Autism Spectrum Disorder(ASD)is a complicated neurodevelopmen-tal disorder that is often identified in toddlers.The microarray data is used as a diagnostic tool to identify the genetics of the disorder.However,microarr...Autism Spectrum Disorder(ASD)is a complicated neurodevelopmen-tal disorder that is often identified in toddlers.The microarray data is used as a diagnostic tool to identify the genetics of the disorder.However,microarray data is large and has a high volume.Consequently,it suffers from the problem of dimensionality.In microarray data,the sample size and variance of the gene expression will lead to overfitting and misclassification.Identifying the autism gene(feature)subset from microarray data is an important and challenging research area.It has to be efficiently addressed to improve gene feature selection and classification.To overcome the challenges,a novel Intelligent Hybrid Ensem-ble Gene Selection(IHEGS)model is proposed in this paper.The proposed model integrates the intelligence of different feature selection techniques over the data partitions.In this model,the initial gene selection is carried out by data perturba-tion,and thefinal autism gene subset is obtained by functional perturbation,which reduces the problem of dimensionality in microarray data.The functional perturbation module employs three meta-heuristic swarm intelligence-based tech-niques for gene selection.The obtained gene subset is validated by the Deep Neural Network(DNN)model.The proposed model is implemented using python with six National Center for Biotechnology Information(NCBI)gene expression datasets.From the comparative study with other existing state-of-the-art systems,the proposed model provides stable results in terms of feature selection and clas-sification accuracy.展开更多
Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty per...Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world population.The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains.This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques.Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy.There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind,relative humidity,Instant Wind Speed in paddy cultivation.The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series.A Robust Optimized Artificial Neural Network(ROANN)Algorithm with Genetic Algorithm(GA)and Multi Objective Particle Swarm Optimization Algorithm(MOPSO)proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation.A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database.The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database.Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm.The reason for improving the growth of paddy was identified using the output of the Neural Network.Performance metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed algorithm.Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.展开更多
Human Activity Recognition(HAR)from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications,video-based behavior monitoring,video classi...Human Activity Recognition(HAR)from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications,video-based behavior monitoring,video classification,and indexing,patient monitoring,robotics,and behavior analysis.Although many techniques are available for HAR in video analysis tasks,most of them are not focusing on behavioral analysis.Hence,a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this work.The most essential aim of this work is to recognize the complex activities that are useful in many tasks that are based on object detection,modelling of individual frame characteristics,and communication among them.Moreover,this work focuses on finding out the human actions from various video resolutions,invariant human poses,and nearness of multi objects.First,we identify the key and essential frames of each activity using histogram differences.Secondly,Discrete Wavelet Transform(DWT)is used in this system to extract coefficients from the sequence of key-frames where the activity is localized in space.Finally,an Adaptive Weighted Flow Net(AWFN)algorithm is proposed in this work for effective video activity recognition.Moreover,the proposed algorithm has been evaluated by comparing it with the existing Visual Geometry Group(VGG-16)convolution neural networks for making performance comparisons.This work focuses on competent deep learning-based feature extraction to discriminate the activities for performing the classification accuracy.The proposed model has been evaluated with VGG-16 using a combination of regular UCF-101 activity datasets and also in very challenging Low-quality videos such as HMDB51.From these investigations,it is proved that the proposed AWFN approach gives higher detection accuracy of 96%.It is approximately 0.3%to 7.88%of higher accuracy than state-of-art methods.展开更多
文摘Autism Spectrum Disorder(ASD)is a complicated neurodevelopmen-tal disorder that is often identified in toddlers.The microarray data is used as a diagnostic tool to identify the genetics of the disorder.However,microarray data is large and has a high volume.Consequently,it suffers from the problem of dimensionality.In microarray data,the sample size and variance of the gene expression will lead to overfitting and misclassification.Identifying the autism gene(feature)subset from microarray data is an important and challenging research area.It has to be efficiently addressed to improve gene feature selection and classification.To overcome the challenges,a novel Intelligent Hybrid Ensem-ble Gene Selection(IHEGS)model is proposed in this paper.The proposed model integrates the intelligence of different feature selection techniques over the data partitions.In this model,the initial gene selection is carried out by data perturba-tion,and thefinal autism gene subset is obtained by functional perturbation,which reduces the problem of dimensionality in microarray data.The functional perturbation module employs three meta-heuristic swarm intelligence-based tech-niques for gene selection.The obtained gene subset is validated by the Deep Neural Network(DNN)model.The proposed model is implemented using python with six National Center for Biotechnology Information(NCBI)gene expression datasets.From the comparative study with other existing state-of-the-art systems,the proposed model provides stable results in terms of feature selection and clas-sification accuracy.
基金support of RUSA-Phase 2.0 grant sanctioned vide Letter No.F.24-51/2014-U,Policy(TNMulti-Gen),Dep.of Edn.Govt.of India,Dt.09.10.2018.
文摘Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world population.The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains.This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques.Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy.There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind,relative humidity,Instant Wind Speed in paddy cultivation.The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series.A Robust Optimized Artificial Neural Network(ROANN)Algorithm with Genetic Algorithm(GA)and Multi Objective Particle Swarm Optimization Algorithm(MOPSO)proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation.A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database.The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database.Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm.The reason for improving the growth of paddy was identified using the output of the Neural Network.Performance metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed algorithm.Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.
文摘Human Activity Recognition(HAR)from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications,video-based behavior monitoring,video classification,and indexing,patient monitoring,robotics,and behavior analysis.Although many techniques are available for HAR in video analysis tasks,most of them are not focusing on behavioral analysis.Hence,a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this work.The most essential aim of this work is to recognize the complex activities that are useful in many tasks that are based on object detection,modelling of individual frame characteristics,and communication among them.Moreover,this work focuses on finding out the human actions from various video resolutions,invariant human poses,and nearness of multi objects.First,we identify the key and essential frames of each activity using histogram differences.Secondly,Discrete Wavelet Transform(DWT)is used in this system to extract coefficients from the sequence of key-frames where the activity is localized in space.Finally,an Adaptive Weighted Flow Net(AWFN)algorithm is proposed in this work for effective video activity recognition.Moreover,the proposed algorithm has been evaluated by comparing it with the existing Visual Geometry Group(VGG-16)convolution neural networks for making performance comparisons.This work focuses on competent deep learning-based feature extraction to discriminate the activities for performing the classification accuracy.The proposed model has been evaluated with VGG-16 using a combination of regular UCF-101 activity datasets and also in very challenging Low-quality videos such as HMDB51.From these investigations,it is proved that the proposed AWFN approach gives higher detection accuracy of 96%.It is approximately 0.3%to 7.88%of higher accuracy than state-of-art methods.
