Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-through...Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.展开更多
This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized...This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized pipeline.Unlike prior works that address these tasks in isolation,our approach combines You Only Look Once(YOLO)v10 detection,ByteTrack tracking,optical-flow density estimation,Long Short-Term Memory-based(LSTM-based)trajectory forecasting,and hybrid Speeded-Up Robust Feature(SURF)+Gray-Level Co-occurrence Matrix(GLCM)feature engineering with VGG16 classification.Upon the validation across datasets(UAVDT and UAVID)our framework achieved a detection accuracy of 94.2%,and 92.3%detection accuracy when conducting a real-time UAV field validation.Our comprehensive evaluations,including multi-metric analyses,ablation studies,and cross-dataset validations,confirm the framework’s accuracy,efficiency,and generalizability.These results highlight the novelty of integrating complementary methods into a single framework,offering a practical solution for accurate and efficient UAV-based traffic monitoring.展开更多
The cervical spine injury represents a potential devastating disease with 6% associated in-hospital mortality （lain et al., 2015）. Neurological deterioration ranging from complete spinal cord injury （SCI） to incom...The cervical spine injury represents a potential devastating disease with 6% associated in-hospital mortality （lain et al., 2015）. Neurological deterioration ranging from complete spinal cord injury （SCI） to incomplete SCI or single radiculopathy are potential consequences of the blunt trauma over this region. The subaxial cervical spine accounts the vast majority of cervical injuries, making up two thirds of all cervical fractures （Alday, 1996）. Few classifications （Holdsworth, 1970; White et al., 1975; Mien et al., 1982; Denis, 1984; Vaccaro et al., 2007） have been proposed to describe injuries of the cervical spine for several reasons. First, to delineate the best treatment in each case; second, to determinate an accurate neurological prognosis, and third, to establish a standard way to communicate and describe specific characteristics of cervical injuries patterns. Classical systems are primarily descriptive and no single system has gained widespread use, largely because of restrictions in clinical relevance and its complexity.展开更多
Recently, researches on distributed data mining by making use of grid are in trend. This paper introduces a data mining algorithm by means of distributed decision-tree,which has taken the advantage of conveniences and...Recently, researches on distributed data mining by making use of grid are in trend. This paper introduces a data mining algorithm by means of distributed decision-tree,which has taken the advantage of conveniences and services supplied by the computing platform-grid,and can perform a data mining of distributed classification on grid.展开更多
Driven by both the“new engineering”initiative and the energy revolution,the traditional engineering education model can hardly meet the demand of the energy and electric power industry for diversified and interdisci...Driven by both the“new engineering”initiative and the energy revolution,the traditional engineering education model can hardly meet the demand of the energy and electric power industry for diversified and interdisciplinary outstanding engineers.Based on the“industry-university-research-application”four-in-one collaborative education concept,this paper constructs a new training system centered on classified cultivation and classified evaluation.The system aims to solve core problems such as homogeneous training,disconnection between industry and academia,single evaluation method,and insufficient faculty.Through measures including modular courses,the dual-tutor system,and diversified practical platforms,it realizes differentiated and precise talent training,so as to deliver outstanding engineers with the ability to“define problems,break through technologies,and create value”for the energy and electric power industry.展开更多
Existing weakly supervised semantic segmentation(WSSS)methods based on image-level labels always rely on class activation maps(CAMs),which measure the relationships between features and classifiers.However,CAMs only f...Existing weakly supervised semantic segmentation(WSSS)methods based on image-level labels always rely on class activation maps(CAMs),which measure the relationships between features and classifiers.However,CAMs only focus on the most discriminative regions of images,resulting in their poor coverage performance.We attribute this to the deficiency in the recognition ability of a single classifier and the negative impacts caused by magnitudes during the CAMs normalisation process.To address the aforementioned issues,we propose to construct selective multiple classifiers(SMC).During the training process,we extract multiple prototypes for each class and store them in the corresponding memory bank.These prototypes are divided into foreground and background prototypes,with the former used to identify foreground objects and the latter aimed at preventing the false activation of background pixels.As for the inference stage,multiple prototypes are adaptively selected from the memory bank for each image as SMC.Subsequently,CAMs are generated by measuring the angle between SMC and features.We enhance the recognition ability of classifiers by adaptively constructing multiple classifiers for each image,while only relying on angle measurement to generate CAMs can alleviate the suppression phenomenon caused by magnitudes.Furthermore,SMC can be integrated into other WSSS approaches to help generate better CAMs.Extensive experiments conducted on standard WSSS benchmarks such as PASCAL VOC 2012 and MS COCO 2014 demonstrate the superiority of our proposed method.展开更多
Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions f...Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera angles.This research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)videos.The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale images.The YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further processing.The joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are included.These features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity classification.Real-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing methods.In particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.展开更多
Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the ident...Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the identification of risk factors associated with genetic disorders.Methods:Our study introduces a novel two-tiered analytical framework to raise the precision and reliability of genetic data interpretation.It is initiated by extracting and analyzing salient features from DNA sequences through a CNN-based feature analysis,taking advantage of the power inherent in Convolutional neural networks(CNNs)to attain complex patterns and minute mutations in genetic data.This study embraces an elite collection of machine learning classifiers interweaved through a stern voting mechanism,which synergistically joins the predictions made from multiple classifiers to generate comprehensive and well-balanced interpretations of the genetic data.Results:This state-of-the-art method was further tested by carrying out an empirical analysis on a variants'dataset of DNA sequences taken from patients affected by breast cancer,juxtaposed with a control group composed of healthy people.Thus,the integration of CNNs with a voting-based ensemble of classifiers returned outstanding outcomes,with performance metrics accuracy,precision,recall,and F1-scorereaching the outstanding rate of 0.88,outperforming previous models.Conclusions:This dual accomplishment underlines the transformative potential that integrating deep learning techniques with ensemble machine learning might provide in real added value for further genetic diagnostics and prognostics.These results from this study set a new benchmark in the accuracy of disease diagnosis through DNA sequencing and promise future studies on improved personalized medicine and healthcare approaches with precise genetic information.展开更多
Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery conditi...Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery condition monitoring because that can fully use available data and computational power.Since significant accidents might be caused if wrong fault alarms are given for machine condition monitoring,interpretable machine learning models,integrate signal processing knowledge to enhance trustworthiness of models,are gradually becoming a research hotspot.A previous spectrum-based and interpretable optimized weights method has been proposed to indicate faulty and fundamental frequencies when the analyzed data only contains a healthy type and a fault type.Considering that multiclass fault types are naturally met in practice,this work aims to explore the interpretable optimized weights method for multiclass fault type scenarios.Therefore,a new multiclass optimized weights spectrum(OWS)is proposed and further studied theoretically and numerically.It is found that the multiclass OWS is capable of capturing the characteristic components associated with different conditions and clearly indicating specific fault characteristic frequencies(FCFs)corresponding to each fault condition.This work can provide new insights into spectrum-based fault classification models,and the new multiclass OWS also shows great potential for practical applications.展开更多
The categorization of brain tumors is a significant issue for healthcare applications.Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease.Brain tumors po...The categorization of brain tumors is a significant issue for healthcare applications.Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease.Brain tumors possess high changes in terms of size,shape,and amount,and hence the classification process acts as a more difficult research problem.This paper suggests a deep learning model using the magnetic resonance imaging technique that overcomes the limitations associated with the existing classification methods.The effectiveness of the suggested method depends on the coyote optimization algorithm,also known as the LOBO algorithm,which optimizes the weights of the deep-convolutional neural network classifier.The accuracy,sensitivity,and specificity indices,which are obtained to be 92.40%,94.15%,and 91.92%,respectively,are used to validate the effectiveness of the suggested method.The result suggests that the suggested strategy is superior for effectively classifying brain tumors.展开更多
The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic techno...The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic technology,plays a pivotal role in ensuring mine safety by enabling real-time identifi cation and accurate classification of vibration signals such as microseismic signals,blasting signals,and noise.These classifications are critical for improving the efficacy of ground pressure monitoring systems,conducting stability analyses of deep rock masses,and implementing timely and precise roadway support measures.Such eff orts are essential for mitigating ground pressure disasters and ensuring safe mining operations.This study proposes an artificial intelligence-based automatic classification network model for mine vibration signals.Based on conventional convolutional neural networks,the proposed model further incorporates long short-term memory(LSTM)networks and attention mechanisms.The LSTM component eff ectively captures temporal correlations in time-series mining vibration data,while the attention mechanism enhances the models’ability to focus on critical features within the data.To validate the eff ectiveness of our proposed model,a dataset comprising 480,526 waveform records collected in 2022 by the microseismic monitoring system at Guangxi Shanhu Tungsten Mine was used for training,validation,and testing purposes.Results demonstrate that the proposed artifi cial intelligence-based classifi cation method achieves a higher recognition accuracy of 92.21%,significantly outperforming traditional manual classification methods.The proposed model represents a signifi cant advancement in ground pressure monitoring and disaster mitigation.展开更多
