Article
Physics, Multidisciplinary
Tamas Sandor Biro, Andras Telcs, Mate Jozsa, Zoltan Neda
Summary: The article introduces an entropic distance analog quantity called gintropy based on the density of the Gini index in the Lorenz map, which can be used for pairwise mapping and ranking between countries and regions based on income and wealth inequality. The generalization to f-gintropy using a function of income or wealth value distinguishes regional inequalities more sensitively than the original construction.
Article
Physics, Multidisciplinary
Jazmin S. De la Cruz-Garcia, Juan Bory-Reyes, Aldo Ramirez-Arellano
Summary: Decision trees are data mining tools that create tree-like models. This paper introduces a decision tree based on a two-parameter fractional Tsallis entropy, which can generate a more sensitive measure for classification.
Article
Computer Science, Artificial Intelligence
Xiaoyu Han, Xiubin Zhu, Witold Pedrycz, Zhiwu Li
Summary: This study designs a three-way classification mechanism by combining fuzzy decision trees and expressing uncertainty. A fuzzy decision tree is constructed through generalization and the three-way decision model is widely used. An efficient way to flag uncertain data is proposed, which is not possible with commonly used fuzzy decision trees. The developed mechanism consists of two stages: building a fuzzy decision tree and determining the uncertainty level to reject instances. The rejection quality is quantified in terms of accuracy and coefficient, and the mechanism performs better than other three-way decision models.
APPLIED SOFT COMPUTING
(2023)
Article
Environmental Sciences
Ismail Serkan Uncu, Mehmet Kayakus
Summary: Light is a crucial environmental factor for the growth and development of plants, with different wavelengths of light affecting plants differently. This study examined the effects of red and blue LED light sources on wheat plants, finding that the ideal light source for plant growth may vary depending on environmental conditions.
FRESENIUS ENVIRONMENTAL BULLETIN
(2021)
Article
Computer Science, Artificial Intelligence
G. Sekhar Reddy, Suneetha Chittineni
Summary: Information efficiency is becoming increasingly important in the development and application sectors of information technology. This paper introduces a new classification algorithm, C4.5-SHO, which combines the classical C4.5 decision tree approach with the Selfish Herd Optimization algorithm to optimize decision tree gain by tuning the information gain weights. The proposed method's robustness is evaluated on various datasets and compared with existing classifiers, showing improved accuracy and performance.
PEERJ COMPUTER SCIENCE
(2021)
Article
Social Sciences, Interdisciplinary
Besma Belhadj, Firas Kaabi, Mejda Bouanani
Summary: The Gini index, a classic measure of inequality, is based on the Lorenz curve which illustrates the distribution of income. This paper introduces a fuzzy version of the Gini index and proposes an axiomatic measure of inequality.
SOCIAL INDICATORS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Patrick P. K. Chan, Juan Zheng, Han Liu, E. C. C. Tsang, Daniel S. Yeung
Summary: This study examines the robustness of classical decision tree (DT) and fuzzy decision tree (FDT) in an adversarial environment. Experimental results show that the fuzzifying process increases the robustness of DT, but FDT with more membership functions is more vulnerable to attacks.
APPLIED SOFT COMPUTING
(2021)
Article
Telecommunications
N. C. Sri Harsha, Y. Girish Venkata Sai Anudeep, Kudarvalli Vikash, D. Venkata Ratnam
Summary: With the increasing number of smartphone users and availability of sensor data, there is a growing interest in sensor-based human activity recognition. Machine learning algorithms can identify different human activities using smartphone sensor data. The research results are beneficial for detecting abnormal features of older people.
WIRELESS PERSONAL COMMUNICATIONS
(2021)
Article
Engineering, Marine
Demetris Demetriou, Constantine Michailides, George Papanastasiou, Toula Onoufriou
Summary: This paper introduces a new method for predicting coastal zone significant wave heights using machine learning models combined with meteorological and structural data, and finds that this approach can improve model classification performance. The study also shows that redundancy of training parameters can lead to overfitting, reducing the model's generalization ability.
Article
Computer Science, Artificial Intelligence
Lukasz Gadomer, Zenon A. Sosnowski
Summary: This paper discusses the use of various pruning methods to achieve better performance in C-fuzzy decision trees and Cluster-context fuzzy decision trees in C-fuzzy random forest. The experiments show that pruning generally reduces computation time and enhances classification accuracy, emphasizing the importance of selecting the appropriate pruning method based on the characteristics of the problem.
Article
Psychology, Multidisciplinary
Aixin Cai, Maohong Liu, Huan Liu
Summary: The performance evaluation of salespeople is crucial for guiding their behavior. This research proposes an integrated evaluation model based on the decision tree model, considering both work efficiency and effectiveness. It utilizes the Data Envelopment Analysis (DEA) model to quantify work efficiency and measure work effectiveness through sales amounts. The proposed model is proven to be stable and applicable through a case study.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Forestry
Mostarin Ara, Bradley D. Pinno, Francis Scaria, Robert E. Froese, Mike Bokalo
Summary: The positive effect of thinning on individual tree growth is well-known, but the long-term growth dynamics of individual trees after thinning remain uncertain. In this study, we used an individual tree growth model to investigate the thinning response of lodgepole pine over a rotation. Our findings show that thinning increased the overall growth and reduced growth variability of individual trees throughout the rotation. Pre-commercial thinning followed by commercial thinning resulted in maximum growth and less growth variability. The positive effect of thinning was immediate and diminished over time, with most of the response occurring within the first 10-15 years.
Article
Computer Science, Artificial Intelligence
Manish Aggarwal
Summary: A general entropy framework is proposed, which redefines existing fuzzy entropy functions and extends them to the probabilistic-fuzzy domain, demonstrating their usefulness in a real world case study.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Aerospace
Fikret Saygin, Yasemin Sisman, Orhan Dengiz, Aziz Sisman
Summary: This study developed a landslide susceptibility map in the high-risk region of Atakum district, Samsun province, Turkey. The map was created using topographic, geological, land use, and soil indicators weighted by the Fuzzy-Analytic Hierarchy Process (Fuzzy-AHP) approach, and integrated with the Geographic Information System (GIS). The predictability of the susceptibility map was also assessed using the decision tree algorithm CHAID. The results showed high accuracy for the 'very low' and 'low' susceptibility classes, but lower accuracy for the 'high' class.
ADVANCES IN SPACE RESEARCH
(2023)
Article
Biodiversity Conservation
Minwen He, Weifei Li, Pengcheng Wang, Chonghuai Yao
Summary: Rapid urban expansion has worsened the degradation of ecosystem services. This study examines the allocation of ecosystem services provided by blue-green infrastructure (BGI) in Wuhan, with a focus on regulating services. The results show significant inequities in the allocation of regulating services based on population, while allocation based on space is relatively equitable. The study also identifies areas with sufficient and insufficient allocation of regulating services, providing insights for urban planning and interventions.
ECOLOGICAL INDICATORS
(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)