叶端定时是航空发动机叶片叶端振动非接触测量的有效手段,但其采样模式决定了所采信号具有高度欠采样特征,需要进行抗混叠频谱分析从而提取转子叶片固有频率这一关键指标。利用了前向平滑策略的改进多重信号分类法(multiple sIgnal clas...叶端定时是航空发动机叶片叶端振动非接触测量的有效手段,但其采样模式决定了所采信号具有高度欠采样特征,需要进行抗混叠频谱分析从而提取转子叶片固有频率这一关键指标。利用了前向平滑策略的改进多重信号分类法(multiple sIgnal classification,MUSIC)能实现抗混叠但无法充分发挥平滑方法的优势。因此,提出适用于叶端定时信号处理的前后向平滑MUSIC法,通过建立传感器的对称布局条件,利用前后向平滑方法代替前向平滑方法,得到更准确的自相关矩阵估计,进而提高叶片固有频率估计性能,并通过仿真和试验验证了在样本数量、算法参数等相同的情况下,前后向平滑MUSIC法的混叠与噪声抑制能力得到了提升。展开更多
BACKGROUND Musical hallucinations(MH)involve the false perception of music in the absence of external stimuli which links with different etiologies.The pathomechanisms of MH encompass various conditions.The etiologica...BACKGROUND Musical hallucinations(MH)involve the false perception of music in the absence of external stimuli which links with different etiologies.The pathomechanisms of MH encompass various conditions.The etiological classification of MH is of particular importance and offers valuable insights to understand MH,and further to develop the effective treatment of MH.Over the recent decades,more MH cases have been reported,revealing newly identified medical and psychiatric causes of MH.Functional imaging studies reveal that MH activates a wide array of brain regions.An up-to-date analysis on MH,especially on MH comorbid psychiatric conditions is warranted.AIM To propose a new classification of MH;to study the age and gender differences of MH in mental disorders;and neuropathology of MH.METHODS Literatures searches were conducted using keywords such as“music hallucination,”“music hallucination and mental illness,”“music hallucination and gender difference,”and“music hallucination and psychiatric disease”in the databases of PubMed,Google Scholar,and Web of Science.MH cases were collected and categorized based on their etiologies.The t-test and ANOVA were employed(P<0.05)to compare the age differences of MH different etiological groups.Function neuroimaging studies of neural networks regulating MH and their possible molecular mechanisms were discussed.RESULTS Among the 357 yielded publications,294 MH cases were collected.The average age of MH cases was 67.9 years,with a predominance of females(66.8%females vs 33.2%males).MH was classified into eight groups based on their etiological mechanisms.Statistical analysis of MH cases indicates varying associations with psychiatric diagnoses.CONCLUSION We carried out a more comprehensive review of MH studies.For the first time according to our knowledge,we demonstrated the psychiatric conditions linked and/or associated with MH from statistical,biological and molecular point of view.展开更多
Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services...Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.展开更多
实际变压器局部放电定位过程中放电源数目是未知的,常利用传统高分辨波达方向(direction of arrival,DOA)估计算法解决放电定位问题,但在信源数欠估计、过估计情况下存在定位精度低、误差大的问题。为此,本文提出了一种基于改进盖氏圆(g...实际变压器局部放电定位过程中放电源数目是未知的,常利用传统高分辨波达方向(direction of arrival,DOA)估计算法解决放电定位问题,但在信源数欠估计、过估计情况下存在定位精度低、误差大的问题。为此,本文提出了一种基于改进盖氏圆(geschgorin disk estimator,GDE)准则联合多重信号分类(multiple signal classification,MUSIC)算法的变压器局部放电多目标定位方法。首先,利用改进盖氏圆准则确定真实放电源数目;然后,在信源数确定的情况下利用MUSIC算法对多个局部放电源的波达方向进行估计。仿真结果表明,本方法定位精度高,且在白噪声和空间色噪声的情况下仍能对放电源的俯仰角和方位角进行准确估计,能够满足实际工程需求。展开更多
With the development of new media technology and the popularity of the TikTok platform in China,a large number of popular vocal music teachers have flocked to online platforms for teaching.Online vocal music education...With the development of new media technology and the popularity of the TikTok platform in China,a large number of popular vocal music teachers have flocked to online platforms for teaching.Online vocal music education in China is undergoing a transformation and facing challenges.This study adopts an exploratory research approach,interviewing students learning pop vocal music,and observing popular pop teachers on TikTok.The advantages,disadvantages,techniques,and methods of domestic TikTok pop vocal music teaching were investigated and studied,and a series of experiences and suggestions for optimizing TikTok teaching were put forward.The results of this study are helpful for understanding the advantages and disadvantages of TikTok pop vocal music teaching and grasping the correct development direction.These guidance and suggestions can stimulate teachers’creativity and improve their vocal music teaching level.展开更多
The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textile...The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.展开更多
Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronar...Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.展开更多
In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in spec...In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in specific genes,type ofβ-cell impairment,degree of insulin resistance,and clinical characteristics of metabolic profiles.Improved classification methods enable healthcare providers to formulate blood glucose management strategies more precisely.Applying these updated classification systems,will assist clinicians in further optimising treatment plans,including targeted drug therapies,personalized dietary advice,and specific exercise plans.Ultimately,this will facilitate stricter blood glucose control,minimize the risks of hypoglycaemia and hyperglycaemia,and reduce long-term complications associated with diabetes.展开更多
