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
Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide ...Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts.For Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse.The researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL.The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field.Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were presented.Convolutional Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the field.Finally,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.展开更多
Aiming at the limitations of rapid fault diagnosis of blast furnace, a novel strategy based on cost-conscious least squares support vector machine （LS-SVM） is proposed to solve this problem. Firstly, modified discre...Aiming at the limitations of rapid fault diagnosis of blast furnace, a novel strategy based on cost-conscious least squares support vector machine （LS-SVM） is proposed to solve this problem. Firstly, modified discrete particle swarm optimization is applied to optimize the feature selection and the LS-SVM parameters. Secondly, cost-con- scious formula is presented for fitness function and it contains in detail training time, recognition accuracy and the feature selection. The CLS-SVM algorithm is presented to increase the performance of the LS-SVM classifier. The new method can select the best fault features in much shorter time and have fewer support vectbrs and better general- ization performance in the application of fault diagnosis of the blast furnace. Thirdly, a gradual change binary tree is established for blast furnace faults diagnosis. It is a multi-class classification method based on center-of-gravity formula distance of cluster. A gradual change classification percentage ia used to select sample randomly. The proposed new metbod raises the sped of diagnosis, optimizes the classifieation scraraey and has good generalization ability for fault diagnosis of the application of blast furnace.展开更多
Thermoelectric materials have aroused widespread concern due to their unique ability to directly convert heat to electricity without any moving parts or noxious emissions.Taking advantages of two-dimensional structure...Thermoelectric materials have aroused widespread concern due to their unique ability to directly convert heat to electricity without any moving parts or noxious emissions.Taking advantages of two-dimensional structures of thermoelectric films,the potential applications of thermoelectric materials are diversified,particularly in microdevices.Well-controlled nanostructures in thermoelectric films are effective to optimize the electrical and thermal transport,which can significantly improve the performance of thermoelectric materials.In this paper,various physical and chemical approaches to fabricate thermoelectric films,including inorganic,organic,and inorganic–organic composites,are summarized,where more attentions are paid on the inorganic thermoelectric films for their excellent thermoelectric responses.Additionally,strategies for enhancing the performance of thermoelectric films are also discussed.展开更多
As the key technology of extracting remote sensing information,the classification of remote sensing images has always been the research focus in the field of remote sensing. The paper introduces the classification pro...As the key technology of extracting remote sensing information,the classification of remote sensing images has always been the research focus in the field of remote sensing. The paper introduces the classification process and system of remote sensing images. According to the recent research status of domestic and international remote sensing classification methods,the new study dynamics of remote sensing classification,such as artificial neural networks,support vector machine,active learning and ensemble multi-classifiers,were introduced,providing references for the automatic and intelligent development of remote sensing images classification.展开更多
In recent years,smart textiles have attracted the attention of scholars from all walks of life,but there is an imbalance between functionality and usability,which affects their marketization process.Firstly,five repre...In recent years,smart textiles have attracted the attention of scholars from all walks of life,but there is an imbalance between functionality and usability,which affects their marketization process.Firstly,five representative smart textiles are introduced and their respective wearability is described around preparation methods.Secondly,it is concluded that the preparation methods of smart textiles can be divided into two categories:fiber methods and finishing methods.The fiber methods refer to making smart fibers into smart textiles.Textiles made by fiber methods are breathable and feel good in the hand,but the mechanical properties are influenced by the production equipment,and the process cost is high.The finishing methods refer to the functional finishing of ordinary textiles.Although the finishing method is simple and convenient,it may reduce the comfort of the textile.Finally,applications and new research in various fields of smart textiles are presented with promising prospects.It is anticipated that this review will serve as a theoretical basis for future research and development of smart textiles.Researchers are expected to create new technologies to overcome the tension between functionality and usability,as well as to increase user comfort and convenience.展开更多
Text classification has always been an increasingly crucial topic in natural language processing.Traditional text classification methods based on machine learning have many disadvantages such as dimension explosion,da...Text classification has always been an increasingly crucial topic in natural language processing.Traditional text classification methods based on machine learning have many disadvantages such as dimension explosion,data sparsity,limited generalization ability and so on.Based on deep learning text classification,this paper presents an extensive study on the text classification models including Convolutional Neural Network-Based(CNN-Based),Recurrent Neural Network-Based(RNN-based),Attention Mechanisms-Based and so on.Many studies have proved that text classification methods based on deep learning outperform the traditional methods when processing large-scale and complex datasets.The main reasons are text classification methods based on deep learning can avoid cumbersome feature extraction process and have higher prediction accuracy for a large set of unstructured data.In this paper,we also summarize the shortcomings of traditional text classification methods and introduce the text classification process based on deep learning including text preprocessing,distributed representation of text,text classification model construction based on deep learning and performance evaluation.展开更多
