A study of mathematical model for corruption and its control, that is Mathematical Corruption Model, it is statistically fit as well as valid for measuring corruption in the society. This is the mathematically method for measuring corruption in the society. When quantity of corruption is known, then there is no difficulty to remove the corruption from the society. Sometimes in corruption problems, there is no a priori information available, therefore the Mathematical Corruption Model (or MC model) is known as black box model
Mathematical Modelling: A Comparatively Mathematical Study Model Base between...IOSR Journals
In this paper, we have studied on the topic of „Corruption‟. Also, I will try to find or study the effect of corruption on the Development of the country or any country of the world. Therefore, how find the solution of the problem of corruption will be destroyed completely from the society. We have observed that the Development of the country depends upon Corruption. That is, when the Corruption increases, Development decreases automatically of any country of the world. Therefore, I will try to find the formula on the problem of „Relation between the Corruption and Development of any field or any country of the world‟. Also, I have to highlight the concept of „Application of Mathematical modeling in the interesting problem “corruption” in every field of our country or world .Also, Applied Mathematics focuses on the formulation and study of Mathematical Models .Thus the activity of Applied Mathematics is vitally connected with Research in Pure Mathematics. So I will try to study on it and find, what is corruption and quantity of corruption and also find the growth of corruption and how it will decay? Now we convert this areal world problem to mathematics problem and find some formulae on it such as Mathematical Corruption Growth formula, Mathematical Constant corruption level formula and Mathematical decay of corruption formula.
Micro finance and its impacts on empowerment of dalit women in cuddalore dist...RAVICHANDIRANG
Micro finance is the model of empowerment of the local people. Micro finance means providing very small loan to poor families rural, urban and semi urban areas. It is the major provision of financial services such as like savings, insurance, marketing, credit, thrift, production, investment, fund transferred and disadvantaged segment of society. Micro finance is one of the major tools for women empowerment and also it provides develop the society. This paper mainly focused on micro finance and its impact on empowerment of Dalit women in Cuddalore district.
The analysis of the data has been done using excel statistical sof.docxmattinsonjanel
The analysis of the data has been done using excel statistical software. First, the demand and popularity of each product has been analyzed using pie charts. The extracts from excel shows the distributions of the three product lines across age, sex and education. The three types of bicycles have analyzed in terms of the number of customers using them, sex, and education levels.
The low product line has the highest demand as 80 customers selected, followed by middle product line with 61 customers and finally upper product line. The following extracts shows the demand of the three bicycles on the basis of number of customers, sex, and education.
Analyzing the popularity and demand for three bicycles using sex showed that males have a higher proportion of using bicycles than females. This is show in the following extract and chart.
Also, the level of education determines the use of bicycles. The demand for bicycles varies across the different levels of education. The analysis revealed that non-college high school diploma do not use bicycles. The following pie chart shows the proportion of each education level with respect to the use of bicycles.
Education
Number of Customers
Percentage
Non-High School Diploma
0
0%
High School Diploma
2
1%
Some-College -level work
67
37%
College Degree
97
54%
Graduate Degree at work
14
8%
However, the use of the three products line varied greatly with the age of customers. The following frequency distribution table shows the age group of customers and the frequency of using the three products line.
Bin
Frequency
Cumulative %
Bin
Frequency
Cumulative %
20
10
5.56%
25
62
34.44%
25
62
40.00%
30
45
59.44%
30
45
65.00%
35
32
77.22%
35
32
82.78%
40
16
86.11%
40
16
91.67%
20
10
91.67%
45
8
96.11%
45
8
96.11%
50
7
100.00%
50
7
100.00%
More
0
100.00%
More
0
100.00%
As it can be seen from the histogram, the distribution of age of customers and the frequency on uses of bikes is negatively skewed. That is, at early ages, customers use bicycles more than old ages. At age group 20-25, the demand of bicycles is high and it decreases as age increases. The mean age, median age, mode of an average customer is showed in the following table. The table also shows the average income that most customers receive,
Mean Age
28.98889
Mode Age
25
Median Age
27
Average Income
35672.22
Median Income
34000
More analysis have been done on individual products lines in order to determine the mean age of a customer at a given product line; average salary, average miles/ week, average times/ week among other analysis. The following discussion focuses on each of the three product lines.
a) Lower Product Line.
The following analysis shows the profile of an average customer who chooses to by Low Product Line.
Mean Age
28.6
Sex
Males
55%
Females
45%
Status
Single:
36%
Married.
64%
Mean Salary
30700
Average Miles
88
Average Time/week
3.01 ...
Deep learning approach analysis model prediction and classification poverty s...IAESIJAI
The problem of poverty is a scourge for every developing country coupled with the economic crisis that occurred during the coronavirus disease (COVID-19) pandemic. The impact of these problems is felt directly by the people in Indonesia, especially in the Province of West Sumatra. This study aims to predict and classify the level of poverty status by developing an analytical model based on the deep learning (DL) approach. The methods used in this study include the K-means method, artificial neural network (ANN), and support vector machine (SVM). The analytical model will be optimized using the pearson correlation (PC) method to measure the accuracy of the analysis. The variable indicator uses the parameters of population (X1), poverty rate (X2), income (X3), and poverty percentage (X4). The results of the study present prediction and classification output with a validity level of accuracy of 99.8%. Based on these results, it can be concluded that the proposed DL analysis model can present an updated analytical model that is quite effective in carrying out the prediction and classification process. The research findings also contribute to the initial handling of the problem of poverty.
