Filter Feature Research Articles

Wavelet structuring element-based morphological filtering method in wire rope inspection signal denoising

Aiming at the multi-source interference signals in wire rope inspection, while common morphological filtering methods with traditional structuring elements are hard to distinguish, a new wavelet structuring element-based one-dimensional (1D) generalized morphological filtering method is proposed. First, principles and related theories of the generalized morphological filter are introduced and then the new wavelet structuring elements are analyzed. Thereafter, simulation and comparison regarding common structuring element morphological filter and the new wavelet function-based models are conducted through signal-to-noise ratio (SNR) and mean square error (MSE) analysis. Meanwhile, the influence of different noises and main structuring element parameters of length L, amplitude H to the signal processing results are revealed. Finally, experiments for different wire rope defects inspection by magnetic flux leakage testing are conducted, and characterizations of the wavelet structuring element-based morphological filtering methods are presented and compared through six case studies. Besides, the superior performance of the wavelet structuring morphological gradient models are compared and validated by short-time Fourier transform, SNR, MSE analysis, and quantitative defect recognition. The comparison results show that db4 and sym4 wavelet structuring elements-based 1D morphological filter features the highest wire rope defect detection and signal recognition accuracy under the gradient operation, which demonstrates the feasibility and effectiveness of the proposed methods. Additionally, advantages and disadvantages of the new models are summarized and discussed.

ASFFuse: Infrared and visible image fusion model based on adaptive selection feature maps

Researchers continuously modify deep learning network architecture for improved fusion results. However, little attention is given to the influence of noise feature maps generated during the convolution process on the fusion outcomes. Here, we aim to minimize the influence of noisy feature maps on fusion results and propose a fusion model, the infrared and visible image fusion model based on adaptive selection feature maps (ASFFuse). We propose an adaptive selection feature maps module (ASFM). ASFM measures the amount of information contained in each feature map and filters out feature maps that contain more noise information. Additionally, we introduce a feature enhancement module (FEM) to enrich the fusion image with more source image information. For unsupervised training of the proposed model, we propose a texture loss function based on contrast learning. This loss function preserves the texture information of the image in a better way and makes the fusion image have a better visual effect. Our ASFFuse model has been shown to outperform state-of-the-art models in both quantitative and qualitative evaluations in extensive experiments on the TNO and RoadScene datasets. The code is available at https://github.com/LKZ1584905069/ASFFuse.

Quasi-elliptic band pass filter using resonators based on coupling theory for ultra-wide band applications

PurposeThis paper aims to present the simulation, fabrication and testing of a novel ultra-wide band (UWB) band-pass filters (BPFs) with better transmission and rejection characteristics on a low-loss Taconic substrate and analyze using the coupled theory of resonators for UWB range covering L, S, C and X bands for radars, global positioning system (GPS) and satellite communication applications.Design/methodology/approachThe filter is designed with a bent coupled transmission line on the top copper layer. Defected ground structures (DGSs) like complementary split ring resonators (CSRRs), V-shaped resonators, rectangular slots and quad circle slots (positioned inwards and outwards) are etched in the ground layer of the filter. The circular orientation of V-shaped resonators adds compactness when linearly placed. By arranging the quad circle slots outwards and inwards at the corner and core of the ground plane, respectively, two filters (Filters I and II) are designed, fabricated and measured. These two filters feature a quasi-elliptic response with transmission zeros (TZs) on either side of the bandpass response, making it highly selective and reflection poles (RPs), resulting in a low-loss filter response. The transmission line model and coupled line theory are implemented to analyze the proposed filters.FindingsTwo filters by placing the quad circle slots outwards (Filter I) and inwards (Filter II) were designed, fabricated and tested. The fabricated model (Filter I) provides transmission with a maximum insertion loss of 2.65 dB from 1.5 GHz to 9.2 GHz. Four TZs and five RPs are observed in the frequency response. The lower and upper stopband band width (BW) of the measured Filter I are 1.2 GHz and 5.5 GHz of upper stopband BW with rejection level greater than 10 dB, respectively. Filter II (inward quad circle slots) operates from 1.4 GHz to 9.05 GHz with 1.65 dB maximum insertion loss inside the passband with four TZs and four RPs, which, in turn, enhances the filter characteristics in terms of selectivity, flatness and stopband. Moreover, 1 GHz BW of lower and upper stopbands are observed. Thus, the fabricated filters (Filters I and II) are therefore evaluated, and the outcomes show good agreement with the electromagnetic simulation response.Research limitations/implicationsThe limitation of this work is the back radiation caused by DGS, which can be eradicated by placing the filter in the cavity and retaining its performance.Practical implicationsThe proposed UWB BPFs with novel resonators find their role in the UWB range covering L, S, C and X bands for radars, GPS and satellite communication applications.Originality/valueTo the best of the authors’ knowledge, for the first time, the authors develop a compact UWB BPFs (Filters I and II) with BW greater than 7.5 GHz by combining reformed coupled lines and DGS resonators (CSRRs, V-shaped resonators [modified hairpin resonators], rectangular slots and quad circle slots [inwards and outwards]) for radars, GPS and satellite communication applications.

