The elemental composition of coal and biomass provides significant parameters used in the design of almost all energy conversion systems and projects.The laboratory tests to determine the elemental composition of coal...The elemental composition of coal and biomass provides significant parameters used in the design of almost all energy conversion systems and projects.The laboratory tests to determine the elemental composition of coal and biomass is time-consuming and costly.However,limited research has suggested that there is a correlation between parameters obtained from elemental and proximate analyses of these materials.In this study,some predictive models of the elemental composition of coal and biomass using soft computing and regression analyses have been developed.Thirty-one samples including parameters of elemental and proximate analyses were used during the analyses to develop multiple prediction models.Dependent variables for multiple prediction models were selected as carbon,hydrogen,and oxygen.Using volatile matter,fixed carbon,moisture and ash contents as independent variables,three different prediction models were developed for each dependent parameter using ANFIS,ANN,and MLR.In addition,a routine for selecting the best predictive model was suggested in the study.The reliability of the established models was tested by using various prediction performance indices and the models were found to be satisfactory.Therefore,the developed models can be used to determine the elemental composition of coal and biomass for practical purposes.展开更多
Recently,many rapid developments in digital medical imaging have made further contributions to health care systems.The segmentation of regions of interest in medical images plays a vital role in assisting doctors with...Recently,many rapid developments in digital medical imaging have made further contributions to health care systems.The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses.Many factors like image contrast and quality affect the result of image segmentation.Due to that,image contrast remains a challenging problem for image segmentation.This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans.The proposed work consists of two stages:enhancement by fractional Rényi entropy,and MRI Kidney deep segmentation.The proposed enhancement model exploits the pixel’s probability representations for image enhancement.Since fractional Rényi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels,yielding an overall better details of the kidney MRI scans.In the second stage,the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans.The experimental results showed an average of 95.60%dice similarity index coefficient,which indicates best overlap between the segmented bodies with the ground truth.It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.展开更多
The task of mining erasable patterns(EPs)is a data mining problem that can help factory managers come up with the best product plans for the future.This problem has been studied by many scientists in recent times,and ...The task of mining erasable patterns(EPs)is a data mining problem that can help factory managers come up with the best product plans for the future.This problem has been studied by many scientists in recent times,and many approaches for mining EPs have been proposed.Erasable closed patterns(ECPs)are an abbreviated representation of EPs and can be con-sidered condensed representations of EPs without information loss.Current methods of mining ECPs identify huge numbers of such patterns,whereas intelligent systems only need a small number.A ranking process therefore needs to be applied prior to use,which causes a reduction in efficiency.To overcome this limitation,this study presents a robust method for mining top-rank-k ECPs in which the mining and ranking phases are combined into a single step.First,we propose a virtual-threshold-based pruning strategy to improve the mining speed.Based on this strategy and dPidset structure,we then develop a fast algorithm for mining top-rank-k ECPs,which we call TRK-ECP.Finally,we carry out experiments to compare the runtime of our TRK-ECP algorithm with two algorithms modified from dVM and TEPUS(Top-rank-k Erasable Pattern mining Using the Subsume concept),which are state-of-the-art algorithms for mining top-rank-k EPs.The results for the running time confirm that TRK-ECP outperforms the other experimental approaches in terms of mining the top-rank-k ECPs.展开更多
In recent years,the detection of fake job descriptions has become increasingly necessary because social networking has changed the way people access burgeoning information in the internet age.Identifying fraud in job ...In recent years,the detection of fake job descriptions has become increasingly necessary because social networking has changed the way people access burgeoning information in the internet age.Identifying fraud in job descriptions can help jobseekers to avoid many of the risks of job hunting.However,the problem of detecting fake job descriptions comes up against the problem of class imbalance when the number of genuine jobs exceeds the number of fake jobs.This causes a reduction in the predictability and performance of traditional machine learning models.We therefore present an efficient framework that uses an oversampling technique called FJD-OT(Fake Job Description Detection Using Oversampling Techniques)to improve the predictability of detecting fake job descriptions.In the proposed framework,we apply several techniques including the removal of stop words and the use of a tokenizer to preprocess the text data in the first module.We then use a bag of words in combination with the term frequency-inverse document frequency(TF-IDF)approach to extract the features from the text data to create the feature dataset in the second module.Next,our framework applies k-fold cross-validation,a commonly used technique to test the effectiveness of machine learning models,that splits the experimental dataset[the Employment Scam Aegean(ESA)dataset in our study]into training and test sets for evaluation.The training set is passed through the third module,an oversampling module in which the SVMSMOTE method is used to balance data before training the classifiers in the last module.The experimental results indicate that the proposed approach significantly improves the predictability of fake job description detection on the ESA dataset based on several popular performance metrics.展开更多
This paper presents the mathematical analysis of the dynamical system for avian influenza.The proposed model considers a nonlinear dynamical model of birds and human.The half-saturated incidence rate is used for the t...This paper presents the mathematical analysis of the dynamical system for avian influenza.The proposed model considers a nonlinear dynamical model of birds and human.The half-saturated incidence rate is used for the transmission of avian influenza infection.Rigorous mathematical results are presented for the proposed models.The local and global dynamics of each model are presented and proven that when R0<1,then the disease-free equilibrium of each model is stable both locally and globally,and when R0>1,then the endemic equilibrium is stable both locally and globally.The numerical results obtained for the proposed model shows that influenza could be eliminated from the community if the threshold is not greater than unity.展开更多
文摘The elemental composition of coal and biomass provides significant parameters used in the design of almost all energy conversion systems and projects.The laboratory tests to determine the elemental composition of coal and biomass is time-consuming and costly.However,limited research has suggested that there is a correlation between parameters obtained from elemental and proximate analyses of these materials.In this study,some predictive models of the elemental composition of coal and biomass using soft computing and regression analyses have been developed.Thirty-one samples including parameters of elemental and proximate analyses were used during the analyses to develop multiple prediction models.Dependent variables for multiple prediction models were selected as carbon,hydrogen,and oxygen.Using volatile matter,fixed carbon,moisture and ash contents as independent variables,three different prediction models were developed for each dependent parameter using ANFIS,ANN,and MLR.In addition,a routine for selecting the best predictive model was suggested in the study.The reliability of the established models was tested by using various prediction performance indices and the models were found to be satisfactory.Therefore,the developed models can be used to determine the elemental composition of coal and biomass for practical purposes.
