Article
Computer Science, Artificial Intelligence
Wenqi Cao, Cong Zhang
Summary: The paper introduces an effective Parallel Integrated Neural Network System (PINN) which combines GRNN and ADGWO for high accuracy prediction of complex problems. PINN demonstrates higher prediction accuracy in soil heavy metal datasets compared to several comparative models, particularly with an increase of 8.05% over Wavelet Neural Network.
APPLIED SOFT COMPUTING
(2021)
Article
Environmental Sciences
Weige Nie, Ou Ao, Huiming Duan
Summary: This paper studies the evolution of carbon emissions, establishes a grey model and expands its modeling structure. By combining the classical feedforward neural network model with the external influencing factors of carbon emissions, a grey model is established and the properties and parameters of the model are studied and optimized. Finally, the validity and feasibility of the model are analyzed using carbon emissions data from Beijing from 2009 to 2018.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Yi-Chung Hu, Wen-Bao Wang
Summary: This study establishes interval grey prediction models for energy demand forecasting without statistical assumptions. The proposed models use neural networks for nonlinear regression analysis and grey prediction to derive the tendencies of energy demand's upper and lower limits. The best non-fuzzy performance value for each time point is obtained using the estimated upper and lower limits. The proposed models perform well in forecasting accuracy compared to other interval grey prediction models.
Article
Computer Science, Artificial Intelligence
Yafang Liu, Chaozhong Wu, Jianghui Wen, Xinping Xiao, Zhijun Chen
Summary: This paper proposes a grey convolutional neural network called G-CNN for traffic flow prediction by analyzing the influence of traffic accident information on traffic flow. A grey fixed-weight clustering method is developed to extract the characteristics of traffic accident grey information. The spatiotemporal characteristics of traffic flow are fused with accident characteristics and served as input to the G-CNN model, which demonstrates better accuracy and stability in predicting future traffic flow.
Article
Environmental Sciences
Langfu Cui, Qingzhen Zhang, Liman Yang, Chenggang Bai
Summary: A prediction model for an inertial platform, called SGMNN, has been proposed in this paper, which combines a sliding window, grey theory, and neural network. The experiment results show that the SGMNN model performs the best in predicting the inertial platform drift rate compared with other prediction models.
Article
Engineering, Multidisciplinary
Wanli Xie, Wen-Ze Wu, Zhenguo Xu, Caixia Liu, Keyun Zhao
Summary: This study introduces a novel fractional order neural grey system model that utilizes QR decomposition to reduce the number of conditions and improve the stability of parameter estimation. The model's validity is validated through real-world examples, and experimental results indicate its higher accuracy.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Operations Research & Management Science
Yi-Chung Hu
Summary: Tourism demand forecasting is crucial in supporting governments to formulate development policies for the travel industry. This study proposed a fractional grey prediction model with Fourier series that assigns appropriate weights to samples for higher prediction accuracy. Experimental results showed the proposed model performs well compared to other prediction models.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Umar Farooq, Muhammad Wasif Shabir, Muhammad Awais Javed, Muhammad Imran
Summary: This paper presents two energy prediction techniques for fog nodes, based on Recursive Least Square and Artificial Neural Network, to enable intelligent energy-aware task offloading. Simulation results show that the ANN-based technique has up to 20% less root mean square error compared to the RLS-based technique.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Aamer Abbas Shah, Khubab Ahmed, Xueshan Han, Adil Saleem
Summary: This paper presents a prediction error-based power forecasting method for a Photovoltaic system using a grey box neural network based on the PVUSA model. Through a real case study, it is demonstrated that the proposed scheme efficiently predicts PV power with better accuracy compared to conventional black box neural network models.
Article
Infectious Diseases
Xinxing Li, Ziyi Zhang, Ding Xu, Congming Wu, Jianping Li, Yongjun Zheng
Summary: The drug resistance of animal-derived pathogens is on the rise, presenting a significant threat to animal and human health. By analyzing the drug resistance data of Escherichia coli, a new approach for predicting drug resistance trends has been developed.
Article
Thermodynamics
Huiying Zhang, Suying Yan, Hong Gao, Xue Yuan, Tingzhen Ming, Mohammad Hossein Ahmadi, Xiaoyan Zhao
Summary: This study analyzed the effects of the dispersant concentration, mass fraction, temperature, and standing time on the thermal conductivity of CNT-nanofluid experimentally, and established a BP neural network prediction model. The model was optimized using genetic algorithms, and the experimental range was expanded. Results showed that temperature had a significant impact on thermal conductivity, and the optimal ratio of CNT particles to dispersant concentration was found to be 1:2.
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER
(2021)
Article
Energy & Fuels
J. Femila Roseline, D. Dhanya, Saravana Selvan, M. Yuvaraj, P. Duraipandy, S. Sandeep Kumar, A. Rajendra Prasad, Ravishankar Sathyamurthy, V. Mohanavel
Summary: This article presents a study on an artificial neural network model for predicting the energy generation of photovoltaic and hybrid photovoltaic-wind energy systems based on weather factors. The results show that the proposed classifier is efficient in terms of reduced mean squared error with increased accuracy, and it holds valuable implications for the development of future smart grids.
Article
Construction & Building Technology
Yifan Zhao, Wei Li, Jili Zhang, Changwei Jiang, Siyu Chen
Summary: With increasing energy consumption, achieving energy-saving operation of air-conditioning systems is crucial for improving building energy efficiency. This paper proposes a real-time energy consumption prediction model for air-conditioning systems based on a long short-term memory neural network, which can select the optimal operating strategy by predicting energy consumption and achieve greater energy conservation.
ENERGY AND BUILDINGS
(2023)
Article
Multidisciplinary Sciences
Hima Elsa Shaji, Arun K. Tangirala, Lelitha Vanajakshi
Summary: This study proposes an iterative joint clustering and prediction approach to improve the accuracy of travel time predictions. By creating data clusters that are sensitive to the quality of predictions, this method has been validated in real-world traffic scenarios.
Article
Computer Science, Artificial Intelligence
T. Geetha, A. J. Deepa
Summary: Due to the expansion of Internet traffic and threats in the cloud environment, intrusion detection is becoming more challenging. This paper proposes a Fisher kernel based PCA dimensionality reduction algorithm and grey wolf optimizer based weight dropped BiLSTM classifier (FKPCA-GWO WDBiLSTM) to overcome the limitations of conventional machine learning methods and achieve high accuracy and performance in intrusion detection.
KNOWLEDGE-BASED SYSTEMS
(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)