PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations最新文献
We explain some of the goals of modern representation theory, aiming at categorical methods. We develop one of the most astonishing invariant, Hochschild (co-)homology and we explain on the example of the recent solution of a question due to Rickard how it is possible to reduce fairly abstract questions to explicit methods finally solved by computers.We explain some of the goals of modern representation theory, aiming at categorical methods. We develop one of the most astonishing invariant, Hochschild (co-)homology and we explain on the example of the recent solution of a question due to Rickard how it is possible to reduce fairly abstract questions to explicit methods finally solved by computers.
{"title":"On equivalences between categories of representations","authors":"A. Zimmermann","doi":"10.1063/1.5139120","DOIUrl":"https://doi.org/10.1063/1.5139120","url":null,"abstract":"We explain some of the goals of modern representation theory, aiming at categorical methods. We develop one of the most astonishing invariant, Hochschild (co-)homology and we explain on the example of the recent solution of a question due to Rickard how it is possible to reduce fairly abstract questions to explicit methods finally solved by computers.We explain some of the goals of modern representation theory, aiming at categorical methods. We develop one of the most astonishing invariant, Hochschild (co-)homology and we explain on the example of the recent solution of a question due to Rickard how it is possible to reduce fairly abstract questions to explicit methods finally solved by computers.","PeriodicalId":209108,"journal":{"name":"PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122505842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Indonesian government has made various efforts in drug prevention policies through criminal law. However, law enforcement officials still contemplate the Drugs Law is oriented towards imprisonment, so drug abuse is considered as a criminal act. In fact, the government has declared 2014 as the year to save the victims of drug abuse through rehabilitation. In this paper, a drug transmission model with the criminal law aspect is presented and analyzed. The optimal control strategy is then applied in the form of rehabilitation efforts. Based on the model analysis, we found two equilibriums, namely the drugs-free equilibrium and the drug addiction equilibrium. The stability of the equilibriums depends on the basic reproduction number. The spread of drug addicts persists in the population if the basic reproduction number greater than unity. Based on the simulation, it can be seen that criminal law give a significant impact to decrease the number of mild drug addicts and also the number of heavy levels of drug addicts. Furthermore, the existence of the optimal control variable is determined through the Pontryagin’s Maximum Principle method. The comparison of simulation results with and without control shows that rehabilitation efforts can reduce drug addicts transmission.
{"title":"Mathematical model analysis of a drug transmission with criminal law and its optimal control","authors":"Muhammad Hafiruddin, Fatmawati, Miswanto","doi":"10.1063/1.5139156","DOIUrl":"https://doi.org/10.1063/1.5139156","url":null,"abstract":"The Indonesian government has made various efforts in drug prevention policies through criminal law. However, law enforcement officials still contemplate the Drugs Law is oriented towards imprisonment, so drug abuse is considered as a criminal act. In fact, the government has declared 2014 as the year to save the victims of drug abuse through rehabilitation. In this paper, a drug transmission model with the criminal law aspect is presented and analyzed. The optimal control strategy is then applied in the form of rehabilitation efforts. Based on the model analysis, we found two equilibriums, namely the drugs-free equilibrium and the drug addiction equilibrium. The stability of the equilibriums depends on the basic reproduction number. The spread of drug addicts persists in the population if the basic reproduction number greater than unity. Based on the simulation, it can be seen that criminal law give a significant impact to decrease the number of mild drug addicts and also the number of heavy levels of drug addicts. Furthermore, the existence of the optimal control variable is determined through the Pontryagin’s Maximum Principle method. The comparison of simulation results with and without control shows that rehabilitation efforts can reduce drug addicts transmission.","PeriodicalId":209108,"journal":{"name":"PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130583663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Let X be a finite set of m elements. Let On(X) be the set of all n-ary operations on X. On On(X), it is defined operation “+” with (f+g)(x¯)=f(g(x_1),g(x_2),…,g(x_n)), for all f, g ∈ On(X) and x¯=(x1,x2,…,xn) ∈ Xn, so that (On(X), +) is a semigroup. In this paper we give some properties of idempotent elements, regular elements, coregular elements, left zero elements and right identity elements on On(X). Moreover, from this properties, we provide some properties of nontrivial subsemigroups of the semigroup (On(X), +).Let X be a finite set of m elements. Let On(X) be the set of all n-ary operations on X. On On(X), it is defined operation “+” with (f+g)(x¯)=f(g(x_1),g(x_2),…,g(x_n)), for all f, g ∈ On(X) and x¯=(x1,x2,…,xn) ∈ Xn, so that (On(X), +) is a semigroup. In this paper we give some properties of idempotent elements, regular elements, coregular elements, left zero elements and right identity elements on On(X). Moreover, from this properties, we provide some properties of nontrivial subsemigroups of the semigroup (On(X), +).