In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue...In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.展开更多
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based...With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.展开更多
Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subse...Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.展开更多
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp...Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.展开更多
With the widespread use of upper gastrointestinal endoscopy,more and more gastric polyps(GPs)are being detected.Traditional management strategies often rely on histopathologic examination,which can be time-consuming a...With the widespread use of upper gastrointestinal endoscopy,more and more gastric polyps(GPs)are being detected.Traditional management strategies often rely on histopathologic examination,which can be time-consuming and may not guide immediate clinical decisions.This paper aims to introduce a novel classification system for GPs based on their potential risk of malignant transformation,categorizing them as"good","bad",and"ugly".A review of the literature and clinical case analysis were conducted to explore the clinical implications,management strategies,and the system's application in endoscopic practice.Good polyps,mainly including fundic gland polyps and inflammatory fibrous polyps,have a low risk of malignancy and typically require minimal or no intervention.Bad polyps,mainly including hyperplastic polyps and adenomas,pose an intermediate risk of malignancy,necessitating closer monitoring or removal.Ugly polyps,mainly including type 3 neuroendocrine tumors and early gastric cancer,indicate a high potential for malignancy and require urgent and comprehensive treatment.The new classification system provides a simplified and practical framework for diagnosing and managing GPs,improving diagnostic accuracy,guiding individualized treatment,and promoting advancements in endoscopic techniques.Despite some challenges,such as the risk of misclassification due to similar endoscopic appearances,this system is essential for the standardized management of GPs.It also lays the foundation for future research into biomarkers and the development of personalized medicine.展开更多
In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This...In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This method combines two core modules:a simple parameter-free self-attention and cross-attention.By analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot conditions.The proposed cross-correlation network integrates these two modules and is trained in an end-to-end manner.The simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between images.Compared with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target objects.Experimental results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.展开更多
As a unique form of education,music education influences individuals’thoughts,emotions,and overall qualities through the medium of music.It has become an indispensable component of modern educational systems.Whether ...As a unique form of education,music education influences individuals’thoughts,emotions,and overall qualities through the medium of music.It has become an indispensable component of modern educational systems.Whether viewed broadly as an art form that enhances individuals’aesthetic,moral,and humanistic literacy,or narrowly as systematic instruction within school settings,music education plays a crucial role in students’holistic development.It not only cultivates musical literacy but also promotes intellectual,emotional,and social growth.Thus,music education holds significant social and cultural value in fostering creativity,inspiring emotions,and shaping character.展开更多
With the continuous development and maturation of artificial intelligence(AI)technology,the influence on music artists is becoming increasingly prominent.While a large number of musicians have benefited from AI techno...With the continuous development and maturation of artificial intelligence(AI)technology,the influence on music artists is becoming increasingly prominent.While a large number of musicians have benefited from AI technology and achieved considerable success,there are also many who have fallen into difficulties due to the emergence of AI technology.This article expounds the positive and negative impacts that artificial intelligence technology has brought to modern music artists based on the phenomena in reality.This paper will prompt music artists to think about how to use technological means to bring convenience to themselves.At the same time,they will also take a series of actions to avoid risks and minimize the adverse impact of artificial intelligence technology on music artists.展开更多
