In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential grow...In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of data.These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality.On the other hand,this growth also made it difficult to comprehend and utilize data for various purposes.The results of imaging data can become biased because of extraneous features present in larger datasets.Feature selection gives a chance to decrease the number of components in such large datasets.Through selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization precision.The correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast control.This paper presents a review of feature selection techniques and attributes selection measures for medical imaging.This review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining algorithms.The review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced data.Moreover,this review provides the importance of feature selection for correct classification of medical infections.In the end,critical analysis and future directions are provided.展开更多
Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s economy.The manual inspection of fruit diseases is a chaotic process that is both time and co...Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s economy.The manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an expert.Hence,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf images.According to the literature,many automated methods have been developed for the recognition of fruit diseases at the early stage.However,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant features.Image processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these challenges.Therefore,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based selection.The proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final recognition.The experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.展开更多
Gait recognition is an active research area that uses a walking theme to identify the subject correctly.Human Gait Recognition(HGR)is performed without any cooperation from the individual.However,in practice,it remain...Gait recognition is an active research area that uses a walking theme to identify the subject correctly.Human Gait Recognition(HGR)is performed without any cooperation from the individual.However,in practice,it remains a challenging task under diverse walking sequences due to the covariant factors such as normal walking and walking with wearing a coat.Researchers,over the years,have worked on successfully identifying subjects using different techniques,but there is still room for improvement in accuracy due to these covariant factors.This paper proposes an automated model-free framework for human gait recognition in this article.There are a few critical steps in the proposed method.Firstly,optical flow-based motion region esti-mation and dynamic coordinates-based cropping are performed.The second step involves training a fine-tuned pre-trained MobileNetV2 model on both original and optical flow cropped frames;the training has been conducted using static hyperparameters.The third step proposed a fusion technique known as normal distribution serially fusion.In the fourth step,a better optimization algorithm is applied to select the best features,which are then classified using a Bi-Layered neural network.Three publicly available datasets,CASIA A,CASIA B,and CASIA C,were used in the experimental process and obtained average accuracies of 99.6%,91.6%,and 95.02%,respectively.The proposed framework has achieved improved accuracy compared to the other methods.展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ...Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.展开更多
Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Par...Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges.However,there are inherent limitations in Particle Swarm Optimization,such as the delicate balance between exploration and exploitation,susceptibility to local optima,and suboptimal convergence rates,hinder its performance.To tackle these issues,this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization,tailored for wrapper-based feature selection.The proposed approach integrates:(1)a fitness-landscape adaptive strategy to dynamically balance exploration and exploitation,(2)the lever principle within Opposition-Based Learning to improve search efficiency,and(3)a Local Selection and Re-optimization mechanism combined with random perturbation to expedite convergence and enhance the quality of the optimal feature subset.The effectiveness of is rigorously evaluated on 24 benchmark datasets and compared against 13 advancedmetaheuristic algorithms.Experimental results demonstrate that the proposed method outperforms the compared algorithms in classification accuracy on over half of the datasets,whilst also significantly reducing the number of selected features.These findings demonstrate its effectiveness and robustness in feature selection tasks.展开更多
Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an in...Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an interpretable machine learning approach to UCS prediction is presented,pairing five models(Random Forest(RF),Gradient Boosting(GB),Extreme Gradient Boosting(XGB),CatBoost,and K-Nearest Neighbors(KNN))with SHapley Additive exPlanations(SHAP)for enhanced interpretability and to guide feature removal.A complete dataset of 12 geotechnical and chemical parameters,i.e.,Atterberg limits,compaction properties,stabilizer chemistry,dosage,curing time,was used to train and test the models.R2,RMSE,MSE,and MAE were used to assess performance.Initial results with all 12 features indicated that boosting-based models(GB,XGB,CatBoost)exhibited the highest predictive accuracy(R^(2)=0.93)with satisfactory generalization on test data,followed by RF and KNN.SHAP analysis consistently picked CaO content,curing time,stabilizer dosage,and compaction parameters as the most important features,aligning with established soil stabilization mechanisms.Models were then re-trained on the top 8 and top 5 SHAP-ranked features.Interestingly,GB,XGB,and CatBoost maintained comparable accuracy with reduced input sets,while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality.The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss.The suggested hybrid approach offers an explainable,interpretable,and cost-effective tool for geotechnical engineering practice.