New Applications of Big Data Technology: Integration of Data Mining and Artificial Intelligence

Topic Information

Dear Colleagues,

The landscape of data mining and machine learning is rapidly evolving, fuelled by advancements in algorithms, computational power, and the availability of vast datasets. This Topic will explore the latest trends and innovations shaping the future of these fields. Key areas of interest include, but are not limited to, deep learning architectures, reinforcement learning, unsupervised and semi-supervised learning techniques, federated learning, and the integration of machine learning with big data technologies. We invite contributions that address novel approaches and methodologies, including improvements in model interpretability, the development of more efficient algorithms, and the application of machine learning in diverse domains such as healthcare, finance, engineering, material science, and social networks. Special emphasis will be placed on emerging topics like generative AI, explainable AI (XAI), edge AI, and the ethical implications of AI deployment. In the realm of data mining, we are particularly interested in new techniques for anomaly detection, pattern recognition, and predictive analytics. Papers exploring the convergence of data mining with AI technologies, such as using deep learning for feature extraction or leveraging generative models for data augmentation, are highly encouraged. By bringing together cutting-edge research and practical applications, this Topic will provide a comprehensive overview of the current state and future directions of data mining and machine learning. We encourage submissions that offer theoretical insights, empirical studies, and case studies demonstrating the transformative impacts of these technologies. Join us in contributing to this exciting discourse and advancing our field through collaborative knowledge-sharing.

Prof. Dr. Xujuan Zhou
Prof. Dr. Yuefeng Li
Prof. Dr. Raj Gururajan
Prof. Dr. Ji Zhang
Prof. Dr. Revathi Venkataraman
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Data data	2.2	4.3	2016	26.8 Days	CHF 1600	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Information information	2.4	6.9	2010	16.4 Days	CHF 1600	Submit
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600	Submit

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Published Papers (4 papers)

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All Applied Sciences Data Electronics Information Mathematics

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22 pages, 4481 KiB  

Open AccessArticle

A Clustering Algorithm Based on Local Relative Density

by Yujuan Zou, Zhijian Wang, Xiangchen Wang and Taizhi Lv

Electronics 2025, 14(3), 481; https://doi.org/10.3390/electronics14030481 - 24 Jan 2025

Viewed by 485

Abstract

DBSCAN and DPC are typical density-based clustering algorithms. These two algorithms have their drawbacks, such as difficulty in clustering when there are significant differences in density between clusters. This study proposes a clustering algorithm, RDBSCAN, which is based on local relative density, drawing [...] Read more.

DBSCAN and DPC are typical density-based clustering algorithms. These two algorithms have their drawbacks, such as difficulty in clustering when there are significant differences in density between clusters. This study proposes a clustering algorithm, RDBSCAN, which is based on local relative density, drawing on the extension strategy of DBSCAN and the allocation mechanism of DPC. The algorithm first uses k-nearest neighbors to calculate the original local density, then sorts the points in descending order of this density. It then selects the point with the highest original local density from the unprocessed points as the local center of the next cluster. Based on this local center, RDBSCAN calculates the local relative density, determines the core objects, and performs cluster expansion. Drawing on the allocation mechanism of DPC, the algorithm performs a secondary allocation for points in clusters that are too small to complete the final clustering. Comparative experiments using RDBSCAN and eight other clustering algorithms were conducted, and the test results show that RDBSCAN ranks first in clustering performance metrics among all algorithms on synthetic datasets and second on real-world datasets. Full article

(This article belongs to the Topic New Applications of Big Data Technology: Integration of Data Mining and Artificial Intelligence)

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24 pages, 2674 KiB  

Open AccessArticle

Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards

by Eyad Btoush, Xujuan Zhou, Raj Gururajan, Ka Ching Chan and Omar Alsodi

Appl. Sci. 2025, 15(3), 1081; https://doi.org/10.3390/app15031081 - 22 Jan 2025

Viewed by 657

Abstract

The rapid advancement of technology has increased the complexity of cyber fraud, presenting a growing challenge for the banking sector to efficiently detect fraudulent credit card transactions. Conventional detection approaches face challenges in adapting to the continuously evolving tactics of fraudsters. This study [...] Read more.