{"title":"Special elements of semigroup of n-ary operations","authors":"Mara Hidayati, Y. Susanti","doi":"10.1063/1.5139130","DOIUrl":"https://doi.org/10.1063/1.5139130","url":null,"abstract":"Let X be a finite set of m elements. Let On(X) be the set of all n-ary operations on X. On On(X), it is defined operation “+” with (f+g)(x¯)=f(g(x_1),g(x_2),…,g(x_n)), for all f, g ∈ On(X) and x¯=(x1,x2,…,xn) ∈ Xn, so that (On(X), +) is a semigroup. In this paper we give some properties of idempotent elements, regular elements, coregular elements, left zero elements and right identity elements on On(X). Moreover, from this properties, we provide some properties of nontrivial subsemigroups of the semigroup (On(X), +).Let X be a finite set of m elements. Let On(X) be the set of all n-ary operations on X. On On(X), it is defined operation “+” with (f+g)(x¯)=f(g(x_1),g(x_2),…,g(x_n)), for all f, g ∈ On(X) and x¯=(x1,x2,…,xn) ∈ Xn, so that (On(X), +) is a semigroup. In this paper we give some properties of idempotent elements, regular elements, coregular elements, left zero elements and right identity elements on On(X). Moreover, from this properties, we provide some properties of nontrivial subsemigroups of the semigroup (On(X), +).","PeriodicalId":209108,"journal":{"name":"PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123482810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kiki Aristiawati, T. Siswantining, Devvi Sarwinda, S. Soemartojo
Chronic Obstructive Pulmonary Disease (COPD) is one of the most causes of death in the world. World Health Organization (WHO) reported that in 2016 COPD was the third leading cause of death worldwide with around 3 million deaths, equivalent to 5.2% of deaths worldwide. For this reason, further research needs to be done on CPOD. Unfortunately, the data collected in the study does not contain all the desired data, is called as a missing value. Missing value is a problem for all types of data analysis. Several ways that can be applied to handle missing value, by filtering data (ignore or remove data) and imputing data. Ignoring or removing data can reduce the amount of information contained in the data and can cause low accuracy to generate from the data analysis process. To overcome this problem, imputation data will be carried out at the preprocessing stage to obtain complete data which is expected to increase the accuracy of the data analysis performed. Many imputations method can be used, such as mean imputation and Fuzzy C-Means (FCM). Fuzzy C-Means is a clustering method that allows one part of the data to belong to two or more groups based on their membership function. The complete dataset was trained with Decision Tree classifier to observe the performance in terms of accuracy for mean and FCM method. The analysis of proposed imputation on classification shows that FCM slightly accurate compare to mean imputation method.Chronic Obstructive Pulmonary Disease (COPD) is one of the most causes of death in the world. World Health Organization (WHO) reported that in 2016 COPD was the third leading cause of death worldwide with around 3 million deaths, equivalent to 5.2% of deaths worldwide. For this reason, further research needs to be done on CPOD. Unfortunately, the data collected in the study does not contain all the desired data, is called as a missing value. Missing value is a problem for all types of data analysis. Several ways that can be applied to handle missing value, by filtering data (ignore or remove data) and imputing data. Ignoring or removing data can reduce the amount of information contained in the data and can cause low accuracy to generate from the data analysis process. To overcome this problem, imputation data will be carried out at the preprocessing stage to obtain complete data which is expected to increase the accuracy of the data analysis performed. Many imputations method can be used, such as mean im...
