Article
Computer Science, Hardware & Architecture
Liwei Tian, Li Feng, Lei Yang, Yuankai Guo
Summary: The paper introduces a hybrid model named LSTM-BO-LightGBM based on LSTM and LightGBM for stock price fluctuation prediction, which demonstrates better prediction accuracy and generalization ability compared to other models.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Anika Kanwal, Man Fai Lau, Sebastian P. H. Ng, Kwan Yong Sim, Siva Chandrasekaran
Summary: This research proposes a hybrid deep learning-based predictive model for timely and efficient prediction of stock prices. The model combines a Bidirectional Cuda Deep Neural Network Long Short-Term Memory and a one-dimensional Convolutional Neural Network, and has shown accurate prediction results for supporting informed investment decisions.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Yang Yujun, Yang Yimei, Zhou Wang
Summary: The study proposes a hybrid prediction model based on LSTM and VMD for forecasting stock prices. By decomposing the stock price time series into stable subsequences, training and predicting, the model demonstrates high prediction accuracy. The experimental results show an accuracy of over 0.991 on each dataset, making it an effective tool for stock market prediction.
Article
Computer Science, Artificial Intelligence
Hadi Rezaei, Hamidreza Faaljou, Gholamreza Mansourfar
Summary: The combination of new deep learning and decomposition algorithms has improved the accuracy and performance of financial time series analysis. Combining CEEMD with CNN and LSTM can produce better prediction results.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Shengting Wu, Yuling Liu, Ziran Zou, Tien-Hsiung Weng
Summary: This paper proposes a stock price prediction method S_I_LSTM that combines multiple data sources and investor sentiment using sentiment analysis and convolutional neural network, as well as long short-term memory network for predicting the China Shanghai A-share market. Experimental results show that the method outperforms traditional methods on real data sets of five listed companies.
CONNECTION SCIENCE
(2022)
Article
Computer Science, Information Systems
Gourav Bathla, Rinkle Rani, Himanshu Aggarwal
Summary: This study investigates the possibility of using deep learning to predict high variations in stock prices in 2020 and proposes a corresponding neural network model. The experimental analysis demonstrates that the LSTM model, with the set up used in this study, is able to predict stock prices with sufficient accuracy, with MAPE values better than traditional data analytics techniques.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Burak Gulmez
Summary: The stock market is a financial market where shares of publicly listed corporations are bought and sold, and it reflects a country's economic health. Investing in stocks carries risks, but it has the potential for significant long-term returns. Artificial intelligence, including the stock market, is increasingly prevalent in the financial sector.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Environmental Sciences
Noratiqah Mohd Ariff, Mohd Aftar Abu Bakar, Han Ying Lim
Summary: This study aims to predict the daily average concentration of PM10 in Malaysia using a hybrid model that combines the k-means clustering technique and the long short-term memory (LSTM) model. The hybrid model showed comparable prediction performance to the univariate LSTM model with a relative percentage difference (RPD) less than or equal to 50% for 43 stations. It also fit the actual data trend well and had a shorter training time, making it more competitive and suitable for real applications.
Article
Physics, Multidisciplinary
Pin Lv, Qinjuan Wu, Jia Xu, Yating Shu
Summary: The study proposes a new stock index forecasting model that accurately predicts stock market fluctuations by decomposing time series and using a hybrid model. The results indicate that the model outperforms seven reference models and provides valuable quantitative investment references.
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
Computer Science, Artificial Intelligence
Shilpa Gite, Hrituja Khatavkar, Ketan Kotecha, Shilpi Srivastava, Priyam Maheshwari, Neerav Pandey
Summary: The stock market is influenced by complex sentiments and media releases, making price predictions challenging. This paper proposes using machine learning and LSTM to improve accuracy by incorporating sentiment analysis. LSTM has proven effective in learning long-term dependencies, and when combined with historical stock data and news sentiment, it can enhance predictive models.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Jose Mejia, Liliana Avelar-Sosa, Boris Mederos, Everardo Santiago Ramirez, Jose David Diaz Roman
Summary: The study presents an architecture for predicting time series with nonlinear dependencies by combining convolutional and LSTM layers, showing better results compared to other methods in various error measures.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Marine
Jinya Xu, Jiaye Gong, Luyao Wang, Yunbo Li
Summary: This study applies the LSTM neural network technique to predict the course changing of a ship in different wave conditions and improves the quality of the navigation database through K-means clustering analysis. The study finds that the initial database obtained by the K-means clustering analysis affects prediction accuracy, and different input features also affect the accuracy of navigation prediction. Finally, multi-task learning is used to improve the neural network and successfully predict the course of an autopilot ship in waves.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Guangyu Mu, Nan Gao, Yuhan Wang, Li Dai
Summary: This paper combines multi-source data, sentiment analysis, swarm intelligence algorithm, and deep learning to build the MS-SSA-LSTM model for predicting stock prices. The experiments demonstrate that the model outperforms others and has high universal applicability.
Article
Computer Science, Artificial Intelligence
Konark Yadav, Milind Yadav, Sandeep Saini
Summary: Researchers developed a deep learning model for live stock price predictions, utilizing different deep learning techniques and experimenting on stock data of four companies, the results show that the new model outperforms other models in terms of RMSE and computation time.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(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)