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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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Application of symbolic classifiers and Multi-Ensemble Threshold Techniques for Android Malware Detection

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BIG DATA AND COGNITIVE COMPUTING 2025年第2期9卷 27-27页

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

Android malware detection using artificial intelligence today is a mandatory tool to prevent cyber attacks. To address this problem in this paper the proposed methodology consists of the application of genetic programming symbolic classifier (GPSC) to obtain symbolic expressions (SEs) that can detect if the android is malware or not. To find the optimal combination of GPSC hyperparameter values the random hyperparameter values search method (RHVS) method and the GPSC were trained using 5-fold cross-validation (5FCV). It should be noted that the initial dataset is highly imbalanced (publicly available dataset). This problem was addressed by applying various preprocessing and oversampling techniques thus creating a huge number of balanced dataset variations and on each dataset variation the GPSC was trained. Since the dataset has many input variables three different approaches were considered: the initial investigation with all input variables, input variables with high feature importance, application of principal component analysis. After the SEs with the highest classification performance were obtained they were used in threshold-based voting ensembles and the threshold values were adjusted to improve classification performance. Multi-TBVE has been developed and using them the robust system for Android malware detection was achieved with the highest accuracy of 0.98 was obtained.

关键词： Android malware detection data preprocessing genetic programming symbolic classifier threshold based voting ensembles

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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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Improvement of pulsars detection using dataset balancing methods and symbolic classification ensemble

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ASTRONOMY AND COMPUTING 2024年 47卷

作者： Andelic, N. Univ Rijeka Fac Engn Vukovarska 58 Rijeka 5100 Croatia

Highly accurate detection of pulsars is mandatory. With the application of machine learning (ML) algorithms, the detection of pulsars can certainly be improved if the dataset is balanced. In this paper, the publicly available dataset (HTRU2) is highly imbalanced so various balancing methods were applied. The balanced dataset was used in genetic programming symbolic classifier (GPSC) to obtain symbolic expressions (SEs) that can detect pulsars with high classification accuracy. To find the optimal combination of GPSC hyperparameters the random hyperparameter search (RHS) method was developed and applied. The GPSC was trained using 5fold cross -validation so after each training a total of 5 SEs were obtained. The best set of SEs are selected based on their classification performance and all of them are applied on the original dataset. The best classification accuracy (ACC), the area under receiver operating characteristic (AUC), precision, recall, and f1 -score were achieved in the case of the dataset balanced with the AllKNN method i.e. all mean evaluation metric values are equal to 0.995. The ensemble consisted of 25 SEs that achieved the ACC = 0.978, AUC = 0.9452 , Precision = 0.905, Recall = 0.9963, and F1 - Score = 0.94877, on the original dataset.

关键词： Dataset balancing methods genetic programming symbolic classifier Pulsars detection

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