Artistic Guidance and Emotional Expression Pathways in Piano Performance - Based on Deep Score Characterization Fusion

Abstract In the evolving domain of artificial intelligence and deep learning, the pedagogical applications of piano performance— specifically its artistic instruction and emotional expressiveness—have garnered increasing scholarly attention. This research leverages MIDI music file formats to mine necessary data, engaging in a detailed examination of the structural and characteristic elements of MIDI files. To optimize the precision of mathematical models applied in this study, MIDI music data underwent preliminary preprocessing steps, including hexadecimal encoding and feature filtering, which facilitated the transformation of MIDI features into vectors representing emotional attributes. The study introduced the development of a BiLSTM_BLS model tailored to articulate the nuances of piano performance and emotional expression. This model synthesis integrates features reflective of emotional states induced by piano playing. A mathematically derived loss function was utilized to gauge the model’s fidelity, thereby enabling an assessment of its efficacy in the realms of artistic instruction and emotional conveyance in piano performance. Findings indicate a proximity between the emotional states of Anger (0.392) and Stress (0.385), with Depressed (0.422) presenting a slightly elevated distance from these states. Moreover, the BiLSTM_BLS model achieved a performance score of 3.214 in the evaluation of artistic expression, which corresponds to 64.28 on a standardized 100-point scale. This investigation contributes to the enhancement of musical skills and theoretical knowledge among pianists, promotes cognitive development, augments emotional expression, and nurtures creativity.

A data-driven workflow for prediction of fracturing parameters with machine learning

In the realm of unconventional reservoir hydraulic fracturing design, the conventional optimization of mechanistic model parameters is a time-consuming process that impedes its responsiveness to the swift demands of on-site development. This study, rooted in Xinjiang oilfield data, delves into the utilization of machine learning methods for extensive field data. The research systematically elucidates the training and optimization procedures of a production forecasting model, achieving effective optimization of hydraulic fracturing design parameters. By employing polynomial feature cross-construction generate composite features, feature filtering is performed using the maximal information coefficient. Subsequently, wrapper-style feature selection techniques, including ridge regression and decision trees, are applied to ascertain the optimal combinations of model input parameters. The integration of stacking during model training enhances performance, while stratified K-fold cross-validation is implemented to mitigate the risk of overfitting. The ultimate optimization of hydraulic fracturing design parameters is realized through a competitive learning particle swarm algorithm. Results indicate that the accuracy of the data-driven production forecasting model can reach 85%. This model proficiently learns patterns from mature blocks and effectively applies them to optimize new blocks. Furthermore, expert validation confirms that the optimization results align closely with actual field conditions.

Optimal Electricity Load Interruption Based on Time Series Classification With Super Learner and Feature Filtering

Optimal Electricity Load Interruption Based on Time Series Classification With Super Learner and Feature Filtering

Multi-parameter MRI radiomic features may contribute to predict progression-free survival in patients with WHO grade II meningiomas.