In general, urban accessibility shows the ease of reaching destinations and the
interaction between the land-use and transportation systems. Integrating transport
and land-use mix is one of the goals of planning policies around the world. In this
paper, an attempt is made to assess the urban accessibility through commuters’ travel
behavior for shopping trip only. The effects of trip characteristics like trip length, trip
time and trip cost and socio-economic characteristics like gender, age, income,
occupation and vehicle ownership on travel behavior and mode choice are studied for
shopping trips for different mixed land use zones (wards) of Vadodara city. Urban
accessibility Index is prepared for different neighborhoods. It is found that the change
in the land-use mix affects the commuters' travel behaviour and mode choice selection
Mathematical Modelling: A Comparatively Mathematical Study Model Base between...IOSR Journals
In this paper, we have studied on the topic of „Corruption‟. Also, I will try to find or study the effect of corruption on the Development of the country or any country of the world. Therefore, how find the solution of the problem of corruption will be destroyed completely from the society. We have observed that the Development of the country depends upon Corruption. That is, when the Corruption increases, Development decreases automatically of any country of the world. Therefore, I will try to find the formula on the problem of „Relation between the Corruption and Development of any field or any country of the world‟. Also, I have to highlight the concept of „Application of Mathematical modeling in the interesting problem “corruption” in every field of our country or world .Also, Applied Mathematics focuses on the formulation and study of Mathematical Models .Thus the activity of Applied Mathematics is vitally connected with Research in Pure Mathematics. So I will try to study on it and find, what is corruption and quantity of corruption and also find the growth of corruption and how it will decay? Now we convert this areal world problem to mathematics problem and find some formulae on it such as Mathematical Corruption Growth formula, Mathematical Constant corruption level formula and Mathematical decay of corruption formula.
Micro finance and its impacts on empowerment of dalit women in cuddalore dist...RAVICHANDIRANG
Micro finance is the model of empowerment of the local people. Micro finance means providing very small loan to poor families rural, urban and semi urban areas. It is the major provision of financial services such as like savings, insurance, marketing, credit, thrift, production, investment, fund transferred and disadvantaged segment of society. Micro finance is one of the major tools for women empowerment and also it provides develop the society. This paper mainly focused on micro finance and its impact on empowerment of Dalit women in Cuddalore district.
The analysis of the data has been done using excel statistical sof.docxmattinsonjanel
The analysis of the data has been done using excel statistical software. First, the demand and popularity of each product has been analyzed using pie charts. The extracts from excel shows the distributions of the three product lines across age, sex and education. The three types of bicycles have analyzed in terms of the number of customers using them, sex, and education levels.
The low product line has the highest demand as 80 customers selected, followed by middle product line with 61 customers and finally upper product line. The following extracts shows the demand of the three bicycles on the basis of number of customers, sex, and education.
Analyzing the popularity and demand for three bicycles using sex showed that males have a higher proportion of using bicycles than females. This is show in the following extract and chart.
Also, the level of education determines the use of bicycles. The demand for bicycles varies across the different levels of education. The analysis revealed that non-college high school diploma do not use bicycles. The following pie chart shows the proportion of each education level with respect to the use of bicycles.
Education
Number of Customers
Percentage
Non-High School Diploma
0
0%
High School Diploma
2
1%
Some-College -level work
67
37%
College Degree
97
54%
Graduate Degree at work
14
8%
However, the use of the three products line varied greatly with the age of customers. The following frequency distribution table shows the age group of customers and the frequency of using the three products line.
Bin
Frequency
Cumulative %
Bin
Frequency
Cumulative %
20
10
5.56%
25
62
34.44%
25
62
40.00%
30
45
59.44%
30
45
65.00%
35
32
77.22%
35
32
82.78%
40
16
86.11%
40
16
91.67%
20
10
91.67%
45
8
96.11%
45
8
96.11%
50
7
100.00%
50
7
100.00%
More
0
100.00%
More
0
100.00%
As it can be seen from the histogram, the distribution of age of customers and the frequency on uses of bikes is negatively skewed. That is, at early ages, customers use bicycles more than old ages. At age group 20-25, the demand of bicycles is high and it decreases as age increases. The mean age, median age, mode of an average customer is showed in the following table. The table also shows the average income that most customers receive,
Mean Age
28.98889
Mode Age
25
Median Age
27
Average Income
35672.22
Median Income
34000
More analysis have been done on individual products lines in order to determine the mean age of a customer at a given product line; average salary, average miles/ week, average times/ week among other analysis. The following discussion focuses on each of the three product lines.
a) Lower Product Line.
The following analysis shows the profile of an average customer who chooses to by Low Product Line.
Mean Age
28.6
Sex
Males
55%
Females
45%
Status
Single:
36%
Married.
64%
Mean Salary
30700
Average Miles
88
Average Time/week
3.01 ...
Deep learning approach analysis model prediction and classification poverty s...IAESIJAI
The problem of poverty is a scourge for every developing country coupled with the economic crisis that occurred during the coronavirus disease (COVID-19) pandemic. The impact of these problems is felt directly by the people in Indonesia, especially in the Province of West Sumatra. This study aims to predict and classify the level of poverty status by developing an analytical model based on the deep learning (DL) approach. The methods used in this study include the K-means method, artificial neural network (ANN), and support vector machine (SVM). The analytical model will be optimized using the pearson correlation (PC) method to measure the accuracy of the analysis. The variable indicator uses the parameters of population (X1), poverty rate (X2), income (X3), and poverty percentage (X4). The results of the study present prediction and classification output with a validity level of accuracy of 99.8%. Based on these results, it can be concluded that the proposed DL analysis model can present an updated analytical model that is quite effective in carrying out the prediction and classification process. The research findings also contribute to the initial handling of the problem of poverty.
