Article
Computer Science, Artificial Intelligence
Trinh D. D. Nguyen, Loan T. T. Nguyen, Lung Vu, Bay Vo, Witold Pedrycz
Summary: This research addresses the problem of mining high-utility itemsets in dynamic unit profit databases, introducing a novel algorithm iEFIM-Closed that outperforms state-of-the-art algorithms in sparse databases.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jun-Feng Qu, Philippe Fournier-Viger, Mengchi Liu, Bo Hang, Chunyang Hu
Summary: This paper proposes a new algorithm called Hamm for mining high utility itemsets. Hamm utilizes a TV (prefix Tree and utility Vector) structure to mine high utility itemsets in a one-phase manner without candidate generation. Experimental results show that Hamm outperforms other algorithms in terms of performance.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Hai Duong, Tien Hoang, Thong Tran, Tin Truong, Bac Le, Philippe Fournier-Viger
Summary: Closed high utility itemsets (CHUIs) and maximal high utility itemsets (MaxHUIs) are important concise representations of HUIs. Mining these representations is crucial for generating meaningful high utility association rules. However, existing algorithms suffer from long runtimes, high memory usage, and scalability issues. To address this, this paper proposes two efficient algorithms that can mine these representations faster.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
N. T. Tung, Trinh D. D. Nguyen, Loan T. T. Nguyen, Bay Vo
Summary: The study of High-Utility Itemset Mining (HUIM) and Frequent Itemset Mining (FIM) is crucial as it explains consumer behavior and provides actionable advice for improving business results. This paper presents strategies for making database scanning more efficient and reducing the number of candidates using strict upper-bound approaches. It also introduces a novel algorithm to efficiently solve the problem.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
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
Rage Uday Kiran, Pamalla Veena, Penugonda Ravikumar, Bathala Venus Vikranth Raj, Minh-Son Dao, Koji Zettsu, Sai Chithra Bommisetti
Summary: Spatial high-utility itemset (SHUI) mining is an important data analysis technique that aims to locate geographically interesting itemsets with high utility in a spatiotemporal database. However, the existing SHUI-Miner algorithm has performance issues when dealing with high-dimensional spatiotemporal databases. This paper proposes a novel algorithm called high-dimensional SHUI-miner (HDSHUI-Miner) that outperforms SHUI-Miner in terms of memory consumption, runtime, and scalability, as demonstrated by experimental results on seven real-world databases. Two real-world case studies are also presented to illustrate the usefulness of the proposed algorithm.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Kuldeep Singh, Rajiv Kumar, Bhaskar Biswas
Summary: HUIM and HAUIM are subdivisions of data mining that focus on obtaining promising patterns in quantitative datasets, with applications in market analysis, bioinformatics, text mining, network analysis, product recommendation, and e-learning.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Wei Fang, Qiang Zhang, Hengyang Lu, Jerry Chun-Wei Lin
Summary: This study proposes an improved binary particle swarm optimization (HUIM-IBPSO) for high-utility itemset mining (HUIM), addressing the issues of exponential growth search space and time-consuming process in traditional exact algorithms. The proposed approach incorporates multiple adjustment strategies to keep the same HUIs, enhance search ability, avoid premature convergence, and improve efficiency in mining HUIs.
APPLIED SOFT COMPUTING
(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
Manijeh Hajihoseini, Mohammad Karim Sohrabi
Summary: The FHAUI mining problem considers the effect of itemset length on the calculated utility, avoiding large sets containing low utility items. The HiFAM method is proposed to efficiently explore FHAUIs by extending the MHAI method and introducing the FAUL structure. Through a depth-first exploration process, the complete set of FHAUIs can be extracted. Pruning techniques are also used to reduce memory consumption and time complexity.