Considering strip steel surface defect samples, a multi-class classification method was proposed based on enhanced least squares twin support vector machines （ELS-TWSVMs） and binary tree. Firstly, pruning region sam...Considering strip steel surface defect samples, a multi-class classification method was proposed based on enhanced least squares twin support vector machines （ELS-TWSVMs） and binary tree. Firstly, pruning region samples center method with adjustable pruning scale was used to prune data samples. This method could reduce classifierr s training time and testing time. Secondly, ELS-TWSVM was proposed to classify the data samples. By introducing error variable contribution parameter and weight parameter, ELS-TWSVM could restrain the impact of noise sam- ples and have better classification accuracy. Finally, multi-class classification algorithms of ELS-TWSVM were pro- posed by combining ELS-TWSVM and complete binary tree. Some experiments were made on two-dimensional data- sets and strip steel surface defect datasets. The experiments showed that the multi-class classification methods of ELS-TWSVM had higher classification speed and accuracy for the datasets with large-scale, unbalanced and noise samples.展开更多
Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on t...Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the literature.Furthermore,several open-source tools implementing these approaches have also been developed.However,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ones.Therefore,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.Research limitations:Three real-world datasets differ in the following aspects:statement,data quality,and purposes.Additionally,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification approach.In the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings.Practical implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification tasks.Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels.With ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable future.Originality/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution.(3)The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution.展开更多
Feature selection is a crucial technique in text classification for improving the efficiency and effectiveness of classifiers or machine learning techniques by reducing the dataset’s dimensionality.This involves elim...Feature selection is a crucial technique in text classification for improving the efficiency and effectiveness of classifiers or machine learning techniques by reducing the dataset’s dimensionality.This involves eliminating irrelevant,redundant,and noisy features to streamline the classification process.Various methods,from single feature selection techniques to ensemble filter-wrapper methods,have been used in the literature.Metaheuristic algorithms have become popular due to their ability to handle optimization complexity and the continuous influx of text documents.Feature selection is inherently multi-objective,balancing the enhancement of feature relevance,accuracy,and the reduction of redundant features.This research presents a two-fold objective for feature selection.The first objective is to identify the top-ranked features using an ensemble of three multi-univariate filter methods:Information Gain(Infogain),Chi-Square(Chi^(2)),and Analysis of Variance(ANOVA).This aims to maximize feature relevance while minimizing redundancy.The second objective involves reducing the number of selected features and increasing accuracy through a hybrid approach combining Artificial Bee Colony(ABC)and Genetic Algorithms(GA).This hybrid method operates in a wrapper framework to identify the most informative subset of text features.Support Vector Machine(SVM)was employed as the performance evaluator for the proposed model,tested on two high-dimensional multiclass datasets.The experimental results demonstrated that the ensemble filter combined with the ABC+GA hybrid approach is a promising solution for text feature selection,offering superior performance compared to other existing feature selection algorithms.展开更多
The Ms8.0 Wenchuan earthquake of 2008 dramatically changed the terrain surface and caused long-term increases in the scale and frequency of landslides and debris flows.The changing trend of landslides in the earthquak...The Ms8.0 Wenchuan earthquake of 2008 dramatically changed the terrain surface and caused long-term increases in the scale and frequency of landslides and debris flows.The changing trend of landslides in the earthquake-affected area over the decade since the earthquake remains largely unknown.In this study,we were able to address this issue using supervised classification methods and multitemporal remote sensing images to study landslide evolution in the worst-affected area(Mianyuan River Basin)over a period of ten years.Satellite images were processed using the maximum likelihood method and random forest algorithm to automatically map landslide occurrence from 2007 to 2018.The principal findings are as follows:(1)when compared with visual image analysis,the random forest algorithm had a good average accuracy rate of 87%for landslide identification;(2)postevent landslide occurrence has generally decreased with time,but heavy monsoonal seasons have caused temporary spikes in activity;and(3)the postearthquake landslide activity in the Mianyuan River Basin can be divided into a strong activity period(2008 to 2011),medium activity period(2012 to 2016),and weak activity period(post 2017).Landslide activity remains above the prequake level,with damaging events being rare but continuing to occur.Long-term remote sensing and on-site monitoring are required to understand the evolution of landslide activity after strong earthquakes.展开更多