The study examines the socio-cultural context of Russian music in the decades immediately preceding and following the October Revolution of 1917,focusing on several aspects:personalities representing pre-and post-revo...The study examines the socio-cultural context of Russian music in the decades immediately preceding and following the October Revolution of 1917,focusing on several aspects:personalities representing pre-and post-revolutionary Russian(in exile)and Soviet(at home)musical activity and culture;second-tier national composers;“industrial music”of the early Soviet period;and Soviet cultural management in music behind the Iron Curtain.Portraits of composers little known to Western listeners and academic audiences are presented.The spectrum of musical styles and aesthetics is considered.Issues of ethnic diversity and religious affiliation of Russian composers are relevantly explored.Songs of the First Revolution(1905),the Civil War,and Soviet mass songs are briefly touched upon.The scope and choice of themes of the study provide a holistic approach to topics that are usually addressed separately:before or after the 1917 Revolution;classical music or folk/popular songs.展开更多
文摘叶端定时是航空发动机叶片叶端振动非接触测量的有效手段,但其采样模式决定了所采信号具有高度欠采样特征,需要进行抗混叠频谱分析从而提取转子叶片固有频率这一关键指标。利用了前向平滑策略的改进多重信号分类法(multiple sIgnal classification,MUSIC)能实现抗混叠但无法充分发挥平滑方法的优势。因此,提出适用于叶端定时信号处理的前后向平滑MUSIC法,通过建立传感器的对称布局条件,利用前后向平滑方法代替前向平滑方法,得到更准确的自相关矩阵估计,进而提高叶片固有频率估计性能,并通过仿真和试验验证了在样本数量、算法参数等相同的情况下,前后向平滑MUSIC法的混叠与噪声抑制能力得到了提升。
文摘BACKGROUND Musical hallucinations(MH)involve the false perception of music in the absence of external stimuli which links with different etiologies.The pathomechanisms of MH encompass various conditions.The etiological classification of MH is of particular importance and offers valuable insights to understand MH,and further to develop the effective treatment of MH.Over the recent decades,more MH cases have been reported,revealing newly identified medical and psychiatric causes of MH.Functional imaging studies reveal that MH activates a wide array of brain regions.An up-to-date analysis on MH,especially on MH comorbid psychiatric conditions is warranted.AIM To propose a new classification of MH;to study the age and gender differences of MH in mental disorders;and neuropathology of MH.METHODS Literatures searches were conducted using keywords such as“music hallucination,”“music hallucination and mental illness,”“music hallucination and gender difference,”and“music hallucination and psychiatric disease”in the databases of PubMed,Google Scholar,and Web of Science.MH cases were collected and categorized based on their etiologies.The t-test and ANOVA were employed(P<0.05)to compare the age differences of MH different etiological groups.Function neuroimaging studies of neural networks regulating MH and their possible molecular mechanisms were discussed.RESULTS Among the 357 yielded publications,294 MH cases were collected.The average age of MH cases was 67.9 years,with a predominance of females(66.8%females vs 33.2%males).MH was classified into eight groups based on their etiological mechanisms.Statistical analysis of MH cases indicates varying associations with psychiatric diagnoses.CONCLUSION We carried out a more comprehensive review of MH studies.For the first time according to our knowledge,we demonstrated the psychiatric conditions linked and/or associated with MH from statistical,biological and molecular point of view.
文摘Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.
文摘实际变压器局部放电定位过程中放电源数目是未知的,常利用传统高分辨波达方向(direction of arrival,DOA)估计算法解决放电定位问题,但在信源数欠估计、过估计情况下存在定位精度低、误差大的问题。为此,本文提出了一种基于改进盖氏圆(geschgorin disk estimator,GDE)准则联合多重信号分类(multiple signal classification,MUSIC)算法的变压器局部放电多目标定位方法。首先,利用改进盖氏圆准则确定真实放电源数目;然后,在信源数确定的情况下利用MUSIC算法对多个局部放电源的波达方向进行估计。仿真结果表明,本方法定位精度高,且在白噪声和空间色噪声的情况下仍能对放电源的俯仰角和方位角进行准确估计,能够满足实际工程需求。
文摘With the development of new media technology and the popularity of the TikTok platform in China,a large number of popular vocal music teachers have flocked to online platforms for teaching.Online vocal music education in China is undergoing a transformation and facing challenges.This study adopts an exploratory research approach,interviewing students learning pop vocal music,and observing popular pop teachers on TikTok.The advantages,disadvantages,techniques,and methods of domestic TikTok pop vocal music teaching were investigated and studied,and a series of experiences and suggestions for optimizing TikTok teaching were put forward.The results of this study are helpful for understanding the advantages and disadvantages of TikTok pop vocal music teaching and grasping the correct development direction.These guidance and suggestions can stimulate teachers’creativity and improve their vocal music teaching level.
