Search Results (86)

Cloud computing environments generate high-dimensional, large-scale, and highly dynamic network traffic, making intrusion diagnosis challenging due to evolving attack patterns, severe traffic imbalance, and limited availability of labeled data. To address these challenges, this study presents an unsupervised, cloud-centric intrusion diagnosis framework that integrates autoencoder-based representation learning with density-based attack categorization. A dual-stage autoencoder is trained exclusively on benign traffic to learn compact latent representations and to identify anomalous flows using reconstruction-error analysis, enabling effective anomaly detection without prior attack labels. The detected anomalies are subsequently grouped using density-based learning to uncover latent attack structures and support fine-grained multiclass intrusion diagnosis under varying attack densities. Experiments conducted on the large-scale CSE-CIC-IDS2018 dataset demonstrate that the proposed framework achieves an anomaly detection accuracy of 99.46%, with high recall and low false-negative rates in the optimal latent-space configuration. The density-based classification stage achieves an overall multiclass attack classification accuracy of 98.79%, effectively handling both majority and minority attack categories. Clustering quality evaluation reports a Silhouette Score of 0.9857 and a Davies–Bouldin Index of 0.0091, indicating strong cluster compactness and separability. Comparative analysis against representative supervised and unsupervised baselines confirms the framework’s scalability and robustness under highly imbalanced cloud traffic, highlighting its suitability for future Internet cloud security ecosystems. Full article

Trajectory clustering is of great significance for identifying behavioral patterns and vessel types of non-cooperative ships. However, existing trajectory clustering methods suffer from limitations in extracting cross-spatiotemporal scale features and modeling the coupling relationship between positional and motion features, which restricts clustering performance. To address this, this study proposes a deep ship trajectory clustering method based on feature embedding representation learning (ERL-DTC). The method designs a Temporal Attention-based Multi-scale feature Aggregation Network (TA-MAN) to achieve dynamic fusion of trajectory features from micro to macro scales. A Dual-feature Self-attention Fusion Encoder (DualSFE) is employed to decouple and jointly represent the spatiotemporal position and motion features of trajectories. A two-stage optimization strategy of “pre-training and joint training” is adopted, combining contrastive loss and clustering loss to jointly constrain the embedding representation learning, ensuring it preserves trajectory similarity relationships while being adapted to the clustering task. Experiments on a public vessel trajectory dataset show that for a four-class task (K = 4), ERL-DTC improves ACC by approximately 14.1% compared to the current best deep clustering method, with NMI and ARI increasing by about 28.9% and 30.2%, respectively. It achieves the highest Silhouette Coefficient (SC) and the lowest Davies-Bouldin Index (DBI), indicating a tighter and more clearly separated cluster structure. Furthermore, its inference efficiency is improved by two orders of magnitude compared to traditional point-matching-based methods, without significantly increasing runtime due to model complexity. Ablation studies and parameter sensitivity analysis further validate the necessity of each module design and the rationality of hyperparameter settings. This research provides an efficient and robust solution for feature learning and clustering of vessel trajectories across spatiotemporal scales. Full article

Complex odor emissions from wastewater treatment plants consist of multiple volatile compounds that exhibit heterogeneous temporal dynamics and low linear correlations, making accurate prediction and interpretation difficult when analyzed on a single-compound basis. This study investigates whether clustering can serve not only as an exploratory tool but as an essential preprocessing step to enhance machine-learning performance in multi-odor prediction systems. A total of 22 designated odorants were continuously monitored, and their pairwise dependencies were evaluated using Pearson correlation and mutual information. Data-driven clustering was performed through K-means, hierarchical linkage, and principal-component–based latent grouping, and the resulting structures were quantitatively compared with functional-group-based chemical classifications using the consistency ratio and Jaccard similarity index. Cluster validity was further examined using the Silhouette Coefficient, Davies–Bouldin Index, and Calinski–Harabasz Index. The predictive contribution of clustering was verified by training XGBoost regression models on both raw and cluster-structured datasets. The clustered dataset yielded higher predictive accuracy, with increased R² and reduced MAE and RMSE across most odorants. SHAP analysis further confirmed that clustering improved model interpretability by stabilizing feature contributions and reducing noise-driven importance shifts. The findings demonstrate that clustering is not a supplementary diagnostic tool, but a prerequisite for building reliable, high-performance machine-learning models in complex odor systems. This integrative framework offers a methodological foundation for multi-odor forecasting, source tracking, and next-generation odor management platforms. Full article