In general, urban accessibility shows the ease of reaching destinations and the
interaction between the land-use and transportation systems. Integrating transport
and land-use mix is one of the goals of planning policies around the world. In this
paper, an attempt is made to assess the urban accessibility through commuters’ travel
behavior for shopping trip only. The effects of trip characteristics like trip length, trip
time and trip cost and socio-economic characteristics like gender, age, income,
occupation and vehicle ownership on travel behavior and mode choice are studied for
shopping trips for different mixed land use zones (wards) of Vadodara city. Urban
accessibility Index is prepared for different neighborhoods. It is found that the change
in the land-use mix affects the commuters' travel behaviour and mode choice selection
A rule-based machine learning model for financial fraud detectionIJECEIAES
Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.
Deep Learning Machine Learning model of my thesis research (http://lup.lub.lu.se/student-papers/record/8905790), to see if similar results may be obtained using Deep Learning methods (Keras/TensorFlow) for automated weight selection rather than manual weight selection.
Ahmed Badawi
American University of the Middle East,
Kuwait
Economy & Business
14th International Conference
1–5 September 2015
Elenite Holiday Village, Bulgaria
http://www.sciencebg.net/
Providing Accommodation and Food Services to the Population of the RegionYogeshIJTSRD
Analyzing development processes of each providing accommodation and food services to the population of the region, the sequence of choosing and modeling the main factors which influence their development are represented through simulation schemes in this article. Mukhitdinov Khudoyar Suyunovich | Rakhimov Abdihakim Muhammadiyevich "Providing Accommodation and Food Services to the Population of the Region" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Research Development and Scientific Excellence in Academic Life , March 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38500.pdf Paper Url: https://www.ijtsrd.com/economics/other/38500/providing-accommodation-and-food-services-to-the-population-of-the-region/mukhitdinov-khudoyar-suyunovich
Construction of a robust prediction model to forecast the likelihood of a cre...AdekunleJoseph4
We will utilize machine learning methodologies to build a prognostic model utilizing the dataset furnished by the organization. The dataset was first transformed. This transformation was done to decode categorical variables which by default where either in dummy variable or dichotomous. After this, a preliminary analysis was conducted to explore and to understand the data structure. Exploratory analysis, to detect missing values and outliers. Considering the nature of the dataset, which contains both qualitative and quantitative variables. A bar chart was used for qualitative variables, while boxplot and density plot was used for the quantitative or continuous variables.
Presentation given at the OECD Gender Budgeting Experts Meeting, Vienna, Austria. 18-19 June 2018
For more information see http://www.oecd.org/gov/budgeting/gender-budgeting-experts-meeting-2018.htm
The Effects of Automation. How the development of new technologies affects t...Dominik Batorski
The Effects of Automation
Rapid development of digital technologies affects the demand for work in various occupations. Some professions are becoming increasingly popular but many others are less needed due to automation and greater efficiency of workers through the use of new technologies. In this paper we examine the dynamics of occupational structure in Poland, analyzing how changes in employment in the various professions depend on the risk of computerization. To this end we use the probabilities of computerisation of different occupations that were estimated by Frey and Osborne (2013). Using longitudinal data from Social Diagnosis study, i.e. a large survey conducted every two years from 2003 to 2015 with over 26000 of respondents in each wave, we make an empirical verification of the effects of work automation in different occupation predicted by Frey and Osborne. We verify whether the probability of automation of a given profession explains the risk of the job loss, as well as how large was the risk of unemployment associated with automation in recent years. Finally, we discuss the situation of people who lost their jobs estimating the scale of technological unemployment in Poland. The results demonstrated in this paper suggests that the automation is an important factor in the dynamics of the labor market.
E-payment systems in B2B commerce: A supply Chain Management Perspective.
The payments industry has advanced remarkably during the last decade. With the new technologies and increasing mobility, payments can be realized much more efficiently, and the trend is growing rapidly. Advanced e-payment systems are not just helping current businesses to offer better service for their customers but also thanks to e-payments systems it is much easier to build a company from scratch, supply chain finance is much more manageable, sustainability is achieved on a completely different edge that companies willing introduce e-invoicing systems for their customers and much more. However, despite the new developments in the payments industry, for the business-to-business (B2B) transactions, the industry response in much slower compared to business-to-customer (B2C). Introducing electronic payment systems to B2B commerce is increasing visibility and connectivity as well as the simplicity of the transaction process, and this is a radical change for an industry whose major challenges are inefficiency and poor information. This paper is designed to present the compressive benefit analysis of electronic payment systems for B2B industries and motivations and possible barriers for its future applications in the industry.
Application of Semiparametric Non-Linear Model on Panel Data with Very Small ...IOSRJM
-This research work investigated the behaviour of a new semiparametric non-linear (SPNL) model on
a set of panel data with very small time point (T = 1). The SPNL model incorporates the relationship between
individual independent variable and unobserved heterogeneity variable. Five different estimation techniques
namely; Least Square (LS), Generalized Method of Moments (GMM), Continuously Updating (CU), Empirical
Likelihood (EL) and Exponential Tilting (ET) Estimators were employed for the estimation; for the purpose of
modelling the metrical response variable non-linearly on a set of independent variables. The performances of
these estimators on the SPNL model were examined for different parameters in the model using the Least
Square Error (LSE), Mean Absolute Error (MAE) and Median Absolute Error (MedAE) criteria at the lowest
time point (T = 1). The results showed that the ET estimator which provided the least errors of estimation is
relatively more efficient for the proposed model than any of the other estimators considered. It is therefore
recommended that the ET estimator should be employed to estimate the SPNL model for panel data with very
small time point.
A rule-based machine learning model for financial fraud detectionIJECEIAES
Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.
Deep Learning Machine Learning model of my thesis research (http://lup.lub.lu.se/student-papers/record/8905790), to see if similar results may be obtained using Deep Learning methods (Keras/TensorFlow) for automated weight selection rather than manual weight selection.
