techniques like Improved Supervised Principal Component Analysis and Support Vector Machines, with validation on various experimental datasets. The proposed work was compared with traditional techniques, demonstrating optimum accuracy, recall, precision, and training time.
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING
(2022)
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
Chuanxing Geng, Songcan Chen
Summary: This article focuses on extending the fitting hyperplanes for each class in generalized eigenvalue proximal support vector machine (GEPSVM) from single one to multiple ones. A novel multiplane convex proximal support vector machine (MCPSVM) is proposed as an extension, which offers advantages in terms of classification performance and flexibility compared to existing methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Software Engineering
Tomas Skersys, Paulius Danenas, Rimantas Butleris
JOURNAL OF SYSTEMS AND SOFTWARE
(2018)
Article
Mathematics, Applied
Konstantinas Korovkinas, Paulius Danenas, Gintautas Garsva
NONLINEAR ANALYSIS-MODELLING AND CONTROL
(2020)
Article
Computer Science, Artificial Intelligence
Paulius Danenas, Tomas Skersys, Rimantas Butleris
DATA & KNOWLEDGE ENGINEERING
(2020)
Article
Chemistry, Multidisciplinary
Paulius Danenas, Tomas Skersys, Rimantas Butleris
APPLIED SCIENCES-BASEL
(2020)
Article
Chemistry, Multidisciplinary
Tomas Skersys, Paulius Danenas, Rimantas Butleris, Armantas Ostreika, Jonas Ceponis
Summary: The developed solution is mainly applied in problem domain analysis and system design, utilizing SBVR business vocabularies, UML use case models, and model-to-model transformation technology. This solution innovatively presents a set of model-based transformations for extracting well-structured SBVR business vocabularies from visual UML use case models, providing more flexibility to the model development process.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Multidisciplinary
Tomas Skersys, Paulius Danenas, Egle Mickeviciute, Rimantas Butleris
Summary: This paper presents an approach for extracting SBVR process rules from BPMN processes using model-to-model transformation technology. The experimental results show that the specified transformation rules and algorithms are sufficient for the given scope, providing a solid background for practical application and future developments of the solution.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Evaldas Vaiciukynas, Paulius Danenas, Vilius Kontrimas, Rimantas Butleris
Summary: The study suggests that ensemble forecasting of time series using meta-learning to adaptively predict the diversity and size of the ensemble yields better results. By ranking different forecasting methods and selecting the best ones for ensemble formation, the proposed approach outperforms existing benchmarks with weighted pooling achieving the best overall performance.
Proceedings Paper
Mathematics, Applied
Tomas Skersys, Paulius Danenas, Rimantas Butleris
INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM-2018)
(2019)
Article
Computer Science, Software Engineering
Konstantinas Korovkinas, Paulius Danenas, Gintautas Garsva
BALTIC JOURNAL OF MODERN COMPUTING
(2019)
Proceedings Paper
Computer Science, Information Systems
Konstantinas Korovkinas, Paulius Danenas, Gintautas Garsva
INFORMATION AND SOFTWARE TECHNOLOGIES, ICIST 2018
(2018)
Article
Computer Science, Software Engineering
Konstantinas Korovkinas, Paulius Danenas, Gintautas Garsva
BALTIC JOURNAL OF MODERN COMPUTING
(2017)
Article
Computer Science, Information Systems
Paulius Danenas, Tomas Skersys
Summary: This study explores the application of natural language processing in visual model-to-model transformations, focusing on information extraction from process and use case models. State-of-the-art NLP tools combined with formal regular expressions were used to solve relation extraction tasks. The best performance was achieved using Stanza for phrase extraction and conjunctive phrase processing.
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)