In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and ta...In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.展开更多
RETRACTION:P.Goyal and R.Malviya,“Challenges and Opportunities of Big Data Analytics in Healthcare,”Health Care Science 2,no.5(2023):328-338,https://doi.org/10.1002/hcs2.66.The above article,published online on 4 Oc...RETRACTION:P.Goyal and R.Malviya,“Challenges and Opportunities of Big Data Analytics in Healthcare,”Health Care Science 2,no.5(2023):328-338,https://doi.org/10.1002/hcs2.66.The above article,published online on 4 October 2023 in Wiley Online Library(wileyonlinelibrary.com),has been retracted by agreement between the journal Editor-in-Chief,Zongjiu Zhang;Tsinghua University Press;and John Wiley&Sons Ltd.展开更多
This study evaluates the use of predictive analytics to forecast customer turnover in subscription-based Services in order to develop a predictive model to help small and medium-sized enterprises manage customer churn...This study evaluates the use of predictive analytics to forecast customer turnover in subscription-based Services in order to develop a predictive model to help small and medium-sized enterprises manage customer churn in the face of digital disruption.The research uses a quantitative approach focusing on empirical customer data to accurately predict buying trends and adapt marketing techniques.Demand forecasts in the health sector are important,as in every sector.In particular,the material forecast and stock forecasting of the purchasing unit of hospitals are among the areas that receive significant attention.Four classifiers(Random Forest,Logistic Regression,Gradient Boosting and XGBoost)are trained and evaluated using various performance indicators as part of a systematic approach involving Kaggle data collection,preparation and model selection.The results show excellent accuracy in predicting customer attrition,but there are limitations in precision and recall,indicating room for improvement.Confusion matrices provide information about the performance of each classifier,allowing for continuous improvement of predictive analytics techniques.Ethical concerns are rigorously addressed throughout the work process to guarantee appropriate data and machine learning methodologies.The proposals emphasize the proactive use of predictive analytics to identify at-risk customers and implement targeted retention strategies.Incorporating new data sources,improving customer experience,and utilizing collaborative churn management methods are recommended to increase forecast accuracy and business outcomes.Finally,this research provides important insights into the usefulness of predictive analytics for customer churn forecasting as well as practical recommendations for businesses seeking to increase customer retention and reduce churn risk.By leveraging empirical research findings and implementing ethical and rigorous churn control strategies,businesses can achieve long-term success in today’s changing market environment.展开更多
The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent ...The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.展开更多
广州地区的高温天气主要是受副热带高压和台风外围下沉气流的影响所致。文中采用BDA(Bogus Data Assimilation)方法,探讨BDA方案对广州地区台风背景条件下高温预报的改进能力。选取2005年7月中旬广州地区出现的高温天气进行研究。这是...广州地区的高温天气主要是受副热带高压和台风外围下沉气流的影响所致。文中采用BDA(Bogus Data Assimilation)方法,探讨BDA方案对广州地区台风背景条件下高温预报的改进能力。选取2005年7月中旬广州地区出现的高温天气进行研究。这是比较典型的受副热带高压和台风(海棠)共同影响造成高温的天气过程。分析有无采用BDA方案的模式初始场,结果表明:采用BDA方案同化Bogus模型可以调整台风中心位置和强度,使所得到的初始场中心位置与观测更为接近,台风强度(气压梯度力、风速)比未用Bogus的情况强,与观测值更为接近。数值模拟的结果表明,采用了BDA方案的敏感试验可以更好地预报台风路径和台风中心强度变化,从而更好地预报高温天气,对高温区分布、日平均温度大小等的预报都有改进。文中对引起这种预报差异的原因进行了讨论,并探讨高温预报改进的可能机制。大气下沉运动的增强是高温预报改进的主要原因。敏感试验由于广州中低层大气的水汽减少,大气的下沉增强,致使天空的云量减少,对太阳短波辐射的阻挡减小,从而地面吸收热量增多,温度升高,输送给大气的感热增加,大气温升高。采用BDA方案可以改进模式在台风"海棠"过程对广州高温的预报。展开更多
