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Due to ease of dissemination, humankind is facing an “infodemic” that has spread through electronic and social media. Therefore, there is a need to combat fake news using text classification techniques. However, textual data contains a lot of redundant useless features which can cause issues during the learning and classification phase. Therefore, an effective feature selection method is required to select the important features only. Filter-based methods exist in the literature for feature selection but their performance is average at best. Similarly, many wrapper-based methods also exist but very few are specialized for textual features. In this study, a meta-heuristic based filter-wrapper approach to feature selection is proposed for fake news classification. The proposed algorithm combines three filter-based measures with Binary Dragonfly Algorithm. Moreover, a mechanism for dynamically adjusting the exploratory and exploitative behavior of the said algorithm is also proposed. The hybrid model is evaluated on three datasets of fake news. Additionally, it is also evaluated on the task of sentiment analysis of news. Both binary and multi-class classification tasks were used in our experiments. The proposed algorithm has been compared with several state-of-the-art wrapper-based and filter-based feature selection methods. For fake news detection, Macro F-1 Scores of 0.897 , 0.782 and 0.667 were achieved on the three datasets. Moreover, for multi-class sentiment analysis task, Macro F-1 Scores of 0.553 and 0.597 were achieved.

Rank aggregation is the combination of several ranked lists from a set of candidates to achieve a better ranking by combining information from different sources. In feature selection problem, due to the heterogeneity of methods, there are some base rankers (Filter-based methods) that are of diverse quality and usually the ground truth of ratings is not available. Existing rank aggregation methods that take the diverse quality of base rankers into account do not have any explicit approach for appropriate weighting, require prior assumptions, and suffers from high computational complexity. In this paper, to overcome these challenges, an efficient unsupervised method is introduced for estimating the base rankers’ qualities and aggregating the rankers based on the estimated weights. We first compute the ratio of disagreement between base rankers in ordering different element pairs and then estimate the accuracies in a way that to minimize the discrepancy between these computed ratios and their analytical counterparts. We use the weighted majority voting method for obtaining the aggregated results. To resolve the probable inconsistencies in the final aggregation, the result is formed as a graph, and a greedy algorithm is used to find an acyclic subgraph with the highest weigh. To demonstrate the performance of the proposed method, nine standard UCI datasets are used. The obtained results by the proposed method have higher values of classifier measures than the existing baseline Feature Selection methods and rank aggregation-based multi-filter methods in the most datasets. The experiments show that rank aggregation-based Feature Selection methods outperform individual methods. The proposed method also shows the weight of each Filter-based Feature Selection method, in which the MRMR method has a higher weight than other methods.

This paper describes the modeling, simulation, and experimental validation of a Hybrid supercapacitor–battery Energy Storage System (HESS) for photovoltaic (PV) modules under partial shading. The system is intended to provide an uninterruptible power supply for a DC primary load. The Hybrid Power System (HPS) architecture includes a DC/DC boost converter with a Maximum Power Point Tracking (MPPT) algorithm that optimizes photovoltaic (PV) energy extraction. Furthermore, two bidirectional DC–DC converters are dedicated to the battery and supercapacitor subsystems to allow the bidirectional power flow within the HPS. The proposed HESS is evaluated through MATLAB/Simulink simulations and experimentally validated on a prototype using real-time hardware based on the dSPACE DS1104. To optimize power flow within the HPS, two energy management strategies are implemented: the Thermostat-Based Method (TBM) and the Filter-Based Method (FBM). The results indicate that the thermostat-based strategy provides better battery protection under shading conditions. Indeed, with this approach, the battery can remain in standby for 300 s under total permanent shading (100%), and for up to 30 min under dynamic partial shading, thereby reducing battery stress and extending its lifetime.

Industrial control systems (ICS) are crucial for automating and optimizing industrial operations but are increasingly vulnerable to cyberattacks due to their interconnected nature. High-dimensional ICS datasets pose challenges for effective anomaly detection and classification. This study aims to enhance ICS security by improving attack detection through an optimized feature selection framework that balances dimensionality reduction and classification accuracy. The study utilizes the HAI dataset, comprising 54,000 time series records with 225 features representing normal and anomalous ICS behaviors. A hybrid feature selection approach integrating wrapper and filter methods was employed. Initially, a Genetic Algorithm (GA) identified 118 relevant features. Further refinement was conducted using filter-based methods—Symmetrical Uncertainty (SU), Information Gain (IG), and Gain Ratio (GR)—leading to a final subset of 104 optimal features. These features were used to train classification models (Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM)) with a 70:30 train-test split and tenfold cross-validation. The proposed feature selection method significantly improved classification accuracy, achieving 98.86% (NB), 99.91% (RF), and 97.97% (SVM). Compared to the full dataset (225 features), which yielded 97.51%, 99.93%, and 96.17%, respectively, our optimized feature subset maintained or enhanced classification performance while reducing computational complexity. This research demonstrates the effectiveness of a hybrid feature selection approach in improving ICS anomaly detection. By reducing feature dimensionality without compromising accuracy, the proposed method enhances ICS security, offering a scalable and efficient solution for real-time attack detection.

