Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article FW-S3PFCM: Feature-Weighted Safe-Semi Possibilistic Fuzzy C-Means Clustering(Springer, 2026) Khezri, Shirin; Aghazadeh, Nasser; Hashemzadeh, Mahdi; Golzari Oskouei, AminThe safe semi-supervised fuzzy c-means clustering (S3FCM) method is a well-known clustering method that can produce successful results by incorporating prior knowledge of the class distribution. Its process is fast and simple but still has two limitations. The first issue is that it gives equal weight to all data features, while in real-world applications, different features usually have different importance. Secondly, S3FCM is very sensitive to noise and outliers. This paper proposes an extension of the S3FCM, entitled FW-S3PFCM, to mitigate these shortcomings. The proposed method uses a local feature weighting scheme to consider the different feature weights in the clustering process. Additionally, a possibilistic version of the S3FCM is designed to reduce the sensitivity to noise and outliers. The effectiveness of the proposed method is comprehensively evaluated on various benchmark datasets, and its performance is compared with the state-of-the-arts methods. To practically asses the FW-S3FCM, a real-world dataset of brain MRI images and its segmentation performance are analyzed as well. The average Accuracy, F1-score, Sensitivity, and Precision measures obtained by FW-S3FCM are 0.9682, 0.9826, 0.9743, and 0.9925, respectively, which are better than the competitors' performance.Article Storage Protein Allergen Sensitization Patterns in Children: Insights from Multiplex Microarray Profiling and Hierarchical Clustering(Wiley, 2025) Caka, Canan; Ozcivici, Engin; Karakus, Ceyda Oksel; Sekerel, Bulent EnisBackground Storage proteins (SPs), including 2S albumins, vicilins, and legumins, are key allergenic molecules (AMs) of peanuts, tree nuts (TNs), and sesame. Their structural stability contributes to allergenicity and sensitization. This study explored SP AM clustering patterns and evaluated the test performance of multiplex microarray (MM) testing in a pediatric cohort. Methods We retrospectively analyzed 350 children (median age: 3.7 years) with detectable SP sensitizations (>= 0.1 kU(A)/L) using the ALEX(2) MM platform. Sensitization interrelationships were analyzed using correlation heatmaps, hierarchical clustering (HC), dimensionality reduction, and feature elimination. Predictive utility was assessed through ROC curve analysis at different sensitization cut-offs (>0.1 and >0.3 kU(A)/L) and total IgE thresholds (>0, >20, and >50 kU/L). Results HC identified a broad SP cluster spanning peanuts, TNs, sesame, poppy seed, and buckwheat. Strong correlations and early HC linkages suggested extensive cross-sensitization (e.g., Ana o 3-Pis v 1 and Jug r 4-Cor a 9), alongside evidence of co-sensitization and molecular spreading. Unexpected clustering of structurally dissimilar peanut and pistachio AMs pointed to shared epitopes and/or cross-contamination. 2S albumins (Ara h 2, Cor a 14, Jug r 1, Ana o 3, and Ses i 1) were most predictive for clinical reactivity. Lower cut-offs and exclusion of patients with low total IgE improved test performance. Alpha-hairpinin (Pap s 2S albumin) showed potential as specific markers. Conclusions MM testing enables detailed SP sensitization profiling. Cluster-based interpretation may clarify cross- vs. co-sensitization, supporting informed clinical decisions. Use of recombinant AMs and IgE stratification may further enhance MM utility in food allergy diagnostics.Article A Robust Possibilistic Semi-Supervised Fuzzy Clustering Algorithm With Neighborhood-Aware Feature Weighting(Springer Heidelberg, 2025) Moghaddam, Arezou Najafi; Aghazadeh, Nasser; Hashemzadeh, Mahdi; Oskouei, Amin GolzariThe Semi-Supervised Fuzzy C-Means (SSFCM) method integrates class distribution information with fuzzy logic to overcome the challenges of semi-supervised clustering methods. While the inclusion of label information in the objective function improves the quality of the clustering method, semi-supervised fuzzy techniques still encounter important limitations, including (1) sensitivity to noise and outliers, (2) uniform feature importance, (3) neglecting the influences of neighborhood in the clustering process. In this paper, an improved semi-supervised clustering algorithm is presented to address these challenges. First, the algorithm reduces the sensitivity to noise and outliers by integrating the possibilistic fuzzy C-means algorithm into the SSFCM method. Second, a dynamic feature weighting method assigns different weights to the features in each cluster, which improves the performance of the algorithm in imbalanced datasets. Third, the proposed algorithm introduces a neighborhood mechanism that incorporates the neighbor's trade-off weighting and feature weighting strategy considering a strong metric. Finally, a robust kernel metric is used to further improve the performance on complex and nonlinear datasets. Extensive experiments are conducted on several benchmark datasets to evaluate the performance of the proposed method. The results show that the proposed method outperforms the current state-of-the-art techniques. The implementation source codes of the proposed method are publicly available at https://github.com/Amin-Golzari-Oskouei/RPSSFC-NAFW.Article Citation - Scopus: 5Event Distortion-Based Clustering Algorithm for Energy Harvesting Wireless Sensor Networks(Springer, 2022) Al-Qamaji, A.; Atakan, B.Wireless sensor networks (WSNs) consist of compact deployed sensor nodes which collectively report their sensed readings about an event to the Base Station (BS). In WSNs, due to the dense deployment, sensor readings can be spatially correlated and it is nonessential to transmit all their readings to the BS. Therefore, for more energy efficient, it is vital to choose which sensor node should report their sensed readings to the BS. In this paper, the event distortion-based clustering (EDC) algorithm is proposed for the spatially correlated sensor nodes. Here, the sensor nodes are assumed to harvest energy from ambient electromagnetic radiation source. The EDC algorithm allows the energy-harvesting sensor nodes to select and eliminate nonessential nodes while maintain an acceptable level of distortion at the BS. To measure the reliability, a theoretical framework of the distortion function is first derived for both single-hop and two-hop communication scenarios. Then, based on the derived theoretical framework, the EDC algorithm is introduced. Through extensive simulations, the performance of the EDC algorithm is evaluated in terms of achievable distortion level, number of alive nodes and harvested energy levels. As a result, EDC algorithm can successfully exploit both the spatial correlation and energy harvesting to improve the energy efficiency while preserving an acceptable level of distortion. Furthermore, the performance comparisons reveal that the two-hop communication model outperforms the single-hop model in terms of the distortion and energy-efficiency. © 2021, The Author(s).