Customer segmentation is essential in financial services for designing targeted interventions, managing dormant portfolios, and supporting marketing re-engagement strategies. Traditional approaches such as Recency–Frequency–Monetary (RFM) analysis offer interpretability but often lack the flexibility needed to capture heterogeneous behavioral patterns. This study presents an automated segmentation framework that integrates machine learning-based clustering with RFM-based interpretability benchmarks. KMeans and Hierarchical clustering are evaluated across multiple values of k using internal validity metrics (Silhouette Coefficient, Davies–Bouldin Index) and interpretability alignment measures (Adjusted Rand Index, Normalized Mutual Information, Homogeneity, Completeness, and V-Measure). The Hungarian algorithm is used to align machine-learned clusters with RFM segments for comparability. The framework reveals behavioral subgroups not captured by RFM alone, demonstrating that machine learning can expose hidden heterogeneity within dormant customer populations. While outcome-based financial validation is not yet feasible due to the cold-start nature of the deployment environment, the study provides a reproducible, scalable pipeline for segmentation that balances analytical rigor with business interpretability. The findings highlight how data-driven clustering can refine traditional segmentation logic, supporting more nuanced portfolio monitoring and re-engagement strategies in financial services. Full article

Smart farming relies heavily on IoT automation and data-driven decision making, but this growing connectivity also increases exposure to cyberattacks. Flow-based unsupervised intrusion detection is a privacy-preserving alternative to signature and payload inspection, yet it still faces three challenges: loss of subtle anomaly cues during Autoencoder (AE) compression, instability of fixed reconstruction-error thresholds, and performance degradation of clustering in noisy high-dimensional spaces. To address these issues, we propose a fog-aware two-stage hierarchical AE with latent-space gating, followed by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for attack categorization. A shallow AE compresses the input into a compact 21-dimensional latent space, reducing computational demand for fog-node deployment. A deep AE then computes reconstruction-error scores to isolate malicious behavior while denoising latent features. Only high-error latent vectors are forwarded to DBSCAN, which improves cluster separability, reduces noise sensitivity, and avoids predefined cluster counts or labels. The framework is evaluated on two benchmark datasets. On CIC IoT-DIAD 2024, it achieves 98.99% accuracy, 0.9897 F1-score, 0.895 Adjusted Rand Index (ARI), and 0.019 Davies–Bouldin Index (DBI). To examine generalizability beyond smart farming traffic, we also evaluate the framework on the CSE-CIC-IDS2018 benchmark, where it achieves 99.33% accuracy, 0.9928 F1-score, 0.9013 ARI, and 0.0174 DBI. These results confirm that the proposed model can reliably detect and categorize major cyberattack families across distinct IoT threat landscapes while remaining compatible with resource-constrained fog computing environments. Full article

In this paper, we propose a novel sparse clustering method designed for high-dimensional mixed-type data, integrating Azzalini’s score-based encoding for ordinal variables. Our approach aims to retain the inherent nature of each variable type—continuous, ordinal, and nominal—while enhancing clustering quality and interpretability. To this end, we extend classical distance metrics and adapt the Davies–Bouldin Index (DBI) to better reflect the structure of mixed data. We also introduce a weighted formulation that accounts for the distinct contributions of variable types in the clustering process. Empirical results on simulated and real-world datasets demonstrate that our method consistently achieves better separation and coherence of clusters compared to traditional techniques, while effectively identifying the most informative variables. This work opens promising directions for clustering in complex, high-dimensional settings such as marketing analytics and customer segmentation. Full article

This study investigated the use of K-means and hierarchical clustering, to group Italian Mediterranean buffalo using routinely collected test-day records. The analysis was first conducted on a combined dataset comprising three buffalo herds and subsequently on each herd individually. The main objective was to determine whether data-driven groupings could be implemented to support improvements in general herd management strategies. Results indicated that K-means consistently outperformed hierarchical clustering across all datasets, as reflected by average silhouette scores (0.17–0.18 vs. 0.10–0.12 for K-means and hierarchical, respectively), favorable Davies–Bouldin Index (DBI; 2.05–2.16 vs. 2.11–2.5 for K-means and hierarchical, respectively) and Calinski–Harabasz Index values (CHI; 1034–3877 vs. 729–2109 for K-means and hierarchical, respectively). K-means identified two clusters in the combined dataset and in two of the three herds, while three clusters were identified in the remaining herd. Cluster composition analysis revealed that days in milk and milk yield were the main discriminating factors when two clusters were formed. When three clusters emerged, K-means also identified a subgroup of animals that differed from the others in both age and lactation stage. These findings were supported by the analysis of variance (ANOVA), which showed statistically significant differences among most of the evaluated variables. Full article