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the los...Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the loss in the production of cotton.Although several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex background.Due to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease classification.Therefore in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×1000.After that,the extracted features are serially concatenated having a feature vector lengthN×2000.Themost prominent features are selected usingEmperor PenguinOptimizer(EPO)method.The method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle cotton-leaf-infection-II.The EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,respectively.The classification is performed using 5,7,and 10 folds cross-validation.The Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.展开更多
To solve the problems of the AMR-WB+(Extended Adaptive Multi-Rate-WideBand) semi-open-loop coding mode selection algorithm,features for ACELP(Algebraic Code Excited Linear Prediction) and TCX(Transform Coded eXcitatio...To solve the problems of the AMR-WB+(Extended Adaptive Multi-Rate-WideBand) semi-open-loop coding mode selection algorithm,features for ACELP(Algebraic Code Excited Linear Prediction) and TCX(Transform Coded eXcitation) classification are investigated.11 classifying features in the AMR-WB+ codec are selected and 2 novel classifying features,i.e.,EFM(Energy Flatness Measurement) and stdEFM(standard deviation of EFM),are proposed.Consequently,a novel semi-open-loop mode selection algorithm based on EFM and selected AMR-WB+ features is proposed.The results of classifying test and listening test show that the performance of the novel algorithm is much better than that of the AMR-WB+ semi-open-loop coding mode selection algorithm.展开更多
Machine learning potentials are promising in atomistic simulations due to their comparable accuracy to first-principles theory but much lower computational cost.However,the reliability,speed,and transferability of ato...Machine learning potentials are promising in atomistic simulations due to their comparable accuracy to first-principles theory but much lower computational cost.However,the reliability,speed,and transferability of atomistic machine learning potentials depend strongly on the way atomic configurations are represented.A wise choice of descriptors used as input for the machine learning program is the key for a successful machine learning representation.Here we develop a simple and efficient strategy to automatically select an optimal set of linearly-independent atomic features out of a large pool of candidates,based on the correlations that are intrinsic to the training data.Through applications to the construction of embedded atom neural network potentials for several benchmark molecules with less redundant linearly-independent embedded density descriptors,we demonstrate the efficiency and accuracy of this new strategy.The proposed algorithm can greatly simplify the initial selection of atomic features and vastly improve the performance of the atomistic machine learning potentials.展开更多
The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.展开更多
Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software ...Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software defect prediction can be effectively performed using traditional features,but there are some redundant or irrelevant features in them(the presence or absence of this feature has little effect on the prediction results).These problems can be solved using feature selection.However,existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset.In order to reduce the impact of these shortcomings,this paper proposes a new feature selection method Cubic TraverseMa Beluga whale optimization algorithm(CTMBWO)based on the improved Beluga whale optimization algorithm(BWO).The goal of this study is to determine how well the CTMBWO can extract the features that are most important for correctly predicting software defects,improve the accuracy of fault prediction,reduce the number of the selected feature and mitigate the risk of overfitting,thereby achieving more efficient resource utilization and better distribution of test workload.The CTMBWO comprises three main stages:preprocessing the dataset,selecting relevant features,and evaluating the classification performance of the model.The novel feature selection method can effectively improve the performance of SDP.This study performs experiments on two software defect datasets(PROMISE,NASA)and shows the method’s classification performance using four detailed evaluation metrics,Accuracy,F1-score,MCC,AUC and Recall.The results indicate that the approach presented in this paper achieves outstanding classification performance on both datasets and has significant improvement over the baseline models.展开更多
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp...Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.展开更多
Purpose-Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios.Existing clothing recognition methods based on RGBD clothing images often ...Purpose-Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios.Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations,leading to compromised performance and efficiency.Design/methodology/approach-To address this issue,this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection(MEDFS)to select global and local features,thereby reducing the dimensionality of the feature representation and improving performance.Specifically,by combining three global features and three local features,a low-dimensional embedding is constructed to capture the correlations between features and categories.The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection.The optimization objective is solved using an alternating iterative strategy,ensuring convergence.Findings-Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.Originality/value-This paper introduces a novel approach for multi-category clothing recognition and retrieval,incorporating the selection of global and local features.The proposed method holds potential for practical applications in real-world clothing scenarios.展开更多