Ahmed Badawi
American University of the Middle East,
Kuwait
Economy & Business
14th International Conference
1–5 September 2015
Elenite Holiday Village, Bulgaria
http://www.sciencebg.net/
Providing Accommodation and Food Services to the Population of the RegionYogeshIJTSRD
Analyzing development processes of each providing accommodation and food services to the population of the region, the sequence of choosing and modeling the main factors which influence their development are represented through simulation schemes in this article. Mukhitdinov Khudoyar Suyunovich | Rakhimov Abdihakim Muhammadiyevich "Providing Accommodation and Food Services to the Population of the Region" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Research Development and Scientific Excellence in Academic Life , March 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38500.pdf Paper Url: https://www.ijtsrd.com/economics/other/38500/providing-accommodation-and-food-services-to-the-population-of-the-region/mukhitdinov-khudoyar-suyunovich
Construction of a robust prediction model to forecast the likelihood of a cre...AdekunleJoseph4
We will utilize machine learning methodologies to build a prognostic model utilizing the dataset furnished by the organization. The dataset was first transformed. This transformation was done to decode categorical variables which by default where either in dummy variable or dichotomous. After this, a preliminary analysis was conducted to explore and to understand the data structure. Exploratory analysis, to detect missing values and outliers. Considering the nature of the dataset, which contains both qualitative and quantitative variables. A bar chart was used for qualitative variables, while boxplot and density plot was used for the quantitative or continuous variables.
Presentation given at the OECD Gender Budgeting Experts Meeting, Vienna, Austria. 18-19 June 2018
For more information see http://www.oecd.org/gov/budgeting/gender-budgeting-experts-meeting-2018.htm
The Effects of Automation. How the development of new technologies affects t...Dominik Batorski
The Effects of Automation
Rapid development of digital technologies affects the demand for work in various occupations. Some professions are becoming increasingly popular but many others are less needed due to automation and greater efficiency of workers through the use of new technologies. In this paper we examine the dynamics of occupational structure in Poland, analyzing how changes in employment in the various professions depend on the risk of computerization. To this end we use the probabilities of computerisation of different occupations that were estimated by Frey and Osborne (2013). Using longitudinal data from Social Diagnosis study, i.e. a large survey conducted every two years from 2003 to 2015 with over 26000 of respondents in each wave, we make an empirical verification of the effects of work automation in different occupation predicted by Frey and Osborne. We verify whether the probability of automation of a given profession explains the risk of the job loss, as well as how large was the risk of unemployment associated with automation in recent years. Finally, we discuss the situation of people who lost their jobs estimating the scale of technological unemployment in Poland. The results demonstrated in this paper suggests that the automation is an important factor in the dynamics of the labor market.
E-payment systems in B2B commerce: A supply Chain Management Perspective.
The payments industry has advanced remarkably during the last decade. With the new technologies and increasing mobility, payments can be realized much more efficiently, and the trend is growing rapidly. Advanced e-payment systems are not just helping current businesses to offer better service for their customers but also thanks to e-payments systems it is much easier to build a company from scratch, supply chain finance is much more manageable, sustainability is achieved on a completely different edge that companies willing introduce e-invoicing systems for their customers and much more. However, despite the new developments in the payments industry, for the business-to-business (B2B) transactions, the industry response in much slower compared to business-to-customer (B2C). Introducing electronic payment systems to B2B commerce is increasing visibility and connectivity as well as the simplicity of the transaction process, and this is a radical change for an industry whose major challenges are inefficiency and poor information. This paper is designed to present the compressive benefit analysis of electronic payment systems for B2B industries and motivations and possible barriers for its future applications in the industry.
Application of Semiparametric Non-Linear Model on Panel Data with Very Small ...IOSRJM
-This research work investigated the behaviour of a new semiparametric non-linear (SPNL) model on
a set of panel data with very small time point (T = 1). The SPNL model incorporates the relationship between
individual independent variable and unobserved heterogeneity variable. Five different estimation techniques
namely; Least Square (LS), Generalized Method of Moments (GMM), Continuously Updating (CU), Empirical
Likelihood (EL) and Exponential Tilting (ET) Estimators were employed for the estimation; for the purpose of
modelling the metrical response variable non-linearly on a set of independent variables. The performances of
these estimators on the SPNL model were examined for different parameters in the model using the Least
Square Error (LSE), Mean Absolute Error (MAE) and Median Absolute Error (MedAE) criteria at the lowest
time point (T = 1). The results showed that the ET estimator which provided the least errors of estimation is
relatively more efficient for the proposed model than any of the other estimators considered. It is therefore
recommended that the ET estimator should be employed to estimate the SPNL model for panel data with very
small time point.
Exploring 3D-Virtual Learning Environments with Adaptive RepetitionsIOSRJM
In spatial tasks, the use of cognitive aids reduce mental load and therefore being appealing to trainers and trainees. However, these aids can act as shortcuts and prevents the trainees from active exploration which is necessary to perform the task independently in non-supervised environment. In this paper we used adaptive repetition as control strategy to explore the 3D- Virtual Learning environments. The proposed approach enables the trainee to get the benefits of cognitive support while at the same time he is actively involved in the learning process. Experimental results show the effectiveness of the proposed approach.