Machine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature(seven well logs and one facies log) were used to classify four facies. Data preprocessing ...Machine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature(seven well logs and one facies log) were used to classify four facies. Data preprocessing and preparation involve two processes: data cleaning and feature scaling. Several machine learning algorithms, including Linear Regression(LR), Decision Tree(DT), Support Vector Machine(SVM),Random Forest(RF), and Gradient Boosting(GB) for classification, were tested using different iterations and various combinations of features and parameters. The support vector radial kernel training model achieved an accuracy of 72.49% without grid search and 64.02% with grid search, while the blind-well test scores were 71.01% and 69.67%, respectively. The Decision Tree(DT) Hyperparameter Optimization model showed an accuracy of 64.15% for training and 67.45% for testing. In comparison, the Decision Tree coupled with grid search yielded better results, with a training score of 69.91% and a testing score of67.89%. The model's validation was carried out using the blind well validation approach, which achieved an accuracy of 69.81%. Three algorithms were used to generate the gradient-boosting model. During training, the Gradient Boosting classifier achieved an accuracy score of 71.57%, and during testing, it achieved 69.89%. The Grid Search model achieved a higher accuracy score of 72.14% during testing. The Extreme Gradient Boosting model had the lowest accuracy score, with only 66.13% for training and66.12% for testing. For validation, the Gradient Boosting(GB) classifier model achieved an accuracy score of 75.41% on the blind well test, while the Gradient Boosting with Grid Search achieved an accuracy score of 71.36%. The Enhanced Random Forest and Random Forest with Bagging algorithms were the most effective, with validation accuracies of 78.30% and 79.18%, respectively. However, the Random Forest and Random Forest with Grid Search models displayed significant variance between their training and testing scores, indicating the potential for overfitting. Random Forest(RF) and Gradient Boosting(GB) are highly effective for facies classification because they handle complex relationships and provide high predictive accuracy. The choice between the two depends on specific project requirements, including interpretability, computational resources, and data nature.展开更多
基金the Deanship of Research and Graduate Studies at King Khalid University,KSA,for funding this work through the Large Research Project under grant number RGP2/164/46.
文摘Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD)(IITP-2025-RS-2022-00156326,50)grant funded by theKorea government(Ministry of Science and ICT)supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized pipeline.Unlike prior works that address these tasks in isolation,our approach combines You Only Look Once(YOLO)v10 detection,ByteTrack tracking,optical-flow density estimation,Long Short-Term Memory-based(LSTM-based)trajectory forecasting,and hybrid Speeded-Up Robust Feature(SURF)+Gray-Level Co-occurrence Matrix(GLCM)feature engineering with VGG16 classification.Upon the validation across datasets(UAVDT and UAVID)our framework achieved a detection accuracy of 94.2%,and 92.3%detection accuracy when conducting a real-time UAV field validation.Our comprehensive evaluations,including multi-metric analyses,ablation studies,and cross-dataset validations,confirm the framework’s accuracy,efficiency,and generalizability.These results highlight the novelty of integrating complementary methods into a single framework,offering a practical solution for accurate and efficient UAV-based traffic monitoring.
文摘The cervical spine injury represents a potential devastating disease with 6% associated in-hospital mortality （lain et al., 2015）. Neurological deterioration ranging from complete spinal cord injury （SCI） to incomplete SCI or single radiculopathy are potential consequences of the blunt trauma over this region. The subaxial cervical spine accounts the vast majority of cervical injuries, making up two thirds of all cervical fractures （Alday, 1996）. Few classifications （Holdsworth, 1970; White et al., 1975; Mien et al., 1982; Denis, 1984; Vaccaro et al., 2007） have been proposed to describe injuries of the cervical spine for several reasons. First, to delineate the best treatment in each case; second, to determinate an accurate neurological prognosis, and third, to establish a standard way to communicate and describe specific characteristics of cervical injuries patterns. Classical systems are primarily descriptive and no single system has gained widespread use, largely because of restrictions in clinical relevance and its complexity.
文摘Recently, researches on distributed data mining by making use of grid are in trend. This paper introduces a data mining algorithm by means of distributed decision-tree,which has taken the advantage of conveniences and services supplied by the computing platform-grid,and can perform a data mining of distributed classification on grid.