{"title":"Missing values imputation based on fuzzy C-Means algorithm for classification of chronic obstructive pulmonary disease (COPD)","authors":"Kiki Aristiawati, T. Siswantining, Devvi Sarwinda, S. Soemartojo","doi":"10.1063/1.5139149","DOIUrl":"https://doi.org/10.1063/1.5139149","url":null,"abstract":"Chronic Obstructive Pulmonary Disease (COPD) is one of the most causes of death in the world. World Health Organization (WHO) reported that in 2016 COPD was the third leading cause of death worldwide with around 3 million deaths, equivalent to 5.2% of deaths worldwide. For this reason, further research needs to be done on CPOD. Unfortunately, the data collected in the study does not contain all the desired data, is called as a missing value. Missing value is a problem for all types of data analysis. Several ways that can be applied to handle missing value, by filtering data (ignore or remove data) and imputing data. Ignoring or removing data can reduce the amount of information contained in the data and can cause low accuracy to generate from the data analysis process. To overcome this problem, imputation data will be carried out at the preprocessing stage to obtain complete data which is expected to increase the accuracy of the data analysis performed. Many imputations method can be used, such as mean imputation and Fuzzy C-Means (FCM). Fuzzy C-Means is a clustering method that allows one part of the data to belong to two or more groups based on their membership function. The complete dataset was trained with Decision Tree classifier to observe the performance in terms of accuracy for mean and FCM method. The analysis of proposed imputation on classification shows that FCM slightly accurate compare to mean imputation method.Chronic Obstructive Pulmonary Disease (COPD) is one of the most causes of death in the world. World Health Organization (WHO) reported that in 2016 COPD was the third leading cause of death worldwide with around 3 million deaths, equivalent to 5.2% of deaths worldwide. For this reason, further research needs to be done on CPOD. Unfortunately, the data collected in the study does not contain all the desired data, is called as a missing value. Missing value is a problem for all types of data analysis. Several ways that can be applied to handle missing value, by filtering data (ignore or remove data) and imputing data. Ignoring or removing data can reduce the amount of information contained in the data and can cause low accuracy to generate from the data analysis process. To overcome this problem, imputation data will be carried out at the preprocessing stage to obtain complete data which is expected to increase the accuracy of the data analysis performed. Many imputations method can be used, such as mean im...","PeriodicalId":209108,"journal":{"name":"PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134322542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Breast cancer is a malignant disease that triggers the anomalies of the cells proliferation in breast tissue. There are some known factors that have ability to increase someone risk to suffer this disease, i.e., hormone, genetics, lifestyle, etc. One of the important hormone for the growth of breast tissue is estrogen, but it also contributes to breast cancer via DNA damage induced by producing the oxidative metabolites. Also, estrogen can provoke excessive proliferation that triggers the tumorigenesis process, where the key effectors are C-Myc and Cyclin D1 (CycD1). In this paper, we introduce a new mathematical model of the DNA damage as the response of the estrogen involving the G1/S transition phase in cell cycle. The model is a 15-dimensional system of the first order of ODE that shows the chemical reactions between proteins and hormones that play important roles in cell cycle regulations. The model could be a foundation to understand the initial behavior of the breast cancer. We use numerical simulations by using fourth order Runge Kutta method to study the molecular behavior of the normal cells and the anomalies on the abnormal cells that initially lead breast cancer.Breast cancer is a malignant disease that triggers the anomalies of the cells proliferation in breast tissue. There are some known factors that have ability to increase someone risk to suffer this disease, i.e., hormone, genetics, lifestyle, etc. One of the important hormone for the growth of breast tissue is estrogen, but it also contributes to breast cancer via DNA damage induced by producing the oxidative metabolites. Also, estrogen can provoke excessive proliferation that triggers the tumorigenesis process, where the key effectors are C-Myc and Cyclin D1 (CycD1). In this paper, we introduce a new mathematical model of the DNA damage as the response of the estrogen involving the G1/S transition phase in cell cycle. The model is a 15-dimensional system of the first order of ODE that shows the chemical reactions between proteins and hormones that play important roles in cell cycle regulations. The model could be a foundation to understand the initial behavior of the breast cancer. We use numerical simula...