Mathematical Model on Branch Canker Disease in Sylhet, BangladeshIOSRJM
Members of the Camellia genus of plants may be affected by many of the diseases, pathogens and pests that mainly affect the tea plant (Camellia sinensis). One of the most common diseases which are found in tea garden is known as Branch Canker (BC) [1]. Recent outbreaks of these diseases is not only hampering the production of tea, but also stupendously hampering our national economy. In this paper, a simple SEIR model has been used to analyze the dynamics of Branch Canker in the tea garden. The stability of the system is analyzed for the existence of the disease free equilibrium. We established that there exists a disease free equilibrium point that is locally asymptotically stable when the reproduction number Ro< 1 and unstable when Ro> 1. Theoretically, we analyze the BC model. Finally, we numerically tested the theoretical results in MATLAB.
Least Square Plane and Leastsquare Quadric Surface Approximation by Using Mod...IOSRJM
Now a days Surface fitting is applied all engineering and medical fields. Kamron Saniee ,2007 find a simple expression for multivariate LaGrange’s Interpolation. We derive a least square plane and least square quadric surface Approximation from a given N+1 tabular points when the function is unique. We used least square method technique. We can apply this method in surface fitting also.
A Numerical Study of the Spread of Malaria Disease with Self and Cross-Diffus...IOSRJM
: A study of the SIS model of malaria disease with a view to observing the effects of self and crossdiffusion on spatial dynamics is undertaken. Three different cases based on self-diffusion and cross-diffusion are chosen for the investigation. Two cases of cross-diffusion without self-diffusion are also considered in order to see the effects of diffusion on the transmission of malaria. Basic reproductive numbers and bifurcation values are calculated for each case. A series of numerical simulations based on self and cross-diffusion is performed. It is observed that with positive cross-diffusion and self-diffusion in the system, there is a significant increase in the proportion of both infected human and mosquito populations. The proportion of infected humans increases markedly with cross diffusion in the system. This also gives rise to some oscillations across the domain.
Fibonacci and Lucas Identities with the Coefficients in Arithmetic ProgressionIOSRJM
We define an analogous pair of recurrence relations that yield some Fibonacci, Lucas and generalized Fibonacci identities with the coefficients in arithmetic progression. One relation yields same sign identities and the other alternating signs identities. We also show some new results for negative indexed Fibonacci and Lucas Sequences.
A Note on Hessen berg of Trapezoidal Fuzzy Number MatricesIOSRJM
The fuzzy set theory has been applied in many fields such as management, engineering, theory of matrices and so on. in this paper, some elementary operations on proposed trapezoidal fuzzy numbers (TrFNS) are defined. We also have been defined some operations on trapezoidal fuzzy matrices(TrFMs). The notion of Hessenberg fuzzy matrices are introduced and discussed. Some of their relevant properties have also been verified.
Enumeration Class of Polyominoes Defined by Two ColumnIOSRJM
Abacus diagram is a graphical representation for any partition µ of a positive integer t. This study presents the bead positions as a unite square in the graph and de ne a special type of e-abacus called nested chain abacus 픑 which is represented by the connected partition. Furthermore, we redefined the polyominoes as a special type of e-abacus diagram. Also, this study reveals new method of enumerating polyominoes special design when e=2
A New Method for Solving Transportation Problems Considering Average PenaltyIOSRJM
Vogel’s Approximation Method (VAM) is one of the conventional methods that gives better Initial Basic Feasible Solution (IBFS) of a Transportation Problem (TP). This method considers the row penalty and column penalty of a Transportation Table (TT) which are the differences between the lowest and next lowest cost of each row and each column of the TT respectively. In a little bit different way, the current method consider the Average Row Penalty (ARP) and Average Column Penalty (ACP) which are the averages of the differences of cell values of each row and each column respectively from the lowest cell value of the corresponding row and column of the TT. Allocations of costs are started in the cell along the row or column which has the highest ARP or ACP. These cells are called basic cells. The details of the developed algorithm with some numerical illustrations are discussed in this article to show that it gives better solution than VAM and some other familiar methods in some cases.
Effect of Magnetic Field on Peristaltic Flow of Williamson Fluid in a Symmetr...IOSRJM
This paper deals with the influence of magnetic field on peristaltic flow of an incompressible Williamson fluid in a symmetric channel with heat and mass transfer. Convective conditions of heat and mass transfer are employed. Viscous dissipation and Joule heating are taken into consideration.Channel walls have compliant properties. Analysis has been carried out through long wavelength and low Reynolds number approach. Resulting problems are solved for small Weissenberg number. Impacts of variables reflecting the salient features of wall properties, concentration and heat transfer coefficient are pointed out. Trapping phenomenon is also analyzed.
Devaney Chaos Induced by Turbulent and Erratic FunctionsIOSRJM
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A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
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Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Mathematical Modelling: A Study of Corruption in the Society of India
1. IOSR Journal of Mathematics (IOSR-JM)
e-ISSN: 2278-5728, p-ISSN: 2319-765X. Volume 13, Issue 1 Ver. II (Jan. - Feb. 2017), PP 09-17
www.iosrjournals.org
DOI: 10.9790/5728-1301020917 www.iosrjournals.org 9 | Page
Mathematical Modelling: A Study of Corruption in the
Society of India
Dr. Sayaji Rastum Waykar
Assistant Professor, Department of Mathematics, Yashwantrao Chavan Mahavidyalaya, Halkarni, Tal:
Chandgad, Dist: Kolhapur, Maharashtra (India).
Abstract: A study of mathematical model for corruption and its control, that is Mathematical Corruption
Model, it is statistically fit as well as valid for measuring corruption in the society. This is the mathematically
method for measuring corruption in the society. When quantity of corruption is known, then there is no difficulty
to remove the corruption from the society. Sometimes in corruption problems, there is no a priori information
available, therefore the Mathematical Corruption Model (or MC model) is known as black box model.
Keywords: mathematical thinking, corruption mentality, modeling, applied.