基金supported in part by the Six Talent Peaks Project in Jiangsu Province under Grant 013040315in part by the China Textile Industry Federation Science and Technology Guidance Project under Grant 2017107+1 种基金in part by the National Natural Science Foundation of China under Grant 31570714in part by the China Scholarship Council under Grant 202108320290。
文摘The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.
基金the Research Grant of Kwangwoon University in 2024.
文摘Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.
文摘In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in specific genes,type ofβ-cell impairment,degree of insulin resistance,and clinical characteristics of metabolic profiles.Improved classification methods enable healthcare providers to formulate blood glucose management strategies more precisely.Applying these updated classification systems,will assist clinicians in further optimising treatment plans,including targeted drug therapies,personalized dietary advice,and specific exercise plans.Ultimately,this will facilitate stricter blood glucose control,minimize the risks of hypoglycaemia and hyperglycaemia,and reduce long-term complications associated with diabetes.
文摘In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.
基金supported by the National Key Research and Development Program of China No.2023YFA1009500.
文摘With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.
基金supported in part by NIH grants R01NS39600,U01MH114829RF1MH128693(to GAA)。
文摘Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.
基金the Deanship of Scientifc Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/421/45supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1446)+1 种基金supported by theResearchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408).
文摘Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
文摘With the widespread use of upper gastrointestinal endoscopy,more and more gastric polyps(GPs)are being detected.Traditional management strategies often rely on histopathologic examination,which can be time-consuming and may not guide immediate clinical decisions.This paper aims to introduce a novel classification system for GPs based on their potential risk of malignant transformation,categorizing them as"good","bad",and"ugly".A review of the literature and clinical case analysis were conducted to explore the clinical implications,management strategies,and the system's application in endoscopic practice.Good polyps,mainly including fundic gland polyps and inflammatory fibrous polyps,have a low risk of malignancy and typically require minimal or no intervention.Bad polyps,mainly including hyperplastic polyps and adenomas,pose an intermediate risk of malignancy,necessitating closer monitoring or removal.Ugly polyps,mainly including type 3 neuroendocrine tumors and early gastric cancer,indicate a high potential for malignancy and require urgent and comprehensive treatment.The new classification system provides a simplified and practical framework for diagnosing and managing GPs,improving diagnostic accuracy,guiding individualized treatment,and promoting advancements in endoscopic techniques.Despite some challenges,such as the risk of misclassification due to similar endoscopic appearances,this system is essential for the standardized management of GPs.It also lays the foundation for future research into biomarkers and the development of personalized medicine.
基金Supported by the National Pre-research Program during the 14th Five-Year Plan(514010405)。
文摘In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This method combines two core modules:a simple parameter-free self-attention and cross-attention.By analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot conditions.The proposed cross-correlation network integrates these two modules and is trained in an end-to-end manner.The simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between images.Compared with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target objects.Experimental results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.
文摘As a unique form of education,music education influences individuals’thoughts,emotions,and overall qualities through the medium of music.It has become an indispensable component of modern educational systems.Whether viewed broadly as an art form that enhances individuals’aesthetic,moral,and humanistic literacy,or narrowly as systematic instruction within school settings,music education plays a crucial role in students’holistic development.It not only cultivates musical literacy but also promotes intellectual,emotional,and social growth.Thus,music education holds significant social and cultural value in fostering creativity,inspiring emotions,and shaping character.
文摘With the continuous development and maturation of artificial intelligence(AI)technology,the influence on music artists is becoming increasingly prominent.While a large number of musicians have benefited from AI technology and achieved considerable success,there are also many who have fallen into difficulties due to the emergence of AI technology.This article expounds the positive and negative impacts that artificial intelligence technology has brought to modern music artists based on the phenomena in reality.This paper will prompt music artists to think about how to use technological means to bring convenience to themselves.At the same time,they will also take a series of actions to avoid risks and minimize the adverse impact of artificial intelligence technology on music artists.
文摘The study examines the socio-cultural context of Russian music in the decades immediately preceding and following the October Revolution of 1917,focusing on several aspects:personalities representing pre-and post-revolutionary Russian(in exile)and Soviet(at home)musical activity and culture;second-tier national composers;“industrial music”of the early Soviet period;and Soviet cultural management in music behind the Iron Curtain.Portraits of composers little known to Western listeners and academic audiences are presented.The spectrum of musical styles and aesthetics is considered.Issues of ethnic diversity and religious affiliation of Russian composers are relevantly explored.Songs of the First Revolution(1905),the Civil War,and Soviet mass songs are briefly touched upon.The scope and choice of themes of the study provide a holistic approach to topics that are usually addressed separately:before or after the 1917 Revolution;classical music or folk/popular songs.