{"title":"A mathematical modelling for estradiol influence on DNA damage response and G1/S transition phase regulations in early stage of breast cancer","authors":"Mayang Fati Kusuma, F. Adi-Kusumo","doi":"10.1063/1.5139158","DOIUrl":"https://doi.org/10.1063/1.5139158","url":null,"abstract":"Breast cancer is a malignant disease that triggers the anomalies of the cells proliferation in breast tissue. There are some known factors that have ability to increase someone risk to suffer this disease, i.e., hormone, genetics, lifestyle, etc. One of the important hormone for the growth of breast tissue is estrogen, but it also contributes to breast cancer via DNA damage induced by producing the oxidative metabolites. Also, estrogen can provoke excessive proliferation that triggers the tumorigenesis process, where the key effectors are C-Myc and Cyclin D1 (CycD1). In this paper, we introduce a new mathematical model of the DNA damage as the response of the estrogen involving the G1/S transition phase in cell cycle. The model is a 15-dimensional system of the first order of ODE that shows the chemical reactions between proteins and hormones that play important roles in cell cycle regulations. The model could be a foundation to understand the initial behavior of the breast cancer. We use numerical simulations by using fourth order Runge Kutta method to study the molecular behavior of the normal cells and the anomalies on the abnormal cells that initially lead breast cancer.Breast cancer is a malignant disease that triggers the anomalies of the cells proliferation in breast tissue. There are some known factors that have ability to increase someone risk to suffer this disease, i.e., hormone, genetics, lifestyle, etc. One of the important hormone for the growth of breast tissue is estrogen, but it also contributes to breast cancer via DNA damage induced by producing the oxidative metabolites. Also, estrogen can provoke excessive proliferation that triggers the tumorigenesis process, where the key effectors are C-Myc and Cyclin D1 (CycD1). In this paper, we introduce a new mathematical model of the DNA damage as the response of the estrogen involving the G1/S transition phase in cell cycle. The model is a 15-dimensional system of the first order of ODE that shows the chemical reactions between proteins and hormones that play important roles in cell cycle regulations. The model could be a foundation to understand the initial behavior of the breast cancer. We use numerical simula...","PeriodicalId":209108,"journal":{"name":"PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129231257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Let H be a graph. A simple graph G=(V(G), E(G)) admits an H-covering if every edge in E(G) belongs to some subgraphs of G that isomorphic to a given graph H. A graph G is H-magic if there exists a total labeling f: V(G) ∪ E(G) → {1, 2, …, |V(G)|+|E(G)|}, such that all subgraphs H′=(V(H′), E(H′)) of G isomorphic to H have the same weight. In this case, the weight of H′ is defined as the sum of all vertex and edge labels of graph H′ and is denoted by f (H′). Additionally, G is an H-supermagic labeling if f (V(G)) = {1, 2, …, |V(G)|}.This research aims to find an H-supermagic labeling of G, for two cases. In case one, we consider G as edge corona product of a star graph and a cycle and H as edge corona product of a path with length two and a cycle. In case two, we consider G as edge corona product of a book graph and a cycle and H as a edge corona product of a cycle with order 4 and a cycle.Let H be a graph. A simple graph G=(V(G), E(G)) admits an H-covering if every edge in E(G) belongs to some subgraphs of G that isomorphic to a given graph H. A graph G is H-magic if there exists a total labeling f: V(G) ∪ E(G) → {1, 2, …, |V(G)|+|E(G)|}, such that all subgraphs H′=(V(H′), E(H′)) of G isomorphic to H have the same weight. In this case, the weight of H′ is defined as the sum of all vertex and edge labels of graph H′ and is denoted by f (H′). Additionally, G is an H-supermagic labeling if f (V(G)) = {1, 2, …, |V(G)|}.This research aims to find an H-supermagic labeling of G, for two cases. In case one, we consider G as edge corona product of a star graph and a cycle and H as edge corona product of a path with length two and a cycle. In case two, we consider G as edge corona product of a book graph and a cycle and H as a edge corona product of a cycle with order 4 and a cycle.
