Article
Computer Science, Artificial Intelligence
Junqiang Liu, Zhousheng Ye, Xiangcai Yang, Xueling Wang, Linjie Shen, Xiaoning Jiang
Summary: The paper introduces a novel algorithm for mining frequent closed itemsets over data streams with high efficiency and scalability. By introducing an indexed prefix closed itemset tree and novel search strategies, the algorithm outperforms existing algorithms in terms of efficiency and performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Cheng-Wei Wu, JianTao Huang, Yun-Wei Lin, Chien-Yu Chuang, Yu-Chee Tseng
Summary: In this study, two efficient algorithms, DFI-List and DFI-Growth, were proposed for efficiently deriving FIs from FCIs. DFI-List utilizes a vertical index structure called Cid List, while DFI-Growth compresses the information into tree structures and applies a pattern-growth strategy for FI derivation.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Muhang Li, Meng Han, Zhiqiang Chen, Hongxin Wu, Xilong Zhang
Summary: The high-speed and continuous nature of data streams poses challenges in mining high utility itemsets in limited memory space. In order to overcome these challenges and provide users with concise and lossless results, a new closed high utility pattern mining algorithm over data stream is proposed, named FCHM-Stream.
KNOWLEDGE AND INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yaling Xun, Xiaohui Cui, Jifu Zhang, Qingxia Yin
Summary: The article introduces an incremental frequent itemsets mining algorithm based on multi-scale theory called FPMSIM, which constructs a pattern tree using the classic FP-Growth to improve mining efficiency and reduce I/O costs.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Huong Bui, Tu-Anh Nguyen-Hoang, Bay Vo, Ham Nguyen, Tuong Le
Summary: This study proposes an algorithm for mining FWPs over data streams by introducing a sliding window model and SWN-tree to maintain information. Empirical experiments indicate that the algorithm outperforms the state-of-the-art NFWI algorithm in batch mode with sliding window processing.
Article
Computer Science, Artificial Intelligence
Nader Aryabarzan, Behrouz Minaei-Bidgoli
Summary: The study introduces an algorithm named NECLAT-CLOSED for fast mining of frequent closed itemsets. Experimental results show that NECLATCLOSED outperforms leading algorithms in terms of runtime and memory usage, especially in runtime.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Bac Le, Tin Truong, Hai Duong, Philippe Fournier-Viger, Hamido Fujita
Summary: High average-utility itemset mining aims to identify sets of items with high average utility through analyzing a quantitative customer transactional database. To address the issue of sensitive information exposure, this study investigates the problem of hiding frequent high average-utility itemsets (FHAUIs) and proposes an algorithm named H-FHAUI. Experimental results demonstrate that H-FHAUI outperforms the baseline approach in terms of performance.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Jongseong Kim, Unil Yun, Hyunsoo Kim, Taewoong Ryu, Jerry Chun-Wei Lin, Philippe Fournier-Vier, Witold Pedrycz
Summary: This paper proposes a damped window based average utility driven data analytics method, which improves mining efficiency by modifying the importance of items and without generating candidate patterns. Experimental results show that the proposed method outperforms other techniques in terms of runtime and memory usage, and maintains stable performance under various environmental changes.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Information Systems
Hong N. Dao, Penugonda Ravikumar, Palla Likhitha, Rage Uday Kiran, Yutaka Watanobe, Incheon Paik
Summary: Stable periodic-frequent itemset mining is important in big data analytics, and this paper proposes a framework to discover such itemsets in columnar databases. A novel depth-first search algorithm is employed to compress the columnar database into a unified dictionary and recursively mine it to find all stable periodic-frequent itemsets. Experimental results show that the proposed algorithm is computationally efficient and scalable.
Review
Computer Science, Theory & Methods
Lazaro Bustio-Martinez, Rene Cumplido, Martin Letras, Raudel Hernandez-Leon, Claudia Feregrino-Uribe, Jose Hernandez-Palancar
Summary: Frequent Itemsets Mining is a data mining technique that has achieved notable results in various domains. However, the large volume of data in modern datasets has increased the processing time, leading to the need for new methods to accelerate the mining process. Hardware acceleration using GPUs and FPGAs is a successful alternative that can improve performance.
ACM COMPUTING SURVEYS
(2022)
Article
Computer Science, Artificial Intelligence
Majid Seyfi, Richi Nayak, Yue Xu, Shlomo Geva
Summary: The importance of mining discriminative itemsets in data streams is discussed, along with a proposed method using a tilted-time window model. The efficient and high accuracy H-DISSparse algorithm is designed to address the challenges in discriminative itemset mining process, with dynamically adjusted data structures to improve performance.
KNOWLEDGE AND INFORMATION SYSTEMS
(2021)
Article
Computer Science, Information Systems
Ali Abbas Zoraghchian, Mohammad Karim Sohrabi, Farzin Yaghmaee
Summary: Data mining is an essential technique for pattern extraction and information classification. Association rule mining is an important data mining technique that extracts useful rules and knowledge by considering the relationships and association of the data. Parallelizing the mining process using multiple GPUs can significantly reduce execution time and improve efficiency.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Seyed Mohsen Fatemi, Seyed Mohsen Hosseini, Ali Kamandi, Mahmood Shabankhah
Summary: The paper introduces an approximation algorithm for frequent itemset mining by converting the problem into a clustering problem, which significantly improves the algorithm's efficiency. Experimental results show that the proposed algorithm is almost always faster than existing deterministic algorithms while retaining up to 95% accuracy.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Meng Han, Haodong Cheng, Ni Zhang, Xiaojuan Li, Le Wang
Summary: This paper proposes a new algorithm, CHUIDS_OSc, for mining closed high utility itemsets over data streams, which achieves mining with only one scan of the original dataset. It introduces a new utility-list structure for efficient construction and update of batch information, and applies effective pruning strategies to improve the efficiency of the closed itemsets mining process.
KNOWLEDGE AND INFORMATION SYSTEMS
(2023)
Article
Mathematics
Jiang Liu, Jing Li, Feng Ni, Xiang Xia, Shunlong Li, Wenhui Dong
Summary: This article presents a method for discovering maximal frequent itemsets with lower complexity and better performance compared to other algorithms. Additionally, the article explores algebraic properties, provides a recurrence formula, and analyzes the properties of maximal frequent itemsets.
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)