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
Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimo...Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.展开更多
The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f...The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.展开更多
To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network mo...To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.展开更多
Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure p...Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment.Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment.However,traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level.On the other hand,models that focus on global semantic-level information might overlook critical,subtle local pathological features.To address this issue,we propose an adaptive multi-scale feature fusion network called(AMSFuse),which can adaptively combine multi-scale global and local features without compromising their individual representation.Specifically,our model incorporates global features for extracting high-level contextual information from retinal images.Concurrently,local features capture fine-grained details,such as microaneurysms,hemorrhages,and exudates,which are critical for DR diagnosis.These global and local features are adaptively fused using a fusion block,followed by an Integrated Attention Mechanism(IAM)that refines the fused features by emphasizing relevant regions,thereby enhancing classification accuracy for DR classification.Our model achieves 86.3%accuracy on the APTOS dataset and 96.6%RFMiD,both of which are comparable to state-of-the-art methods.展开更多
Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework...Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.展开更多
Although conventional object detection methods achieve high accuracy through extensively annotated datasets,acquiring such large-scale labeled data remains challenging and cost-prohibitive in numerous real-world appli...Although conventional object detection methods achieve high accuracy through extensively annotated datasets,acquiring such large-scale labeled data remains challenging and cost-prohibitive in numerous real-world applications.Few-shot object detection presents a new research idea that aims to localize and classify objects in images using only limited annotated examples.However,the inherent challenge in few-shot object detection lies in the insufficient sample diversity to fully characterize the sample feature distribution,which consequently impacts model performance.Inspired by contrastive learning principles,we propose an Implicit Feature Contrastive Learning(IFCL)module to address this limitation and augment feature diversity for more robust representational learning.This module generates augmented support sample features in a mixed feature space and implicitly contrasts them with query Region of Interest(RoI)features.This approach facilitates more comprehensive learning of both intra-class feature similarity and inter-class feature diversity,thereby enhancing the model’s object classification and localization capabilities.Extensive experiments on PASCAL VOC show that our method achieves a respective improvement of 3.2%,1.8%,and 2.3%on 10-shot of three Novel Sets compared to the baseline model FPD.展开更多
Hematoxylin and Eosin(H&E)images,popularly used in the field of digital pathology,often pose challenges due to their limited color richness,hindering the differentiation of subtle cell features crucial for accurat...Hematoxylin and Eosin(H&E)images,popularly used in the field of digital pathology,often pose challenges due to their limited color richness,hindering the differentiation of subtle cell features crucial for accurate classification.Enhancing the visibility of these elusive cell features helps train robust deep-learning models.However,the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community.To address this challenge,we introduce Salient Features Guided Augmentation(SFGA),an approach that strategically integrates machine learning and image processing.SFGA utilizes machine learning algorithms to identify crucial features within cell images,subsequently mapping these features to appropriate image processing techniques to enhance training images.By emphasizing salient features and aligning them with corresponding image processing methods,SFGA is designed to enhance the discriminating power of deep learning models in cell classification tasks.Our research undertakes a series of experiments,each exploring the performance of different datasets and data enhancement techniques in classifying cell types,highlighting the significance of data quality and enhancement in mitigating overfitting and distinguishing cell characteristics.Specifically,SFGA focuses on identifying tumor cells from tissue for extranodal extension detection,with the SFGA-enhanced dataset showing notable advantages in accuracy.We conducted a preliminary study of five experiments,among which the accuracy of the pleomorphism experiment improved significantly from 50.81%to 95.15%.The accuracy of the other four experiments also increased,with improvements ranging from 3 to 43 percentage points.Our preliminary study shows the possibilities to enhance the diagnostic accuracy of deep learning models and proposes a systematic approach that could enhance cancer diagnosis,contributing as a first step in using SFGA in medical image enhancement.展开更多
Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells an...Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.展开更多
With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of...With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of multimodal approaches for fake news detection has gained significant attention.To solve the problems existing in previous multi-modal fake news detection algorithms,such as insufficient feature extraction and insufficient use of semantic relations between modes,this paper proposes the MFFFND-Co(Multimodal Feature Fusion Fake News Detection with Co-Attention Block)model.First,the model deeply explores the textual content,image content,and frequency domain features.Then,it employs a Co-Attention mechanism for cross-modal fusion.Additionally,a semantic consistency detectionmodule is designed to quantify semantic deviations,thereby enhancing the performance of fake news detection.Experimentally verified on two commonly used datasets,Twitter and Weibo,the model achieved F1 scores of 90.0% and 94.0%,respectively,significantly outperforming the pre-modified MFFFND(Multimodal Feature Fusion Fake News Detection with Attention Block)model and surpassing other baseline models.This improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection.展开更多
Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE...Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.展开更多
Accurate and robust navigation in complex surgical environments is crucial for bronchoscopic surgeries.This study purposes a bronchoscopic lumen feature matching network(BLFM-Net)based on deep learning to address the ...Accurate and robust navigation in complex surgical environments is crucial for bronchoscopic surgeries.This study purposes a bronchoscopic lumen feature matching network(BLFM-Net)based on deep learning to address the challenges of image noise,anatomical complexity,and the stringent real-time requirements.The BLFM-Net enhances bronchoscopic image processing by integrating several functional modules.The FFA-Net preprocessing module mitigates image fogging and improves visual clarity for subsequent processing.The feature extraction module derives multi-dimensional features,such as centroids,area,and shape descriptors,from dehazed images.The Faster RCNN Object detection module detects bronchial regions of interest and generates bounding boxes to localize key areas.The feature matching module accelerates the process by combining detection boxes,extracted features,and a KD-Tree(K-Dimensional Tree)-based algorithm,ensuring efficient and accurate regional feature associations.The BLFM-Net was evaluated on 5212 bronchoscopic images,demonstrating superior performance compared to traditional and other deep learning-based image matching methods.It achieved real-time matching with an average frame time of 6 ms,with a matching accuracy of over 96%.The method remained robust under challenging conditions including frame dropping(0,5,10,20),shadowed regions,and variable lighting,maintaining accuracy of above 94%even with the frame dropping of 20.This study presents BLFM-Net,a deep learning-based matching network designed to enhance and match bronchial features in bronchoscopic images.The BLFM-Net shows improved accuracy,real-time performance,and reliability,making a valuable tool for bronchoscopic surgeries.展开更多
Acute lymphoblastic leukemia(ALL)is characterized by overgrowth of immature lymphoid cells in the bone marrow at the expense of normal hematopoiesis.One of the most prioritized tasks is the early and correct diagnosis...Acute lymphoblastic leukemia(ALL)is characterized by overgrowth of immature lymphoid cells in the bone marrow at the expense of normal hematopoiesis.One of the most prioritized tasks is the early and correct diagnosis of this malignancy;however,manual observation of the blood smear is very time-consuming and requires labor and expertise.Transfer learning in deep neural networks is of growing importance to intricate medical tasks such as medical imaging.Our work proposes an application of a novel ensemble architecture that puts together Vision Transformer and EfficientNetV2.This approach fuses deep and spatial features to optimize discriminative power by selecting features accurately,reducing redundancy,and promoting sparsity.Besides the architecture of the ensemble,the advanced feature selection is performed by the Frog-Snake Prey-Predation Relationship Optimization(FSRO)algorithm.FSRO prioritizes the most relevant features while dynamically reducing redundant and noisy data,hence improving the efficiency and accuracy of the classification model.We have compared our method for feature selection against state-of-the-art techniques and recorded an accuracy of 94.88%,a recall of 94.38%,a precision of 96.18%,and an F1-score of 95.63%.These figures are therefore better than the classical methods for deep learning.Though our dataset,collected from four different hospitals,is non-standard and heterogeneous,making the analysis more challenging,although computationally expensive,our approach proves diagnostically superior in cancer detection.Source codes and datasets are available on GitHub.展开更多
Background:Gene expression profiling plays a key role in cancer research,but its high dimensionality and redundancy pose challenges for effective analysis.Feature selection and robust classification are critical for i...Background:Gene expression profiling plays a key role in cancer research,but its high dimensionality and redundancy pose challenges for effective analysis.Feature selection and robust classification are critical for improving predictive performance,while explainable machine learning techniques support transparency and biomarker discovery.Methods:To propose a hybrid explainable machine learning framework that combines stability-guided multi-source(SGMS)feature selection with classification models for gene expression-based cancer prediction and biomarker identification.SGMS integrates Mutual Information,F-statistic,and random forest(RF)importance to select informative genes.These features are used to train classifiers,including novel elasticnet logistic regression(NEN-LR),RF,and Support Vector Machine(SVM).Performance is evaluated using accuracy,precision,recall,F1-score,and Matthews correlation coefficient(MCC).SHapley Additive exPlanations(SHAP)values are used to interpret gene-level contributions,and co-expression networks help identify functional gene modules.Results:The proposed NEN-LR classifier achieved the highest performance with 99.8%accuracy,99.9%precision,and 0.997 MCC using the top 200 SGMS-selected genes.Biomarker discovery identified both class-specific and shared genes across five cancer types,with top genes like gene_230,gene_5380,and gene_18570 consistently appearing across multiple classes.Visualization tools,including heatmaps,Venn diagrams,and co-expression networks,were used to interpret expression dynamics and regulatory patterns,enhancing the biological relevance of findings.SHAP analysis revealed top biomarkers with strong predictive influence,while co-expression clustering uncovered biologically meaningful gene modules.Other models also showed marked improvement using SGMS-selected features.Conclusion:The proposed framework successfully integrates feature selection,interpretable classification,and biomarker discovery,providing a powerful tool for precision oncology and molecular diagnostics.展开更多
Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irr...Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical history.These challenges often lead to inefficient and less accuratemodels.Traditional predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational cost.This work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel efficiency.Theselected features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction accuracy.This hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the ensemble.These enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional methods.The proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over traditionalmodels.Specifically,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medical datasets for heart disease prediction.展开更多
In the evolving landscape of cyber threats,phishing attacks pose significant challenges,particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy.Conventional and ...In the evolving landscape of cyber threats,phishing attacks pose significant challenges,particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy.Conventional and machine learning(ML)-based detection systems struggle to detect phishing websites owing to their constantly changing tactics.Furthermore,newer phishing websites exhibit subtle and expertly concealed indicators that are not readily detectable.Hence,effective detection depends on identifying the most critical features.Traditional feature selection(FS)methods often struggle to enhance ML model performance and instead decrease it.To combat these issues,we propose an innovative method using explainable AI(XAI)to enhance FS in ML models and improve the identification of phishing websites.Specifically,we employ SHapley Additive exPlanations(SHAP)for global perspective and aggregated local interpretable model-agnostic explanations(LIME)to deter-mine specific localized patterns.The proposed SHAP and LIME-aggregated FS(SLA-FS)framework pinpoints the most informative features,enabling more precise,swift,and adaptable phishing detection.Applying this approach to an up-to-date web phishing dataset,we evaluate the performance of three ML models before and after FS to assess their effectiveness.Our findings reveal that random forest(RF),with an accuracy of 97.41%and XGBoost(XGB)at 97.21%significantly benefit from the SLA-FS framework,while k-nearest neighbors lags.Our framework increases the accuracy of RF and XGB by 0.65%and 0.41%,respectively,outperforming traditional filter or wrapper methods and any prior methods evaluated on this dataset,showcasing its potential.展开更多
This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection,unsupervised clustering,and ensemble learning to improve classification performance in financial transaction m...This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection,unsupervised clustering,and ensemble learning to improve classification performance in financial transaction monitoring systems.The framework is structured into three core layers:(1)feature selection using Recursive Feature Elimination(RFE),Principal Component Analysis(PCA),and Mutual Information(MI)to reduce dimensionality and enhance input relevance;(2)anomaly detection through unsupervised clustering using K-Means,Density-Based Spatial Clustering(DBSCAN),and Hierarchical Clustering to flag suspicious patterns in unlabeled data;and(3)final classification using a voting-based hybrid ensemble of Support Vector Machine(SVM),Random Forest(RF),and Gradient Boosting Classifier(GBC).The experimental evaluation is conducted on a synthetically generated dataset comprising one million financial transactions,with 5% labelled as fraudulent,simulating realistic fraud rates and behavioural features,including transaction time,origin,amount,and geo-location.The proposed model demonstrated a significant improvement over baseline classifiers,achieving an accuracy of 99%,a precision of 99%,a recall of 97%,and an F1-score of 99%.Compared to individual models,it yielded a 9% gain in overall detection accuracy.It reduced the false positive rate to below 3.5%,thereby minimising the operational costs associated with manually reviewing false alerts.The model’s interpretability is enhanced by the integration of Shapley Additive Explanations(SHAP)values for feature importance,supporting transparency and regulatory auditability.These results affirm the practical relevance of the proposed system for deployment in real-time fraud detection scenarios such as credit card transactions,mobile banking,and cross-border payments.The study also highlights future directions,including the deployment of lightweight models and the integration of multimodal data for scalable fraud analytics.展开更多
The complex compositions of high-entropy alloys(HEAs)enable a variety of phase structures like FCC single phase,BCC single phase,or duplex FCC+BCC phase.Accurate and efficient prediction of phase structure is crucial ...The complex compositions of high-entropy alloys(HEAs)enable a variety of phase structures like FCC single phase,BCC single phase,or duplex FCC+BCC phase.Accurate and efficient prediction of phase structure is crucial for accelerating the discovery of new components and designing HEAs with desired phase structure.In this work,five machine learning strategies were utilized to predict the phase structures of HEAs with a dataset of 296.Specifically,a two-step feature selection strategy was proposed,enabling pronounced improvement in the computational efficiency from 2047 to 12 iterations for each model while ensuring fewer input features and higher prediction accuracy.Compared with traditional valence electron concentration criterion,the prediction accuracy of collected dataset was highly improved from 0.79 to 0.98 for random forest.Furthermore,HEAs with compositions of Al_(x)CoCu_(6)Ni_(6)Fe_(6)(x=1,3,6)were developed to validate the prediction results of machine learning models,and the mechanical properties as well as corrosion resistance were investigated.It is found that the higher Al content enhances the yield strength but deteriorates corrosion resistance.The present two-step feature selection strategy provides an alternative method that is feasible for predicting the phase structure of HEAs with high efficiency and accuracy.展开更多
Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart d...Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart disease,but more remains to be accomplished.The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches.By using data fusion from several regions of the country,we intend to increase the accuracy of heart disease prediction.A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern in the data,which cannot be adequately captured by a single feature.We processed the data using techniques including feature scaling,outlier detection and replacement,null and missing value imputation,and more to improve the data quality.Furthermore,the proposed feature engineering method uses the correlation test for numerical features and the chi-square test for categorical features to interact with the feature.To reduce the dimensionality,we subsequently used PCA with 95%variation.To identify patients with heart disease,hyperparameter-based machine learning algorithms like RF,XGBoost,Gradient Boosting,LightGBM,CatBoost,SVM,and MLP are utilized,along with ensemble models.The model’s overall prediction performance ranges from 88%to 92%.In order to attain cutting-edge results,we then used a 1D CNN model,which significantly enhanced the prediction with an accuracy score of 96.36%,precision of 96.45%,recall of 96.36%,specificity score of 99.51%and F1 score of 96.34%.The RF model produces the best results among all the classifiers in the evaluation matrix without feature interaction,with accuracy of 90.21%,precision of 90.40%,recall of 90.86%,specificity of 90.91%,and F1 score of 90.63%.Our proposed 1D CNN model is 7%superior to the one without feature engineering when compared to the suggested approach.This illustrates how interaction-focused feature analysis can produce precise and useful insights for heart disease diagnosis.展开更多
Aimed at the issues of high feature dimensionality,excessive data redundancy,and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition,a recognition method was proposed based on ...Aimed at the issues of high feature dimensionality,excessive data redundancy,and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition,a recognition method was proposed based on CatBoost feature selection and Stacking ensemble learning.First,the method uses a feature selection algorithm to filter important features and remove features with less impact,achieving the effect of data dimensionality reduction.Second,random forests classifier,decision trees,K-nearest neighbor classifier,and light gradient boosting machine were used as base classifiers,and support vector machine was used as meta classifier to fuse and construct the ensemble learning model.This measure increases the accuracy of the classification model while maintaining the diversity of the base classifiers.The experimental results show that the recognition accuracy of the proposed method reaches 94.375%.Compared to the random forest algorithm with the best performance among single classifiers,the accuracy of the proposed method is increased by 1.875%.Compared to the recent deep learning methods(ResNet+GBM+Attention and MVCSNet)on ground-glass pulmonary nodule recognition,the proposed method’s performance is also better or comparative.Experiments show that the proposed model can effectively select features and make recognition on ground-glass pulmonary nodules.展开更多
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.
基金supported by the Science and Technology Project of Henan Province(No.222102210081).
文摘Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.
基金Supported by the Henan Province Key Research and Development Project(231111211300)the Central Government of Henan Province Guides Local Science and Technology Development Funds(Z20231811005)+2 种基金Henan Province Key Research and Development Project(231111110100)Henan Provincial Outstanding Foreign Scientist Studio(GZS2024006)Henan Provincial Joint Fund for Scientific and Technological Research and Development Plan(Application and Overcoming Technical Barriers)(242103810028)。
文摘The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.
基金supported by National Natural Science Foundation of China(No.61862037)Lanzhou Jiaotong University Tianyou Innovation Team Project(No.TY202002)。
文摘To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.
基金supported by the National Natural Science Foundation of China(No.62376287)the International Science and Technology Innovation Joint Base of Machine Vision and Medical Image Processing in Hunan Province(2021CB1013)the Natural Science Foundation of Hunan Province(Nos.2022JJ30762,2023JJ70016).
文摘Globally,diabetic retinopathy(DR)is the primary cause of blindness,affecting millions of people worldwide.This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment.Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment.However,traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level.On the other hand,models that focus on global semantic-level information might overlook critical,subtle local pathological features.To address this issue,we propose an adaptive multi-scale feature fusion network called(AMSFuse),which can adaptively combine multi-scale global and local features without compromising their individual representation.Specifically,our model incorporates global features for extracting high-level contextual information from retinal images.Concurrently,local features capture fine-grained details,such as microaneurysms,hemorrhages,and exudates,which are critical for DR diagnosis.These global and local features are adaptively fused using a fusion block,followed by an Integrated Attention Mechanism(IAM)that refines the fused features by emphasizing relevant regions,thereby enhancing classification accuracy for DR classification.Our model achieves 86.3%accuracy on the APTOS dataset and 96.6%RFMiD,both of which are comparable to state-of-the-art methods.
