Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages ot...Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.展开更多
Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may ...Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reached an impressive 99.77%.Upon testing,the accuracy for male and female datasets was recorded at 92.87%and 75.47%,respectively.The combined dataset accuracy is 93.96%,showcasing a commendable performance overall.Our proposed approach demonstrates versatility in accommodating small and large datasets,offering promising prospects for real-world applications.展开更多
Accurately modeling heavy-tailed data is critical across applied sciences,particularly in finance,medicine,and actuarial analysis.This work presents the heavy-tailed power XLindley distribution(HTPXLD),a unique heavy-...Accurately modeling heavy-tailed data is critical across applied sciences,particularly in finance,medicine,and actuarial analysis.This work presents the heavy-tailed power XLindley distribution(HTPXLD),a unique heavy-tailed distribution.Adding one more parameter to the power XLindley distribution improves this new distribution,especially when modeling leptokurtic lifetime data.The suggested density provides greater flexibility with asymmetric forms and different degrees of peakedness.Its statistical features,like the quantile function,moments,extropy measures,incomplete moments,stochastic ordering,and stress-strength parameters,are explored.We further investigate its use in actuarial science through the computation of pertinent metrics,such as value-at-risk,tail value-at-risk,tail variance,and tail variance premium.To obtain the point and interval parameter estimates,we use the maximum likelihood estimation approach.We do many simulation tests to evaluate the performance of our proposed estimator.Metrics like bias,relative bias,mean squared error,root mean squared error,average interval length,and coverage probability will be used in these tests to assess the estimator’s performance.To illustrate the practical value of our proposed model,we apply it to analyze three real-world datasets.We then compare its performance to established competing models,highlighting its advantages.展开更多
In this paper, a new lifetime class with decreasing failure rate is introduced by compounding truncated binomial distribution with any proper continuous lifetime distribution. The properties of the proposed class are ...In this paper, a new lifetime class with decreasing failure rate is introduced by compounding truncated binomial distribution with any proper continuous lifetime distribution. The properties of the proposed class are discussed, including a formal proof of its probability density function, distribution function and explicit algebraic formulae for its reliability and failure rate functions. A simple EM-type algorithm for iteratively computing maximum likelihood estimates is presented. The Fisher information matrix is derived in order to obtain the asymptotic covariance matrix. This new class of distributions generalizes several distributions which have been introduced and studied in the literature.展开更多
In this paper, we introduce a class of Lindley and Weibull distributions (LW) that are useful for modeling lifetime data with a comprehensive mathematical treatment. The new class of generated distributions includes s...In this paper, we introduce a class of Lindley and Weibull distributions (LW) that are useful for modeling lifetime data with a comprehensive mathematical treatment. The new class of generated distributions includes some well-known distributions, such as exponential, gamma, Weibull, Lindley, inverse gamma, inverse Weibull, inverse Lindley, and others. We provide closed-form expressions for the density, cumulative distribution, survival function, hazard rate function, moments, moments generating function, quantile, and stochastic orderings. Moreover, we discuss maximum likelihood estimation and the algorithm for computing the parameters estimates. Some sub models are discussed as an illustration with real data sets to show the flexibility of this class.展开更多
The study of the relationship between crude oil and its refined products prices may be perceived as an important tool for testing how are the dynamics and the type of integration of the petro-derivatives market in the...The study of the relationship between crude oil and its refined products prices may be perceived as an important tool for testing how are the dynamics and the type of integration of the petro-derivatives market in the United States.In this sense,we have applied a set of causality tests to study the possible presence of asymmetries in the relationship between WTI crude oil and each refined product price and to explore the type of market integration.Furthermore,the application of these causality tests lets us explore the validation of different hypotheses in the literature,such as the Rocket and Feathers hypothesis and the Verleger hypothesis.Our findings reveal that Reformulated Gasoline Blendstock for Oxygen Blending(RBOB),heating oil,diesel and kerosene are supply-driven integrated and conventional gasoline and kerosene are demand-driven integrated when linear effects are assessed.This behaviour changes deeply when the existence of asymmetries is tested,noticing that the Rocket and Feathers hypothesis is not fulfilled when a negative shock appears.Conversely,the Verleger hypothesis is supported when a negative shock appears for conventional gasoline and kerosene.These results provide important policy implications for investors,energy policymakers and refiners.展开更多
基金Researchers supporting Project Number(RSPD2024R576),King Saud University,Riyadh,Saudi Arabia.