The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classifi...The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classification of complex organics,three kinds of fresh leaves were measured by LIBS.100 spectra from 100 samples of each kind of leaves were measured and then they were divided into a training set and a test set in a ratio of 7:3.Two algorithms of chemometric methods including the partial least squares discriminant analysis(PLS-DA) and principal component analysis Mahalanobis distance(PCA-MD) were used to identify these leaves.By using 23 lines from 16 elements or molecules as input data,these two methods can both classify these three kinds of leaves successfully.The classification accuracies of training sets are both up to 100% by PCA-MD and PLS-DA.The classification accuracies of the test set are 93.3% by PCA-MD and 97.8% by PLS-DA.It means that PLS-DA is better than PCA-MD in classifying plant leaves.Because the components in PLS-DA process are more suitable for classification than those in PCA-MD process.We think that this work can provide a reference for plant traceability using LIBS.展开更多
The Gaussian mixture model (GMM), k-nearest neighbor (k-NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Ef...The Gaussian mixture model (GMM), k-nearest neighbor (k-NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Effect of feature selection in EMG signal processing was also verified by comparing classification accuracy of each feature, and the enhancement of classification accuracy by normalization was confirmed. EMG signals were acquired from two electrodes placed on the forearm of twenty eight healthy subjects and used for recognition of wrist motion. Features were extracted from the obtained EMG signals in the time domain and were applied to classification methods. The difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), mean absolute value (MAV), root mean square (RMS) were used for composing 16 double features which were combined of two channels. In the classification methods, the highest accuracy of classification showed in the GMM. The most effective combination of classification method and double feature was (MAV, DAMV) of GMM and its classification accuracy was 96.85%. The results of normalization were better than those of non-normalization in GMM, k-NN, and LDA.展开更多
This paper aims to establish a comparison between both geomagnetic activity classification methods on foF2 diurnal variation over solar cycle phases. It concerns first a comparison of geomagnetic activity occurrences ...This paper aims to establish a comparison between both geomagnetic activity classification methods on foF2 diurnal variation over solar cycle phases. It concerns first a comparison of geomagnetic activity occurrences according to both classification methods;and second the geomagnetic effect on foF2 diurnal variation profiles as defined for the equatorial latitudes. The occurrences of the different disturbed geomagnetic activities (recurrent activity (RA), shock activity (SA) and fluctuant activity (FA)) according to both classifications (ancient classification (AC) and new classification (NC)) have been studied at Dakar ionosonde station (Lat: 14.8°N;Long: 342.6°E). Regarding both classifications, the RA occurs more during the decreasing phase. And it’s observed that the RA occurs the most during the increasing phase for the AC and during the minimum phase for the NC. The maximum gap of occurrence (<img src="Edit_e4627ea9-9a9a-4473-9017-202d04a16377.bmp" alt="" /><span><span style="font-family:Verdana;">) between both classifications is <span style="font-size:10.0pt;font-family:;" "=""><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">-</span></span></span><span style="font-family:;" "=""><span style="font-family:Verdana;">11.1%</span><span style="font-family:Verdana;"> (for the negative value which is observed during the increasing phase) and </span><span style="font-family:Verdana;">+16.74%</span><span style="font-family:Verdana;"> (for the positive one which is observed during the decreasing phase). The occurrence of the SA in relation with both classifications is the lowest during the minimum phase and the maximum occurrence is observed during the maximum and decreasing phases, for the AC, with a value close to </span><span style="font-family:Verdana;">37%</span><span style="font-family:Verdana;"> and for the NC at the maximum phase with a percentage of </span><span style="font-family:Verdana;">54.47%</span><span><span style="font-family:Verdana;">. The maximum gap of occurrence (</span><img src="Edit_20fa141b-ecee-4e06-8024-144ba0969395.bmp" alt="" /></span></span><span style="font-family:Verdana;">) between both classifications is <span style="font-size:10.0pt;font-family:;" "=""><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">-</span></span></span><span style="font-family:;" "=""><span style="font-family:Verdana;">17.85%</span><span style="font-family:Verdana;"> (for the negative value which is observed at maximum phase) and </span><span style="font-family:Verdana;">+13.53%</span><span style="font-family:Verdana;"> (for the positive one which is observed during the decreasing phase). For both classifications, the FA occurs the least during the minimum phase and the most during the maximum phase for the AC and at maximum and decreasing phases with percentage values of occurrence of roughly </span><span style="font-family:Verdana;">37%</span><span><span style="font-family:Verdana;"> for the NC. The maximum gap of occurrence (</span><img src="Edit_eecb8939-783e-4d43-b92c-80c528c1890b.bmp" alt="" /><span style="font-family:Verdana;"></span></span></span><span style="font-family:Verdana;">) between both classifications is <span style="font-size:10.0pt;font-family:;" "=""><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">-</span></span>10% (for the negative value which is observed during the decreasing phase) and </span><span style="font-family:;" "=""><span style="font-family:Verdana;">+20.11%</span><span style="font-family:Verdana;"> (for the positive one which is observed during the maximum phase). foF2 diurnal profiles throughout solar cycle phases concerning the AC and the NC have been compared. The FA diurnal profiles don’t present a difference. The RA and the SA present a difference during minimum and increasing phases and the least at maximum and decreasing phases.</span></span></span>展开更多
The inverse problems for motions of dynamic systems of which are described by system of the ordinary differential equations are examined. The classification of such type of inverse problems is given. It was shown that...The inverse problems for motions of dynamic systems of which are described by system of the ordinary differential equations are examined. The classification of such type of inverse problems is given. It was shown that inverse problems can be divided into two types: synthesis inverse problems and inverse problems of measurement (recognition). Each type of inverse problems requires separate approach to statements and solution methods. The regularization method for obtaining of stable solution of inverse problems was suggested. In some cases, instead of recognition of inverse problems solution, the estimation of solution can be used. Within the framework of this approach, two practical inverse problems of measurement are considered.展开更多