Article
Computer Science, Software Engineering
Anup Brahmavar, Harish Venkatarama, Geetha Maiya
Summary: Market Basket Analysis, considering purchase quantity and unit profit, has been boosted by the increase in revenue information. However, existing algorithms' performance degrades as databases grow, and distributed computing solutions like Apache Hadoop and Apache Spark have proven effective in solving this problem. This study develops a parallel workflow on a Spark cluster to improve algorithm efficiency, and experimental evaluation demonstrates its superiority.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Wei Song, Lu Liu, Chaomin Huang
Summary: The paper introduces an efficient HAIU mining algorithm, HAUIM-GMU, based on generalized maximal utility for mining high average-utility itemsets. The algorithm proposes a new pruning strategy utilizing the concept of support to filter out unpromising itemsets effectively, outperforming existing state-of-the-art algorithms according to extensive experimental results.
KNOWLEDGE AND INFORMATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
N. T. Tung, Loan T. T. Nguyen, Trinh D. D. Nguyen, Bay Vo
Summary: High-utility itemset mining is an effective tool for analyzing customer behavior by identifying the most beneficial itemsets in transaction databases. Traditional algorithms often overlook categorization of items, leading to a limitation in discovering important patterns at higher levels.
APPLIED INTELLIGENCE
(2022)
Article
Multidisciplinary Sciences
Damla Oguz
Summary: High-utility itemset mining aims to discover sets of items that are sold together with utility values that exceed a minimum threshold. The method considers internal and external utility values of the itemsets, and their symmetric effects in determining high-utility itemsets. A proposed asymmetric approach focuses on high external utility values while ignoring internal utility values, leading to more efficient discovery and highlighting the fundamental impact of external utility values.
Article
Computer Science, Artificial Intelligence
Trinh D. D. Nguyen, N. T. Tung, Thiet Pham, Loan T. T. Nguyen
Summary: In the field of data mining, high utility itemset mining (HUIM) is a relevant task for analyzing customer transaction databases. Exploiting frequently purchased items that yield high profit value, HUIM provides useful insights into customer behaviors. This work introduces three new efficient strategies and proposes two new algorithms, MCML+ and MCML++, to significantly improve the performance of multi-level high utility itemset mining using multicore processing. Extensive experiments show that the proposed algorithms outperform previous approaches in terms of running time and scalability.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Chongsheng Zhang, Changchang Liu, Xiangliang Zhang, George Almpanidis
EXPERT SYSTEMS WITH APPLICATIONS
(2017)
Article
Acoustics
George Almpanidis, Margarita Kotti, Constantine Kotropoulos
IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
(2009)
Article
Acoustics
George Almpanidis, Constantine Kotropoulos
SPEECH COMMUNICATION
(2008)
Article
Computer Science, Artificial Intelligence
Chongsheng Zhang, Yuan Zhang, Xianjin Shi, George Almpanidis, Gaojuan Fan, Xiajiong Shen
NEURAL PROCESSING LETTERS
(2019)
Article
Computer Science, Artificial Intelligence
Chongsheng Zhang, Paolo Soda, Jingjun Bi, Gaojuan Fan, George Almpanidis, Salvador Garcia, Weiping Ding
Summary: This study investigates the performance of feature selection and data resampling in two opposite imbalanced classification frameworks, and suggests that both frameworks should be considered for finding the best performing imbalanced classification model. The study also examines the impact of classifiers, IR, and SFR on the performance of imbalance classification.
APPLIED INTELLIGENCE
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Gaojuan Fan, Huaiyuan Xiao, Chongsheng Zhang, George Almpanidis, Philippe Fournier-Viger, Hamido Fujita
Summary: Association rule mining is a popular task in data mining for discovering relationships between co-occurring itemsets in a transactional database. The current algorithms for association rule mining are inefficient when dealing with large volumes of data, and high utility itemset mining (HUIM) has emerged as a popular solution. This paper investigates parallel HUIM algorithms and adapts them for parallel processing using the Apache Spark platform, resulting in improved efficiency.
ADVANCES AND TRENDS IN ARTIFICIAL INTELLIGENCE: THEORY AND PRACTICES IN ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Chongsheng Zhang, George Almpanidis, Faegheh Hasibi, Gaojuan Fan
Article
Computer Science, Information Systems
G. Almpanidis, C. Kotropoulos, I. Pitas
INFORMATION SYSTEMS
(2007)
Article
Computer Science, Artificial Intelligence
A Xafopoulos, C Kotropoulos, G Almpanidis, I Pitas
PATTERN RECOGNITION
(2004)
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)