Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.Wit...Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.展开更多
Lungs are a vital human body organ,and different Obstructive Lung Diseases(OLD)such as asthma,bronchitis,or lung cancer are caused by shortcomings within the lungs.Therefore,early diagnosis of OLD is crucial for such ...Lungs are a vital human body organ,and different Obstructive Lung Diseases(OLD)such as asthma,bronchitis,or lung cancer are caused by shortcomings within the lungs.Therefore,early diagnosis of OLD is crucial for such patients suffering from OLD since,after early diagnosis,breathing exercises and medical precautions can effectively improve their health state.A secure non-invasive early diagnosis of OLD is a primordial need,and in this context,digital image processing supported by Artificial Intelligence(AI)techniques is reliable and widely used in the medical field,especially for improving early disease diagnosis.Hence,this article presents an AIbased non-invasive and secured diagnosis for OLD using physiological and iris features.This research work implements different machine-learning-based techniques which classify various subjects,which are healthy and effective patients.The iris features include gray-level run-length matrix-based features,gray-level co-occurrence matrix,and statistical features.These features are extracted from iris images.Additionally,ten different classifiers and voting techniques,including hard and soft voting,are implemented and tested,and their performances are evaluated using several parameters,which are precision,accuracy,specificity,F-score,and sensitivity.Based on the statistical analysis,it is concluded that the proposed approach offers promising techniques for the non-invasive early diagnosis of OLD with an accuracy of 97.6%.展开更多
Bioactive compounds in plants,which can be synthesized using N-arylationmethods such as the Buchwald-Hartwig reaction,are essential in drug discovery for their pharmacological effects.Important descriptors are necessa...Bioactive compounds in plants,which can be synthesized using N-arylationmethods such as the Buchwald-Hartwig reaction,are essential in drug discovery for their pharmacological effects.Important descriptors are necessary for the estimation of yields in these reactions.This study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for evaluation.The algorithms were evaluated based on computational time and the number of selected descriptors.Analyses show that robust performance is obtained with more descriptors,compared to cases where fewer descriptors are selected.The essential descriptor was deduced based on the frequency of occurrence within the 50 extracted data subsets,and better performance was achieved with the voting ensemble than other algorithms with RMSE of 6.4270 and R^(2) of 0.9423.The results and deductions from this study can be readily applied in the decision-making process of chemical synthesis by saving the computational cost associated with initial descriptor selection for yield estimation.The ensemble model has also shown robust performance in its yield estimation ability and efficiency.展开更多
Difficulty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder(ASD)diseases.These diseases can affect the nerves at any stage of the human bein...Difficulty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder(ASD)diseases.These diseases can affect the nerves at any stage of the human being in childhood,adolescence,and adulthood.ASD is known as a behavioral disease due to the appearances of symptoms over thefirst two years that continue until adulthood.Most of the studies prove that the early detection of ASD helps improve the behavioral characteristics of patients with ASD.The detection of ASD is a very challenging task among various researchers.Machine learning(ML)algorithms still act very intelligent by learning the complex data and pre-dicting quality results.In this paper,ensemble ML techniques for the early detec-tion of ASD are proposed.In this detection,the dataset isfirst processed using three ML algorithms such as sequential minimal optimization with support vector machine,Kohonen self-organizing neural network,and random forest algorithm.The prediction results of these ML algorithms(ensemble)further use the bagging concept called max voting to predict thefinal result.The accuracy,sensitivity,and specificity of the proposed system are calculated using confusion matrix.The pro-posed ensemble technique performs better than state-of-the art ML algorithms.展开更多
基于不同分类器对同一样本分类能力不同,同一分类器对不同样本可分程度不同的思想,为不同样本赋予不同融合权重,提出了一种基于熵的自适应加权投票高分辨距离像(high range resolution profile,HRRP)融合识别方法。该方法将二分类相关...基于不同分类器对同一样本分类能力不同,同一分类器对不同样本可分程度不同的思想,为不同样本赋予不同融合权重,提出了一种基于熵的自适应加权投票高分辨距离像(high range resolution profile,HRRP)融合识别方法。该方法将二分类相关向量机(relevance vector machine,RVM)扩展为多类分类RVM概率模型,并对不同HRRP特征样本进行分类,利用每个多类分类RVM输出的样本后验概率信息计算出的熵值自适应为各个样本赋予权重,使得不同分类器以及同一分类器对不同样本的决策占有不同的比重,熵值越大的样本赋予的融合权重越低,最后通过加权投票方法实现融合识别,得到目标的最终识别结果。仿真实验结果验证了所提方法的有效性。展开更多
基金supported by the National Natural Science Foundation of China(6177340561751312)the Major Scientific and Technological Innovation Projects of Shandong Province(2019JZZY020123)。
文摘Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.