{"title":"H-supermagic labeling on edge coronation of some graphs with a cycle","authors":"H. Sandariria, Y. Susanti","doi":"10.1063/1.5139140","DOIUrl":"https://doi.org/10.1063/1.5139140","url":null,"abstract":"Let H be a graph. A simple graph G=(V(G), E(G)) admits an H-covering if every edge in E(G) belongs to some subgraphs of G that isomorphic to a given graph H. A graph G is H-magic if there exists a total labeling f: V(G) ∪ E(G) → {1, 2, …, |V(G)|+|E(G)|}, such that all subgraphs H′=(V(H′), E(H′)) of G isomorphic to H have the same weight. In this case, the weight of H′ is defined as the sum of all vertex and edge labels of graph H′ and is denoted by f (H′). Additionally, G is an H-supermagic labeling if f (V(G)) = {1, 2, …, |V(G)|}.This research aims to find an H-supermagic labeling of G, for two cases. In case one, we consider G as edge corona product of a star graph and a cycle and H as edge corona product of a path with length two and a cycle. In case two, we consider G as edge corona product of a book graph and a cycle and H as a edge corona product of a cycle with order 4 and a cycle.Let H be a graph. A simple graph G=(V(G), E(G)) admits an H-covering if every edge in E(G) belongs to some subgraphs of G that isomorphic to a given graph H. A graph G is H-magic if there exists a total labeling f: V(G) ∪ E(G) → {1, 2, …, |V(G)|+|E(G)|}, such that all subgraphs H′=(V(H′), E(H′)) of G isomorphic to H have the same weight. In this case, the weight of H′ is defined as the sum of all vertex and edge labels of graph H′ and is denoted by f (H′). Additionally, G is an H-supermagic labeling if f (V(G)) = {1, 2, …, |V(G)|}.This research aims to find an H-supermagic labeling of G, for two cases. In case one, we consider G as edge corona product of a star graph and a cycle and H as edge corona product of a path with length two and a cycle. In case two, we consider G as edge corona product of a book graph and a cycle and H as a edge corona product of a cycle with order 4 and a cycle.","PeriodicalId":209108,"journal":{"name":"PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132730009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Support Vector Machine (SVM) Algorithm is one of the most popular classification method in machine learning and statistics. However, in the presence of outliers, the classifier may be adversely affected. In this paper, we experiment on the hinge loss function of the unconstrained SVM Algorithm to suit prior information about nonlinearly separable sets of Gaussian data. First, we determine if an altered hinge loss function x ↦ max(0, α − x) with several positive values of α will be significantly better in classification compared when α = 1. Then, taking an inspiration from Huber’s least informative distribution model to desensitize regression from outliers, we smoothen the hinge loss function to promote insensitivity of the classification to outliers. Using statistical analysis, we determine that at some level of significance, there is a considerable improvement in classification with respect to the number of misclassified data.The Support Vector Machine (SVM) Algorithm is one of the most popular classification method in machine learning and statistics. However, in the presence of outliers, the classifier may be adversely affected. In this paper, we experiment on the hinge loss function of the unconstrained SVM Algorithm to suit prior information about nonlinearly separable sets of Gaussian data. First, we determine if an altered hinge loss function x ↦ max(0, α − x) with several positive values of α will be significantly better in classification compared when α = 1. Then, taking an inspiration from Huber’s least informative distribution model to desensitize regression from outliers, we smoothen the hinge loss function to promote insensitivity of the classification to outliers. Using statistical analysis, we determine that at some level of significance, there is a considerable improvement in classification with respect to the number of misclassified data.