Ordnance material is the physical basis of ordnance equipment maintenance and support. With the increase of technology content and the enhancement of structural complexity of ordnance equipment,the traditional way of ...Ordnance material is the physical basis of ordnance equipment maintenance and support. With the increase of technology content and the enhancement of structural complexity of ordnance equipment,the traditional way of military self-independent support is unable to meet the troops' requirements. It has become an inevitable trend to integrate ordnance materials with the militarycivilian joint support. However, there is a problem demanding prompt solution,that is,to distinguish the categories of ordnance material that can be supported by civilian source. Based on the inherent properties of ordnance material, a method to classify ordnance materials military-civilian joint support categories based on multiple attribute decision was proposed. The effectiveness was validated through practical cases.展开更多
Earlier analyses of transitions from licensed practical nurse (LPN) to registered nurse (RN) in the North Carolina (NC) nursing workforce in terms of 11 categorical predictors were limited by not considering parsimoni...Earlier analyses of transitions from licensed practical nurse (LPN) to registered nurse (RN) in the North Carolina (NC) nursing workforce in terms of 11 categorical predictors were limited by not considering parsimonious classifications based on these predictors and by substantial amounts of missing data. To address these issues, we formulated adaptive classification methods. Secondary analyses of data collected by the NC State Board of Nursing were also conducted to demonstrate adaptive classification methods by modeling the occurrence of LPN-to-RN transitions in the NC nursing workforce from 2001-2013. These methods combine levels (values) for one or more categorical predictors into parsimonious classifications. Missing values for a predictor are treated as one level for that predictor so that the complete data can be used in the analyses;the missing level is imputed by combining it with other levels of a predictor. An adaptive nested classification generated the best model for predicting an LPN-to-RN transition based on three predictors in order of importance: year of first LPN licensure, work setting at transition, and age at first LPN licensure. These results demonstrate that adaptive classification can identify effective and parsimonious classifications for predicting dichotomous outcomes such as the occurrence of an LPN-to-RN transition.展开更多
Grain-size class-Std(GSCStd) and Grain-size class-dD(GSCdD) methods are simple statistical approaches for classifying bulk grain-size distributions(GSDs) into grain-size fractions. Although these two methods were deve...Grain-size class-Std(GSCStd) and Grain-size class-dD(GSCdD) methods are simple statistical approaches for classifying bulk grain-size distributions(GSDs) into grain-size fractions. Although these two methods were developed based on similar statistical principles, the classification difference between these two methods has not been analyzed. In this study, GSCStd and GSCdD methods are conducted in thirteen grain-size data sequences to examine the applicability for identifying grain size fractions. Results show that, application of the GSCStd method is equivalent to that of the GSCdD method in identifying finer grain-size fractions, and the difference between the two methods mainly comes from the identification of coarse grain-size fractions. Thus, finer grain-size fractions are recommended for use in research of surface aeolian and paleo-aeolian sediments. In addition, our results do not completely agree with previous studies, coarser grain-size fractions in our case suggest that the GSCdD method may not be more applicable than the GSCStd method.展开更多
Satellite image classification is crucial in various applications such as urban planning,environmental monitoring,and land use analysis.In this study,the authors present a comparative analysis of different supervised ...Satellite image classification is crucial in various applications such as urban planning,environmental monitoring,and land use analysis.In this study,the authors present a comparative analysis of different supervised and unsupervised learning methods for satellite image classification,focusing on a case study in Casablanca using Landsat 8 imagery.This research aims to identify the most effective machine-learning approach for accurately classifying land cover in an urban environment.The methodology used consists of the pre-processing of Landsat imagery data from Casablanca city,the authors extract relevant features and partition them into training and test sets,and then use random forest(RF),SVM(support vector machine),classification,and regression tree(CART),gradient tree boost(GTB),decision tree(DT),and minimum distance(MD)algorithms.Through a series of experiments,the authors evaluate the performance of each machine learning method in terms of accuracy,and Kappa coefficient.This work shows that random forest is the best-performing algorithm,with an accuracy of 95.42%and 0.94 Kappa coefficient.The authors discuss the factors of their performance,including data characteristics,accurate selection,and model influencing.展开更多
Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault diagnosis.In this paper,we first introduce RCMDE int...Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault diagnosis.In this paper,we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise,and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor(KNN),termed RCMDE-KNN.The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise,and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy(MPE)and KNN,multi-scale weighted-permutation entropy(MW-PE)and KNN,and multi-scale dispersion entropy(MDE)and KNN,termed MPE-KNN,MW-PE-KNN,and MDE-KNN.It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective,and can obtain a very high recognition rate.展开更多
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.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00460621,Developing BCI-Based Digital Health Technologies for Mental Illness and Pain Management).