文摘Lungs are a vital human body organ,and different Obstructive Lung Diseases(OLD)such as asthma,bronchitis,or lung cancer are caused by shortcomings within the lungs.Therefore,early diagnosis of OLD is crucial for such patients suffering from OLD since,after early diagnosis,breathing exercises and medical precautions can effectively improve their health state.A secure non-invasive early diagnosis of OLD is a primordial need,and in this context,digital image processing supported by Artificial Intelligence(AI)techniques is reliable and widely used in the medical field,especially for improving early disease diagnosis.Hence,this article presents an AIbased non-invasive and secured diagnosis for OLD using physiological and iris features.This research work implements different machine-learning-based techniques which classify various subjects,which are healthy and effective patients.The iris features include gray-level run-length matrix-based features,gray-level co-occurrence matrix,and statistical features.These features are extracted from iris images.Additionally,ten different classifiers and voting techniques,including hard and soft voting,are implemented and tested,and their performances are evaluated using several parameters,which are precision,accuracy,specificity,F-score,and sensitivity.Based on the statistical analysis,it is concluded that the proposed approach offers promising techniques for the non-invasive early diagnosis of OLD with an accuracy of 97.6%.
基金The work described in this paper was substantially supported by the grant from the Research Grants Council of the Hong Kong Special Administrative Region[CityU 11200218]one grant from the Health and Medical Research Fund,the Food and Health Bureau,The Government of the Hong Kong Special Administrative Region[07181426]+1 种基金and the funding from Hong Kong Institute for Data Science(HKIDS)at City University of Hong Kong.The work described in this paper was partially supported by two grants from City University of Hong Kong(CityU 11202219,CityU 11203520)This research was substantially sponsored by the research project(Grant No.32000464)supported by the National Natural Science Foundation of China and was substantially supported by the Shenzhen Research Institute,City University of Hong Kong.The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research with the project number(442/77).
文摘Bioactive compounds in plants,which can be synthesized using N-arylationmethods such as the Buchwald-Hartwig reaction,are essential in drug discovery for their pharmacological effects.Important descriptors are necessary for the estimation of yields in these reactions.This study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for evaluation.The algorithms were evaluated based on computational time and the number of selected descriptors.Analyses show that robust performance is obtained with more descriptors,compared to cases where fewer descriptors are selected.The essential descriptor was deduced based on the frequency of occurrence within the 50 extracted data subsets,and better performance was achieved with the voting ensemble than other algorithms with RMSE of 6.4270 and R^(2) of 0.9423.The results and deductions from this study can be readily applied in the decision-making process of chemical synthesis by saving the computational cost associated with initial descriptor selection for yield estimation.The ensemble model has also shown robust performance in its yield estimation ability and efficiency.
文摘Difficulty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder(ASD)diseases.These diseases can affect the nerves at any stage of the human being in childhood,adolescence,and adulthood.ASD is known as a behavioral disease due to the appearances of symptoms over thefirst two years that continue until adulthood.Most of the studies prove that the early detection of ASD helps improve the behavioral characteristics of patients with ASD.The detection of ASD is a very challenging task among various researchers.Machine learning(ML)algorithms still act very intelligent by learning the complex data and pre-dicting quality results.In this paper,ensemble ML techniques for the early detec-tion of ASD are proposed.In this detection,the dataset isfirst processed using three ML algorithms such as sequential minimal optimization with support vector machine,Kohonen self-organizing neural network,and random forest algorithm.The prediction results of these ML algorithms(ensemble)further use the bagging concept called max voting to predict thefinal result.The accuracy,sensitivity,and specificity of the proposed system are calculated using confusion matrix.The pro-posed ensemble technique performs better than state-of-the art ML algorithms.