The rapid advancement of technology has increased the complexity of cyber fraud, presenting a growing challenge for the banking sector to efficiently detect fraudulent credit card transactions. Conventional detection approaches face challenges in adapting to the continuously evolving tactics of fraudsters. This study addresses these limitations by proposing an innovative hybrid model that integrates Machine Learning (ML) and Deep Learning (DL) techniques through a stacking ensemble and resampling strategies. The hybrid model leverages ML techniques including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Logistic Regression (LR) alongside DL techniques such as Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM) with attention mechanisms. By utilising the stacking ensemble method, the model consolidates predictions from multiple base models, resulting in improved predictive accuracy compared to individual models. The methodology incorporates robust data pre-processing techniques. Experimental evaluations demonstrate the superior performance of the hybrid ML+DL model, particularly in handling class imbalances and achieving a high F1 score, achieving an F1 score of 94.63%. This result underscores the effectiveness of the proposed model in delivering reliable cyber fraud detection, highlighting its potential to enhance financial transaction security. Full article

(This article belongs to the Topic New Applications of Big Data Technology: Integration of Data Mining and Artificial Intelligence)

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21 pages, 10348 KiB  

Open AccessArticle

A Learning Resource Recommendation Method Based on Graph Contrastive Learning

by Jiu Yong, Jianguo Wei, Xiaomei Lei, Jianwu Dang, Wenhuan Lu and Meijuan Cheng

Electronics 2025, 14(1), 142; https://doi.org/10.3390/electronics14010142 - 1 Jan 2025

Viewed by 550

Abstract

The existing learning resource recommendation systems suffer from data sparsity and missing data labels, leading to the insufficient mining of the correlation between users and courses. To address these issues, we propose a learning resource recommendation method based on graph contrastive learning, which [...] Read more.

The existing learning resource recommendation systems suffer from data sparsity and missing data labels, leading to the insufficient mining of the correlation between users and courses. To address these issues, we propose a learning resource recommendation method based on graph contrastive learning, which uses graph contrastive learning to construct an auxiliary recommendation task combined with a main recommendation task, achieving the joint recommendation of learning resources. Firstly, the interaction bipartite graph between the user and the course is input into a lightweight graph convolutional network, and the embedded representation of each node in the graph is obtained after compilation. Then, for the input user–course interaction bipartite graph, noise vectors are randomly added to each node in the embedding space to perturb the embedding of graph encoder node, forming a perturbation embedding representation of the node to enhance the data. Subsequently, the graph contrastive learning method is used to construct auxiliary recommendation tasks. Finally, the main task of recommendation supervision and the constructed auxiliary task of graph contrastive learning are jointly learned to alleviate data sparsity. The experimental results show that the proposed method in this paper has improved the Recall@5 by 5.7% and 11.2% and the NDCG@5 by 0.1% and 6.4%, respectively, on the MOOCCube and Amazon-Book datasets compared with the node enhancement methods. Therefore, the proposed method can significantly improve the mining level of users and courses by using a graph comparison method in the auxiliary recommendation task and has better noise immunity and robustness. Full article

(This article belongs to the Topic New Applications of Big Data Technology: Integration of Data Mining and Artificial Intelligence)

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16 pages, 3708 KiB  

Open AccessArticle

Suppression of Strong Cultural Noise in Magnetotelluric Signals Using Particle Swarm Optimization-Optimized Variational Mode Decomposition

by Zhongda Shang, Xinjun Zhang, Shen Yan and Kaiwen Zhang

Appl. Sci. 2024, 14(24), 11719; https://doi.org/10.3390/app142411719 - 16 Dec 2024

Viewed by 570

Abstract

To effectively separate strong cultural noise in Magnetotelluric (MT) signals under strong interference conditions and restore the true forms of apparent resistivity and phase curves, this paper proposes an improved method for suppressing strong cultural noise based on Particle Swarm Optimization (PSO) and [...] Read more.

To effectively separate strong cultural noise in Magnetotelluric (MT) signals under strong interference conditions and restore the true forms of apparent resistivity and phase curves, this paper proposes an improved method for suppressing strong cultural noise based on Particle Swarm Optimization (PSO) and Variational Mode Decomposition (VMD). First, the effects of two initial parameters, the decomposition scale K and penalty factor α, on the performance of variational mode decomposition are studied. Subsequently, using the PSO algorithm, the optimal combination of influential parameters in the VMD is determined. This optimal parameter set is applied to decompose electromagnetic signals, and Intrinsic Mode Functions (IMFs) are selected for signal reconstruction based on correlation coefficients, resulting in denoised electromagnetic signals. The simulation results show that, compared to traditional algorithms such as Empirical Mode Decomposition (EMD), Intrinsic Time Decomposition (ITD), and VMD, the Normalized Cross-Correlation (NCC) and signal-to-noise ratio (SNR) of the PSO-optimized VMD method for suppressing strong cultural noise increased by 0.024, 0.035, 0.019, and 2.225, 2.446, 1.964, respectively. The processing of field data confirms that this method effectively suppresses strong cultural noise in strongly interfering environments, leading to significant improvements in the apparent resistivity and phase curve data, thereby enhancing the authenticity and reliability of underground electrical structure interpretations. Full article

(This article belongs to the Topic New Applications of Big Data Technology: Integration of Data Mining and Artificial Intelligence)

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