Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amo...Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced tools.The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security.The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms.Here,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting security.Then,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more effectively.The Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis function.Moreover,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security framework.The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.展开更多
This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network(CNN)combined with advanced mathematical methods,such as Independe...This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network(CNN)combined with advanced mathematical methods,such as Independent Component Analysis(ICA),Singular Value Decomposition(SVD),and a dimension-reduction technique like Nonnegative Matrix Factorization(NMF).Due to the highly disproportionate frequency of the fetus’s heart rate compared to the mother’s,the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy.Furthermore,we can disentangle the various components of fetal ECG,which serve as inputs to the CNN model to optimize the actual FECG signal,denoted by FECGr,which is recovered using the SVD-ICA process.The findings demonstrate the efficiency of this innovative approach,which may be deployed in real-time.展开更多
Online search has become very popular,and users can easily search for any movie title;however,to easily search for moving titles,users have to select a title that suits their taste.Otherwise,people will have difficult...Online search has become very popular,and users can easily search for any movie title;however,to easily search for moving titles,users have to select a title that suits their taste.Otherwise,people will have difficulty choosing the film they want to watch.The process of choosing or searching for a film in a large film database is currently time-consuming and tedious.Users spend extensive time on the internet or on several movie viewing sites without success until they find a film that matches their taste.This happens especially because humans are confused about choosing things and quickly change their minds.Hence,the recommendation system becomes critical.This study aims to reduce user effort and facilitate the movie research task.Further,we used the root mean square error scale to evaluate and compare different models adopted in this paper.These models were employed with the aim of developing a classification model for predicting movies.Thus,we tested and evaluated several cooperative filtering techniques.We used four approaches to implement sparse matrix completion algorithms:k-nearest neighbors,matrix factorization,co-clustering,and slope-one.展开更多
文摘Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced tools.The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security.The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms.Here,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting security.Then,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more effectively.The Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis function.Moreover,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security framework.The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.
文摘This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network(CNN)combined with advanced mathematical methods,such as Independent Component Analysis(ICA),Singular Value Decomposition(SVD),and a dimension-reduction technique like Nonnegative Matrix Factorization(NMF).Due to the highly disproportionate frequency of the fetus’s heart rate compared to the mother’s,the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy.Furthermore,we can disentangle the various components of fetal ECG,which serve as inputs to the CNN model to optimize the actual FECG signal,denoted by FECGr,which is recovered using the SVD-ICA process.The findings demonstrate the efficiency of this innovative approach,which may be deployed in real-time.
文摘Online search has become very popular,and users can easily search for any movie title;however,to easily search for moving titles,users have to select a title that suits their taste.Otherwise,people will have difficulty choosing the film they want to watch.The process of choosing or searching for a film in a large film database is currently time-consuming and tedious.Users spend extensive time on the internet or on several movie viewing sites without success until they find a film that matches their taste.This happens especially because humans are confused about choosing things and quickly change their minds.Hence,the recommendation system becomes critical.This study aims to reduce user effort and facilitate the movie research task.Further,we used the root mean square error scale to evaluate and compare different models adopted in this paper.These models were employed with the aim of developing a classification model for predicting movies.Thus,we tested and evaluated several cooperative filtering techniques.We used four approaches to implement sparse matrix completion algorithms:k-nearest neighbors,matrix factorization,co-clustering,and slope-one.