针对北京经济技术开发区(Beijing Economic-Technology Development Area,BDA)企业的合作竞争网络进行实证研究,建立BDA企业合作竞争网络模型,结合UCINET网络分析软件,描绘BDA企业合作竞争网络结构图。根据BDA的发展特征,将企业主要分...针对北京经济技术开发区(Beijing Economic-Technology Development Area,BDA)企业的合作竞争网络进行实证研究,建立BDA企业合作竞争网络模型,结合UCINET网络分析软件,描绘BDA企业合作竞争网络结构图。根据BDA的发展特征,将企业主要分为四个行业:微电子行业、生物医药行业、汽车行业和装备制造业,清晰地描述了BDA企业合作竞争网络关系并且揭示了网络结构特征。研究也显示,这四大行业形成了具有中心节点的网络,并且网络的信息沟通比较顺畅,有利于企业之间的合作与竞争。该模型也有助于优化区域行业分布和资源分配,给管理决策提供研究支持。展开更多
文摘In the competitive retail industry of the digital era,data-driven insights into gender-specific customer behavior are essential.They support the optimization of store performance,layout design,product placement,and targeted marketing.However,existing computer vision solutions often rely on facial recognition to gather such insights,raising significant privacy and ethical concerns.To address these issues,this paper presents a privacypreserving customer analytics system through two key strategies.First,we deploy a deep learning framework using YOLOv9s,trained on the RCA-TVGender dataset.Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate gender classification.Second,we apply AES-128 encryption to customer position data,ensuring secure access and regulatory compliance.Our system achieved overall performance,with 81.5%mAP@50,77.7%precision,and 75.7%recall.Moreover,a 90-min observational study confirmed the system’s ability to generate privacy-protected heatmaps revealing distinct behavioral patterns between male and female customers.For instance,women spent more time in certain areas and showed interest in different products.These results confirm the system’s effectiveness in enabling personalized layout and marketing strategies without compromising privacy.
文摘RETRACTION:P.Goyal and R.Malviya,“Challenges and Opportunities of Big Data Analytics in Healthcare,”Health Care Science 2,no.5(2023):328-338,https://doi.org/10.1002/hcs2.66.The above article,published online on 4 October 2023 in Wiley Online Library(wileyonlinelibrary.com),has been retracted by agreement between the journal Editor-in-Chief,Zongjiu Zhang;Tsinghua University Press;and John Wiley&Sons Ltd.
文摘This study evaluates the use of predictive analytics to forecast customer turnover in subscription-based Services in order to develop a predictive model to help small and medium-sized enterprises manage customer churn in the face of digital disruption.The research uses a quantitative approach focusing on empirical customer data to accurately predict buying trends and adapt marketing techniques.Demand forecasts in the health sector are important,as in every sector.In particular,the material forecast and stock forecasting of the purchasing unit of hospitals are among the areas that receive significant attention.Four classifiers(Random Forest,Logistic Regression,Gradient Boosting and XGBoost)are trained and evaluated using various performance indicators as part of a systematic approach involving Kaggle data collection,preparation and model selection.The results show excellent accuracy in predicting customer attrition,but there are limitations in precision and recall,indicating room for improvement.Confusion matrices provide information about the performance of each classifier,allowing for continuous improvement of predictive analytics techniques.Ethical concerns are rigorously addressed throughout the work process to guarantee appropriate data and machine learning methodologies.The proposals emphasize the proactive use of predictive analytics to identify at-risk customers and implement targeted retention strategies.Incorporating new data sources,improving customer experience,and utilizing collaborative churn management methods are recommended to increase forecast accuracy and business outcomes.Finally,this research provides important insights into the usefulness of predictive analytics for customer churn forecasting as well as practical recommendations for businesses seeking to increase customer retention and reduce churn risk.By leveraging empirical research findings and implementing ethical and rigorous churn control strategies,businesses can achieve long-term success in today’s changing market environment.