文摘Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.
基金Researchers Supporting Project Number(RSPD2024R576),King Saud University,Riyadh,Saudi Arabia。
文摘Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reached an impressive 99.77%.Upon testing,the accuracy for male and female datasets was recorded at 92.87%and 75.47%,respectively.The combined dataset accuracy is 93.96%,showcasing a commendable performance overall.Our proposed approach demonstrates versatility in accommodating small and large datasets,offering promising prospects for real-world applications.
基金supported by researchers Supporting Project Number(RSPD2025R548)King Saud University,Riyadh,Saudi Arabia.
文摘Accurately modeling heavy-tailed data is critical across applied sciences,particularly in finance,medicine,and actuarial analysis.This work presents the heavy-tailed power XLindley distribution(HTPXLD),a unique heavy-tailed distribution.Adding one more parameter to the power XLindley distribution improves this new distribution,especially when modeling leptokurtic lifetime data.The suggested density provides greater flexibility with asymmetric forms and different degrees of peakedness.Its statistical features,like the quantile function,moments,extropy measures,incomplete moments,stochastic ordering,and stress-strength parameters,are explored.We further investigate its use in actuarial science through the computation of pertinent metrics,such as value-at-risk,tail value-at-risk,tail variance,and tail variance premium.To obtain the point and interval parameter estimates,we use the maximum likelihood estimation approach.We do many simulation tests to evaluate the performance of our proposed estimator.Metrics like bias,relative bias,mean squared error,root mean squared error,average interval length,and coverage probability will be used in these tests to assess the estimator’s performance.To illustrate the practical value of our proposed model,we apply it to analyze three real-world datasets.We then compare its performance to established competing models,highlighting its advantages.
文摘In this paper, a new lifetime class with decreasing failure rate is introduced by compounding truncated binomial distribution with any proper continuous lifetime distribution. The properties of the proposed class are discussed, including a formal proof of its probability density function, distribution function and explicit algebraic formulae for its reliability and failure rate functions. A simple EM-type algorithm for iteratively computing maximum likelihood estimates is presented. The Fisher information matrix is derived in order to obtain the asymptotic covariance matrix. This new class of distributions generalizes several distributions which have been introduced and studied in the literature.
文摘In this paper, we introduce a class of Lindley and Weibull distributions (LW) that are useful for modeling lifetime data with a comprehensive mathematical treatment. The new class of generated distributions includes some well-known distributions, such as exponential, gamma, Weibull, Lindley, inverse gamma, inverse Weibull, inverse Lindley, and others. We provide closed-form expressions for the density, cumulative distribution, survival function, hazard rate function, moments, moments generating function, quantile, and stochastic orderings. Moreover, we discuss maximum likelihood estimation and the algorithm for computing the parameters estimates. Some sub models are discussed as an illustration with real data sets to show the flexibility of this class.
文摘The study of the relationship between crude oil and its refined products prices may be perceived as an important tool for testing how are the dynamics and the type of integration of the petro-derivatives market in the United States.In this sense,we have applied a set of causality tests to study the possible presence of asymmetries in the relationship between WTI crude oil and each refined product price and to explore the type of market integration.Furthermore,the application of these causality tests lets us explore the validation of different hypotheses in the literature,such as the Rocket and Feathers hypothesis and the Verleger hypothesis.Our findings reveal that Reformulated Gasoline Blendstock for Oxygen Blending(RBOB),heating oil,diesel and kerosene are supply-driven integrated and conventional gasoline and kerosene are demand-driven integrated when linear effects are assessed.This behaviour changes deeply when the existence of asymmetries is tested,noticing that the Rocket and Feathers hypothesis is not fulfilled when a negative shock appears.Conversely,the Verleger hypothesis is supported when a negative shock appears for conventional gasoline and kerosene.These results provide important policy implications for investors,energy policymakers and refiners.
