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A comprehensive study on symbolic expressions for fault detection-classification in photovoltaic farms

APPLIED ENERGY 2025年 383卷

作者： Andelic, Nikola Segota, Sandi Baressi Mrzljak, Vedran Univ Rijeka Fac Engn Dept Automat & Elect Vukovarska 58 Rijeka 51000 Croatia Univ Rijeka Fac Engn Dept Thermodynam & Energy Engn Vukovarska 58 Rijeka 51000 Croatia

Large-scale photovoltaic (solar) farms playa crucial role in harnessing solar energy for electricity generation through photovoltaic (PV) technology. However, the control and management of such systems pose significant challenges, particularly in fault detection. This paper introduces the application of a genetic programming symbolic classifier (GPSC) to a publicly available dataset for fault detection in photovoltaic farms. Given the imbalanced nature of the original dataset, the study necessitated the application of oversampling techniques to achieve a balanced representation of class samples. Additionally, the impact of scaling and normalizing techniques on the performance of the GPSC was thoroughly investigated. The GPSC was systematically applied to each scaled or normalized balanced dataset variation, and its hyperparameters were fine-tuned using a random hyperparameter values search (RHVS) method. The algorithm underwent training, via a 5-fold cross-validation (5FCV) process, and the best symbolic expressions (SEs) were determined based on accuracy, area under the receiver operating characteristics curve, precision, recall, and F1-score. The research yielded many SEs, which were used to develop a threshold-based voting ensemble (TBVE). The TBVE for each class was tested on the initial dataset and the threshold was finely tuned to achieve even higher classification performance in photovoltaic detection/classification. Results demonstrated that this approach produced highly accurate TBVE for each class (accuracy in the majority of cases equal to 1.0), showcasing the effectiveness of the GPSC and TBVE in fault detection/classification for photovoltaic farms.

关键词： Data preprocessing and oversampling Genetic programming symbolic classifier random hyperparameter value search method Photovoltaic farms fault detection and classification Threshold based voting ensemble

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Robust password security: a genetic programming approach with imbalanced dataset handling

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY 2024年第3期23卷 1761-1786页

作者： Andelic, Nikola Segota, Sandi Baressi Car, Zlatan Univ Rijeka Fac Engn Vukovarska 58 Rijeka 51000 Croatia

Developing a method for determining password strength using artificial intelligence (AI) is crucial as it enhances cybersecurity by providing a more robust defense against unauthorized access. AI can analyze complex patterns and trends, allowing for the identification of weak passwords and potential vulnerabilities more effectively than traditional methods. This proactive approach helps users and organizations strengthen their security posture, reducing the risk of data breaches and unauthorized intrusions. In this paper, the genetic programming symbolic classifier (GPSC) was applied to the publicly available dataset to obtain a set of symbolic expressions for password strength classification with high classification accuracy. One of the problems with the dataset was an imbalance between classes so various oversampling/undersampling techniques have been utilized. The optimal GPSC hyperparameter values were found using the random hyperparameter value search method. The algorithm was trained using fivefold cross-validation (5FCV). One of the problems with the dataset was an imbalance between classes so various oversampling/undersampling techniques have been utilized. To evaluate obtained SEs, the evaluation metric accuracy, area under receiver operating characteristics curve, precision, recall, and f1-score were used. The obtained SEs on balanced dataset variations achieved high classification accuracy (0.99) and with the application of all SEs on the entire original imbalanced dataset achieved the same accuracy.

关键词： Genetic programming symbolic classifier random hyperparameter value search method Fivefold cross-validation Oversampling and undersampling techniques Password strength classification

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