I. Introduction
A study of mathematical model for corruption and its control, in this connection my research is „for
making a mathematical model to measure the corruption from the society‟. We have seen in literature review
that the Secretary-General Ban Ki Moon said that there is no scientific and mathematical method for measuring
corruption from the society. Therefore we will try to study corruption in the society in different ways that is
mathematical study. For collecting the data from Literature Review such as different Research papers from
various journals related to the problem corruption in the society and various books of Mathematical modeling.
Then we will try to understand the corruption problem. The terms „corruption‟, first we will try to explain or
define in different ways that is corruption means „illegal‟ work or duty or behavior of a person which is living in
the society. Such type of concepts we consider the terms „mathematical effected virus corruption parameter‟ or
„MEVCP‟ and it is denoted as variable „Ҝ‟. Then we have seen that the problem of corruption is related to the
peoples who are living in the society. So when we calculate growth of population by using applications of
differential equations, in this way the corruption problem will solve. By taking the terms „mathematical
corruption parameter‟ which is related to the negative characters of the peoples in the society of any country in
the world and then we take some variables and mathematical relations for making a mathematical corruption
model.
By using the application of differential equation, we will form the „model‟. In this model, we use
mathematical effected virus corruption parameter then it will become „corruption model‟. Such type of model is
known as „mathematical corruption model‟. In this way we make some models such as mathematical corruption
growth model in population size as well as in rupees size, mathematical corruption-development model,
mathematical decay of corruption model, mathematical development model and mathematical effected virus
corruption parameter model. Such type of models is combined known as „Mathematical Corruption Model‟. The
function of this model is „for measuring corruption in the society‟ as well as „for removing corruption in the
society‟. Further by using Mathematical modeling technique, we will make „Mathematical Corruption Model‟
for measuring corruption from the society. Such as of the following:
* Mathematical Corruption Growth formula or MC Model:
a] C = , when Ҝ > 0
b] C = , when Ҝ > 0
* Mathematical Constant Corruption level formula:
C = , when Ҝ = 0
* Mathematical Decay of Corruption formula or MCC Model:
a] C = , when Ҝ ≤ 0
b] C = , when Ҝ ≤ 0
* Mathematical relation between Corruption and Development formula or MCD Model:
a] D(c) = D(0) , when Ҝ > 0
b] D(C) = D (0) , when Ҝ > 0
* Mathematical E- virus constant formula or MEVC Model:
2. Mathematical Modelling: A Study of Corruption in the Society of India
DOI: 10.9790/5728-1301020917 www.iosrjournals.org 10 | Page
a] Ҝ= , -1< Ҝ < 1
b] Ҝ = , -1< Ҝ < 1
* Mathematical Development Model or MD Model:
a] D(t) = D(0) , when Ҝ > 0
b] D (t) = D (0) , when Ҝ > 0
II. Methodology
It is as follows:
A modelling task, it requires translations between Mathematics and reality, it is known as mathematical
modelling. In reality, it means according to Pollack (1979), he said that the mathematical modeling means “rest
of the world” that is outside mathematics including society, nature, and everyday life and other scientific
disciplines.
III. Some Illustrations for measuring Corruption in the society
3. Mathematical Corruption growths in various fields of the society (general) in India:
3.1 From Model-I, Model-II, Model-III, and Model-IV, We get final Mathematical Results for corruption in
India (in population size). It is as follows:
MEV Constant
„ Ҝ ‘
Model-I
(0.25%)
Model-II
(0.50%)
Model-III
(0.75%)
Model-IV
(1%)
0 0.0875 0.1750 0.2625 0.3500
0.20 0.2725662 0.5451323 0.8176985 1.0902649
0.40 0.8552174 1.7104348 2.5656522 3.4208695
0.60 4.0241661 5.0843876 7.6265814 10.1687752
0.80 6.2699644 14.0193398 21.0290097 28.0386794
0.9988 17.8059597 35.613963 53.4178793 71.2238390
Regression Square (R2
) 0.985 0.998 0.998 0.998
3. Mathematical Modelling: A Study of Corruption in the Society of India
DOI: 10.9790/5728-1301020917 www.iosrjournals.org 11 | Page
Graph of Mathematical Corruption Model with R2
in polynomial form:
3.2 Appendix-I: Descriptive Statistics
Model-I: Statistical Study Of Corruption For Model-I
Data
x
Sample-I
f
f. x D= (x- X) f.
10 0.139979 1.39979 -78 6084 851.632236
20 0.23091603 4.6183206 -68 4642 1071.91221
30 0.39174815 11.7524445 -58 3364 1317.84078
40 0.7500534 30.002136 -48 2304 1728.12303
50 1.32026249 66.0131245 -38 1444 1906.45904
60 2.29883672 137.930203 -28 784 1802.28799
70 4.18174976 292.722483 -18 324 1354.88692
80 7.70885786 616.708629 -8 64 493.366903
90 14.3733307 1293.59976 2 4 57.4933228
100 27.0600136 2706.00136 12 144 3896.64196
N= .456 5160.74825
S. D. = = 15.739082 15.74
Therefore the standard deviation of corruption in India with related period is 15.74.
Here we have observed that standard deviation or inflation in corruption of India for Model-I is 15.74. Therefore
it is calculated inflation with related period.
Model-II: Statistical Study Of Corruption For Model-II
Data
x
Sample-II
f
f. x D= (x- X) f.
10 0.27996 2.7996 -78 6084 1703.27664
20 0.46186 9.2372 -68 4642 2143.95412
30 0.783512 23.50536 -58 3364 2635.73437
40 1.362514 54.50056 -48 2304 3139.23226
50 2.420388 121.0194 -38 1444 3495.04027
60 4.3777496 262.664976 -28 784 3432.15569
70 8.0381248 562.668736 -18 324 2604.35244
80 14.9448366 1195.58693 -8 64 956.469542
90 28.0759678 2526.8371 2 4 112.303871
100 53.2016022 5320.16022 12 144 7661.03072
N= 10102.4855
S. D. = = 15.6428855 15.64
Therefore the standard deviation of corruption in India with related period is 15.64.
