Web crawlers have evolved from performing a meagre task of collecting statistics,security testing,web indexing and numerous other examples.The size and dynamism of the web are making crawling an interesting and challe...Web crawlers have evolved from performing a meagre task of collecting statistics,security testing,web indexing and numerous other examples.The size and dynamism of the web are making crawling an interesting and challenging task.Researchers have tackled various issues and challenges related to web crawling.One such issue is efficiently discovering hidden web data.Web crawler’s inability to work with form-based data,lack of benchmarks and standards for both performance measures and datasets for evaluation of the web crawlers make it still an immature research domain.The applications like vertical portals and data integration require hidden web crawling.Most of the existing methods are based on returning top k matches that makes exhaustive crawling difficult.The documents which are ranked high will be returned multiple times.The low ranked documents have slim chances of being retrieved.Discovering the hidden web sources and ranking them based on relevance is a core component of hidden web crawlers.The problem of ranking bias,heuristic approach and saturation of ranking algorithm led to low coverage.This research represents an enhanced ranking algorithm based on the triplet formula for prioritizing hidden websites to increase the coverage of the hidden web crawler.展开更多
Diabetes mellitus is a metabolic disease in which blood glucose levels rise as a result of pancreatic insulin production failure.It causes hyperglycemia and chronic multiorgan dysfunction,including blindness,renal fai...Diabetes mellitus is a metabolic disease in which blood glucose levels rise as a result of pancreatic insulin production failure.It causes hyperglycemia and chronic multiorgan dysfunction,including blindness,renal failure,and cardi-ovascular disease,if left untreated.One of the essential checks that are needed to be performed frequently in Type 1 Diabetes Mellitus is a blood test,this procedure involves extracting blood quite frequently,which leads to subject discomfort increasing the possibility of infection when the procedure is often recurring.Exist-ing methods used for diabetes classification have less classification accuracy and suffer from vanishing gradient problems,to overcome these issues,we proposed stacking ensemble learning-based convolutional gated recurrent neural network(CGRNN)Metamodel algorithm.Our proposed method initially performs outlier detection to remove outlier data,using the Gaussian distribution method,and the Box-cox method is used to correctly order the dataset.After the outliers’detec-tion,the missing values are replaced by the data’s mean rather than their elimina-tion.In the stacking ensemble base model,multiple machine learning algorithms like Naïve Bayes,Bagging with random forest,and Adaboost Decision tree have been employed.CGRNN Meta model uses two hidden layers Long-Short-Time Memory(LSTM)and Gated Recurrent Unit(GRU)to calculate the weight matrix for diabetes prediction.Finally,the calculated weight matrix is passed to the soft-max function in the output layer to produce the diabetes prediction results.By using LSTM-based CG-RNN,the mean square error(MSE)value is 0.016 and the obtained accuracy is 91.33%.展开更多
Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools.This stipulation make the dispensation period over-riding,difficult and tiresome to calculate.This paper present ...Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools.This stipulation make the dispensation period over-riding,difficult and tiresome to calculate.This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network(ANN)associated with Opposition based Grey Wolf Optimization Algorithm(OGWA).It identifies the prehistoric language,signs and fonts.It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance.For adaptively determining these weights,this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization,Particle Swarm Optimization and Grey Wolf Opti-mization to the ANN system.Performance results are illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques.In test case 1,the accuracy value of OGWO is 94.89%and in test case 2,the accu-racy value of OGWO is 92.34%,on average,the accuracy of OGWO achieves 5.8%greater accuracy than ANN-GWO,10.1%greater accuracy than ANN-PSO and 22.1%greater accuracy over conventional ANN technique.展开更多
基金Taif University Researchers Supporting Project number(TURSP-2020/98),Taif University,Taif,Saudi Arabia.
文摘Web crawlers have evolved from performing a meagre task of collecting statistics,security testing,web indexing and numerous other examples.The size and dynamism of the web are making crawling an interesting and challenging task.Researchers have tackled various issues and challenges related to web crawling.One such issue is efficiently discovering hidden web data.Web crawler’s inability to work with form-based data,lack of benchmarks and standards for both performance measures and datasets for evaluation of the web crawlers make it still an immature research domain.The applications like vertical portals and data integration require hidden web crawling.Most of the existing methods are based on returning top k matches that makes exhaustive crawling difficult.The documents which are ranked high will be returned multiple times.The low ranked documents have slim chances of being retrieved.Discovering the hidden web sources and ranking them based on relevance is a core component of hidden web crawlers.The problem of ranking bias,heuristic approach and saturation of ranking algorithm led to low coverage.This research represents an enhanced ranking algorithm based on the triplet formula for prioritizing hidden websites to increase the coverage of the hidden web crawler.
文摘Diabetes mellitus is a metabolic disease in which blood glucose levels rise as a result of pancreatic insulin production failure.It causes hyperglycemia and chronic multiorgan dysfunction,including blindness,renal failure,and cardi-ovascular disease,if left untreated.One of the essential checks that are needed to be performed frequently in Type 1 Diabetes Mellitus is a blood test,this procedure involves extracting blood quite frequently,which leads to subject discomfort increasing the possibility of infection when the procedure is often recurring.Exist-ing methods used for diabetes classification have less classification accuracy and suffer from vanishing gradient problems,to overcome these issues,we proposed stacking ensemble learning-based convolutional gated recurrent neural network(CGRNN)Metamodel algorithm.Our proposed method initially performs outlier detection to remove outlier data,using the Gaussian distribution method,and the Box-cox method is used to correctly order the dataset.After the outliers’detec-tion,the missing values are replaced by the data’s mean rather than their elimina-tion.In the stacking ensemble base model,multiple machine learning algorithms like Naïve Bayes,Bagging with random forest,and Adaboost Decision tree have been employed.CGRNN Meta model uses two hidden layers Long-Short-Time Memory(LSTM)and Gated Recurrent Unit(GRU)to calculate the weight matrix for diabetes prediction.Finally,the calculated weight matrix is passed to the soft-max function in the output layer to produce the diabetes prediction results.By using LSTM-based CG-RNN,the mean square error(MSE)value is 0.016 and the obtained accuracy is 91.33%.
文摘Recognizing signs and fonts of prehistoric language is a fairly difficult job that requires special tools.This stipulation make the dispensation period over-riding,difficult and tiresome to calculate.This paper present a technique for recognizing ancient south Indian languages by applying Artificial Neural Network(ANN)associated with Opposition based Grey Wolf Optimization Algorithm(OGWA).It identifies the prehistoric language,signs and fonts.It is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a significant role in its performance.For adaptively determining these weights,this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization,Particle Swarm Optimization and Grey Wolf Opti-mization to the ANN system.Performance results are illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques.In test case 1,the accuracy value of OGWO is 94.89%and in test case 2,the accu-racy value of OGWO is 92.34%,on average,the accuracy of OGWO achieves 5.8%greater accuracy than ANN-GWO,10.1%greater accuracy than ANN-PSO and 22.1%greater accuracy over conventional ANN technique.
