Article
Computer Science, Interdisciplinary Applications
Milad Sharifipour, Ali Nakhaee, Reza Yousefzadeh, Mojtaba Gohari
Summary: The study successfully applied the SFLA algorithm to the well placement problem, achieving better results compared to other algorithms in reservoir optimization.
COMPUTATIONAL GEOSCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Zhenxue He, Limin Xiao, Xiang Wang
Summary: This paper introduces a ternary quantum shuffled frog leaping algorithm (TQSFL) and a minimization algorithm (MA) for ternary FPRM expressions. Experimental results demonstrate the effectiveness of MA in minimizing ternary FPRM expressions.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Yao Huang, Xiao-Ning Shen, Xuan You
Summary: A discrete shuffled frog-leaping algorithm based on heuristic information is proposed for the traveling salesman problem, with four improved searching strategies to enhance the algorithm performance. Validation on TSP instances shows that the proposed algorithm outperforms classical and state-of-the-art algorithms in terms of accuracy and stability.
APPLIED SOFT COMPUTING
(2021)
Article
Energy & Fuels
Yanxiao Wu, Jiaqi Li, Xincheng Tang, Zhuoer Yuan, Xinyu Dong, Zhenchang Fang, Chunhua Sun, Xinqi Qiao, Xinling Li
Summary: This study establishes a simplified mechanism for n-butanol using the decoupling methodology and optimizes the rate constants using the Shuffled Frog Leaping Algorithm. The optimized mechanism shows good agreement with experimental data and is computationally efficient. This study provides a theoretical basis for the application of n-butanol fuel.
Article
Computer Science, Information Systems
Rasmita Dash, Rajashree Dash, Rasmita Rautray
Summary: Microarray technology has been widely used in biomedical research, but its efficient application in this field remains challenging and expensive. This study proposes a new metaheuristic approach, utilizing binary shuffled frog leaping algorithm for gene selection, and demonstrates the superiority of the selected gene subset through comparison with various classifiers.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Xiaoning Shen, Qingzhou Chen, Hongli Pan, Liyan Song, Yinan Guo
Summary: In this study, several issues in mobile crowdsensing are addressed, including the possibility of user mode-switching between tasks and the imbalance in task allocation to users with different reputation levels. To tackle these issues, an optimization model and a multi-stage algorithm are proposed to find optimal solutions.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Qingtao Pan, Jun Tang, Haoran Wang, Hao Li, Xi Chen, Songyang Lao
Summary: This paper proposes an improved self-adaptive differential evolution algorithm SFSADE, which effectively improves the performance of DE by introducing a shuffled frog-leaping strategy, a new mutation strategy, and an adaptive adjustment mechanism for control parameters. A large number of simulation experiments on 25 benchmark functions of CEC 2005 and two nonparametric statistical tests have shown that SFSADE significantly enhances the overall diversity of the population during dynamic evolution.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Multidisciplinary Sciences
Deming Lei, Tian Yi
Summary: The DSFLA algorithm effectively addresses the UPMSP with deteriorating PM and SDST, demonstrating competitive performance in computational experiments.
Article
Computer Science, Artificial Intelligence
Yi Chen, Mingjing Wang, Ali Asghar Heidari, Beibei Shi, Zhongyi Hu, Qian Zhang, Huiling Chen, Majdi Mafarja, Hamza Turabieh
Summary: In this study, a multi-strategy-driven shuffled frog leaping algorithm with horizontal and vertical crossover search (HVSFLA) is proposed for medical image segmentation. The algorithm achieves a better balance between diversification and intensification through horizontal and vertical crossover search. Experimental results demonstrate that HVSFLA outperforms other competing algorithms, showing great potential for medical image segmentation.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Energy & Fuels
Yun Liu, Ali Asghar Heidari, Xiaojia Ye, Chen Chi, Xuehua Zhao, Chao Ma, Hamza Turabieh, Huiling Chen, Rongrong Le
Summary: The study introduces an efficient solver called SFLBS for extracting unknown parameters in photovoltaic systems. Experimental results demonstrate that SFLBS performs well in parameter extraction and evaluation of commercial PV modules, with satisfactory convergence speed.
Article
Computer Science, Artificial Intelligence
Yun Liu, Ali Asghar Heidari, Zhennao Cai, Guoxi Liang, Huiling Chen, Zhifang Pan, Abdulmajeed Alsufyani, Sami Bourouis
Summary: This paper proposes an improved shuffled frog leaping algorithm that incorporates dynamic step size adjustment, specular reflection learning, and simulated annealing mechanisms. Experimental results show that the algorithm achieves superior solutions and performs well in feature selection tasks.
Article
Computer Science, Information Systems
Lingling Fang, Yumeng Jiang
Summary: Rapid extraction of brain lesions is crucial for clinical diagnosis and treatment. This paper proposes a C-MSFLA based on the cerebral hemorrhage clot clustering algorithm and establishes an intracranial blood clot extraction framework, which can automatically and accurately extract blood clots.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Industrial
Jingcao Cai, Deming Lei, Jing Wang, Lei Wang
Summary: This study considers the distributed assembly hybrid flow shop scheduling (DAHFS) problem with actual processing constraints and proposes a new algorithm with reinforcement learning to minimize makespan.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Information Systems
Atulya Gupta, Rajendra Prasad Mahapatra
Summary: Both unit and integration testing are crucial for software applications. Test case prioritization is an important strategy to optimize the testing process. This study introduces a memetics-inspired approach for prioritizing test cases at different levels, which outperforms other algorithms in terms of coverage rate and fault detection.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Computer Science, Artificial Intelligence
Yafei Dong, Quanwang Wu, Junhao Wen
Summary: The multiple traveling salesman problem (mTSP) is an extended version of the well-known traveling salesman problem, involving multiple salesmen visiting a set of cities, aiming to balance workload among salesmen or find the optimal solution when travel time is prioritized. The novel improved shuffled frog-leaping algorithm (ISFLA) proposed in this paper outperforms some state-of-the-art approaches for this problem, demonstrating its superiority in solving the minmax mTSP.
NEURAL COMPUTING & APPLICATIONS
(2021)
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)