The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and immunity.Liver diseases result from factors such as viral infections,obesity,al...The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and immunity.Liver diseases result from factors such as viral infections,obesity,alcohol consumption,injuries,or genetic predispositions.Pose significant health risks and demand timely diagnosis and treatment to enhance survival rates.Traditionally,diagnosing liver diseases relied heavily on clinical expertise,often leading to subjective,challenging,and time-intensive processes.However,early detection is essential for effective intervention,and advancements in machine learning(ML)have demonstrated remarkable success in predicting various conditions,including Chronic Obstructive Pulmonary Disease(COPD),hypertension,and diabetes.This study proposed a novel XGBoost-liver predictor by integrating distinct feature methodologies,including Ranking and Statistical Projection-based strategies to detect early signs of liver disease.The Fisher score method is applied to perform global interpretation analysis,helping to select optimal features by assessing their contributions to the overall model.The performance of the proposed model has been extensively evaluated through k-fold cross-validation tests.Firstly,the performance of the proposed model is evaluated using individual and hybrid features.Secondly,the XGBoost-Liver model performance is compared to that of commonly used classifier algorithms.Thirdly,its performance is compared with the existing state-of-the-art computational models.The experimental results show that the proposed model performed better than the existing predictors,reaching an average accuracy rate of 92.07%.This paper demonstrates the potential of machine learning to improve liver disease prediction,enhance diagnostic accuracy,and enable timely medical interventions for better patient outcomes.展开更多
The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues.Identifiability can be divided into two:subjective identifiability,which is based on psychologi...The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues.Identifiability can be divided into two:subjective identifiability,which is based on psychological perceptions(i.e.,mental space),and objective identifiability,which is based on social media data(i.e.,information space).This study constructs a prediction model for social media data identifiability of users based on a supervised machine learning technique.The findings,based on data from Weibo,a Chinese social media platform,indicate that the top seven features and values for predicting social media identifiability include blog pictures(0.21),blog location(0.14),birthdate(0.12),location(0.10),blog interaction(0.10),school(0.08),and interests and hobbies(0.07).The relationship between machine-predicted and self-reported identifiability was tested using data from 91 participants.Based on the degree of deviation between the two,users can be divided into four categories—normal,conservative,active,and atypical—which reflect their sensitivity to privacy concerns and preferences regarding information disclosure.This study provides insights into the development of privacy protection strategies based on social media data classification.展开更多
The present work seeks to develop a model for measuring efficiency of RCBs in Ghana by means of financial key performance indicators pairing macroeconomic indicators. A stochastic differential equation model for predi...The present work seeks to develop a model for measuring efficiency of RCBs in Ghana by means of financial key performance indicators pairing macroeconomic indicators. A stochastic differential equation model for predicting the efficiency of RCBs in the future is developed and simulated using gaussian jumps to evaluate the models’ performance in unpredicted situations with four distinct phases of efficiency. Unique solution Exit multiple 4-dimensional stochastic differential equations and Macroeconomic model is proven to be the best-fitting model for the data with the lowest information criterion.展开更多
There is growing concern among the users of financial statements about the level of frauds occurring in the business organization.The unexpected collapse of corporate giants such as Enron and WorldCom has driven those...There is growing concern among the users of financial statements about the level of frauds occurring in the business organization.The unexpected collapse of corporate giants such as Enron and WorldCom has driven those interested to pay more attention to the unethical practices in accounting systems.The phenomenon of Earnings Management(EM)is seen as one of the most problematic issues facing the accounting profession during the last few decades.It has been argued that EM misleads the users of financial statements.This is because when the managers alter earnings,the financial statements do not accurately reflect the economic wealth of the company,which ultimately leads to the gross violation of stakeholders'trust.The objective of this paper is firstly to address the ethical issue of EM and evaluate the progress of research in dealing with this issue where Islamic Structural Framework,based on Islamic creed(aqidah),can provide some insights into the behavioral pattern and actions that could be undertaken to reduce or eliminate the practice of EM.Hence,the paper proposes a structural framework of Islamic Aqidah and the practice of EM that functions as a guidance to direct the intentions as well as behaviors of managers towards a proper manner(derived from the Quran and Sunna).展开更多
Environmental degradation is one of the most debatable topics at international forums and it is considered a prime concern for the entire world.Therefore,researchers and policymakers have turned their attention from c...Environmental degradation is one of the most debatable topics at international forums and it is considered a prime concern for the entire world.Therefore,researchers and policymakers have turned their attention from conventional economic growth to green growth.Although the existing literature has discussed several determinants of green growth,the impact of economic policy uncertainty(EPU),renewable energy consumption(RENE),and institutional quality(IQ)on green growth(GGDP)is relatively unexplored.Hence,this study is the earliest attempt to investigate the impact of EPU,IQ,and RENE on GGDP for emerging seven(E-7)countries from 1996 to 2019.In doing so,we apply panel quantile regression(PQR).The empirical findings delineate that EPU has a negative impact on GGDP,whereas IQ and RENE enhance the GGDP in E-7 countries.Based on the outcomes,this study suggests policy implications for achieving targets of the SDG 07,SDG 08,SDG 13,and SDG 16.The governments of these countries can achieve higher GGDP by ensuring political stability and reliable macroeconomic policies and through making such flexible policies that can easily control or address unpredictable future economic issues.展开更多
基金supported by Research Supporting Project Number(RSPD2025R585),King Saud University,Riyadh,Saudi Arabia.
