Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER hav...Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces.Recently,Deep Learning Techniques(DLT)have gained popular-ity in applications of real-world problems including recognition of human emo-tions.The human face reflects emotional states and human intentions.An expression is the most natural and powerful way of communicating non-verbally.Systems which form communications between the two are termed Human Machine Interaction(HMI)systems.FER can improve HMI systems as human expressions convey useful information to an observer.This paper proposes a FER scheme called EECNN(Enhanced Convolution Neural Network with Atten-tion mechanism)to recognize seven types of human emotions with satisfying results in its experiments.Proposed EECNN achieved 89.8%accuracy in classi-fying the images.展开更多
The brain of humans and other organisms is affected in various ways through the electromagneticfield(EMF)radiations generated by mobile phones and cell phone towers.Morphological variations in the brain are caused by t...The brain of humans and other organisms is affected in various ways through the electromagneticfield(EMF)radiations generated by mobile phones and cell phone towers.Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF.Cellular level analysis is used to measure and detect the effect of mobile radiations,but its utilization seems very expensive,and it is a tedious process,where its analysis requires the preparation of cell suspension.In this regard,this research article proposes optimal broadcast-ing learning to detect changes in brain morphology due to the revelation of EMF.Here,Drosophila melanogaster acts as a specimen under the revelation of EMF.Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF.The geometrical characteristics of the brain image of that is microscopic segmented are analyzed.Analysis results reveal the occur-rence of several prejudicial characteristics that can be processed by machine learn-ing techniques.The important prejudicial characteristics are given to four varieties of classifiers such as naïve Bayes,artificial neural network,support vector machine,and unsystematic forest for the classification of open or nonopen micro-scopic image of D.melanogaster brain.The results are attained through various experimental evaluations,and the said classifiers perform well by achieving 96.44%using the prejudicial characteristics chosen by the feature selection meth-od.The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity,where the machine learning techniques produce an effective framework for image processing.展开更多
ions in the ECG signal.The cardiologist and medical specialistfind numerous difficulties in the process of traditional approaches.The specified restrictions are eliminated in the proposed classifier.The fundamental ai...ions in the ECG signal.The cardiologist and medical specialistfind numerous difficulties in the process of traditional approaches.The specified restrictions are eliminated in the proposed classifier.The fundamental aim of this work is tofind the R-R interval.To analyze the blockage,different approaches are implemented,which make the computation as facile with high accuracy.The information are recovered from the MIT-BIH dataset.The retrieved data contain normal and pathological ECG signals.To obtain a noiseless signal,Gaborfilter is employed and to compute the amplitude of the signal,DCT-DOST(Discrete cosine based Discrete orthogonal stock well transform)is implemented.The amplitude is computed to detect the cardiac abnormality.The R peak of the underlying ECG signal is noted and the segment length of the ECG cycle is identified.The Genetic algorithm(GA)retrieves the primary highlights and the classifier integrates the data with the chosen attributes to optimize the identification.In addition,the GA helps in performing hereditary calculations to reduce the problem of multi-target enhancement.Finally,the RBFNN(Radial basis function neural network)is applied,which diminishes the local minima present in the signal.It shows enhancement in characterizing the ordinary and anomalous ECG signals.展开更多
文摘Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces.Recently,Deep Learning Techniques(DLT)have gained popular-ity in applications of real-world problems including recognition of human emo-tions.The human face reflects emotional states and human intentions.An expression is the most natural and powerful way of communicating non-verbally.Systems which form communications between the two are termed Human Machine Interaction(HMI)systems.FER can improve HMI systems as human expressions convey useful information to an observer.This paper proposes a FER scheme called EECNN(Enhanced Convolution Neural Network with Atten-tion mechanism)to recognize seven types of human emotions with satisfying results in its experiments.Proposed EECNN achieved 89.8%accuracy in classi-fying the images.
文摘The brain of humans and other organisms is affected in various ways through the electromagneticfield(EMF)radiations generated by mobile phones and cell phone towers.Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF.Cellular level analysis is used to measure and detect the effect of mobile radiations,but its utilization seems very expensive,and it is a tedious process,where its analysis requires the preparation of cell suspension.In this regard,this research article proposes optimal broadcast-ing learning to detect changes in brain morphology due to the revelation of EMF.Here,Drosophila melanogaster acts as a specimen under the revelation of EMF.Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF.The geometrical characteristics of the brain image of that is microscopic segmented are analyzed.Analysis results reveal the occur-rence of several prejudicial characteristics that can be processed by machine learn-ing techniques.The important prejudicial characteristics are given to four varieties of classifiers such as naïve Bayes,artificial neural network,support vector machine,and unsystematic forest for the classification of open or nonopen micro-scopic image of D.melanogaster brain.The results are attained through various experimental evaluations,and the said classifiers perform well by achieving 96.44%using the prejudicial characteristics chosen by the feature selection meth-od.The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity,where the machine learning techniques produce an effective framework for image processing.
文摘ions in the ECG signal.The cardiologist and medical specialistfind numerous difficulties in the process of traditional approaches.The specified restrictions are eliminated in the proposed classifier.The fundamental aim of this work is tofind the R-R interval.To analyze the blockage,different approaches are implemented,which make the computation as facile with high accuracy.The information are recovered from the MIT-BIH dataset.The retrieved data contain normal and pathological ECG signals.To obtain a noiseless signal,Gaborfilter is employed and to compute the amplitude of the signal,DCT-DOST(Discrete cosine based Discrete orthogonal stock well transform)is implemented.The amplitude is computed to detect the cardiac abnormality.The R peak of the underlying ECG signal is noted and the segment length of the ECG cycle is identified.The Genetic algorithm(GA)retrieves the primary highlights and the classifier integrates the data with the chosen attributes to optimize the identification.In addition,the GA helps in performing hereditary calculations to reduce the problem of multi-target enhancement.Finally,the RBFNN(Radial basis function neural network)is applied,which diminishes the local minima present in the signal.It shows enhancement in characterizing the ordinary and anomalous ECG signals.
