Article
Computer Science, Information Systems
Zahid Halim, Muhammad Nadeem Yousaf, Muhammad Waqas, Muhammad Sulaiman, Ghulam Abbas, Masroor Hussain, Iftekhar Ahmad, Muhammad Hanif
Summary: This study aims to improve the accuracy of classifiers in network security and intrusion detection through an enhanced Genetic Algorithm-based feature selection method, with parameter tuning and a novel fitness function. Results show that using GbFS can significantly improve accuracy.
COMPUTERS & SECURITY
(2021)
Article
Engineering, Industrial
Jiayi Ding, Jianfang Zhou, Wei Cai
Summary: This paper proposes an efficient variable selection-based Kriging model method to approximate the finite element analysis model in reliability analysis of slopes. The variable selection technique successfully solves the curse of dimensionality problem within Kriging model induced by numerous random variables. The implementation procedure of this method for the reliability analysis of slopes is introduced in detail, and the validity is demonstrated through examples.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Materials Science, Multidisciplinary
Shuai Li, Shu Li, Dongrong Liu, Rui Zou, Zhiyuan Yang
Summary: By improving the traditional genetic algorithm as a feature selection method, this study has enhanced the accuracy and stability of hardness prediction for high entropy alloys. Additionally, utilizing the stacking method as an ensemble learning strategy effectively reduced prediction errors.
COMPUTATIONAL MATERIALS SCIENCE
(2022)
Article
Biochemical Research Methods
Chiwen Qu, Lupeng Zhang, Jinlong Li, Fang Deng, Yifan Tang, Xiaomin Zeng, Xiaoning Peng
Summary: This article proposes a novel metaheuristic approach VNLHHO for gene feature extraction by utilizing F-score for gene selection, constructing a variable neighborhood learning strategy, and employing mutation operations and a new activation function to improve accuracy. Experimental results show that VNLHHO achieves high classification accuracy in gene expression data for eight types of tumors.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Che Xu, Shuwen Zhang
Summary: This paper proposes a framework for sequential instance selection based on the Genetic Algorithm to address the balance between individual accuracy and diversity in ensemble models. The framework overcomes the limitations of the Genetic Algorithm in high-dimensional tasks and provides a way to balance accuracy and diversity by searching appropriate training data subsets. The predictions of the component classifiers are combined using a weighted majority voting rule.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Automation & Control Systems
Zhehuang Huang, Jinjin Li, Changzhong Wang
Summary: This article proposes novel distinguishing indicators for fuzzy beta covering (FBC) decision systems. It discusses the granulation structure of FBC and introduces a new multi-granulation distinguishing indicator. The article also presents variants of the distinguishing indicator and develops a forward algorithm for feature subset selection. Experimental results demonstrate the competitive classification performance and strong robustness of the algorithm.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Forestry
Evandro Nunes Miranda, Bruno Henrique Groenner Barbosa, Sergio Henrique Godinho Silva, Cassio Augusto Ussi Monti, David Yue Phin Tng, Lucas Rezende Gomide
Summary: A hybrid method combining genetic algorithms for variables selection and random forest for fitting models of individual tree heights was proposed and compared with other methods using a dataset of 5,608 trees and 189 environmental variables. The optimal set of variables included breast height diameter ratio, competition index, dominant height, soil silt, and boron content, with the proposed hybrid method achieving comparable accuracy in estimating tree heights. The study suggests that this modelling approach could have broader applications in forestry and ecological science.
FOREST ECOLOGY AND MANAGEMENT
(2022)
Article
Automation & Control Systems
Weidong Xie, Yushan Fang, Kun Yu, Xin Min, Wei Li
Summary: MFRAG is a new hybrid feature selection method that mimics the natural principle of survival of the fittest by enhancing the stability and reliability of the selection process through fusion mechanisms and integrated models, and guides the evolutionary process through a set of lists generated by a feature fusion model.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Environmental Sciences
Ozan Nadirgil
Summary: This paper develops and compares 48 hybrid machine learning models for accurate carbon price prediction. By using CEEMDAN, VMD, PE, and multiple types of ML models optimized by GA, the study shows that the CEEMDAN-VMD-BPNN-GA optimized double decomposition hybrid model outperforms the others with a striking R2 value of 0.993, RMSE of 0.0103, MAE of 0.0097, and MAPE of 1.61%.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Computer Science, Interdisciplinary Applications
Weifei Guo, Qi Lei, Yuchuan Song, Xiangfei Lyu
Summary: The paper introduces an LIGA-ESE algorithm for solving AJSSP problems using edge selection encoding, and aims to improve efficiency and convergence speed through novel interactive mechanisms and parameter selection.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Wenbin Pei, Bing Xue, Lin Shang, Mengjie Zhang
Summary: Cost-sensitive learning is a popular approach for addressing class imbalance in machine learning, but the proposed genetic programming-based approach in this paper shows promising results in developing cost-sensitive classifiers independent of manually designed cost matrices. Experiment results on high-dimensional unbalanced datasets demonstrate the effectiveness of this approach.
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
(2021)
Article
Computer Science, Artificial Intelligence
Dipankar Dasgupta, Kishor Datta Gupta
Summary: This study proposed a trustworthy framework for machine learning defense that utilizes an adaptive strategy to inspect inputs and decisions. Experimental results showed that the dual-filtering strategy could effectively mitigate various machine learning attacks with improved accuracy. Inspection of the output decision boundary using classification techniques enhances trustworthiness.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Mathematical & Computational Biology
Depei Wang, Lianglun Cheng, Tao Wang
Summary: This letter presents a framework that combines fair feature selection and fair meta-learning for few-shot classification, aiming to address the issue of unfairness caused by biased data in artificial intelligence decision-making. The proposed method incorporates a pre-processing component, FairGA module, and FairFS module to generate a feature pool and perform representation and fairness constraint classification. Experimental results demonstrate the strong competitive performance of the proposed method on three public benchmarks.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2023)
Article
Mathematics
Ahmed A. Ewees, Mohammed A. A. Al-qaness, Laith Abualigah, Diego Oliva, Zakariya Yahya Algamal, Ahmed M. Anter, Rehab Ali Ibrahim, Rania M. Ghoniem, Mohamed Abd Elaziz
Summary: The paper introduces a novel feature selection method called AOAGA, which combines arithmetic optimization algorithm with genetic algorithm. Through experiments with multiple benchmark datasets, the method shows promising performance.
Article
Computer Science, Hardware & Architecture
Kyle Spurlock, Heba Elgazzar
Summary: Neural networks are popular for their ability to learn complex patterns, but designing them is challenging due to the numerous parameters required. This study proposes an experimental approach to optimize both weights and meta-parameters simultaneously, showing success in simple problems.
JOURNAL OF SUPERCOMPUTING
(2022)
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)