文摘The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and immunity.Liver diseases result from factors such as viral infections,obesity,alcohol consumption,injuries,or genetic predispositions.Pose significant health risks and demand timely diagnosis and treatment to enhance survival rates.Traditionally,diagnosing liver diseases relied heavily on clinical expertise,often leading to subjective,challenging,and time-intensive processes.However,early detection is essential for effective intervention,and advancements in machine learning(ML)have demonstrated remarkable success in predicting various conditions,including Chronic Obstructive Pulmonary Disease(COPD),hypertension,and diabetes.This study proposed a novel XGBoost-liver predictor by integrating distinct feature methodologies,including Ranking and Statistical Projection-based strategies to detect early signs of liver disease.The Fisher score method is applied to perform global interpretation analysis,helping to select optimal features by assessing their contributions to the overall model.The performance of the proposed model has been extensively evaluated through k-fold cross-validation tests.Firstly,the performance of the proposed model is evaluated using individual and hybrid features.Secondly,the XGBoost-Liver model performance is compared to that of commonly used classifier algorithms.Thirdly,its performance is compared with the existing state-of-the-art computational models.The experimental results show that the proposed model performed better than the existing predictors,reaching an average accuracy rate of 92.07%.This paper demonstrates the potential of machine learning to improve liver disease prediction,enhance diagnostic accuracy,and enable timely medical interventions for better patient outcomes.
基金supported by the National Social Science Funds of China(Grant No.21BSH050)Major Project of National Social Science Funds of China(Grant No.20&ZD013).
文摘The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues.Identifiability can be divided into two:subjective identifiability,which is based on psychological perceptions(i.e.,mental space),and objective identifiability,which is based on social media data(i.e.,information space).This study constructs a prediction model for social media data identifiability of users based on a supervised machine learning technique.The findings,based on data from Weibo,a Chinese social media platform,indicate that the top seven features and values for predicting social media identifiability include blog pictures(0.21),blog location(0.14),birthdate(0.12),location(0.10),blog interaction(0.10),school(0.08),and interests and hobbies(0.07).The relationship between machine-predicted and self-reported identifiability was tested using data from 91 participants.Based on the degree of deviation between the two,users can be divided into four categories—normal,conservative,active,and atypical—which reflect their sensitivity to privacy concerns and preferences regarding information disclosure.This study provides insights into the development of privacy protection strategies based on social media data classification.
文摘The present work seeks to develop a model for measuring efficiency of RCBs in Ghana by means of financial key performance indicators pairing macroeconomic indicators. A stochastic differential equation model for predicting the efficiency of RCBs in the future is developed and simulated using gaussian jumps to evaluate the models’ performance in unpredicted situations with four distinct phases of efficiency. Unique solution Exit multiple 4-dimensional stochastic differential equations and Macroeconomic model is proven to be the best-fitting model for the data with the lowest information criterion.
文摘There is growing concern among the users of financial statements about the level of frauds occurring in the business organization.The unexpected collapse of corporate giants such as Enron and WorldCom has driven those interested to pay more attention to the unethical practices in accounting systems.The phenomenon of Earnings Management(EM)is seen as one of the most problematic issues facing the accounting profession during the last few decades.It has been argued that EM misleads the users of financial statements.This is because when the managers alter earnings,the financial statements do not accurately reflect the economic wealth of the company,which ultimately leads to the gross violation of stakeholders'trust.The objective of this paper is firstly to address the ethical issue of EM and evaluate the progress of research in dealing with this issue where Islamic Structural Framework,based on Islamic creed(aqidah),can provide some insights into the behavioral pattern and actions that could be undertaken to reduce or eliminate the practice of EM.Hence,the paper proposes a structural framework of Islamic Aqidah and the practice of EM that functions as a guidance to direct the intentions as well as behaviors of managers towards a proper manner(derived from the Quran and Sunna).
基金supported by Chengdu University of Technology “Double First-Class”initiative Construction Philosophy and Social Sciences Key Construction Project “Research on the Forming Mechanism of Laborers’Democratic Participation in Digital Platform under Algorithm Control”(Project No.:ZDJS202210)the Philosophy and Social Science Research Fund of Chengdu University of Technology“Research on the Guarantee Mechanism of Workers’Right to Speak in the New Business under the People’s Democracy in the Whole Process”(Project No.:YJ2022-YB022)the views expressed in this article are those of the authors and do not represent the foundations.
文摘Environmental degradation is one of the most debatable topics at international forums and it is considered a prime concern for the entire world.Therefore,researchers and policymakers have turned their attention from conventional economic growth to green growth.Although the existing literature has discussed several determinants of green growth,the impact of economic policy uncertainty(EPU),renewable energy consumption(RENE),and institutional quality(IQ)on green growth(GGDP)is relatively unexplored.Hence,this study is the earliest attempt to investigate the impact of EPU,IQ,and RENE on GGDP for emerging seven(E-7)countries from 1996 to 2019.In doing so,we apply panel quantile regression(PQR).The empirical findings delineate that EPU has a negative impact on GGDP,whereas IQ and RENE enhance the GGDP in E-7 countries.Based on the outcomes,this study suggests policy implications for achieving targets of the SDG 07,SDG 08,SDG 13,and SDG 16.The governments of these countries can achieve higher GGDP by ensuring political stability and reliable macroeconomic policies and through making such flexible policies that can easily control or address unpredictable future economic issues.