文摘Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts.For Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse.The researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL.The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field.Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were presented.Convolutional Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the field.Finally,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.
基金Item Sponsored by National Natural Science Foundation of China(60843007,61050006)
文摘Aiming at the limitations of rapid fault diagnosis of blast furnace, a novel strategy based on cost-conscious least squares support vector machine （LS-SVM） is proposed to solve this problem. Firstly, modified discrete particle swarm optimization is applied to optimize the feature selection and the LS-SVM parameters. Secondly, cost-con- scious formula is presented for fitness function and it contains in detail training time, recognition accuracy and the feature selection. The CLS-SVM algorithm is presented to increase the performance of the LS-SVM classifier. The new method can select the best fault features in much shorter time and have fewer support vectbrs and better general- ization performance in the application of fault diagnosis of the blast furnace. Thirdly, a gradual change binary tree is established for blast furnace faults diagnosis. It is a multi-class classification method based on center-of-gravity formula distance of cluster. A gradual change classification percentage ia used to select sample randomly. The proposed new metbod raises the sped of diagnosis, optimizes the classifieation scraraey and has good generalization ability for fault diagnosis of the application of blast furnace.
基金Project supported by the Joint Funds of the National Natural Science Foundation of China(Grant No.U1601213)the National Natural Science Foundation of China(Grant Nos.51601005 and 61704006)+1 种基金the Beijing Natural Science Foundation(Grant No.2182032)the Fundamental Research Funds for the Central Universities
文摘Thermoelectric materials have aroused widespread concern due to their unique ability to directly convert heat to electricity without any moving parts or noxious emissions.Taking advantages of two-dimensional structures of thermoelectric films,the potential applications of thermoelectric materials are diversified,particularly in microdevices.Well-controlled nanostructures in thermoelectric films are effective to optimize the electrical and thermal transport,which can significantly improve the performance of thermoelectric materials.In this paper,various physical and chemical approaches to fabricate thermoelectric films,including inorganic,organic,and inorganic–organic composites,are summarized,where more attentions are paid on the inorganic thermoelectric films for their excellent thermoelectric responses.Additionally,strategies for enhancing the performance of thermoelectric films are also discussed.
基金Supported by the Science Research Foundation(2010Y290) of Yunnan Department of Education
文摘As the key technology of extracting remote sensing information,the classification of remote sensing images has always been the research focus in the field of remote sensing. The paper introduces the classification process and system of remote sensing images. According to the recent research status of domestic and international remote sensing classification methods,the new study dynamics of remote sensing classification,such as artificial neural networks,support vector machine,active learning and ensemble multi-classifiers,were introduced,providing references for the automatic and intelligent development of remote sensing images classification.
基金Innovation Team Building Program of Beijing Institute of Fashion Technology,China。
文摘In recent years,smart textiles have attracted the attention of scholars from all walks of life,but there is an imbalance between functionality and usability,which affects their marketization process.Firstly,five representative smart textiles are introduced and their respective wearability is described around preparation methods.Secondly,it is concluded that the preparation methods of smart textiles can be divided into two categories:fiber methods and finishing methods.The fiber methods refer to making smart fibers into smart textiles.Textiles made by fiber methods are breathable and feel good in the hand,but the mechanical properties are influenced by the production equipment,and the process cost is high.The finishing methods refer to the functional finishing of ordinary textiles.Although the finishing method is simple and convenient,it may reduce the comfort of the textile.Finally,applications and new research in various fields of smart textiles are presented with promising prospects.It is anticipated that this review will serve as a theoretical basis for future research and development of smart textiles.Researchers are expected to create new technologies to overcome the tension between functionality and usability,as well as to increase user comfort and convenience.
基金This work supported in part by the National Natural Science Foundation of China under Grant 61872134,in part by the Natural Science Foundation of Hunan Province under Grant 2018JJ2062in part by Science and Technology Development Center of the Ministry of Education under Grant 2019J01020in part by the 2011 Collaborative Innovative Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province。
文摘Text classification has always been an increasingly crucial topic in natural language processing.Traditional text classification methods based on machine learning have many disadvantages such as dimension explosion,data sparsity,limited generalization ability and so on.Based on deep learning text classification,this paper presents an extensive study on the text classification models including Convolutional Neural Network-Based(CNN-Based),Recurrent Neural Network-Based(RNN-based),Attention Mechanisms-Based and so on.Many studies have proved that text classification methods based on deep learning outperform the traditional methods when processing large-scale and complex datasets.The main reasons are text classification methods based on deep learning can avoid cumbersome feature extraction process and have higher prediction accuracy for a large set of unstructured data.In this paper,we also summarize the shortcomings of traditional text classification methods and introduce the text classification process based on deep learning including text preprocessing,distributed representation of text,text classification model construction based on deep learning and performance evaluation.