The recommendation of electricity retail packages is challenged by diversified user demands and the complexity of evaluation information in liberalized electricity markets. Existing approaches are often limited by the subjectivity of user clustering and the difficulty of accurately capturing cognitive fuzziness and dynamic weight variations in the decision-making process. To address these challenges, this paper proposes a novel recommendation framework that integrates Improved Density Peaks Clustering (IDPC) with group decision-making based on trapezoidal fuzzy numbers. First, an IDPC-based model is constructed to objectively identify and partition users into homogeneous groups based on similar electricity consumption characteristics. Subsequently, a dynamic multi-attribute group decision-making model, which synergizes trapezoidal fuzzy numbers and the Multi-Criteria Compromise Ranking Method (MCRM), is designed to aggregate evaluation information from these user groups and to score the retail packages. Furthermore, a full-ranking recommendation strategy is established based on group satisfaction levels. Finally, a case study using a real-world dataset from a region in Eastern China is conducted. The empirical results demonstrate the framework’s superior performance: the IDPC algorithm achieves a stable Davies–Bouldin index of approximately 1.4, and the final recommendation ranking yields a Spearman correlation coefficient of 0.9 against simulated actual choices, significantly outperforming benchmark methods. This study shows that the proposed method can effectively enhance the precision and relevance of package recommendations, providing crucial decision support for electricity retailers in implementing refined marketing strategies. Full article

Detecting anomalies in network traffic is a central task in cybersecurity and digital infrastructure management. Traditional approaches rely on statistical models, rule-based systems, or machine learning techniques to identify deviations from expected patterns, but often face limitations in generalization across domains. This study proposes a cross-domain data enrichment framework that integrates behavioral embeddings with network traffic features through adversarial autoencoders. Each network traffic record is paired with the most similar behavioral profile embedding from user web activity data (Charles dataset) using cosine similarity, thereby providing contextual enrichment for anomaly detection. The proposed system comprises (i) behavioral profile clustering via autoencoder embeddings and (ii) cross-domain latent alignment through adversarial autoencoders, with a discriminator to enable feature fusion. A Deep Feedforward Neural Network trained on the enriched feature space achieves 97.17% accuracy, 96.95% precision, 97.34% recall, and 97.14% F1-score, with stable cross-validation performance (99.79% average accuracy across folds). Behavioral clustering quality is supported by a silhouette score of 0.86 and a Davies–Bouldin index of 0.57. To assess robustness and transferability, the framework was evaluated on the UNSW-NB15 and the CIC-IDS2017 datasets, where results confirmed consistent performance and reliability when compared to traffic-only baselines. This supports the feasibility of cross-domain alignment and shows that adversarial training enables stable feature integration without evidence of overfitting or memorization. Full article

The growing penetration of distributed photovoltaic (PV) systems presents significant operational challenges for power grids, driven by the scarcity of historical data and the high spatiotemporal variability of PV generation. To address these challenges, we propose Generative Reconstruction and Adaptive Identification via Latents (GRAIL), a unified, end-to-end framework that integrates generative modeling with adaptive clustering to discover latent structures and representative scenarios in PV datasets. GRAIL operates through a closed-loop mechanism where clustering feedback guides a cluster-aware data generation process, and the resulting generative augmentation strengthens partitioning in the latent space. Evaluated on a real-world, multi-site PV dataset with a high missing data rate of $45.4\%$, GRAIL consistently outperforms both classical clustering algorithms and deep embedding-based methods. Specifically, GRAIL achieves a Silhouette Score of 0.969, a Calinski–Harabasz index exceeding $4.132\times {10}^6$, and a Davies–Bouldin index of $0.042$, demonstrating superior intra-cluster compactness and inter-cluster separation. The framework also yields a normalized entropy of $0.994$, which indicates highly balanced partitioning. These results underscore that coupling data generation with clustering is a powerful strategy for expressive and robust structure learning in data-sparse environments. Notably, GRAIL achieves significant performance gains over the strongest deep learning baseline that lacks a generative component, securing the highest composite score among all evaluated methods. The framework is also computationally efficient. Its alternating optimization converges rapidly, and clustering and reconstruction metrics stabilize within approximately six iterations. Beyond quantitative performance, GRAIL produces physically interpretable clusters that correspond to distinct weather-driven regimes and capture cross-site dependencies. These clusters serve as compact and robust state descriptors, valuable for downstream applications such as PV forecasting, dispatch optimization, and intelligent energy management in modern power systems. Full article