Here we have observed that standard deviation or inflation in corruption of India for Model-II is 15.64.
Therefore it is calculated inflation with related period.
4. Mathematical Modelling: A Study of Corruption in the Society of India
DOI: 10.9790/5728-1301020917 www.iosrjournals.org 12 | Page
Model-III: Statistical Study Of Corruption For Model-III
Data
x
Sample-III
f
f. x D= (x- X) f.
10 0.419937 4.19937 -78 6084 2554.89671
20 0.69274808 13.8549616 -68 4642 3215.73659
30 1.17524445 35.2573335 -58 3364 3953.52233
40 2.04372182 81.7488728 -48 2304 4708.73507
50 3.63048604 181.524302 -38 1444 5242.42184
60 6.56643252 393.985951 -28 784 5148.0831
70 12.0568059 843.976413 -18 324 3906.40511
80 22.41649 1793.3192 -8 64 1434.65536
90 42.1124246 3790.11821 2 4 168.449698
100 79.7993516 7979.93516 12 144 11491.1066
N= 170.9136
S. D. = = 15.6431618 15.64
Therefore the standard deviation of corruption in India with related period is 15.64.
Here we have observed that standard deviation or inflation in corruption of India for Model-III is 15.64.
Therefore it is calculated inflation with related period.
Model-IV: Statistical Study Of Corruption For Model-IV
Data
x
Sample-IV
f
f. x D= (x- X) f.
10 0.559916 5.59916 -78 6084 3406.52894
20 0.9236641 18.473282 -68 4642 4287.64875
30 1.5669926 47.009778 -58 3364 5271.36311
40 2.72496242 108.998497 -48 2304 6278.31342
50 4.84064806 242.032403 -38 1444 6989.8958
60 8.75524336 525.314602 -28 784 6864.11079
70 16.0757412 1125.30188 -18 324 5208.54015
80 29.8886536 2391.09229 -8 64 1912.87383
90 56.1498992 5053.49093 2 4 224.599597
100 106.399135 10639.9135 12 144 15321.4754
N= 227.88 20157.2264
S. D. = = 15.6433265 15.64
Therefore the standard deviation of corruption in India with related period is 15.64.
Here we have observed that standard deviation or inflation in corruption of India for Model-IV is 15.64.
Therefore it is calculated inflation with related period.
We have seen from Model-I to Model-IV, the Average inflation is 15.64. This inflation is related with the
Average Mathematical corruption model.
3.3 Appendix-II: Statistical Study Of Corruption
Data
x
Sample Mean
f
f. x D= (x-
X)
f.
10 0.34992825 3.4992825 -78 6084 2128.96347
20 0.58344349 11.6688698 -68 4642 2708.34468
30 0.97891489 29.3674467 -58 3364 3293.06969
40 1.71909124 68.7636496 -48 2304 3960.78622
50 3.04990047 152.495024 -38 1444 4404.05628
60 5.49227635 329.536581 -28 784 4305.94466
70 10.0711448 704.980136 -18 324 3263.05092
80 18.7010505 1496.08404 -8 64 1196.86723
90 35.0911652 3158.20487 2 4 140.364661
100 66.4228065 6642.28065 12 144 9564.88414
N= 143 12597
X=Mean= = = 88.090909 88
Therefore, Mean =88
5. Mathematical Modelling: A Study of Corruption in the Society of India
DOI: 10.9790/5728-1301020917 www.iosrjournals.org 13 | Page
We know that the formula for Standard Deviation is as follows:
Therefore, S. D. = = = =
S. D. = = 15.637129 15.64
Therefore the standard deviation of corruption in India with related period is 15.64.
Here we have observed that standard deviation or inflation in corruption of India for Average Model is 15.64.
Therefore it is calculated inflation with related period.
Statistical Graph of Corruption in India:
The graph of the above data is as follows:
3.4 Appendix-III: Comparative Study Of Mathematical Corruption Model
Model Corruption
‘C’ (crore)
(-ve)
Corruption
‘C’(crore)
(+ve)
Standard
Deviation
(S.D.)
Regression
Square (R2
)
Average Inflation
(CPI)
(Real Data)
Gross
Inflation
(Real Data)
Model-I 0.8750 27.5653738 15.74 0.999 7.979 113.5399
Model-II 1.750 55.3982575 15.64 0.998 7.989 113.5504
Model-III 2.625 83.0943211 15.64 0.998 7.997 113.5589
Model-IV 3.50 110.792428 15.64 0.998 7.998 113.5599
Average Model 2.1875 69.2125951 15.64 - 7.99 -
Regression
Square (R2
)
0.960 0.960 - 0.998 0.907 0.906
From the above table, we have seen that the Average Model the population 71.4000951 crore is corrupted but
among the 2.1875 crore population is in (-ve) corruption and 69.2125951 crore population is in (+ve) corruption.
The graph of above table is as follows:
Graph-I
6. Mathematical Modelling: A Study of Corruption in the Society of India
DOI: 10.9790/5728-1301020917 www.iosrjournals.org 14 | Page
Graph-II
Graph-III
Graph-IV
This shows that the mathematical corruption model is valid for the above illustrations and it is fit statistically.