{"title":"Preconditioning the support vector machine algorithm to suit margin and outlier priors of Gaussian data","authors":"Shaira Lee L. Pabalan, Louie John D. Vallejo","doi":"10.1063/1.5139170","DOIUrl":"https://doi.org/10.1063/1.5139170","url":null,"abstract":"The Support Vector Machine (SVM) Algorithm is one of the most popular classification method in machine learning and statistics. However, in the presence of outliers, the classifier may be adversely affected. In this paper, we experiment on the hinge loss function of the unconstrained SVM Algorithm to suit prior information about nonlinearly separable sets of Gaussian data. First, we determine if an altered hinge loss function x ↦ max(0, α − x) with several positive values of α will be significantly better in classification compared when α = 1. Then, taking an inspiration from Huber’s least informative distribution model to desensitize regression from outliers, we smoothen the hinge loss function to promote insensitivity of the classification to outliers. Using statistical analysis, we determine that at some level of significance, there is a considerable improvement in classification with respect to the number of misclassified data.The Support Vector Machine (SVM) Algorithm is one of the most popular classification method in machine learning and statistics. However, in the presence of outliers, the classifier may be adversely affected. In this paper, we experiment on the hinge loss function of the unconstrained SVM Algorithm to suit prior information about nonlinearly separable sets of Gaussian data. First, we determine if an altered hinge loss function x ↦ max(0, α − x) with several positive values of α will be significantly better in classification compared when α = 1. Then, taking an inspiration from Huber’s least informative distribution model to desensitize regression from outliers, we smoothen the hinge loss function to promote insensitivity of the classification to outliers. Using statistical analysis, we determine that at some level of significance, there is a considerable improvement in classification with respect to the number of misclassified data.","PeriodicalId":209108,"journal":{"name":"PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123661902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given a simple connected undirected graph G and let k be the maximum number of its vertices and its edges. Let f be a bijective labeling from the set of its edges to the set of odd integers from 1 up to 2q − 1, where q is the number of edges of G. The labeling f is called an edge odd graceful labeling on G if the weights of any two different vertices are different, where the weight of a vertex v is defined as the sum mod(2k) of all labels of edges that are incident to v. A graph is called an edge odd graceful graph if it admits an edge odd graceful labeling. In this paper, we show that there are some new classes of graphs that are edge odd graceful.
{"title":"On some new edge odd graceful graphs","authors":"Y. Susanti, Iwan Ernanto, B. Surodjo","doi":"10.1063/1.5139142","DOIUrl":"https://doi.org/10.1063/1.5139142","url":null,"abstract":"Given a simple connected undirected graph G and let k be the maximum number of its vertices and its edges. Let f be a bijective labeling from the set of its edges to the set of odd integers from 1 up to 2q − 1, where q is the number of edges of G. The labeling f is called an edge odd graceful labeling on G if the weights of any two different vertices are different, where the weight of a vertex v is defined as the sum mod(2k) of all labels of edges that are incident to v. A graph is called an edge odd graceful graph if it admits an edge odd graceful labeling. In this paper, we show that there are some new classes of graphs that are edge odd graceful.","PeriodicalId":209108,"journal":{"name":"PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126312688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Winsy Weku, H. Pramoedyo, Agus Widodo, Rahma Fitriani
The covariance function that forms a variogram is an important measurement for spatial dependence and as a linear kriging interpolation tool. The covariance function requires a definite positive guarantee, this means that not all functions can be used. Therefore, this research explores the correlogram and nonmonoton variogram functions and shows it analytically using the Fourier Transform (Bochner’s theorem). In addition, a simple approach is used to determine definite positivity by paying attention to boundaries. Suppose that C : Rd → R is positive definite if it bounded to exponential which is positive definit. Research shows that Nonmonoton Bessel functions that have Exponential bound are positive definite. Multiplication operations of two covariance functions, C1 and C2 in measured spaces indicate that definite positive properties are fulfilled.