文摘Driven by both the“new engineering”initiative and the energy revolution,the traditional engineering education model can hardly meet the demand of the energy and electric power industry for diversified and interdisciplinary outstanding engineers.Based on the“industry-university-research-application”four-in-one collaborative education concept,this paper constructs a new training system centered on classified cultivation and classified evaluation.The system aims to solve core problems such as homogeneous training,disconnection between industry and academia,single evaluation method,and insufficient faculty.Through measures including modular courses,the dual-tutor system,and diversified practical platforms,it realizes differentiated and precise talent training,so as to deliver outstanding engineers with the ability to“define problems,break through technologies,and create value”for the energy and electric power industry.
基金supported by the National Natural Science Foundation of China(Grants 62176097,61433007)Fundamental Research Funds for the Central Universities(Grant 2019kfyXKJC024)the 111 Project on Computational Intelligence and Intelligent Control(Grant B18024).
文摘Existing weakly supervised semantic segmentation(WSSS)methods based on image-level labels always rely on class activation maps(CAMs),which measure the relationships between features and classifiers.However,CAMs only focus on the most discriminative regions of images,resulting in their poor coverage performance.We attribute this to the deficiency in the recognition ability of a single classifier and the negative impacts caused by magnitudes during the CAMs normalisation process.To address the aforementioned issues,we propose to construct selective multiple classifiers(SMC).During the training process,we extract multiple prototypes for each class and store them in the corresponding memory bank.These prototypes are divided into foreground and background prototypes,with the former used to identify foreground objects and the latter aimed at preventing the false activation of background pixels.As for the inference stage,multiple prototypes are adaptively selected from the memory bank for each image as SMC.Subsequently,CAMs are generated by measuring the angle between SMC and features.We enhance the recognition ability of classifiers by adaptively constructing multiple classifiers for each image,while only relying on angle measurement to generate CAMs can alleviate the suppression phenomenon caused by magnitudes.Furthermore,SMC can be integrated into other WSSS approaches to help generate better CAMs.Extensive experiments conducted on standard WSSS benchmarks such as PASCAL VOC 2012 and MS COCO 2014 demonstrate the superiority of our proposed method.
基金funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB Bremen.Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R348),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera angles.This research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)videos.The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale images.The YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further processing.The joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are included.These features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity classification.Real-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing methods.In particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.
文摘Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the identification of risk factors associated with genetic disorders.Methods:Our study introduces a novel two-tiered analytical framework to raise the precision and reliability of genetic data interpretation.It is initiated by extracting and analyzing salient features from DNA sequences through a CNN-based feature analysis,taking advantage of the power inherent in Convolutional neural networks(CNNs)to attain complex patterns and minute mutations in genetic data.This study embraces an elite collection of machine learning classifiers interweaved through a stern voting mechanism,which synergistically joins the predictions made from multiple classifiers to generate comprehensive and well-balanced interpretations of the genetic data.Results:This state-of-the-art method was further tested by carrying out an empirical analysis on a variants'dataset of DNA sequences taken from patients affected by breast cancer,juxtaposed with a control group composed of healthy people.Thus,the integration of CNNs with a voting-based ensemble of classifiers returned outstanding outcomes,with performance metrics accuracy,precision,recall,and F1-scorereaching the outstanding rate of 0.88,outperforming previous models.Conclusions:This dual accomplishment underlines the transformative potential that integrating deep learning techniques with ensemble machine learning might provide in real added value for further genetic diagnostics and prognostics.These results from this study set a new benchmark in the accuracy of disease diagnosis through DNA sequencing and promise future studies on improved personalized medicine and healthcare approaches with precise genetic information.
基金supported by the National Natural Science Foundation of China under Grant Nos.523B2043 and 52475112.
文摘Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry.Machine learning,especially deep learning,has become popular for machinery condition monitoring because that can fully use available data and computational power.Since significant accidents might be caused if wrong fault alarms are given for machine condition monitoring,interpretable machine learning models,integrate signal processing knowledge to enhance trustworthiness of models,are gradually becoming a research hotspot.A previous spectrum-based and interpretable optimized weights method has been proposed to indicate faulty and fundamental frequencies when the analyzed data only contains a healthy type and a fault type.Considering that multiclass fault types are naturally met in practice,this work aims to explore the interpretable optimized weights method for multiclass fault type scenarios.Therefore,a new multiclass optimized weights spectrum(OWS)is proposed and further studied theoretically and numerically.It is found that the multiclass OWS is capable of capturing the characteristic components associated with different conditions and clearly indicating specific fault characteristic frequencies(FCFs)corresponding to each fault condition.This work can provide new insights into spectrum-based fault classification models,and the new multiclass OWS also shows great potential for practical applications.