基金King Saud University,Grant/Award Number:RSP2024R157。
文摘Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.
基金funded by the China Chongqing Municipal Science and Technology Bureau,grant numbers CSTB2024TIAD-CYKJCXX0009,CSTB2024NSCQ-LZX0043,CSTB2022NSCQ-MSX0288Chongqing Municipal Commission of Housing and Urban-Rural Development,grant number CKZ2024-87+3 种基金the Chongqing University of Technology Graduate Education High-Quality Development Project,grant number gzlsz202401the Chongqing University of Technology—Chongqing LINGLUE Technology Co.,Ltd.Electronic Information(Artificial Intelligence)Graduate Joint Training Basethe Postgraduate Education and Teaching Reform Research Project in Chongqing,grant number yjg213116the Chongqing University of Technology-CISDI Chongqing Information Technology Co.,Ltd.Computer Technology Graduate Joint Training Base.
文摘Although conventional object detection methods achieve high accuracy through extensively annotated datasets,acquiring such large-scale labeled data remains challenging and cost-prohibitive in numerous real-world applications.Few-shot object detection presents a new research idea that aims to localize and classify objects in images using only limited annotated examples.However,the inherent challenge in few-shot object detection lies in the insufficient sample diversity to fully characterize the sample feature distribution,which consequently impacts model performance.Inspired by contrastive learning principles,we propose an Implicit Feature Contrastive Learning(IFCL)module to address this limitation and augment feature diversity for more robust representational learning.This module generates augmented support sample features in a mixed feature space and implicitly contrasts them with query Region of Interest(RoI)features.This approach facilitates more comprehensive learning of both intra-class feature similarity and inter-class feature diversity,thereby enhancing the model’s object classification and localization capabilities.Extensive experiments on PASCAL VOC show that our method achieves a respective improvement of 3.2%,1.8%,and 2.3%on 10-shot of three Novel Sets compared to the baseline model FPD.
基金supported by grants fromthe North China University of Technology Research Start-Up Fund(11005136024XN147-14)and(110051360024XN151-97)Guangzhou Development Zone Science and Technology Project(2023GH02)+4 种基金the National Key R&D Program of China(2021YFE0201100 and 2022YFA1103401 to Juntao Gao)National Natural Science Foundation of China(981890991 to Juntao Gao)Beijing Municipal Natural Science Foundation(Z200021 to Juntao Gao)CAS Interdisciplinary Innovation Team(JCTD-2020-04 to Juntao Gao)0032/2022/A,by Macao FDCT,and MYRG2022-00271-FST.
文摘Hematoxylin and Eosin(H&E)images,popularly used in the field of digital pathology,often pose challenges due to their limited color richness,hindering the differentiation of subtle cell features crucial for accurate classification.Enhancing the visibility of these elusive cell features helps train robust deep-learning models.However,the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community.To address this challenge,we introduce Salient Features Guided Augmentation(SFGA),an approach that strategically integrates machine learning and image processing.SFGA utilizes machine learning algorithms to identify crucial features within cell images,subsequently mapping these features to appropriate image processing techniques to enhance training images.By emphasizing salient features and aligning them with corresponding image processing methods,SFGA is designed to enhance the discriminating power of deep learning models in cell classification tasks.Our research undertakes a series of experiments,each exploring the performance of different datasets and data enhancement techniques in classifying cell types,highlighting the significance of data quality and enhancement in mitigating overfitting and distinguishing cell characteristics.Specifically,SFGA focuses on identifying tumor cells from tissue for extranodal extension detection,with the SFGA-enhanced dataset showing notable advantages in accuracy.We conducted a preliminary study of five experiments,among which the accuracy of the pleomorphism experiment improved significantly from 50.81%to 95.15%.The accuracy of the other four experiments also increased,with improvements ranging from 3 to 43 percentage points.Our preliminary study shows the possibilities to enhance the diagnostic accuracy of deep learning models and proposes a systematic approach that could enhance cancer diagnosis,contributing as a first step in using SFGA in medical image enhancement.
基金funded by the China Chongqing Municipal Science and Technology Bureau,grant numbers 2024TIAD-CYKJCXX0121,2024NSCQ-LZX0135Chongqing Municipal Commission of Housing and Urban-Rural Development,grant number CKZ2024-87+3 种基金the Chongqing University of Technology graduate education high-quality development project,grant number gzlsz202401the Chongqing University of Technology-Chongqing LINGLUE Technology Co.,Ltd.,Electronic Information(Artificial Intelligence)graduate joint training basethe Postgraduate Education and Teaching Reform Research Project in Chongqing,grant number yjg213116the Chongqing University of Technology-CISDI Chongqing Information Technology Co.,Ltd.,Computer Technology graduate joint training base.
