Article
Economics
Andres Garcia-Medina, Ester Aguayo-Moreno
Summary: This study predicts the volatility of leading cryptocurrencies using GARCH models, MLP, LSTM, and hybrid models combining LSTM and GARCH. Deep neural network models outperform GARCH models in terms of heteroscedastic error, absolute error, and squared error. Uniform portfolios consistently outperform the stablecoin Tether in terms of volatility forecasting at long horizons. Including transaction volume helps reduce the value at risk or loss probability for uniform portfolios. MLP models provide the best predictive results and are suggested for highly non-linear cryptocurrency market volatility forecasts.
COMPUTATIONAL ECONOMICS
(2023)
Article
Chemistry, Multidisciplinary
Mona AL-Ghamdi, Abdullah AL-Malaise AL-Ghamdi, Mahmoud Ragab
Summary: The ability to accurately predict energy consumption is vital for future growth and development. This study utilized a hybrid DNN and LSTM model to predict household energy consumption, resulting in highly accurate predictions.
APPLIED SCIENCES-BASEL
(2023)
Article
Business, Finance
Qi Shu, Heng Xiong, Wenjun Jiang, Rogemar Mamon
Summary: This study proposes a new perspective on predicting the volatility of non-ferrous metals in the futures market. Two hybrid deep learning architectures are constructed by combining convolutional neural networks (CNN) and long short-term memory (LSTM) models, as well as LSTM networks and various generalized autoregressive conditional heteroscedasticity (GARCH) models. The findings show that the GARCH-LSTM model outperforms other alternatives in predicting commodity volatility. This study marks a significant advancement in enhancing the prediction performance of commodity volatility using integrated deep learning models.
FINANCE RESEARCH LETTERS
(2023)
Article
Computer Science, Information Systems
B. Sunitha Devi, N. Sandhya, K. Shahu Chatrapati
Summary: Navigating the complexities of contemporary agriculture involves overcoming various obstacles such as dietary changes, food safety concerns, and health issues related to soil inconsistencies, climate fluctuations, and diverse agricultural practices. This paper proposes a novel Deep Learning approach that effectively captures and integrates spatial and temporal features to forecast crop yields with minimal error rates, outperforming prevailing machine learning methodologies.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Green & Sustainable Science & Technology
Hongbin Dai, Guangqiu Huang, Huibin Zeng, Fangyu Zhou
Summary: Air pollution has severe impacts on global public health and economic development, prompting the need for accurate prediction of atmospheric pollutant concentrations. The XGBoost-GARCH-MLP hybrid model shows good performance in predicting PM2.5 concentration and volatility, providing valuable insights for environmental policy decision-makers.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Business, Finance
Yuan Yao, Yang Zhao, Yan Li
Summary: Investment expectations have an impact on stock price volatility, and accurately capturing these expectations can help alleviate the problem. This study examines the rational expectations properties of existing volatility models and explores a volatility model based on adaptive expectations. By constructing ADGARCH and LSTM-ADGARCH models under the assumption of adaptive expectations, the study finds that the volatility model based on adaptive expectations has greater explanatory power than one based on rational expectations.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2022)
Article
Energy & Fuels
Huibin Zeng, Bilin Shao, Genqing Bian, Hongbin Dai, Fangyu Zhou
Summary: This study proposes a natural gas load volatility prediction model by combining GARCH family models, XGBoost algorithm, and LSTM network. The model demonstrates good performance and accuracy in predicting the volatility of natural gas load, with an average reduction of 45.404% in the evaluation index of mean squared error.
ENERGY SCIENCE & ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Nan Jing, Zhao Wu, Hefei Wang
Summary: This paper proposes a hybrid model combining deep learning with sentiment analysis for stock price prediction. Real-life experiments conducted validate the model's superior performance in classifying investor sentiments and predicting stock prices.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Construction & Building Technology
Irene Karijadi, Shuo-Yan Chou
Summary: This study proposes a hybrid method based on CEEMDAN, combining RF and LSTM for predicting building energy consumption, with experimental results showing better performance compared to benchmark methods.
ENERGY AND BUILDINGS
(2022)
Article
Computer Science, Artificial Intelligence
Serkan Aras
Summary: Research indicates that using higher model orders improves the accuracy of volatility forecasts for hybrid GARCH models, while stacking ensemble with LASSO produces superior forecasts compared to other hybrid models.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Biology
Zhifang Liao, Peng Lan, Xiaoping Fan, Benjamin Kelly, Aidan Innes, Zhining Liao
Summary: A COVID-19 prediction model based on time-dependent SIRVD using deep learning technology and mathematical model of infectious diseases is proposed in this paper, with experimental results showing a 50% improvement in single-day predictions.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Construction & Building Technology
Xianlei Fu, Maozhi Wu, Robert Lee Kong Tiong, Limao Zhang
Summary: This paper investigates a hybrid deep learning approach, combining graph convolutional network (GCN) and long short-term memory (LSTM) networks, for accurate prediction of geological conditions ahead of tunnel boring machines (TBM). The results from the case study demonstrate that the proposed approach provides estimation with high accuracy, outperforming state-of-the-art methods.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Computer Science, Artificial Intelligence
K. Venkatachalam, Pavel Trojovsky, Dragan Pamucar, Nebojsa Bacanin, Vladimir Simic
Summary: Weather forecasting plays a crucial role in various aspects of modern society, and this study proposes a deep learning model called LSTM and T-LSTM for accurate weather prediction. Evaluation metrics demonstrate the effectiveness and reliability of the T-LSTM method.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Xianlei Fu, Maozhi Wu, Sasthikapreeya Ponnarasu, Limao Zhang
Summary: This study proposes a hybrid deep learning approach, named GCN-LSTM, for accurately predicting the dynamic attitude and position of the tunnel boring machine (TBM). By utilizing key operational parameters and historical values, the model is trained to predict the vertical and horizontal deviations at the articulation and tail of TBM. Shapley Additive exPlanations (SHAP) analysis is performed to improve interpretability and identify key factors. The proposed approach outperforms state-of-the-art methods in terms of accuracy and is suitable for reliable TBM position estimation.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Yongzhi Liu, Wenting Zhang, Ying Yan, Zhixuan Li, Yulin Xia, Shuhong Song
Summary: This study used a LSTM neural network to build a ponding prediction model, with LSTM (msle) identified as the best model for accurately predicting the depth of ponding in the next 1 hour. LSTM (mae) showed better prediction performance when the ponding depth exceeded 30 mm.
APPLIED SCIENCES-BASEL
(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)