文摘The categorization of brain tumors is a significant issue for healthcare applications.Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease.Brain tumors possess high changes in terms of size,shape,and amount,and hence the classification process acts as a more difficult research problem.This paper suggests a deep learning model using the magnetic resonance imaging technique that overcomes the limitations associated with the existing classification methods.The effectiveness of the suggested method depends on the coyote optimization algorithm,also known as the LOBO algorithm,which optimizes the weights of the deep-convolutional neural network classifier.The accuracy,sensitivity,and specificity indices,which are obtained to be 92.40%,94.15%,and 91.92%,respectively,are used to validate the effectiveness of the suggested method.The result suggests that the suggested strategy is superior for effectively classifying brain tumors.
基金supported in part by the National Science Fund for Distinguished Young Scholars under Grant (42025403)the National Key Research and Development Plan of China (2021YFA0716800)the National Key Research and Development Plan of China (2022YFC2903804)。
文摘The increasing risk of ground pressure disasters resulting from deep well mining highlights the urgent need for advanced monitoring and early warning systems.Ground pressure monitoring,supported by microseismic technology,plays a pivotal role in ensuring mine safety by enabling real-time identifi cation and accurate classification of vibration signals such as microseismic signals,blasting signals,and noise.These classifications are critical for improving the efficacy of ground pressure monitoring systems,conducting stability analyses of deep rock masses,and implementing timely and precise roadway support measures.Such eff orts are essential for mitigating ground pressure disasters and ensuring safe mining operations.This study proposes an artificial intelligence-based automatic classification network model for mine vibration signals.Based on conventional convolutional neural networks,the proposed model further incorporates long short-term memory(LSTM)networks and attention mechanisms.The LSTM component eff ectively captures temporal correlations in time-series mining vibration data,while the attention mechanism enhances the models’ability to focus on critical features within the data.To validate the eff ectiveness of our proposed model,a dataset comprising 480,526 waveform records collected in 2022 by the microseismic monitoring system at Guangxi Shanhu Tungsten Mine was used for training,validation,and testing purposes.Results demonstrate that the proposed artifi cial intelligence-based classifi cation method achieves a higher recognition accuracy of 92.21%,significantly outperforming traditional manual classification methods.The proposed model represents a signifi cant advancement in ground pressure monitoring and disaster mitigation.
文摘Machine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature(seven well logs and one facies log) were used to classify four facies. Data preprocessing and preparation involve two processes: data cleaning and feature scaling. Several machine learning algorithms, including Linear Regression(LR), Decision Tree(DT), Support Vector Machine(SVM),Random Forest(RF), and Gradient Boosting(GB) for classification, were tested using different iterations and various combinations of features and parameters. The support vector radial kernel training model achieved an accuracy of 72.49% without grid search and 64.02% with grid search, while the blind-well test scores were 71.01% and 69.67%, respectively. The Decision Tree(DT) Hyperparameter Optimization model showed an accuracy of 64.15% for training and 67.45% for testing. In comparison, the Decision Tree coupled with grid search yielded better results, with a training score of 69.91% and a testing score of67.89%. The model's validation was carried out using the blind well validation approach, which achieved an accuracy of 69.81%. Three algorithms were used to generate the gradient-boosting model. During training, the Gradient Boosting classifier achieved an accuracy score of 71.57%, and during testing, it achieved 69.89%. The Grid Search model achieved a higher accuracy score of 72.14% during testing. The Extreme Gradient Boosting model had the lowest accuracy score, with only 66.13% for training and66.12% for testing. For validation, the Gradient Boosting(GB) classifier model achieved an accuracy score of 75.41% on the blind well test, while the Gradient Boosting with Grid Search achieved an accuracy score of 71.36%. The Enhanced Random Forest and Random Forest with Bagging algorithms were the most effective, with validation accuracies of 78.30% and 79.18%, respectively. However, the Random Forest and Random Forest with Grid Search models displayed significant variance between their training and testing scores, indicating the potential for overfitting. Random Forest(RF) and Gradient Boosting(GB) are highly effective for facies classification because they handle complex relationships and provide high predictive accuracy. The choice between the two depends on specific project requirements, including interpretability, computational resources, and data nature.