Distributed Acoustic Sensing (DAS) is an emerging technology in the maritime domain, enabling the use of existing fiber optic cables to detect acoustic signals in the marine environment. In this study, we present an automated signal detection and classification framework for DAS data that supports near-real-time processing. Using data from the SHEFA-2 cable between the Faroe and Shetland Islands, we develop a method to identify acoustic signals and generate both labeled and unlabeled datasets based on their spectral characteristics. Principal component analysis (PCA) is used to explore separability in the labeled data, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is applied to classify unlabeled data. Experimental validation using clustering metrics shows that with the full dataset, we can achieve a Davies–Bouldin Index of 0.828, a Silhouette Score of 0.124, and a Calinski–Harabasz Index of 189.8. The clustering quality degrades significantly when more than 20% of the labeled data is excluded, highlighting the importance of maintaining sufficient labeled samples for robust classification. Our results demonstrate the potential to distinguish between signal sources such as ships, vehicles, earthquakes, and possible cable damage, offering valuable insights for maritime monitoring and security. Full article

Diesel engines serve as critical power sources across transportation and industrial fields, and their fault diagnosis is essential for ensuring operational safety and system reliability. However, acquiring sufficient and effective operational data remains a significant challenge due to the high complexity of the systems. As a modeling method that incorporates expert knowledge, the belief rule base (BRB) demonstrates strong potential in resolving such challenges. Nevertheless, the reliance on expert knowledge constrains its practical application, particularly in complex engineering scenarios. To overcome this limitation, this study proposes a reliability fault diagnosis method for diesel engines based on the belief rule base with data-driven initialization (DI-BRB-R), which aims to improve modeling capability under conditions of limited expert knowledge. Specifically, the approach first employs fuzzy c-means clustering with the Davies–Bouldin index (DBI-FCM) to initialize attribute reference values. Then, a Gaussian membership function with Laplace smoothing (LS-GMF) is developed to initialize the rule belief degrees. Furthermore, to guarantee the reliability of the model optimization process, a group of reliability guidelines is introduced. Finally, the effectiveness of the proposed method is validated through an example of fault diagnosis of the WD615 diesel engine. Full article

This paper presents a novel approach to supplier clustering by utilizing the Arithmetic Optimizer Algorithm (AOA), addressing the complex challenge of supplier segmentation in modern supply chain management. The AOA framework is applied to solve the multi-criteria clustering problem inherent to supplier classification. Using a real-world dataset of 500 suppliers with 12 performance criteria, including cost, quality, delivery reliability, and sustainability metrics, our method demonstrates effective clustering performance compared to conventional techniques. The AOA achieves a silhouette coefficient of 56.5% and a Davies–Bouldin index of 56.6%, outperforming several other state-of-the-art metaheuristic algorithms, including the Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Harris Hawks Optimization (HHO). The algorithm’s robustness is validated through extensive sensitivity analysis and statistical tests. The results indicate that the proposed approach successfully identifies distinct supplier segments with approximately 85% accuracy, enabling more effective supplier relationship management strategies. Full article

This paper presents a novel approach to water access segmentation by introducing an improved version of the Salp Swarm Algorithm (ISSA), addressing the complex challenge of household water access classification in developing regions. The proposed enhancement incorporates dynamic exploration–exploitation balancing and feature-aware mechanisms into the original SSA framework, significantly improving cluster quality and interpretability. Using a real-world dataset of 500 households from the El Hajeb region in Morocco and 12 socio-economic criteria, our method demonstrates superior clustering performance compared to conventional techniques. The ISSA achieves a 25% improvement in the silhouette coefficient (0.732 vs. 0.480) and a 22% reduction in the Davies–Bouldin index (0.421 vs. 0.645) compared to the standard SSA and other state-of-the-art metaheuristic algorithms. Five distinct water access segments are identified, enabling targeted infrastructure development strategies across different community types. The approach provides regional planners with essential insights into the spatial distribution of water access patterns and their relationship with socio-economic factors. Full article

In this paper, the concept of symmetry is utilized to evaluate the distribution characteristics of flow fields—that is, flow fields with balanced displacement generally exhibit good spatial symmetry. In the late stage of water-flooding reservoir development, identifying flow field distribution and implementing targeted adjustments are crucial for improving development efficiency and enhancing oil recovery. This study establishes a quantitative evaluation index system integrating both static geological and dynamic production factors to comprehensively characterize flow field distribution in ultra-high water-cut reservoirs. The system incorporates residual oil potential abundance, water-flooding ratio, and water influx intensity as key indicators. A flow field classification method based on the K-Means clustering algorithm was proposed, with the Davies–Bouldin index applied to evaluate clustering validity. The approach was validated using the Egg model, where the flow field was effectively classified into four types: inefficient retention field, effective displacement field, dominant displacement field, and extreme displacement field. Adjustment measures were then applied based on classification results. The findings demonstrate that the proposed method weakens dominant displacement areas while expanding effective and inefficient displacement zones, leading to a 1.1 percentage point increase in recovery factor. This research provides a practical and quantitative tool for flow field diagnosis and adjustment, offering valuable technical guidance for managing ultra-high water-cut reservoirs. Full article