Article
Computer Science, Information Systems
Ruijin Wang, Xikai Pei, Juyi Zhu, Zhiyang Zhang, Xin Huang, Jiayi Zhai, Fengli Zhang
Summary: This paper proposes a model fusion-based time series forecasting method to improve the accuracy and efficiency of predictions using multivariate grey model and artificial fish swarm algorithm. Two fusion models based on data decomposition and weighted summation achieve good prediction results in different scenarios.
INFORMATION SCIENCES
(2022)
Article
Business
Youyang Ren, Lin Xia, Yuhong Wang
Summary: Global warming and environmental degradation pose significant threats to human survival, prompting China's energy sector to enhance efforts in clean energy generation due to the conflict between rising carbon emissions and carbon neutrality goals. This paper proposes a seasonal optimized multivariate grey model that improves fitting and prediction accuracy of China's hydropower generation through optimization algorithms and dummy variable supplementation. The model is validated and compared with other methods, showing a mean absolute percentage error of 3.87% and 0.83% for the training and test groups. Finally, the paper predicts China's hydropower generation from 2022 to 2025 based on the power generation during the country's 13th Five-Year Plan Period.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Article
Green & Sustainable Science & Technology
Ming Xie, Shuli Yan, Lifeng Wu, Liying Liu, Yongfeng Bai, Linghui Liu, Yanzeng Tong
Summary: This paper proposes a novel robust reweighted multivariate grey model (RWGM(1,N)) for accurately forecasting national-level greenhouse gas emissions. The model reduces overfitting with weighted factors and employs LASSO regression for variable selection, showing higher predictive accuracy and robust performance in simulating GHG emissions in EU member countries.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Green & Sustainable Science & Technology
Yongtong Li, Yan Chen, Yuliang Wang
Summary: This study uses different orders of grey models to classify and predict carbon emissions in the Beijing-Tianjin-Hebei region, selecting scenarios suitable for local development. The results show that Beijing has already achieved peak carbon, Tianjin may not reach its target by 2030, and Hebei is expected to reach its target by 2030. For future development, a high rate of carbon emission reduction is ideal for improving air quality in Beijing, a low-speed growth scenario is more appropriate for Tianjin, and a low-rate carbon reduction scenario aligns with pollution and carbon reduction efforts in Hebei. Additionally, population policies in Beijing, Tianjin, and industrial structure reform in Hebei are favorable to local development.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Computer Science, Information Systems
Qinpei Zhao, Guangda Yang, Kai Zhao, Jiaming Yin, Weixiong Rao, Lei Chen
Summary: This article proposes a methodology to measure the upper limit of predictability for multivariate time series with multivariate constraint relations. The key of the methodology is a novel entropy named Multivariate Constraint Sample Entropy (McSE) that incorporates the multivariate constraint relations for better predictability. The authors conducted a systematic evaluation over eight datasets and compared existing methods with their proposed predictability, finding that their method achieved higher predictability. They also discovered that forecasting algorithms that capture the multivariate constraint relation information can achieve higher accuracy, confirming the importance of multivariate constraint relations for predictability.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Automation & Control Systems
S. H. Li, L. Zhu, Y. Wu, X. Q. Lei
Summary: A novel grey multivariate model is proposed for forecasting landslide displacement. The model outperforms other common models in terms of prediction accuracy and effectiveness, providing an effective method for forecasting landslide displacement.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Green & Sustainable Science & Technology
Meng Xiangmei, Tu Leping, Yan Chen, Wu Lifeng
Summary: The study introduces a grey multivariate convolution model to predict water consumption in 31 regions in China, showing high prediction accuracy and a downward trend in water consumption in over 50% of regions. These predictive results are important for water resources management in China.
SUSTAINABLE PRODUCTION AND CONSUMPTION
(2021)
Article
Green & Sustainable Science & Technology
Chengguang Liu, Jiaqi Zhang, Xixi Luo, Yulin Yang, Chao Hu
Summary: The construction of high-speed rail lines in China has increased the freight capacity of conventional railways, but recent energy policy adjustments have led to a decline in rail freight volumes. This study combines grey relational analysis and deep neural networks to predict rail freight demand. The proposed GRA-DAE-NN model is accurate and easy to interpret, outperforming other conventional prediction models. The method not only accurately predicts rail freight demand but also helps transportation companies understand the key factors influencing demand changes.
Article
Computer Science, Artificial Intelligence
Kai Zhang, Kedong Yin, Wendong Yang
Summary: In grey system theory, the performance of a grey forecasting model depends on efficiently measuring grey information from data. This study proposed a probabilistic accumulation operator-based grey forecasting model (PGM(1,1)) that uses a Bernoulli distribution to simulate valid/invalid grey information. PGM(1,1) achieved state-of-the-art performance by not being affected by invalid grey information, as demonstrated through comprehensive analysis and comparison with other models on five public datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Environmental Sciences
Xiaojie Wu, Pingping Xiong, Lingshan Hu, Hui Shu
Summary: This paper introduces a new carbon emission prediction model by incorporating new information priority operator and nonlinear parameter. The new model is applied to predict carbon emissions in different regions and trends, and demonstrates higher accuracy and forecasting ability.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Business
Sumana Biswas, Ismail Ali, Ripon K. Chakrabortty, Hasan Huseyin Turan, Sondoss Elsawah, Michael J. Ryan
Summary: In this research, a dynamic model for product family evolution combined with forecasting is proposed, taking into account market demand, customer needs, and technological requirements. The evaluations of product family evolution are based on Grey Relational Analysis and Fuzzy Analytical Hierarchy Process. A data-driven neural network-based forecasting model is also introduced. Numerical simulation and a case study of Apple's iPhone product family demonstrate the effectiveness of the developed approach in identifying influential key design features and best performed products for future product evolution.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2022)
Article
Materials Science, Multidisciplinary
Ali Naderi Bakhtiyari, Yongling Wu, Liyong Wang, Zhiwen Wang, Hongyu Zheng
Summary: This article introduces a method to improve the laser machining of sapphire by using a silicon (Si) plate as an absorptive material. The effects of processing parameters on the machining performance are investigated and the optimal parameters are obtained. This integrated Taguchi and GRA methodology offers an alternative for optimizing laser machining processes.
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
(2023)
Article
Engineering, Multidisciplinary
Hong Yang, Maozhu Wang, Guohui Li
Summary: This study proposes a multi-factor forecasting model for carbon emissions, considering the various influencing factors and randomness of carbon emissions. The model decomposes carbon emissions using GPPE-SSD and applies TDO-BiLSTM for forecasting. The results show that the proposed model outperforms other comparative models in terms of forecasting accuracy.
Article
Green & Sustainable Science & Technology
Xiaohui Gao
Summary: In this paper, a dual integrated hybrid model is presented for accurate prediction of the potential amount of wind power generation by incorporating random forest (RF) with extreme gradient boosting (XGB), empirical mode decomposition (EMD), and a fractional order accumulation seasonal grey model (FSGM).
Article
Computer Science, Artificial Intelligence
Yaoguo Dang, Yifan Zhang, Junjie Wang
Summary: To address the problem of the grey multivariate prediction model's inability to accurately simulate systems with periodic oscillations, a novel multivariate grey model named the GM(1,N|sin) power model is proposed. This model incorporates a power exponential term and dynamic sinusoidal function to represent the nonlinear relationship and periodic oscillations of the independent and dependent variables, respectively. Through case studies on electricity consumption and PM2.5 concentrations, the GM(1,N|sin) power model outperforms alternative models in accurately predicting time series with periodic oscillations.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
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)