This study aims to investigate the potential value of radiomic features from multi-parameter MRI in predicting progression-free survival (PFS) of patients with WHO grade II meningiomas. Kaplan-Meier survival curves were used for survival analysis of clinical features. A total of 851 radiomic features were extracted based on tumor region segmentation from each sequence, and Max-Relevance and Min-Redundancy (mRMR) algorithm was applied to filter and select radiomic features. Bagged AdaBoost, Stochastic Gradient Boosting, Random Forest, and Neural Network models were built based on selected features. Discriminative abilities of models were evaluated using receiver operating characteristics (ROC) and area under the curve (AUC). Our study enrolled 164 patients with WHO grade II meningiomas. Female gender (p=0.023), gross total resection (GTR) (p<0.001), age <68 years old (p=0.023), and edema index <2.3 (p=0.006) are protective factors for PFS in these patients. Both the Bagged AdaBoost model and the Neural Network model achieved the best performance on test set with an AUC of 0.927 (95% CI, Bagged AdaBoost: 0.834-1.000; Neural Network: 0.836-1.000). The Bagged AdaBoost model and the Neural Network model based on radiomic features demonstrated decent predictive ability for PFS in patients with WHO grade II meningiomas who underwent operation using preoperative multi-parameter MR images, thus bringing benefit for patient prognosis prediction in clinical practice. Our study emphasizes the importance of utilizing advanced imaging techniques such as radiomics to improve personalized treatment strategies for meningiomas by providing more accurate prognostic information that can guide clinicians toward better decision-making processes when treating their patients' conditions effectively while minimizing risks associated with unnecessary interventions or treatments that may not be beneficial.

Classification of Breast Cancer using Ensemble Filter Feature Selection with Triplet Attention Based Efficient Net Classifier

In medical imaging, the effective detection and classification of Breast Cancer (BC) is a current research important task because of the still existing difficulty to distinguish abnormalities from normal breast tissues due to their subtle appearance and ambiguous margins and distinguish abnormalities from the normal breast. Moreover, BC detection based on an automated detection model is needed, because manual diagnosis faces problems due to cost and shortage of skilled manpower, and also takes a very long time. Using deep learning and ensemble feature selection techniques, in this paper, a novel framework is introduced for classifying BC from histopathology images. The five primary steps of the suggested framework are as follows: 1) to make the largest original dataset and then deep learning model with data augmentation to improve the learning. 2) The best features are selected by an Ensemble Filter Feature selection Method (EFFM) which combines the best feature subsets to produce the final feature subsets. 3) Then the pruned Convolution Neural Network (CNN) model is utilized to extract the optimal features. 4) Finally, the classification is done through the Triplet Attention based Efficient Network (TAENet) classifier. The suggested model produces a 98% accuracy rate after being trained and tested on two different histopathology imaging datasets including images from four different data cohorts. Subsequently, the suggested strategy outperforms the conventional ones since the ensemble filter habitually acquires the best features, and experimental results demonstrate the importance of the proposed approach

A Multimodal Multi-Objective Evolutionary Algorithm for Filter Feature Selection in Multi-Label Classification

A Multimodal Multi-Objective Evolutionary Algorithm for Filter Feature Selection in Multi-Label Classification

Novel Synthesis of Generalized Strongly Coupled Resonator Triplet Filters With Complex Reflection Zeros

Complex reflection zeros (CRZs) are introduced into generalized strongly coupled resonator triplet (GSCRT) filter synthesis for the first time to reliably eliminate negative couplings, thus facilitating physical implementation. CRZs bring more flexibility in synthesis and can be preassigned and regulated as needed to guarantee nonnegative couplings in different topologies and achieve varying electrical performance. Besides, CRZs bring the advantage of self-suppressed spikes, which can save extra band-stop modules in the system. To provide useful guidelines for designers, the selection of suitable CRZs is discussed in detail. With the introduced CRZs, the responses of GSCRT filters feature nonideal “in-between” performance, which shows a tradeoff between electrical performance and physical realization. Also, a comparison between the performance of GSCRT filters, extracted-zero, and path filters is given, which aims to help designers choose the most suitable configuration in practical use. Finally, the good measurement results of an anti-interference satellite filter have strongly validated the proposed theories.