基金Item Sponsored by National Natural Science Foundation of China(61050006)
文摘Considering strip steel surface defect samples, a multi-class classification method was proposed based on enhanced least squares twin support vector machines （ELS-TWSVMs） and binary tree. Firstly, pruning region samples center method with adjustable pruning scale was used to prune data samples. This method could reduce classifierr s training time and testing time. Secondly, ELS-TWSVM was proposed to classify the data samples. By introducing error variable contribution parameter and weight parameter, ELS-TWSVM could restrain the impact of noise sam- ples and have better classification accuracy. Finally, multi-class classification algorithms of ELS-TWSVM were pro- posed by combining ELS-TWSVM and complete binary tree. Some experiments were made on two-dimensional data- sets and strip steel surface defect datasets. The experiments showed that the multi-class classification methods of ELS-TWSVM had higher classification speed and accuracy for the datasets with large-scale, unbalanced and noise samples.
基金the Natural Science Foundation of China(Grant Numbers 72074014 and 72004012).
文摘Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the literature.Furthermore,several open-source tools implementing these approaches have also been developed.However,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ones.Therefore,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.Research limitations:Three real-world datasets differ in the following aspects:statement,data quality,and purposes.Additionally,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification approach.In the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings.Practical implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification tasks.Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels.With ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable future.Originality/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution.(3)The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution.
基金supported by Universiti Sains Malaysia(USM)and School of Computer Sciences,USM。
文摘Feature selection is a crucial technique in text classification for improving the efficiency and effectiveness of classifiers or machine learning techniques by reducing the dataset’s dimensionality.This involves eliminating irrelevant,redundant,and noisy features to streamline the classification process.Various methods,from single feature selection techniques to ensemble filter-wrapper methods,have been used in the literature.Metaheuristic algorithms have become popular due to their ability to handle optimization complexity and the continuous influx of text documents.Feature selection is inherently multi-objective,balancing the enhancement of feature relevance,accuracy,and the reduction of redundant features.This research presents a two-fold objective for feature selection.The first objective is to identify the top-ranked features using an ensemble of three multi-univariate filter methods:Information Gain(Infogain),Chi-Square(Chi^(2)),and Analysis of Variance(ANOVA).This aims to maximize feature relevance while minimizing redundancy.The second objective involves reducing the number of selected features and increasing accuracy through a hybrid approach combining Artificial Bee Colony(ABC)and Genetic Algorithms(GA).This hybrid method operates in a wrapper framework to identify the most informative subset of text features.Support Vector Machine(SVM)was employed as the performance evaluator for the proposed model,tested on two high-dimensional multiclass datasets.The experimental results demonstrated that the ensemble filter combined with the ABC+GA hybrid approach is a promising solution for text feature selection,offering superior performance compared to other existing feature selection algorithms.
基金financially supported by the National Key R&D Program(No.2018YFC1505402)the Key Research and Development Program of Sichuan Province(No.2023YFS0435)+1 种基金the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project(No.SKLGP2014Z004)the Science and Technology Innovation Fund of Sichuan Earthquake Agency(No.201901)。
文摘The Ms8.0 Wenchuan earthquake of 2008 dramatically changed the terrain surface and caused long-term increases in the scale and frequency of landslides and debris flows.The changing trend of landslides in the earthquake-affected area over the decade since the earthquake remains largely unknown.In this study,we were able to address this issue using supervised classification methods and multitemporal remote sensing images to study landslide evolution in the worst-affected area(Mianyuan River Basin)over a period of ten years.Satellite images were processed using the maximum likelihood method and random forest algorithm to automatically map landslide occurrence from 2007 to 2018.The principal findings are as follows:(1)when compared with visual image analysis,the random forest algorithm had a good average accuracy rate of 87%for landslide identification;(2)postevent landslide occurrence has generally decreased with time,but heavy monsoonal seasons have caused temporary spikes in activity;and(3)the postearthquake landslide activity in the Mianyuan River Basin can be divided into a strong activity period(2008 to 2011),medium activity period(2012 to 2016),and weak activity period(post 2017).Landslide activity remains above the prequake level,with damaging events being rare but continuing to occur.Long-term remote sensing and on-site monitoring are required to understand the evolution of landslide activity after strong earthquakes.