文摘Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer.However,this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size.Pathologists often refer to surrounding cells to identify abnormalities.To emulate this slide examination behavior,this study proposes a Multi-Scale Feature Fusion Network(MSFF-Net)for detecting cervical abnormal cells.MSFF-Net employs a Cross-Scale Pooling Model(CSPM)to effectively capture diverse features and contextual information,ranging from local details to the overall structure.Additionally,a Multi-Scale Fusion Attention(MSFA)module is introduced to mitigate the impact of cell size variations by adaptively fusing local and global information at different scales.To handle the complex environment of cervical cell images,such as cell adhesion and overlapping,the Inner-CIoU loss function is utilized to more precisely measure the overlap between bounding boxes,thereby improving detection accuracy in such scenarios.Experimental results on the Comparison detector dataset demonstrate that MSFF-Net achieves a mean average precision(mAP)of 63.2%,outperforming state-of-the-art methods while maintaining a relatively small number of parameters(26.8 M).This study highlights the effectiveness of multi-scale feature fusion in enhancing the detection of cervical abnormal cells,contributing to more accurate and efficient cervical cancer screening.
基金supported by Communication University of China(HG23035)partly supported by the Fundamental Research Funds for the Central Universities(CUC230A013).
文摘With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of multimodal approaches for fake news detection has gained significant attention.To solve the problems existing in previous multi-modal fake news detection algorithms,such as insufficient feature extraction and insufficient use of semantic relations between modes,this paper proposes the MFFFND-Co(Multimodal Feature Fusion Fake News Detection with Co-Attention Block)model.First,the model deeply explores the textual content,image content,and frequency domain features.Then,it employs a Co-Attention mechanism for cross-modal fusion.Additionally,a semantic consistency detectionmodule is designed to quantify semantic deviations,thereby enhancing the performance of fake news detection.Experimentally verified on two commonly used datasets,Twitter and Weibo,the model achieved F1 scores of 90.0% and 94.0%,respectively,significantly outperforming the pre-modified MFFFND(Multimodal Feature Fusion Fake News Detection with Attention Block)model and surpassing other baseline models.This improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection.
基金supported by the National Key Research and Development Program of China(2023YFB3307800)National Natural Science Foundation of China(62394343,62373155)+2 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024A26)Fundamental Research Funds for the Central Universities.
文摘Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.
基金funded by the National Natural Science Foundation of China(Grant No.52175028).
文摘Accurate and robust navigation in complex surgical environments is crucial for bronchoscopic surgeries.This study purposes a bronchoscopic lumen feature matching network(BLFM-Net)based on deep learning to address the challenges of image noise,anatomical complexity,and the stringent real-time requirements.The BLFM-Net enhances bronchoscopic image processing by integrating several functional modules.The FFA-Net preprocessing module mitigates image fogging and improves visual clarity for subsequent processing.The feature extraction module derives multi-dimensional features,such as centroids,area,and shape descriptors,from dehazed images.The Faster RCNN Object detection module detects bronchial regions of interest and generates bounding boxes to localize key areas.The feature matching module accelerates the process by combining detection boxes,extracted features,and a KD-Tree(K-Dimensional Tree)-based algorithm,ensuring efficient and accurate regional feature associations.The BLFM-Net was evaluated on 5212 bronchoscopic images,demonstrating superior performance compared to traditional and other deep learning-based image matching methods.It achieved real-time matching with an average frame time of 6 ms,with a matching accuracy of over 96%.The method remained robust under challenging conditions including frame dropping(0,5,10,20),shadowed regions,and variable lighting,maintaining accuracy of above 94%even with the frame dropping of 20.This study presents BLFM-Net,a deep learning-based matching network designed to enhance and match bronchial features in bronchoscopic images.The BLFM-Net shows improved accuracy,real-time performance,and reliability,making a valuable tool for bronchoscopic surgeries.
文摘Acute lymphoblastic leukemia(ALL)is characterized by overgrowth of immature lymphoid cells in the bone marrow at the expense of normal hematopoiesis.One of the most prioritized tasks is the early and correct diagnosis of this malignancy;however,manual observation of the blood smear is very time-consuming and requires labor and expertise.Transfer learning in deep neural networks is of growing importance to intricate medical tasks such as medical imaging.Our work proposes an application of a novel ensemble architecture that puts together Vision Transformer and EfficientNetV2.This approach fuses deep and spatial features to optimize discriminative power by selecting features accurately,reducing redundancy,and promoting sparsity.Besides the architecture of the ensemble,the advanced feature selection is performed by the Frog-Snake Prey-Predation Relationship Optimization(FSRO)algorithm.FSRO prioritizes the most relevant features while dynamically reducing redundant and noisy data,hence improving the efficiency and accuracy of the classification model.We have compared our method for feature selection against state-of-the-art techniques and recorded an accuracy of 94.88%,a recall of 94.38%,a precision of 96.18%,and an F1-score of 95.63%.These figures are therefore better than the classical methods for deep learning.Though our dataset,collected from four different hospitals,is non-standard and heterogeneous,making the analysis more challenging,although computationally expensive,our approach proves diagnostically superior in cancer detection.Source codes and datasets are available on GitHub.