CrossNet: Cross-scene Background Subtraction Network via 3D Optical Flow

This paper investigates an intriguing yet unsolved problem of cross-scene background subtraction for training only one deep model to process large-scale video streaming. We propose an end-to-end cross-scene background subtraction network via 3D optical flow, dubbed CrossNet. First, we design a new motion descriptor, hierarchical 3D optical flows (3D-HOP), to observe fine-grained motion. Then, we build a cross-modal dynamic feature filter (CmDFF) to enable the motion and appearance feature interaction. CrossNet exhibits better generalization since the proposed modules are encouraged to learn more discriminative semantic information between the foreground and the background. Furthermore, we design a loss function to balance the size diversity of foreground instances since small objects are usually missed due to training bias. Our whole background subtraction model is called Hierarchical Optical Flow Attention Model (HOFAM). Unlike most of the existing stochastic-process-based and CNN-based background subtraction models, HOFAM will avoid inaccurate online model updating, not heavily rely on scene-specific information, and well represent ambient motion in the open world. Experimental results on several well-known benchmarks demonstrate that it outperforms state-of-the-art by a large margin. The proposed framework can be flexibly integrated into arbitrary streaming media systems in a plug-and-play form. Codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/dongzhang89/HOFAM</uri> .

FG-HFS: A feature filter and group evolution hybrid feature selection algorithm for high-dimensional gene expression data

High dimensional and small samples characterize gene expression data and contain a large number of genes unrelated to disease. Feature selection improves the efficiency of disease diagnosis by selecting a small number of important genes. Unfortunately, existing algorithms do not consider the correlation between features, and search algorithms tend to fall into the local optimal solution in the feature search process. To this end, this paper proposes a feature filter and group evolution hybrid feature selection algorithm (FG-HFS) for high-dimensional gene expression data. Unlike existing algorithms, we propose using spectral clustering to group redundant features into a group. Then, we propose a redundant feature filter algorithm. According to the principle of approximate Markov blanket, grouped feature groups are filtered to delete these redundant features. Among them, filtered features are evenly divided by density according to the feature exponential strategy. Most importantly, we propose using the group evolution multi-objective genetic algorithm to search the filtered feature subsets and evaluate the candidate feature subsets according to the in-group and out-group so as to select the feature subsets with the highest accuracy and the least number. Experimental results show that the average accuracy (ACC) and Matthews correlation coefficient (MCC) indexes of the selected feature subsets (FSs) by the FG-HFS algorithm on 5 gene expression datasets are 92.76% and 88.76%, respectively, which are significantly better than the existing algorithms. In addition, the FSs and ACC/FSs indexes of the FG-HFS algorithm are also better than the existing algorithms, which fully proves the superiority of the FG-HFS algorithm. More importantly, the Wilcoxon and Friedman statistical experiments results show that the feature selection effect of FG-HFS algorithm is significantly better than that of existing algorithms, no matter in pairwise comparison or multiple comparison.

Efficient Extraction and Automated Thyroid Prediction with an Optimized Gated Recurrent Unit in Recurrent Neural Networks

Computer-aided tools are becoming increasingly important in medical diagnostics. This paper introduces the Efficient Feature Extraction Based Recurrent Neural Network (FERNN) for computer-aided thyroid disease prediction. The FERNN model uses a Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) optimized with the COOT Optimization Algorithm.The study begins by gathering data from an open-source system and preprocessing it using min-max normalization to address missing values. The preprocessed data undergoes a two-level feature extraction (TLFE) procedure. In the first level, a ranked filter feature set technique is used to prioritize features based on medical expert recommendations. In the second level, a variety of metrics, including information gain, gain ratio, chi-square, and relief, are used to rank and select features. A composite measure guided by fuzzy logic is then used to select a judicious subset of features. The FERNN model uses the GRU-RNN to classify thyroid diseases in the databases. To optimise, the COOT optimization method is employed. The model's weights. The FERNN model was put into practise in MATLAB and assessed with a variety of statistical metrics, including kappa, accuracy, precision, recall, sensitivity, specificity, and the F-measure. The proposed methodology was benchmarked against traditional techniques, including the deep belief neural network (DBN), artificial neural network (ANN), and support vector machine (SVM).

Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction

The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper, and evolutionary were used. Then seven algorithms Bayes net, Naïve Bayes (BN), multivariate linear model (MLM), Support Vector Machine (SVM), logit boost, j48, and Random Forest were applied to identify the best models for heart disease prediction. Precision, F-measure, Specificity, Accuracy, Sensitivity, ROC area, and PRC were measured to compare feature selection methods' effect on prediction algorithms. The results demonstrate that feature selection resulted in significant improvements in model performance in some methods (e.g., j48), whereas it led to a decrease in model performance in other models (e.g. MLP, RF). SVM-based filtering methods have a best-fit accuracy of 85.5. In fact, in a best-case scenario, filtering methods result in + 2.3 model accuracy. SVM-CFS/information gain/Symmetrical uncertainty methods have the highest improvement in this index. The filter feature selection methods with the highest number of features selected outperformed other methods in terms of models' ACC, Precision, and F-measures. However, wrapper-based and evolutionary algorithms improved models' performance from sensitivity and specificity points of view.

Machine learning model for predicting immediate postoperative desaturation using spirometry signal data

Postoperative desaturation is a common post-surgery pulmonary complication. The real-time prediction of postoperative desaturation can become a preventive measure, and real-time changes in spirometry data can provide valuable information on respiratory mechanics. However, there is a lack of related research, specifically on using spirometry signals as inputs to machine learning (ML) models. We developed an ML model and postoperative desaturation prediction index (DPI) by analyzing intraoperative spirometry signals in patients undergoing laparoscopic surgery. We analyzed spirometry data from patients who underwent laparoscopic, robot-assisted gynecologic, or urologic surgery, identifying postoperative desaturation as a peripheral arterial oxygen saturation level below 95%, despite facial oxygen mask usage. We fitted the ML model on two separate datasets collected during different periods. (Datasets A and B). Dataset A (Normal 133, Desaturation 74) was used for the entire experimental process, including ML model fitting, statistical analysis, and DPI determination. Dataset B (Normal 20, Desaturation 4) was only used for verify the ML model and DPI. Four feature categories—signal property, inter-/intra-position correlation, peak value/interval variability, and demographics—were incorporated into the ML models via filter and wrapper feature selection methods. In experiments, the ML model achieved an adequate predictive capacity for postoperative desaturation, and the performance of the DPI was unbiased.

A radiomics based method for prediction of prostate cancer Gleason score using enlarged region of interest

BackgroundProstate cancer (PCa) is one of the most common cancers in men worldwide, and its timely diagnosis and treatment are becoming increasingly important. MRI is in increasing use to diagnose cancer and to distinguish between non-clinically significant and clinically significant PCa, leading to more precise diagnosis and treatment. The purpose of this study is to present a radiomics-based method for determining the Gleason score (GS) for PCa using tumour heterogeneity on multiparametric MRI (mp-MRI).MethodsTwenty-six patients with biopsy-proven PCa were included in this study. The quantitative T2 values, apparent diffusion coefficient (ADC) and signal enhancement rates (α) were calculated using multi-echo T2 images, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), for the annotated region of interests (ROI). After texture feature analysis, ROI range expansion and feature filtering was performed. Then obtained data were put into support vector machine (SVM), K-Nearest Neighbor (KNN) and other classifiers for binary classification.ResultsThe highest classification accuracy was 73.96% for distinguishing between clinically significant (Gleason 3 + 4 and above) and non-significant cancers (Gleason 3 + 3) and 83.72% for distinguishing between Gleason 3 + 4 from Gleason 4 + 3 and above, which was achieved using initial ROIs drawn by the radiologists. The accuracy improved when using expanded ROIs to 80.67% using SVM and 88.42% using Bayesian classification for distinguishing between clinically significant and non-significant cancers and Gleason 3 + 4 from Gleason 4 + 3 and above, respectively.ConclusionsOur results indicate the research significance and value of this study for determining the GS for prostate cancer using the expansion of the ROI region.