This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—cove...This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—covering momentum,volatility,volume,and trend-related technical indicators—are subjected to three distinct feature selection approaches.Specifically,mutual information(MI),recursive feature elimination(RFE),and random forest importance(RFI).By extracting an optimal set of 20 predictors,the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability.These feature subsets are integrated into support vector regression(SVR),Huber regressors,and k-nearest neighbors(KNN)models to forecast the prices of three leading cryptocurrencies—Bitcoin(BTC/USDT),Ethereum(ETH/USDT),and Binance Coin(BNB/USDT)—across horizons ranging from 1 to 20 days.Model evaluation employs the coefficient of determination(R2)and the root mean squared logarithmic error(RMSLE),alongside a walk-forward validation scheme to approximate real-world trading contexts.Empirical results indicate that incorporating momentum and volatility measures substantially improves predictive accuracy,with particularly pronounced effects observed at longer forecast windows.Moreover,indicators related to volume and trend provide incremental benefits in select market conditions.Notably,an 80%–85% reduction in the original feature set frequently maintains or enhances model performance relative to the complete indicator set.These findings highlight the critical role of targeted feature selection in addressing high-dimensional financial data challenges while preserving model robustness.This research advances the field of cryptocurrency forecasting by offering a rigorous comparison of feature selection methods and their effects on multiple digital assets and prediction horizons.The outcomes highlight the importance of dimension-reduction strategies in developing more efficient and resilient forecasting algorithms.Future efforts should incorporate high-frequency data and explore alternative selection techniques to further refine predictive accuracy in this highly volatile domain.展开更多
文摘In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of data.These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality.On the other hand,this growth also made it difficult to comprehend and utilize data for various purposes.The results of imaging data can become biased because of extraneous features present in larger datasets.Feature selection gives a chance to decrease the number of components in such large datasets.Through selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization precision.The correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast control.This paper presents a review of feature selection techniques and attributes selection measures for medical imaging.This review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining algorithms.The review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced data.Moreover,this review provides the importance of feature selection for correct classification of medical infections.In the end,critical analysis and future directions are provided.
基金This research work was partially supported by Chiang Mai University.
文摘Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s economy.The manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an expert.Hence,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf images.According to the literature,many automated methods have been developed for the recognition of fruit diseases at the early stage.However,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant features.Image processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these challenges.Therefore,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based selection.The proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final recognition.The experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.
基金supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘Gait recognition is an active research area that uses a walking theme to identify the subject correctly.Human Gait Recognition(HGR)is performed without any cooperation from the individual.However,in practice,it remains a challenging task under diverse walking sequences due to the covariant factors such as normal walking and walking with wearing a coat.Researchers,over the years,have worked on successfully identifying subjects using different techniques,but there is still room for improvement in accuracy due to these covariant factors.This paper proposes an automated model-free framework for human gait recognition in this article.There are a few critical steps in the proposed method.Firstly,optical flow-based motion region esti-mation and dynamic coordinates-based cropping are performed.The second step involves training a fine-tuned pre-trained MobileNetV2 model on both original and optical flow cropped frames;the training has been conducted using static hyperparameters.The third step proposed a fusion technique known as normal distribution serially fusion.In the fourth step,a better optimization algorithm is applied to select the best features,which are then classified using a Bi-Layered neural network.Three publicly available datasets,CASIA A,CASIA B,and CASIA C,were used in the experimental process and obtained average accuracies of 99.6%,91.6%,and 95.02%,respectively.The proposed framework has achieved improved accuracy compared to the other methods.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia Grant No.KFU253765.
文摘Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金supported by National Natural Science Foundation of China(62106092)Natural Science Foundation of Fujian Province(2024J01822,2024J01820,2022J01916)Natural Science Foundation of Zhangzhou City(ZZ2024J28).