文摘Background:Gene expression profiling plays a key role in cancer research,but its high dimensionality and redundancy pose challenges for effective analysis.Feature selection and robust classification are critical for improving predictive performance,while explainable machine learning techniques support transparency and biomarker discovery.Methods:To propose a hybrid explainable machine learning framework that combines stability-guided multi-source(SGMS)feature selection with classification models for gene expression-based cancer prediction and biomarker identification.SGMS integrates Mutual Information,F-statistic,and random forest(RF)importance to select informative genes.These features are used to train classifiers,including novel elasticnet logistic regression(NEN-LR),RF,and Support Vector Machine(SVM).Performance is evaluated using accuracy,precision,recall,F1-score,and Matthews correlation coefficient(MCC).SHapley Additive exPlanations(SHAP)values are used to interpret gene-level contributions,and co-expression networks help identify functional gene modules.Results:The proposed NEN-LR classifier achieved the highest performance with 99.8%accuracy,99.9%precision,and 0.997 MCC using the top 200 SGMS-selected genes.Biomarker discovery identified both class-specific and shared genes across five cancer types,with top genes like gene_230,gene_5380,and gene_18570 consistently appearing across multiple classes.Visualization tools,including heatmaps,Venn diagrams,and co-expression networks,were used to interpret expression dynamics and regulatory patterns,enhancing the biological relevance of findings.SHAP analysis revealed top biomarkers with strong predictive influence,while co-expression clustering uncovered biologically meaningful gene modules.Other models also showed marked improvement using SGMS-selected features.Conclusion:The proposed framework successfully integrates feature selection,interpretable classification,and biomarker discovery,providing a powerful tool for precision oncology and molecular diagnostics.
文摘Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical history.These challenges often lead to inefficient and less accuratemodels.Traditional predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational cost.This work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel efficiency.Theselected features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction accuracy.This hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the ensemble.These enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional methods.The proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over traditionalmodels.Specifically,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medical datasets for heart disease prediction.
文摘In the evolving landscape of cyber threats,phishing attacks pose significant challenges,particularly through deceptive webpages designed to extract sensitive information under the guise of legitimacy.Conventional and machine learning(ML)-based detection systems struggle to detect phishing websites owing to their constantly changing tactics.Furthermore,newer phishing websites exhibit subtle and expertly concealed indicators that are not readily detectable.Hence,effective detection depends on identifying the most critical features.Traditional feature selection(FS)methods often struggle to enhance ML model performance and instead decrease it.To combat these issues,we propose an innovative method using explainable AI(XAI)to enhance FS in ML models and improve the identification of phishing websites.Specifically,we employ SHapley Additive exPlanations(SHAP)for global perspective and aggregated local interpretable model-agnostic explanations(LIME)to deter-mine specific localized patterns.The proposed SHAP and LIME-aggregated FS(SLA-FS)framework pinpoints the most informative features,enabling more precise,swift,and adaptable phishing detection.Applying this approach to an up-to-date web phishing dataset,we evaluate the performance of three ML models before and after FS to assess their effectiveness.Our findings reveal that random forest(RF),with an accuracy of 97.41%and XGBoost(XGB)at 97.21%significantly benefit from the SLA-FS framework,while k-nearest neighbors lags.Our framework increases the accuracy of RF and XGB by 0.65%and 0.41%,respectively,outperforming traditional filter or wrapper methods and any prior methods evaluated on this dataset,showcasing its potential.
基金funded by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Grant No.KFU241683].
文摘This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection,unsupervised clustering,and ensemble learning to improve classification performance in financial transaction monitoring systems.The framework is structured into three core layers:(1)feature selection using Recursive Feature Elimination(RFE),Principal Component Analysis(PCA),and Mutual Information(MI)to reduce dimensionality and enhance input relevance;(2)anomaly detection through unsupervised clustering using K-Means,Density-Based Spatial Clustering(DBSCAN),and Hierarchical Clustering to flag suspicious patterns in unlabeled data;and(3)final classification using a voting-based hybrid ensemble of Support Vector Machine(SVM),Random Forest(RF),and Gradient Boosting Classifier(GBC).The experimental evaluation is conducted on a synthetically generated dataset comprising one million financial transactions,with 5% labelled as fraudulent,simulating realistic fraud rates and behavioural features,including transaction time,origin,amount,and geo-location.The proposed model demonstrated a significant improvement over baseline classifiers,achieving an accuracy of 99%,a precision of 99%,a recall of 97%,and an F1-score of 99%.Compared to individual models,it yielded a 9% gain in overall detection accuracy.It reduced the false positive rate to below 3.5%,thereby minimising the operational costs associated with manually reviewing false alerts.The model’s interpretability is enhanced by the integration of Shapley Additive Explanations(SHAP)values for feature importance,supporting transparency and regulatory auditability.These results affirm the practical relevance of the proposed system for deployment in real-time fraud detection scenarios such as credit card transactions,mobile banking,and cross-border payments.The study also highlights future directions,including the deployment of lightweight models and the integration of multimodal data for scalable fraud analytics.
