| 1. |
- Alzghoul, Ahmad, et al.
(författare)
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Addressing concept drift to improve system availability by updating one-class data-driven models
- 2015
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Ingår i: Evolving Systems. - : Springer. - 1868-6478 .- 1868-6486. ; 6:3, s. 187-198
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Tidskriftsartikel (refereegranskat)abstract
- Data-driven models have been used to detect system faults, thereby increasing industrial system availability. The ability to search data streams while dealing with concept drift are challenges for data-driven models. The objective of this work is to demonstrate a general method to manage concept drift when using one-class data-driven models. The method has been used to develop an automatically retrained and updated polygon-based model. In this paper, the available industrial data allowed for use of one-class data-driven models, and the polygon-based model was selected because it has previously been successful. Possible scenarios that allow one-class data-driven models to be retrained or updated were identified. Based on the identified scenarios, a method to automatically update a polygon-based model online is proposed. The method has been tested and verified using data collected from a Bosch Rexroth Mellansel AB hydraulic drive system. Data representing relevant faults was inserted into the data set in close collaboration with engineers from the company. The results show that the developed polygon-based model method was able to address the concept drift issue and was able to significantly improve the classification accuracy compared to the static polygon-based model. Thereby, the model could significantly improve industrial system availability when applied in the relevant production process. This paper shows that the developed polygon-based model requires small memory space while its updating procedure is simple and fast. Finally, the identified scenarios may be helpful as input for supporting other one-class data-driven models to cope with concept drift, thus increasing the generalizability of the results.
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| 2. |
- Mostafa, Reham R., et al.
(författare)
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AEOWOA: hybridizing whale optimization algorithm with artificial ecosystem-based optimization for optimal feature selection and global optimization
- 2024
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Ingår i: Evolving Systems. - : SPRINGER HEIDELBERG. - 1868-6478 .- 1868-6486.
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Tidskriftsartikel (refereegranskat)abstract
- The process of data classification involves determining the optimal number of features that lead to high accuracy. However, feature selection (FS) is a complex task that necessitates robust metaheuristics due to its challenging NP-hard nature. This paper introduces a hybrid algorithm that combines the Artificial Ecosystem Optimization (AEO) operators with the Whale Optimization Algorithm (WOA) to enhance numerical optimization and FS. While the WOA algorithm, inspired by the hunting behavior of whales, has been successful in solving various optimization problems, it can sometimes be limited in its ability to explore and may become trapped in local optima. To address this limitation, the authors propose the use of AEO operators to improve the exploration process of the WOA algorithm. The authors conducted experiments to evaluate the effectiveness of their proposed method, called AEOWOA, using the CEC'20 test suite for numerical optimization and sixteen datasets for FS. They compared the results with those obtained from other optimization methods. Through experimental and statistical analyses, it was observed that AEOWOA delivers efficient search results with faster convergence, reducing the feature size by up to 89% while achieving up to 94% accuracy. These findings shed light on potential future research directions in this field.
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| 3. |
- Nordahl, Christian, 1991-, et al.
(författare)
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EvolveCluster : an evolutionary clustering algorithm for streaming data
- 2022
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Ingår i: Evolving Systems. - : SPRINGER HEIDELBERG. - 1868-6478 .- 1868-6486. ; :4, s. 603-623
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Tidskriftsartikel (refereegranskat)abstract
- Data has become an integral part of our society in the past years, arriving faster and in larger quantities than before. Traditional clustering algorithms rely on the availability of entire datasets to model them correctly and efficiently. Such requirements are not possible in the data stream clustering scenario, where data arrives and needs to be analyzed continuously. This paper proposes a novel evolutionary clustering algorithm, entitled EvolveCluster, capable of modeling evolving data streams. We compare EvolveCluster against two other evolutionary clustering algorithms, PivotBiCluster and Split-Merge Evolutionary Clustering, by conducting experiments on three different datasets. Furthermore, we perform additional experiments on EvolveCluster to further evaluate its capabilities on clustering evolving data streams. Our results show that EvolveCluster manages to capture evolving data stream behaviors and adapts accordingly.
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| 4. |
- Papastergiou, Spyridon, et al.
(författare)
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Handling of advanced persistent threats and complex incidents in healthcare, transportation and energy ICT infrastructures
- 2021
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Ingår i: Evolving Systems. - : Springer Science and Business Media LLC. - 1868-6478 .- 1868-6486. ; 12, s. 91-108
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, the use of information technologies in Critical Infrastructures is gradually increasing. Although this brings benefits, it also increases the possibility of security attacks. Despite the availability of various advanced incident handling techniques and tools, there is still no easy, structured, standardized and trusted way to manage and forecast interrelated cybersecurity incidents. This paper introduces CyberSANE, a novel dynamic and collaborative, warning and response system, which supports security officers and operators to recognize, identify, dynamically analyse, forecast, treat and respond to security threats and risks and and it guides them to handle effectively cyber incidents. The components of CyberSANE are described along with a description of the CyberSANE data flow. The main novelty of the CyberSANE system is the fact that it enables the combination of active incident handling approaches with reactive approaches to support incidents of compound, highly dependent Critical Information Infrastructures. The benefits and added value of using CyberSANE is described with the aid of a set of cyber-attack scenarios.
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| 5. |
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| 6. |
- Tzanetos, Alexandros, et al.
(författare)
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Sonar inspired optimization (SIO) in engineering applications
- 2020
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Ingår i: Evolving Systems. - : Springer. - 1868-6478 .- 1868-6486. ; 11:3, s. 531-539
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Tidskriftsartikel (refereegranskat)abstract
- Recently, a new Nature Inspired Intelligent scheme has been proposed and presented, named Sonar Inspired Optimization (SIO). This algorithm is inspired by the SONAR mechanism, which is used by Warships to detect targets and avoid mines. In this paper, improvements have been done to the SIO approach in an attempt to increase the performance of the algorithm. Also, results from experiments in known constrained Engineering applications are presented and discussed. SIO tackles with these problems, managing to overcome the performance of other Nature Inspired metaheuristics, heuristics and mathematical approaches in most of the cases.
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