文摘Feature selection serves as a critical preprocessing step inmachine learning,focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms.Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges.However,there are inherent limitations in Particle Swarm Optimization,such as the delicate balance between exploration and exploitation,susceptibility to local optima,and suboptimal convergence rates,hinder its performance.To tackle these issues,this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization,tailored for wrapper-based feature selection.The proposed approach integrates:(1)a fitness-landscape adaptive strategy to dynamically balance exploration and exploitation,(2)the lever principle within Opposition-Based Learning to improve search efficiency,and(3)a Local Selection and Re-optimization mechanism combined with random perturbation to expedite convergence and enhance the quality of the optimal feature subset.The effectiveness of is rigorously evaluated on 24 benchmark datasets and compared against 13 advancedmetaheuristic algorithms.Experimental results demonstrate that the proposed method outperforms the compared algorithms in classification accuracy on over half of the datasets,whilst also significantly reducing the number of selected features.These findings demonstrate its effectiveness and robustness in feature selection tasks.
文摘Unconfined Compressive Strength(UCS)is a key parameter for the assessment of the stability and performance of stabilized soils,yet traditional laboratory testing is both time and resource intensive.In this study,an interpretable machine learning approach to UCS prediction is presented,pairing five models(Random Forest(RF),Gradient Boosting(GB),Extreme Gradient Boosting(XGB),CatBoost,and K-Nearest Neighbors(KNN))with SHapley Additive exPlanations(SHAP)for enhanced interpretability and to guide feature removal.A complete dataset of 12 geotechnical and chemical parameters,i.e.,Atterberg limits,compaction properties,stabilizer chemistry,dosage,curing time,was used to train and test the models.R2,RMSE,MSE,and MAE were used to assess performance.Initial results with all 12 features indicated that boosting-based models(GB,XGB,CatBoost)exhibited the highest predictive accuracy(R^(2)=0.93)with satisfactory generalization on test data,followed by RF and KNN.SHAP analysis consistently picked CaO content,curing time,stabilizer dosage,and compaction parameters as the most important features,aligning with established soil stabilization mechanisms.Models were then re-trained on the top 8 and top 5 SHAP-ranked features.Interestingly,GB,XGB,and CatBoost maintained comparable accuracy with reduced input sets,while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality.The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss.The suggested hybrid approach offers an explainable,interpretable,and cost-effective tool for geotechnical engineering practice.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
基金supported by the Technology Development Program of MSS[No.S3033853]by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A4A1031509).
文摘Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the loss in the production of cotton.Although several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex background.Due to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease classification.Therefore in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×1000.After that,the extracted features are serially concatenated having a feature vector lengthN×2000.Themost prominent features are selected usingEmperor PenguinOptimizer(EPO)method.The method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle cotton-leaf-infection-II.The EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,respectively.The classification is performed using 5,7,and 10 folds cross-validation.The Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.
文摘To solve the problems of the AMR-WB+(Extended Adaptive Multi-Rate-WideBand) semi-open-loop coding mode selection algorithm,features for ACELP(Algebraic Code Excited Linear Prediction) and TCX(Transform Coded eXcitation) classification are investigated.11 classifying features in the AMR-WB+ codec are selected and 2 novel classifying features,i.e.,EFM(Energy Flatness Measurement) and stdEFM(standard deviation of EFM),are proposed.Consequently,a novel semi-open-loop mode selection algorithm based on EFM and selected AMR-WB+ features is proposed.The results of classifying test and listening test show that the performance of the novel algorithm is much better than that of the AMR-WB+ semi-open-loop coding mode selection algorithm.
