In this work,a Leslie–Gower prey-predator model with two discrete delays has been investigated.The positivity,boundedness and persistence of the delayed system have been discussed.The system exhibits the phenomenon o...In this work,a Leslie–Gower prey-predator model with two discrete delays has been investigated.The positivity,boundedness and persistence of the delayed system have been discussed.The system exhibits the phenomenon of Hopf bifurcation with respect to both delays.The conditions for occurrence of Hopf bifurcation are obtained for different combinations of delays.It is shown that delay induces the complexity in the system and brings the periodic oscillations,quasi-periodic oscillations and chaos.The properties of periodic solution have been determined using central manifold and normal form theory.Further,the global stability of the system has been established for different cases of discrete delays.The numerical computation has also been performed to verify analytical results.展开更多
The objective of this paper is to provide a better rendition of Generation Z purchase intentions of retail products through Facebook.The study gyrated around the favorable attitude formation of Generation Z translatin...The objective of this paper is to provide a better rendition of Generation Z purchase intentions of retail products through Facebook.The study gyrated around the favorable attitude formation of Generation Z translating into intentions to purchase retail products through Facebook.The role of antecedents of attitude,namely enjoyment,credibility,and peer communication was also explored.The main purpose was to analyze the F-commerce pervasiveness(retail purchases through Facebook)among Generation Z in India and how could it be materialized effectively.A conceptual fac¸ade was proposed after trotting out germane and urbane literature.The study focused exclusively on Generation Z population.The data were statistically analyzed using partial least squares structural equation modelling.The study found the proposed conceptual model had a high prediction power of Generation Z intentions to purchase retail products through Facebook verifying the materialization of F-commerce.Enjoyment,credibility,and peer communication were proved to be good predictors of attitude(R^(2)=0.589)and furthermore attitude was found to be a stellar antecedent to purchase intentions(R^(2)=0.540).展开更多
Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs.Machine Learning(ML)models are also being used to make predictions in these applications.Accurate forecasting re...Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs.Machine Learning(ML)models are also being used to make predictions in these applications.Accurate forecasting requires knowledge of these machine learning models and their distinguishing features.The datasets we use as input for each of these different types of ML models,yielding different results.The choice of an ML model for a dataset is critical.A loan risk model is used to show how ML models for a dataset can be linked together.The purpose of this study is to look into how we could use machine learning to quantify or forecast mortgage credit risk.This phrase refers to the process of evaluating massive amounts of data in order to derive useful information for making decisions in a variety of fields.If credit risk is considered,a method based on an examination of what caused and how mortgage credit risk affected credit defaults during the still-current economic crisis of 2021 will be tried.Various approaches to credit risk calculation will be examined,ranging from the most basic to the most complex.In addition,we will conduct a case study on a sample of mortgage loans and compare the results of three different analytical approaches,logistic regression,decision tree,and gradient boost to see which one produced the most commercially useful insights.展开更多
文摘In this work,a Leslie–Gower prey-predator model with two discrete delays has been investigated.The positivity,boundedness and persistence of the delayed system have been discussed.The system exhibits the phenomenon of Hopf bifurcation with respect to both delays.The conditions for occurrence of Hopf bifurcation are obtained for different combinations of delays.It is shown that delay induces the complexity in the system and brings the periodic oscillations,quasi-periodic oscillations and chaos.The properties of periodic solution have been determined using central manifold and normal form theory.Further,the global stability of the system has been established for different cases of discrete delays.The numerical computation has also been performed to verify analytical results.
文摘The objective of this paper is to provide a better rendition of Generation Z purchase intentions of retail products through Facebook.The study gyrated around the favorable attitude formation of Generation Z translating into intentions to purchase retail products through Facebook.The role of antecedents of attitude,namely enjoyment,credibility,and peer communication was also explored.The main purpose was to analyze the F-commerce pervasiveness(retail purchases through Facebook)among Generation Z in India and how could it be materialized effectively.A conceptual fac¸ade was proposed after trotting out germane and urbane literature.The study focused exclusively on Generation Z population.The data were statistically analyzed using partial least squares structural equation modelling.The study found the proposed conceptual model had a high prediction power of Generation Z intentions to purchase retail products through Facebook verifying the materialization of F-commerce.Enjoyment,credibility,and peer communication were proved to be good predictors of attitude(R^(2)=0.589)and furthermore attitude was found to be a stellar antecedent to purchase intentions(R^(2)=0.540).
文摘Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs.Machine Learning(ML)models are also being used to make predictions in these applications.Accurate forecasting requires knowledge of these machine learning models and their distinguishing features.The datasets we use as input for each of these different types of ML models,yielding different results.The choice of an ML model for a dataset is critical.A loan risk model is used to show how ML models for a dataset can be linked together.The purpose of this study is to look into how we could use machine learning to quantify or forecast mortgage credit risk.This phrase refers to the process of evaluating massive amounts of data in order to derive useful information for making decisions in a variety of fields.If credit risk is considered,a method based on an examination of what caused and how mortgage credit risk affected credit defaults during the still-current economic crisis of 2021 will be tried.Various approaches to credit risk calculation will be examined,ranging from the most basic to the most complex.In addition,we will conduct a case study on a sample of mortgage loans and compare the results of three different analytical approaches,logistic regression,decision tree,and gradient boost to see which one produced the most commercially useful insights.
