Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of ma...Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of machine learning models for customer churn prediction, focusing on the U.S. context. The research evaluates the performance of logistic regression, random forest, and neural networks using industry-specific datasets, considering the economic impact and practical implications of the findings. The exploratory data analysis reveals unique patterns and trends in the U.S. banking and finance industry, such as the age distribution of customers and the prevalence of dormant accounts. The study incorporates macroeconomic factors to capture the potential influence of external conditions on customer churn behavior. The findings highlight the importance of leveraging advanced machine learning techniques and comprehensive customer data to develop effective churn prevention strategies in the U.S. context. By accurately predicting customer churn, financial institutions can proactively identify at-risk customers, implement targeted retention strategies, and optimize resource allocation. The study discusses the limitations and potential future improvements, serving as a roadmap for researchers and practitioners to further advance the field of customer churn prediction in the evolving landscape of the U.S. banking and finance industry.展开更多
How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable i...How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].展开更多
Introduction: Wise prescription of antibiotics is an ethical duty of physicians in view of rising antimicrobial resistance in the community, it should be balanced between the health requirements of the patients and re...Introduction: Wise prescription of antibiotics is an ethical duty of physicians in view of rising antimicrobial resistance in the community, it should be balanced between the health requirements of the patients and resulting long-term antibiotics resistance. Overuse of antimicrobials is a major cause of emerging resistance to antimicrobials. There are multiple factors in the community that influence the physician’s antibiotic prescriptions. Methods: This is a systematic case-control study on antibiotics prescription for paediatric patients attending Latifa Hospital for Women and Children (LWCH), Dubai Health Authority, to know the effects of behavioral interventions on rates of inappropriate antimicrobials prescription by doctors in the Paediatric Emergency Department. Results: The results of our study showed the effectiveness of behavioral insights by peer comparison in antibiotic use among paediatricians in Latifa Hospital had a statistical significance (P = 0.0038). The rate of the prescription decreased from 41% to 21%, a difference of 20%. Conclusion: The study concluded behavioural intervention is an effective measure in reducing the improper prescription of antibiotics in the hospital setting.展开更多
文摘Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of machine learning models for customer churn prediction, focusing on the U.S. context. The research evaluates the performance of logistic regression, random forest, and neural networks using industry-specific datasets, considering the economic impact and practical implications of the findings. The exploratory data analysis reveals unique patterns and trends in the U.S. banking and finance industry, such as the age distribution of customers and the prevalence of dormant accounts. The study incorporates macroeconomic factors to capture the potential influence of external conditions on customer churn behavior. The findings highlight the importance of leveraging advanced machine learning techniques and comprehensive customer data to develop effective churn prevention strategies in the U.S. context. By accurately predicting customer churn, financial institutions can proactively identify at-risk customers, implement targeted retention strategies, and optimize resource allocation. The study discusses the limitations and potential future improvements, serving as a roadmap for researchers and practitioners to further advance the field of customer churn prediction in the evolving landscape of the U.S. banking and finance industry.
文摘How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].
文摘Introduction: Wise prescription of antibiotics is an ethical duty of physicians in view of rising antimicrobial resistance in the community, it should be balanced between the health requirements of the patients and resulting long-term antibiotics resistance. Overuse of antimicrobials is a major cause of emerging resistance to antimicrobials. There are multiple factors in the community that influence the physician’s antibiotic prescriptions. Methods: This is a systematic case-control study on antibiotics prescription for paediatric patients attending Latifa Hospital for Women and Children (LWCH), Dubai Health Authority, to know the effects of behavioral interventions on rates of inappropriate antimicrobials prescription by doctors in the Paediatric Emergency Department. Results: The results of our study showed the effectiveness of behavioral insights by peer comparison in antibiotic use among paediatricians in Latifa Hospital had a statistical significance (P = 0.0038). The rate of the prescription decreased from 41% to 21%, a difference of 20%. Conclusion: The study concluded behavioural intervention is an effective measure in reducing the improper prescription of antibiotics in the hospital setting.