基金the Shenzhen Fundamental Research Fund(No.JCYJ20210324122801005)the Fundamental Research Funds for the Central Universities(No.HIT.OCEF.2023022).
文摘The complex compositions of high-entropy alloys(HEAs)enable a variety of phase structures like FCC single phase,BCC single phase,or duplex FCC+BCC phase.Accurate and efficient prediction of phase structure is crucial for accelerating the discovery of new components and designing HEAs with desired phase structure.In this work,five machine learning strategies were utilized to predict the phase structures of HEAs with a dataset of 296.Specifically,a two-step feature selection strategy was proposed,enabling pronounced improvement in the computational efficiency from 2047 to 12 iterations for each model while ensuring fewer input features and higher prediction accuracy.Compared with traditional valence electron concentration criterion,the prediction accuracy of collected dataset was highly improved from 0.79 to 0.98 for random forest.Furthermore,HEAs with compositions of Al_(x)CoCu_(6)Ni_(6)Fe_(6)(x=1,3,6)were developed to validate the prediction results of machine learning models,and the mechanical properties as well as corrosion resistance were investigated.It is found that the higher Al content enhances the yield strength but deteriorates corrosion resistance.The present two-step feature selection strategy provides an alternative method that is feasible for predicting the phase structure of HEAs with high efficiency and accuracy.
基金supported by the Competitive Research Fund of the University of Aizu,Japan(Grant No.P-13).
文摘Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart disease,but more remains to be accomplished.The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches.By using data fusion from several regions of the country,we intend to increase the accuracy of heart disease prediction.A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern in the data,which cannot be adequately captured by a single feature.We processed the data using techniques including feature scaling,outlier detection and replacement,null and missing value imputation,and more to improve the data quality.Furthermore,the proposed feature engineering method uses the correlation test for numerical features and the chi-square test for categorical features to interact with the feature.To reduce the dimensionality,we subsequently used PCA with 95%variation.To identify patients with heart disease,hyperparameter-based machine learning algorithms like RF,XGBoost,Gradient Boosting,LightGBM,CatBoost,SVM,and MLP are utilized,along with ensemble models.The model’s overall prediction performance ranges from 88%to 92%.In order to attain cutting-edge results,we then used a 1D CNN model,which significantly enhanced the prediction with an accuracy score of 96.36%,precision of 96.45%,recall of 96.36%,specificity score of 99.51%and F1 score of 96.34%.The RF model produces the best results among all the classifiers in the evaluation matrix without feature interaction,with accuracy of 90.21%,precision of 90.40%,recall of 90.86%,specificity of 90.91%,and F1 score of 90.63%.Our proposed 1D CNN model is 7%superior to the one without feature engineering when compared to the suggested approach.This illustrates how interaction-focused feature analysis can produce precise and useful insights for heart disease diagnosis.
基金the National Natural Science Foundation of China(No.62271466)the Natural Science Foundation of Beijing(No.4202025)+1 种基金the Tianjin IoT Technology Enterprise Key Laboratory Research Project(No.VTJ-OT20230209-2)the Guizhou Provincial Sci-Tech Project(No.ZK[2022]-012)。
文摘Aimed at the issues of high feature dimensionality,excessive data redundancy,and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition,a recognition method was proposed based on CatBoost feature selection and Stacking ensemble learning.First,the method uses a feature selection algorithm to filter important features and remove features with less impact,achieving the effect of data dimensionality reduction.Second,random forests classifier,decision trees,K-nearest neighbor classifier,and light gradient boosting machine were used as base classifiers,and support vector machine was used as meta classifier to fuse and construct the ensemble learning model.This measure increases the accuracy of the classification model while maintaining the diversity of the base classifiers.The experimental results show that the recognition accuracy of the proposed method reaches 94.375%.Compared to the random forest algorithm with the best performance among single classifiers,the accuracy of the proposed method is increased by 1.875%.Compared to the recent deep learning methods(ResNet+GBM+Attention and MVCSNet)on ground-glass pulmonary nodule recognition,the proposed method’s performance is also better or comparative.Experiments show that the proposed model can effectively select features and make recognition on ground-glass pulmonary nodules.