MCFilter: feature filter based on motion-correlation for LiDAR SLAM

PurposeThis study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose estimation accuracy and improve the overall system performance in outdoor environments.Design/methodology/approachDistinct from traditional approaches, MCFilter emphasizes enhancing point cloud data quality at the pixel level. This framework hinges on two primary elements. First, the D-Tracker, a tracking algorithm, is grounded on multiresolution three-dimensional (3D) descriptors and adeptly maintains a balance between precision and efficiency. Second, the R-Filter introduces a pixel-level attribute named motion-correlation, which effectively identifies and removes dynamic points. Furthermore, designed as a modular component, MCFilter ensures seamless integration into existing LiDAR SLAM systems.FindingsBased on rigorous testing with public data sets and real-world conditions, the MCFilter reported an increase in average accuracy of 12.39% and reduced processing time by 24.18%. These outcomes emphasize the method’s effectiveness in refining the performance of current LiDAR SLAM systems.Originality/valueIn this study, the authors present a novel 3D descriptor tracker designed for consistent feature point matching across successive frames. The authors also propose an innovative attribute to detect and eliminate noise points. Experimental results demonstrate that integrating this method into existing LiDAR SLAM systems yields state-of-the-art performance.

Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples.

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.

Implementation Chatbot on Discord for Information Assistance and Conflict Prevention

Discord, which was originally created for the gamer community, can now be found used by hobby groups and communities that are used for shared learning purposes. But the downside is the gamer culture that comes with it. Rude and toxic words that are synonymous with the gamer community should be avoided in study group communities. Meanwhile, the facilities for minimizing harsh and toxic words are still limited to word filters that can be tricked so that they can still be sent to the chat room. This can trigger conflict and interfere with learning activities together. This paper proposed an information assistance chatbot that is able to answer question, and conflict prevention with detection toxic sentences using pre-processing from NLP (Natural Language Processing) and text classification so that the chatbot is able to limit toxic sentences a little more accurately than the word filter feature alone. Also, Chatbots are given the ability to determine the value / level of toxic conversations so that they are had been able to determine the punishment action to be carried out by warning, suspending, or even being issued for the most severe cases. In addition, by looking at the frequency of sending messages from several senders, which indicates toxic, it was able to determine when the conflict occurs. The result shows that chatbot can work fine to answer question and detecting toxic include do punishment to toxic sender. With 10% error on detecting conflict and 30% error on answer question. That 30% error false positive on make an answer that should not be answered.

Novel Network Intrusion Detection Based on Feature Filtering Using FLAME and New Cuckoo Selection in a Genetic Algorithm

Recently, networks have faced a significant challenge in terms of security due to constant unauthorized access by hackers, resulting in the compromise of network user data. To enhance network security, there are various approaches that can be employed, including the utilization of firewalls, encryption, and antivirus software. Among these methods, one type of system that can be implemented is an intrusion detection system (IDS), which actively monitors the network to identify any intrusions. In order to effectively detect any unauthorized or malicious activities, sophisticated techniques such as genetic algorithms, cuckoo searches, and FLAME are employed. This research proposes a novel IDS that aims to improve the detection of intrusions. The proposed IDS initially conducts feature filtration using fuzzy clustering through the local approximation of the membership algorithm (FLAME), which effectively reduces the number of features that need to be analyzed and processed. Subsequently, the system detects attacks by implementing an extended classifier system (XCS) that incorporates a genetic algorithm (GA) to enhance the accuracy of intrusion detection. By incorporating the cuckoo search and selection within GA, while considering different crossover and mutation probabilities, instead of solely relying on traditional GA, it is anticipated that intrusion detection accuracy will be improved. To evaluate the performance of the proposed IDS, it was tested on the KDD99 dataset and compared with several other IDSs that were also tested on the same dataset. The experimental results clearly indicate that the proposed IDS significantly enhances the detection rate (DR) and accuracy while also significantly reducing the false alarm rate (FAR) and these impressive results were 100%, 99.99% and 0.05% respectively. The performance comparisons reveal that, overall, the proposed IDS outperforms several existing IDSs in terms of intrusion detection performance.