基金supported by the Fundamental Research Funds for the Central Universities of Ministry of Education of China(No.JB190501)Science and Technology Innovation Team of Shaanxi Province(No.2019TD-002)National Natural Science Foundation of China(No.11774277)。
文摘The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classification of complex organics,three kinds of fresh leaves were measured by LIBS.100 spectra from 100 samples of each kind of leaves were measured and then they were divided into a training set and a test set in a ratio of 7:3.Two algorithms of chemometric methods including the partial least squares discriminant analysis(PLS-DA) and principal component analysis Mahalanobis distance(PCA-MD) were used to identify these leaves.By using 23 lines from 16 elements or molecules as input data,these two methods can both classify these three kinds of leaves successfully.The classification accuracies of training sets are both up to 100% by PCA-MD and PLS-DA.The classification accuracies of the test set are 93.3% by PCA-MD and 97.8% by PLS-DA.It means that PLS-DA is better than PCA-MD in classifying plant leaves.Because the components in PLS-DA process are more suitable for classification than those in PCA-MD process.We think that this work can provide a reference for plant traceability using LIBS.
基金Project(NIPA-2012-H0401-12-1007) supported by the MKE(The Ministry of Knowledge Economy), Korea, supervised by the NIPAProject(2010-0020163) supported by Key Research Institute Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology, Korea
文摘The Gaussian mixture model (GMM), k-nearest neighbor (k-NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Effect of feature selection in EMG signal processing was also verified by comparing classification accuracy of each feature, and the enhancement of classification accuracy by normalization was confirmed. EMG signals were acquired from two electrodes placed on the forearm of twenty eight healthy subjects and used for recognition of wrist motion. Features were extracted from the obtained EMG signals in the time domain and were applied to classification methods. The difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), mean absolute value (MAV), root mean square (RMS) were used for composing 16 double features which were combined of two channels. In the classification methods, the highest accuracy of classification showed in the GMM. The most effective combination of classification method and double feature was (MAV, DAMV) of GMM and its classification accuracy was 96.85%. The results of normalization were better than those of non-normalization in GMM, k-NN, and LDA.
文摘This paper aims to establish a comparison between both geomagnetic activity classification methods on foF2 diurnal variation over solar cycle phases. It concerns first a comparison of geomagnetic activity occurrences according to both classification methods;and second the geomagnetic effect on foF2 diurnal variation profiles as defined for the equatorial latitudes. The occurrences of the different disturbed geomagnetic activities (recurrent activity (RA), shock activity (SA) and fluctuant activity (FA)) according to both classifications (ancient classification (AC) and new classification (NC)) have been studied at Dakar ionosonde station (Lat: 14.8°N;Long: 342.6°E). Regarding both classifications, the RA occurs more during the decreasing phase. And it’s observed that the RA occurs the most during the increasing phase for the AC and during the minimum phase for the NC. The maximum gap of occurrence (<img src="Edit_e4627ea9-9a9a-4473-9017-202d04a16377.bmp" alt="" /><span><span style="font-family:Verdana;">) between both classifications is <span style="font-size:10.0pt;font-family:;" "=""><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">-</span></span></span><span style="font-family:;" "=""><span style="font-family:Verdana;">11.1%</span><span style="font-family:Verdana;"> (for the negative value which is observed during the increasing phase) and </span><span style="font-family:Verdana;">+16.74%</span><span style="font-family:Verdana;"> (for the positive one which is observed during the decreasing phase). The occurrence of the SA in relation with both classifications is the lowest during the minimum phase and the maximum occurrence is observed during the maximum and decreasing phases, for the AC, with a value close to </span><span style="font-family:Verdana;">37%</span><span style="font-family:Verdana;"> and for the NC at the maximum phase with a percentage of </span><span style="font-family:Verdana;">54.47%</span><span><span style="font-family:Verdana;">. The maximum gap of occurrence (</span><img src="Edit_20fa141b-ecee-4e06-8024-144ba0969395.bmp" alt="" /></span></span><span style="font-family:Verdana;">) between both classifications is <span style="font-size:10.0pt;font-family:;" "=""><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">-</span></span></span><span style="font-family:;" "=""><span style="font-family:Verdana;">17.85%</span><span style="font-family:Verdana;"> (for the negative value which is observed at maximum phase) and </span><span style="font-family:Verdana;">+13.53%</span><span style="font-family:Verdana;"> (for the positive one which is observed during the decreasing phase). For both classifications, the FA occurs the least during the minimum phase and the most during the maximum phase for the AC and at maximum and decreasing phases with percentage values of occurrence of roughly </span><span style="font-family:Verdana;">37%</span><span><span style="font-family:Verdana;"> for the NC. The maximum gap of occurrence (</span><img src="Edit_eecb8939-783e-4d43-b92c-80c528c1890b.bmp" alt="" /><span style="font-family:Verdana;"></span></span></span><span style="font-family:Verdana;">) between both classifications is <span style="font-size:10.0pt;font-family:;" "=""><span style="font-family:Verdana, Helvetica, Arial;white-space:normal;background-color:#FFFFFF;">-</span></span>10% (for the negative value which is observed during the decreasing phase) and </span><span style="font-family:;" "=""><span style="font-family:Verdana;">+20.11%</span><span style="font-family:Verdana;"> (for the positive one which is observed during the maximum phase). foF2 diurnal profiles throughout solar cycle phases concerning the AC and the NC have been compared. The FA diurnal profiles don’t present a difference. The RA and the SA present a difference during minimum and increasing phases and the least at maximum and decreasing phases.</span></span></span>
文摘The inverse problems for motions of dynamic systems of which are described by system of the ordinary differential equations are examined. The classification of such type of inverse problems is given. It was shown that inverse problems can be divided into two types: synthesis inverse problems and inverse problems of measurement (recognition). Each type of inverse problems requires separate approach to statements and solution methods. The regularization method for obtaining of stable solution of inverse problems was suggested. In some cases, instead of recognition of inverse problems solution, the estimation of solution can be used. Within the framework of this approach, two practical inverse problems of measurement are considered.
