Research Article
An Effective Clustering Based Privacy Preserving Model Against Feature Attacks
Muhammad Zulqurnain*
,
Muazzam Ali Khan Khattak,
Adeel Anjum,
Tehsin Kanwal
Issue:
Volume 14, Issue 3, June 2025
Pages:
44-59
Received:
21 March 2025
Accepted:
19 April 2025
Published:
19 June 2025
Abstract: The rise in healthcare-related illnesses has generated a substantial amount of patient data, making the safeguarding of patient data imperative. Existing privacy protection methods face challenges, including longer execution times, compromised data quality, and increased information loss as data dimensions expand. Effective attribute selection is vital to enhance preservation methods. Our research introduces a privacy-preserving clustering approach that addresses these concerns through two stages: feature selection and anonymization. The first stage selects relevant features using symmetrical uncertainty (SU) and eliminates duplicates with Kendall’s Tau Correlation Coefficient. The Utility Preserved Anonymization (UPA) algorithm is employed in the second phase to achieve privacy preservation. Additionally, our approach reduces data dimensionality to simplify cluster creation for anonymization. Experimental analysis on real-time data demonstrates the strategy’s effectiveness, with outstanding sensitivity (97.85%) and accuracy (95%), efficiently eliminating unnecessary features and simplifying clustering complexity.
Abstract: The rise in healthcare-related illnesses has generated a substantial amount of patient data, making the safeguarding of patient data imperative. Existing privacy protection methods face challenges, including longer execution times, compromised data quality, and increased information loss as data dimensions expand. Effective attribute selection is v...
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Research Article
A Hybrid Adaptive Neuro-fuzzy Inference System and Physics-informed Neural Network (ANFIS-PINN) for Complex System Modeling
Oleg Agamalov*
Issue:
Volume 14, Issue 3, June 2025
Pages:
60-69
Received:
16 June 2025
Accepted:
27 June 2025
Published:
28 July 2025
Abstract: This work explores the integration of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Physics-Informed Neural Networks (PINN) into a novel hybrid ANFIS-PINN framework. The proposed system aims to leverage the complementary strengths of both paradigms to address limitations inherent in individual approaches. ANFIS offers inherent interpretability, robust uncertainty handling, and adaptability to nonlinear relationships, applying the expert knowledge in the considered area, while PINN excels at incorporating physical laws, enhancing data efficiency, and improving generalization. The synergistic combination is envisioned to yield a more robust, interpretable, and physically consistent artificial intelligence (AI) solution, particularly for complex scientific and engineering problems characterized by nonlinearity, uncertainty, and sparse data, based on measurement data, a nonformal human expert's experience, and formal known physical laws. This paper details the foundational principles of ANFIS and PINN, outlines the compelling rationale for their integration, proposes several conceptual architectures and implementation strategies, and discusses the challenges and future directions for this promising hybrid AI paradigm.
Abstract: This work explores the integration of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Physics-Informed Neural Networks (PINN) into a novel hybrid ANFIS-PINN framework. The proposed system aims to leverage the complementary strengths of both paradigms to address limitations inherent in individual approaches. ANFIS offers inherent interpretability...
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