{"title":"Positive definite functions of non monoton variogram to define the spatial dependency of correlogram","authors":"Winsy Weku, H. Pramoedyo, Agus Widodo, Rahma Fitriani","doi":"10.1063/1.5139186","DOIUrl":"https://doi.org/10.1063/1.5139186","url":null,"abstract":"The covariance function that forms a variogram is an important measurement for spatial dependence and as a linear kriging interpolation tool. The covariance function requires a definite positive guarantee, this means that not all functions can be used. Therefore, this research explores the correlogram and nonmonoton variogram functions and shows it analytically using the Fourier Transform (Bochner’s theorem). In addition, a simple approach is used to determine definite positivity by paying attention to boundaries. Suppose that C : Rd → R is positive definite if it bounded to exponential which is positive definit. Research shows that Nonmonoton Bessel functions that have Exponential bound are positive definite. Multiplication operations of two covariance functions, C1 and C2 in measured spaces indicate that definite positive properties are fulfilled.","PeriodicalId":209108,"journal":{"name":"PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122255975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. D. Urrutia, Paul Ryan A. Longhas, Francis Leo T. Mingo
The researcher aim to forecast the Gross Domestic Product (GDP) of the Philippines from the 1st Quarter of 2018 to 4th Quarter of 2022. Furthermore, this study determines the most suitable model among Autoregressive Integrated Moving Average and Bayesian Artificial Neural Network that can forecast the GDP of the Philippines. The researcher used the data ranging from the 1st Quarter of 1990 up to 4th Quarter of 2017 with a total of 112 observations. Statistical test are conducted within the study to be able to formulate and compare the statistical model ARIMA and Bayesian ANN. It is concluded in this study that the ARIMA(1,1,1) and Bayesian ANN can forecast the GDP of the Philippines. The researcher use Forecasting accuracy such as MSE, NMSE, MAE, RMSE, and MAPE to compare the performance of two models. In this paper, the best fitted model obtained is Bayesian ANN. Paired T-test concludes that there is no significant difference between actual and predicted value. This study helps economics specifically in economic forecasting and economic analysis.
{"title":"Forecasting the Gross Domestic Product of the Philippines using Bayesian artificial neural network and autoregressive integrated moving average","authors":"J. D. Urrutia, Paul Ryan A. Longhas, Francis Leo T. Mingo","doi":"10.1063/1.5139182","DOIUrl":"https://doi.org/10.1063/1.5139182","url":null,"abstract":"The researcher aim to forecast the Gross Domestic Product (GDP) of the Philippines from the 1st Quarter of 2018 to 4th Quarter of 2022. Furthermore, this study determines the most suitable model among Autoregressive Integrated Moving Average and Bayesian Artificial Neural Network that can forecast the GDP of the Philippines. The researcher used the data ranging from the 1st Quarter of 1990 up to 4th Quarter of 2017 with a total of 112 observations. Statistical test are conducted within the study to be able to formulate and compare the statistical model ARIMA and Bayesian ANN. It is concluded in this study that the ARIMA(1,1,1) and Bayesian ANN can forecast the GDP of the Philippines. The researcher use Forecasting accuracy such as MSE, NMSE, MAE, RMSE, and MAPE to compare the performance of two models. In this paper, the best fitted model obtained is Bayesian ANN. Paired T-test concludes that there is no significant difference between actual and predicted value. This study helps economics specifically in economic forecasting and economic analysis.","PeriodicalId":209108,"journal":{"name":"PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128937295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations
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