文摘Ordnance material is the physical basis of ordnance equipment maintenance and support. With the increase of technology content and the enhancement of structural complexity of ordnance equipment,the traditional way of military self-independent support is unable to meet the troops' requirements. It has become an inevitable trend to integrate ordnance materials with the militarycivilian joint support. However, there is a problem demanding prompt solution,that is,to distinguish the categories of ordnance material that can be supported by civilian source. Based on the inherent properties of ordnance material, a method to classify ordnance materials military-civilian joint support categories based on multiple attribute decision was proposed. The effectiveness was validated through practical cases.
文摘Earlier analyses of transitions from licensed practical nurse (LPN) to registered nurse (RN) in the North Carolina (NC) nursing workforce in terms of 11 categorical predictors were limited by not considering parsimonious classifications based on these predictors and by substantial amounts of missing data. To address these issues, we formulated adaptive classification methods. Secondary analyses of data collected by the NC State Board of Nursing were also conducted to demonstrate adaptive classification methods by modeling the occurrence of LPN-to-RN transitions in the NC nursing workforce from 2001-2013. These methods combine levels (values) for one or more categorical predictors into parsimonious classifications. Missing values for a predictor are treated as one level for that predictor so that the complete data can be used in the analyses;the missing level is imputed by combining it with other levels of a predictor. An adaptive nested classification generated the best model for predicting an LPN-to-RN transition based on three predictors in order of importance: year of first LPN licensure, work setting at transition, and age at first LPN licensure. These results demonstrate that adaptive classification can identify effective and parsimonious classifications for predicting dichotomous outcomes such as the occurrence of an LPN-to-RN transition.
基金supported by project funding from Chongqing Normal University (No. 12XLB009)Key Projects in the National Science & Technology Program (No. 2006BAD26B0302)
文摘Grain-size class-Std(GSCStd) and Grain-size class-dD(GSCdD) methods are simple statistical approaches for classifying bulk grain-size distributions(GSDs) into grain-size fractions. Although these two methods were developed based on similar statistical principles, the classification difference between these two methods has not been analyzed. In this study, GSCStd and GSCdD methods are conducted in thirteen grain-size data sequences to examine the applicability for identifying grain size fractions. Results show that, application of the GSCStd method is equivalent to that of the GSCdD method in identifying finer grain-size fractions, and the difference between the two methods mainly comes from the identification of coarse grain-size fractions. Thus, finer grain-size fractions are recommended for use in research of surface aeolian and paleo-aeolian sediments. In addition, our results do not completely agree with previous studies, coarser grain-size fractions in our case suggest that the GSCdD method may not be more applicable than the GSCStd method.
文摘Satellite image classification is crucial in various applications such as urban planning,environmental monitoring,and land use analysis.In this study,the authors present a comparative analysis of different supervised and unsupervised learning methods for satellite image classification,focusing on a case study in Casablanca using Landsat 8 imagery.This research aims to identify the most effective machine-learning approach for accurately classifying land cover in an urban environment.The methodology used consists of the pre-processing of Landsat imagery data from Casablanca city,the authors extract relevant features and partition them into training and test sets,and then use random forest(RF),SVM(support vector machine),classification,and regression tree(CART),gradient tree boost(GTB),decision tree(DT),and minimum distance(MD)algorithms.Through a series of experiments,the authors evaluate the performance of each machine learning method in terms of accuracy,and Kappa coefficient.This work shows that random forest is the best-performing algorithm,with an accuracy of 95.42%and 0.94 Kappa coefficient.The authors discuss the factors of their performance,including data characteristics,accurate selection,and model influencing.
基金supported by National Natural Science Foundation of China(No.61871318 and 61833013)Shaanxi Provincial Key Research and Development Project(No.2019GY-099).
文摘Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault diagnosis.In this paper,we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise,and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor(KNN),termed RCMDE-KNN.The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise,and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy(MPE)and KNN,multi-scale weighted-permutation entropy(MW-PE)and KNN,and multi-scale dispersion entropy(MDE)and KNN,termed MPE-KNN,MW-PE-KNN,and MDE-KNN.It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective,and can obtain a very high recognition rate.