基金supported by CAS Project for Young Scientists in Basic Research(YSBR-005)the National Natural Science Foundation of China(No.22073089 and No.22033007)+1 种基金Anhui Initiative in Quantum Information Technologies(AHY090200)the Fundamental Research Funds for Central Universities(WK2060000017)。
文摘Machine learning potentials are promising in atomistic simulations due to their comparable accuracy to first-principles theory but much lower computational cost.However,the reliability,speed,and transferability of atomistic machine learning potentials depend strongly on the way atomic configurations are represented.A wise choice of descriptors used as input for the machine learning program is the key for a successful machine learning representation.Here we develop a simple and efficient strategy to automatically select an optimal set of linearly-independent atomic features out of a large pool of candidates,based on the correlations that are intrinsic to the training data.Through applications to the construction of embedded atom neural network potentials for several benchmark molecules with less redundant linearly-independent embedded density descriptors,we demonstrate the efficiency and accuracy of this new strategy.The proposed algorithm can greatly simplify the initial selection of atomic features and vastly improve the performance of the atomistic machine learning potentials.
文摘The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.
文摘Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software defect prediction can be effectively performed using traditional features,but there are some redundant or irrelevant features in them(the presence or absence of this feature has little effect on the prediction results).These problems can be solved using feature selection.However,existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset.In order to reduce the impact of these shortcomings,this paper proposes a new feature selection method Cubic TraverseMa Beluga whale optimization algorithm(CTMBWO)based on the improved Beluga whale optimization algorithm(BWO).The goal of this study is to determine how well the CTMBWO can extract the features that are most important for correctly predicting software defects,improve the accuracy of fault prediction,reduce the number of the selected feature and mitigate the risk of overfitting,thereby achieving more efficient resource utilization and better distribution of test workload.The CTMBWO comprises three main stages:preprocessing the dataset,selecting relevant features,and evaluating the classification performance of the model.The novel feature selection method can effectively improve the performance of SDP.This study performs experiments on two software defect datasets(PROMISE,NASA)and shows the method’s classification performance using four detailed evaluation metrics,Accuracy,F1-score,MCC,AUC and Recall.The results indicate that the approach presented in this paper achieves outstanding classification performance on both datasets and has significant improvement over the baseline models.
基金the Deanship of Scientifc Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/421/45supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1446)+1 种基金supported by theResearchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408).
文摘Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
文摘Purpose-Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios.Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations,leading to compromised performance and efficiency.Design/methodology/approach-To address this issue,this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection(MEDFS)to select global and local features,thereby reducing the dimensionality of the feature representation and improving performance.Specifically,by combining three global features and three local features,a low-dimensional embedding is constructed to capture the correlations between features and categories.The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection.The optimization objective is solved using an alternating iterative strategy,ensuring convergence.Findings-Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.Originality/value-This paper introduces a novel approach for multi-category clothing recognition and retrieval,incorporating the selection of global and local features.The proposed method holds potential for practical applications in real-world clothing scenarios.
文摘This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical indicators.In this work,over 130 technical indicators—covering momentum,volatility,volume,and trend-related technical indicators—are subjected to three distinct feature selection approaches.Specifically,mutual information(MI),recursive feature elimination(RFE),and random forest importance(RFI).By extracting an optimal set of 20 predictors,the proposed framework aims to mitigate redundancy and overfitting while enhancing interpretability.These feature subsets are integrated into support vector regression(SVR),Huber regressors,and k-nearest neighbors(KNN)models to forecast the prices of three leading cryptocurrencies—Bitcoin(BTC/USDT),Ethereum(ETH/USDT),and Binance Coin(BNB/USDT)—across horizons ranging from 1 to 20 days.Model evaluation employs the coefficient of determination(R2)and the root mean squared logarithmic error(RMSLE),alongside a walk-forward validation scheme to approximate real-world trading contexts.Empirical results indicate that incorporating momentum and volatility measures substantially improves predictive accuracy,with particularly pronounced effects observed at longer forecast windows.Moreover,indicators related to volume and trend provide incremental benefits in select market conditions.Notably,an 80%–85% reduction in the original feature set frequently maintains or enhances model performance relative to the complete indicator set.These findings highlight the critical role of targeted feature selection in addressing high-dimensional financial data challenges while preserving model robustness.This research advances the field of cryptocurrency forecasting by offering a rigorous comparison of feature selection methods and their effects on multiple digital assets and prediction horizons.The outcomes highlight the importance of dimension-reduction strategies in developing more efficient and resilient forecasting algorithms.Future efforts should incorporate high-frequency data and explore alternative selection techniques to further refine predictive accuracy in this highly volatile domain.