基金The research project,“Research on Power Safety Assisted Decision System Based on Large Language Models”(Project Number:JSDL24051414020001)acknowledges with gratitude the financial and logistical support it has received.
文摘The rapid development and increased installed capacity of new energy sources such as wind and solar power pose new challenges for power grid fault diagnosis.This paper presents an innovative framework,the Intelligent Power Stability and Scheduling(IPSS)System,which is designed to enhance the safety,stability,and economic efficiency of power systems,particularly those integrated with green energy sources.The IPSS System is distinguished by its integration of a CNN-Transformer predictive model,which leverages the strengths of Convolutional Neural Networks(CNN)for local feature extraction and Transformer architecture for global dependency modeling,offering significant potential in power safety diagnostics.TheIPSS System optimizes the economic and stability objectives of the power grid through an improved Zebra Algorithm,which aims tominimize operational costs and grid instability.Theperformance of the predictive model is comprehensively evaluated using key metrics such as Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and Coefficient of Determination(R2).Experimental results demonstrate the superiority of the CNN-Transformer model,with the lowest RMSE and MAE values of 0.0063 and 0.00421,respectively,on the training set,and an R2 value approaching 1,at 0.99635,indicating minimal prediction error and strong data interpretability.On the test set,the model maintains its excellence with the lowest RMSE and MAE values of 0.009 and 0.00673,respectively,and an R2 value of 0.97233.The IPSS System outperforms other models in terms of prediction accuracy and explanatory power and validates its effectiveness in economic and stability analysis through comparative studies with other optimization algorithms.The system’s efficacy is further supported by experimental results,highlighting the proposed scheme’s capability to reduce operational costs and enhance system stability,making it a valuable contribution to the field of green energy systems.
文摘广州地区的高温天气主要是受副热带高压和台风外围下沉气流的影响所致。文中采用BDA(Bogus Data Assimilation)方法,探讨BDA方案对广州地区台风背景条件下高温预报的改进能力。选取2005年7月中旬广州地区出现的高温天气进行研究。这是比较典型的受副热带高压和台风(海棠)共同影响造成高温的天气过程。分析有无采用BDA方案的模式初始场,结果表明:采用BDA方案同化Bogus模型可以调整台风中心位置和强度,使所得到的初始场中心位置与观测更为接近,台风强度(气压梯度力、风速)比未用Bogus的情况强,与观测值更为接近。数值模拟的结果表明,采用了BDA方案的敏感试验可以更好地预报台风路径和台风中心强度变化,从而更好地预报高温天气,对高温区分布、日平均温度大小等的预报都有改进。文中对引起这种预报差异的原因进行了讨论,并探讨高温预报改进的可能机制。大气下沉运动的增强是高温预报改进的主要原因。敏感试验由于广州中低层大气的水汽减少,大气的下沉增强,致使天空的云量减少,对太阳短波辐射的阻挡减小,从而地面吸收热量增多,温度升高,输送给大气的感热增加,大气温升高。采用BDA方案可以改进模式在台风"海棠"过程对广州高温的预报。
文摘针对北京经济技术开发区(Beijing Economic-Technology Development Area,BDA)企业的合作竞争网络进行实证研究,建立BDA企业合作竞争网络模型,结合UCINET网络分析软件,描绘BDA企业合作竞争网络结构图。根据BDA的发展特征,将企业主要分为四个行业:微电子行业、生物医药行业、汽车行业和装备制造业,清晰地描述了BDA企业合作竞争网络关系并且揭示了网络结构特征。研究也显示,这四大行业形成了具有中心节点的网络,并且网络的信息沟通比较顺畅,有利于企业之间的合作与竞争。该模型也有助于优化区域行业分布和资源分配,给管理决策提供研究支持。