基金funded by the deanship of scientific research at princess Nourah bint Abdulrahman University through the fast-track research-funding program.
文摘Recently,many rapid developments in digital medical imaging have made further contributions to health care systems.The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses.Many factors like image contrast and quality affect the result of image segmentation.Due to that,image contrast remains a challenging problem for image segmentation.This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans.The proposed work consists of two stages:enhancement by fractional Rényi entropy,and MRI Kidney deep segmentation.The proposed enhancement model exploits the pixel’s probability representations for image enhancement.Since fractional Rényi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels,yielding an overall better details of the kidney MRI scans.In the second stage,the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans.The experimental results showed an average of 95.60%dice similarity index coefficient,which indicates best overlap between the segmented bodies with the ground truth.It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.
文摘The task of mining erasable patterns(EPs)is a data mining problem that can help factory managers come up with the best product plans for the future.This problem has been studied by many scientists in recent times,and many approaches for mining EPs have been proposed.Erasable closed patterns(ECPs)are an abbreviated representation of EPs and can be con-sidered condensed representations of EPs without information loss.Current methods of mining ECPs identify huge numbers of such patterns,whereas intelligent systems only need a small number.A ranking process therefore needs to be applied prior to use,which causes a reduction in efficiency.To overcome this limitation,this study presents a robust method for mining top-rank-k ECPs in which the mining and ranking phases are combined into a single step.First,we propose a virtual-threshold-based pruning strategy to improve the mining speed.Based on this strategy and dPidset structure,we then develop a fast algorithm for mining top-rank-k ECPs,which we call TRK-ECP.Finally,we carry out experiments to compare the runtime of our TRK-ECP algorithm with two algorithms modified from dVM and TEPUS(Top-rank-k Erasable Pattern mining Using the Subsume concept),which are state-of-the-art algorithms for mining top-rank-k EPs.The results for the running time confirm that TRK-ECP outperforms the other experimental approaches in terms of mining the top-rank-k ECPs.
文摘In recent years,the detection of fake job descriptions has become increasingly necessary because social networking has changed the way people access burgeoning information in the internet age.Identifying fraud in job descriptions can help jobseekers to avoid many of the risks of job hunting.However,the problem of detecting fake job descriptions comes up against the problem of class imbalance when the number of genuine jobs exceeds the number of fake jobs.This causes a reduction in the predictability and performance of traditional machine learning models.We therefore present an efficient framework that uses an oversampling technique called FJD-OT(Fake Job Description Detection Using Oversampling Techniques)to improve the predictability of detecting fake job descriptions.In the proposed framework,we apply several techniques including the removal of stop words and the use of a tokenizer to preprocess the text data in the first module.We then use a bag of words in combination with the term frequency-inverse document frequency(TF-IDF)approach to extract the features from the text data to create the feature dataset in the second module.Next,our framework applies k-fold cross-validation,a commonly used technique to test the effectiveness of machine learning models,that splits the experimental dataset[the Employment Scam Aegean(ESA)dataset in our study]into training and test sets for evaluation.The training set is passed through the third module,an oversampling module in which the SVMSMOTE method is used to balance data before training the classifiers in the last module.The experimental results indicate that the proposed approach significantly improves the predictability of fake job description detection on the ESA dataset based on several popular performance metrics.
基金The corresponding authors extend their appreciation to the Deanship of Scientific Research,University of Hafr Al Batin for funding this work through the research group project no.(G-108-2020).
文摘This paper presents the mathematical analysis of the dynamical system for avian influenza.The proposed model considers a nonlinear dynamical model of birds and human.The half-saturated incidence rate is used for the transmission of avian influenza infection.Rigorous mathematical results are presented for the proposed models.The local and global dynamics of each model are presented and proven that when R0<1,then the disease-free equilibrium of each model is stable both locally and globally,and when R0>1,then the endemic equilibrium is stable both locally and globally.The numerical results obtained for the proposed model shows that influenza could be eliminated from the community if the threshold is not greater than unity.