Also we can calculate the corruption in the following type:
Corruption
(Population)
crore
Model-I
(0.25%)
Model-II
(0.50%)
Model-III
(0.75%)
Model-IV
(1%)
Average Model
(crore)
Regression
Square (R2
)
First stage Corruption 1.1277836 2.2555671 3.3833507 4.5111345 2.8194590 0.960
Medium stage
Corruption
10.2941305 19.1037274 28.6555911 38.2074546 24.0652259 0.971
Final stage
Corruption
17.8059597 35.6139630 53.4178793 71.2238390 44.5154102 0.960
Graph-V: Graph of Mathematical Corruption Model with R2
in Exponential Form:
7. Mathematical Modelling: A Study of Corruption in the Society of India
DOI: 10.9790/5728-1301020917 www.iosrjournals.org 15 | Page
We have observed that from Graph-I, Graph-II, Graph-III, Graph-IV and Graph-V the regression
square (R2
) is less than or equal to 1. Therefore the Average mathematical corruption model is fit statistically.
Also the mathematical results are valid with the real data of the society of India.
From the table and graph- V, the population distributed in the form of corruption, it is as follows:
We know that MCD Model, D(C) = D (0)× [1 + Ҝ] 𝐶
, here we take Ҝ = 0.59976
Therefore C= 71.4000951, D(C) =?
Initially C=0, D (0) = 0.35 crore (1% of 35 crore), therefore MCD Model, we have
When C= 71.4000951 crore, then
D(C) = 0.35× [1 + 0.59976]71.4000951
Therefore D(C) = 129898031223302.47 crore
Also, when C= 2.1875 crore, then
D(C) = [2.1875/71.4000951]× [129898031223302]
Therefore, D(C) = 3.979713e12 crore
When C = 69.2125951 crore, then
D(C) = [69.2125951/71.4000951] × [129898031223302]
Therefore, D(C) = 1.259183e14 crore
Therefore,
Total population corrupted: 71.4000951 crore, Amount Rs. 129898031223302.47 crore
Among –Ve corruption: 2.1875 crore, Amount Rs. 3.979713e12 crore
Among +Ve corruption: 69.2125951 crore, Amount Rs. 1.259183e14 crore
Also we will try to find the corruption for Average model in the following types,
At First stage corruption, when C = 2.8194590 crore, then
D(C) = [2.8194590/71.4000951]× [129898031223302]
Therefore, D(C) = 5.129436e12 crore
At Medium stage corruption, when C = 24.0652259 crore, then
D(C) = [24.0652259/71.4000951]× [129898031223302]
Therefore, D(C) = 4.378181e13 crore
At Final stage corruption, when C = 44.5154103 crore, then
D(C) = [44.5154103 /71.4000951]× [129898031223302]
Therefore, D(C) = 8.098679e13 crore
Therefore,
First stage corruption C = 2.8194590 crore, Amount Rs. 5.129436e12 crore
Medium stage corruption C = 24.0652259 crore, Amount Rs. 4.378181e13 crore
Final stage corruption C = 44.5154102 crore, Amount Rs. 8.098679e13 crore
Now we can write the above data in tabular form as follows:
Corruption Population (crore) Amount in rupees (crore)
First stage corruption 2.8194590 5.129436e12
Medium stage
corruption
24.0652259 4.378181e13
Final stage corruption 44.5154102 8.098679e13
Regression Square (R2
) 0.907 0.907
Graph-VI
8. Mathematical Modelling: A Study of Corruption in the Society of India
DOI: 10.9790/5728-1301020917 www.iosrjournals.org 16 | Page
Graph-VII
Therefore we have seen all the above various mathematical results and graphs of various illustrations
then we can conclude that the mathematical corruption model is fit as well as valid for measuring corruption
from the society of the country India or any country of the world. From Appendix-III, we have seen that the
Average Model, the population 71.4000951 crore is corrupted but among the 2.1875 crore population is in (-ve)
corruption and 69.2125951 crore population is in (+ve) corruption. Also, from Graph the Regression Square
(R2
) is less than or equal to 1. Therefore the mathematical corruption model is fit statistically for the above
illustrations. It is also valid with the real data of the society in India.
III. Conclusion & Recommendations
We have observed and it concluded that our mathematical results are the mean values of four models
with related corruption when the inflation will be approximately 15.64 among 100 years from base after
freedom. Then the mathematical results are as follows:
First stage corruption:
When 0 < Ҝ ≤ 0.40 C = 2.8194590 crore D(C) = 5.129436e12 crore
Medium stage corruption:
When 0.40 < Ҝ ≤ 0.80 C = 24.0652259 crore D(C) = 4.378181e13 crore
Final stage corruption:
When 0.80 < Ҝ < 1 C = 44.5154102 crore D(C) = 8.098679e13 crore
Graph of Mathematical Corruption Model of the Society of India
Therefore,
Total population corrupted: 71.4000951 crore, Amount Rs. 129898031223302.47 crore
Among –Ve corruption: 2.1875 crore, Amount Rs. 3.979713e12 crore
Among +Ve corruption: 69.2125951 crore, Amount Rs. 1.259183e14 crore
9. Mathematical Modelling: A Study of Corruption in the Society of India
DOI: 10.9790/5728-1301020917 www.iosrjournals.org 17 | Page
According to the data provided by the Swiss Banking Association Report (2006), India has more black
money than the rest of the world combined. To put things in perspective, Indian-owned Swiss bank Account
assets are worth 13 times the country‟s national debt.
Also we observed that „the corruption and inflation are related to each other‟. When corruption increases then
inflation increases and vice versa.
Recommendations:
To reduce the MEVC parameter Ҝ (to zero) from the society.
To increase transparency and efficiency of the judiciary.
To implement strictly laws and statutes in all governments/private offices in India for controlling a bribe.
No excuse for minor mistakes also.
To increase the social welfare in the society.
To reduce inequality in income.
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