This work presents a novel approach for optimal feature subset (OFS) selection in weather prediction (WP), addressing the challenge of handling a large number of features. The proposed method is a filter-based technique utilizing an intuitionistic fuzzy inference system (IFIS) designed to assess relationships between meteorological features while incorporating geographical factors. The core focus is on the utilisation of the 'hesitation degree' (HD) as a measure of feature importance, a concept applied for the first time in this domain. The method is compared against traditional and state-of-the-art algorithms, including custom fuzzy inference systems (FIS) and several variations of IFIS, showcasing its superiority in terms of accuracy (ACC), precision (PRE), recall (REC), and f1-score (F1S) across various classifiers. The computational analysis affirms the simplicity and efficiency of the proposed method. The main contributions encompass the development of a computationally efficient filter-based feature selection (FS) method, the integration of geographical features, and the emphasis on the HD for a nuanced FS, demonstrating robust performance in scenarios involving nonlinear relationships between features and the target feature.

A novel temporal contrast filter (TCF)-based method was developed to detect supersonic small infrared targets. The proposed hysteresis threshold-based detection followed by the TCF can enhance the accuracy of the target position, the robustness to background clutter and the velocity range of moving targets compared with the conventional temporal variance filter-based method.

The ratio between streamflow and base flow for 3 catchments from lowland area of North-Eastern Romania were calculated with six different separation methods: the local minimum method, Talaksen filter, Chapman filter, recursive digital filter, WHAT model, and the Ekchardt filter. In agreement with an increase in precipitation levels in the past decades all filter-based methods indicate a slight increase in Base Flow Index (BFI) values throughout the study period (1981–2013). The Eckhardt filter associated with Chapman filter are the most appropriate methods to evaluate the ratio between streamflow and base flow for this area. Both methods suggest the identification of parameters a and BFI max ( a = 0.925, BFI max = 0.5–0.7). Taking into account the highly variable hydrological regime throughout the year, and the fact that 35% of the hydrographic network displays ephemeral stream, the values obtained for the BFI based on these algorithms are the following: BFI = 0.58 for basins developed on porous aquifers with perennial stream (asuming a = 925 and BFI max = 0.7) and BFI = 0.52 for basins developed on porous aquifers, but with ephemeral stream (asuming a = 925 and BFI max = 0.5).

Base flow indices for low land area from North-Eastern part of Romania are compared, which were calculated with six different separation methods: the local minimum method (LMM), Talaksen filter, Chapman filter, recursive digital filter (RDF) WHAT model and the Ekchardt filter. All filter-based methods indicate a slight increase in BFI values throughout the study period (1981-2013), in agreement with an increase in precipitation levels in the area over the past decades. The correlation matrix between the different values obtained for the BFI indicates that the most appropriate methods for the study area are the Chapman filter and the Eckhardt filter (r=0.98). Both methods suggest the identification of parameters a and BFImax, which, when adjusted for the lowland area of North-Eastern Romania (a=0.925, BFImax=0.7-0.8), indicate that, in the area in question, BFI values exceed 0.5.). This indicates the need for a careful reevaluation of the region from a hydrological point of view, one that takes into account the changes in land use and the numerous hydro-technical works of the past decades.

The aim of the present investigation is to simulate and analyze the tip leakage flow structure and instantaneous evolution of tip vortex cavitation in a scaled axial-flow pump model. The improved filter-based turbulence model based on the density correction and a homogeneous cavitation model were used for implementing this work. The results show that when entering into the tip clearance, the backward flow separates from the blade tip near the pressure side, resulting in the generation of a corner vortex with high magnitude of turbulence kinetic energy. Then, at the exit of the tip clearance, the leakage jets would re-attach on the blade tip wall. Moreover, the maximum swirling strength method was employed in identifying the TLV core and a counter-rotating induced vortex near the end-wall successfully. The three-dimensional cavitation patterns and in-plain cavitation structures obtained by the improved numerical method agree well with the experimental results. At the sheet cavitation trailing edge in the tip region, the perpendicular cavitation cloud induced by TLV sheds and migrates toward the pressure side of the neighboring blade. During its migration, it breaks down abruptly and generates a large number of small-scale cavities, leading to severe degradation of the pump performance, which is similar with the phenomenon observed by Tan et al. [35].