Pub Date : 2018-10-01DOI: 10.1109/ICICOS.2018.8621720
Widya Mas Septiawan, S. Endah
Air pollution currently occurs in developed and developing countries and can disrupt environmental conditions and public health. Determining the level of air pollution (air pollutants) or air quality can be seen from a group of sensitive parameters such as NO2, O3, PM10, PM2.5, and SO2. This study predicts data on air pollutant concentrations over time (time series data) to determine future air quality conditions that are good or bad for health and the environment. Data predictions can use algorithms from artificial neural networks, one of which is the Backpropagation Through Time (BPTT) algorithm. BPTT is a learning algorithm developed from the backpropagation algorithm that is applied to the Recurrent Neural Network (RNN) network architecture. BPTT algorithm and RNN architecture have the advantage of predicting time series data because they not only consider the latest inputs, but also all previous inputs in the network. This study proposes to apply the BPTT algorithm by comparing Elman RNN, Jordan RNN, and hybrid network architecture to predict the time series data of air pollutant concentration in determining air quality. The architecture that is suitable for predicting air pollutant concentrations in determining air quality is Jordan RNN which is based on MAPE testing of 6.481% to 7.177% for each data, and the average MAPE prediction for new input data is 5.9024%. Based on the air quality category, the prediction category of the three architectures produces the same category between prediction categories with air quality categories from real data or in other words, the three architectures are suitable for predicting air quality.
{"title":"Suitable Recurrent Neural Network for Air Quality Prediction With Backpropagation Through Time","authors":"Widya Mas Septiawan, S. Endah","doi":"10.1109/ICICOS.2018.8621720","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621720","url":null,"abstract":"Air pollution currently occurs in developed and developing countries and can disrupt environmental conditions and public health. Determining the level of air pollution (air pollutants) or air quality can be seen from a group of sensitive parameters such as NO2, O3, PM10, PM2.5, and SO2. This study predicts data on air pollutant concentrations over time (time series data) to determine future air quality conditions that are good or bad for health and the environment. Data predictions can use algorithms from artificial neural networks, one of which is the Backpropagation Through Time (BPTT) algorithm. BPTT is a learning algorithm developed from the backpropagation algorithm that is applied to the Recurrent Neural Network (RNN) network architecture. BPTT algorithm and RNN architecture have the advantage of predicting time series data because they not only consider the latest inputs, but also all previous inputs in the network. This study proposes to apply the BPTT algorithm by comparing Elman RNN, Jordan RNN, and hybrid network architecture to predict the time series data of air pollutant concentration in determining air quality. The architecture that is suitable for predicting air pollutant concentrations in determining air quality is Jordan RNN which is based on MAPE testing of 6.481% to 7.177% for each data, and the average MAPE prediction for new input data is 5.9024%. Based on the air quality category, the prediction category of the three architectures produces the same category between prediction categories with air quality categories from real data or in other words, the three architectures are suitable for predicting air quality.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116178071","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}
Pub Date : 2018-10-01DOI: 10.1109/ICICOS.2018.8621711
Radite Purwahana, S. Suryono, J. E. Suseno
The analysis used in dealing with maternal mortality factors in the postpartum period can be used as a reference in preventing maternal death in the postpartum period. Appropriate analysis is needed to reduce maternal mortality rates in the postpartum period. This study uses multinomial logistic regression to analyze the data of mothers dying in the postpartum period based on the main variables causing maternal death. Multinomial logistic regression process is carried out by looking at data records of variables that influence maternal mortality. In the first experiment using data from midwife visits for seven days, the results of the multinomial logistic regression process with the highest maternal mortality occurred on the fourth day with anogenital variables reaching a percentage of 32.4% of the causes of maternal death. Multinomial logistic regression processes are combined with cloud computing technology so that data can be processed more quickly and can be used together.
{"title":"Cloud-Based Multinomial Logistic Regression for Analyzing Maternal Mortality Data in Postpartum Period","authors":"Radite Purwahana, S. Suryono, J. E. Suseno","doi":"10.1109/ICICOS.2018.8621711","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621711","url":null,"abstract":"The analysis used in dealing with maternal mortality factors in the postpartum period can be used as a reference in preventing maternal death in the postpartum period. Appropriate analysis is needed to reduce maternal mortality rates in the postpartum period. This study uses multinomial logistic regression to analyze the data of mothers dying in the postpartum period based on the main variables causing maternal death. Multinomial logistic regression process is carried out by looking at data records of variables that influence maternal mortality. In the first experiment using data from midwife visits for seven days, the results of the multinomial logistic regression process with the highest maternal mortality occurred on the fourth day with anogenital variables reaching a percentage of 32.4% of the causes of maternal death. Multinomial logistic regression processes are combined with cloud computing technology so that data can be processed more quickly and can be used together.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121486420","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}
Pub Date : 2018-10-01DOI: 10.1109/ICICOS.2018.8621664
Pattarapong Pakpoom, P. Charnsethikul
In this work, we model a personnel scheduling problem with uncertain demand as a two-stage stochastic integer program. The model is a large integer program with a large number of columns and constraints which creates difficulty for optimization process. We apply column generation method and Benders' decomposition technique to solve the problem. We test our proposed algorithm on some generated instances and obtain satisfying results showing improvement in obtaining good solutions quickly over solving MIP on GAMS with CPLEX solver.
{"title":"A Column Generation Approach for Personnel Sched uling with Discrete Uncertain Requirements","authors":"Pattarapong Pakpoom, P. Charnsethikul","doi":"10.1109/ICICOS.2018.8621664","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621664","url":null,"abstract":"In this work, we model a personnel scheduling problem with uncertain demand as a two-stage stochastic integer program. The model is a large integer program with a large number of columns and constraints which creates difficulty for optimization process. We apply column generation method and Benders' decomposition technique to solve the problem. We test our proposed algorithm on some generated instances and obtain satisfying results showing improvement in obtaining good solutions quickly over solving MIP on GAMS with CPLEX solver.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124676571","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}
Pub Date : 2018-10-01DOI: 10.1109/ICICOS.2018.8621698
Widodo, W. Wibowo
Privacy preserving data publishing is a growing research in computer science. Many of study on this research focus on single sensitive attribute. While many of multiple sensitive attributes researches do not set the sensitive attribute distribution. It is necessary for ensuring p-sensitive property since multiple sensitive attributes not just apply the model in single sensitive attribute. It needs more setting. This research discusses a distribution model to set sensitive attribute values when p-sensitive is applied on multiple sensitive attributes. We build a rule to distribute sensitive values. The result shows that this distribution model satisfies privacy guarantee that is provided by p-sensitive.
{"title":"A Distributional Model of Sensitive Values on p-Sensitive in Multiple Sensitive Attributes","authors":"Widodo, W. Wibowo","doi":"10.1109/ICICOS.2018.8621698","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621698","url":null,"abstract":"Privacy preserving data publishing is a growing research in computer science. Many of study on this research focus on single sensitive attribute. While many of multiple sensitive attributes researches do not set the sensitive attribute distribution. It is necessary for ensuring p-sensitive property since multiple sensitive attributes not just apply the model in single sensitive attribute. It needs more setting. This research discusses a distribution model to set sensitive attribute values when p-sensitive is applied on multiple sensitive attributes. We build a rule to distribute sensitive values. The result shows that this distribution model satisfies privacy guarantee that is provided by p-sensitive.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"442 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114583217","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}
Pub Date : 2018-10-01DOI: 10.1109/ICICOS.2018.8621662
Muhammad Rizal Khaefi, P.Jutta Prahara, Muhammad Rheza, Dikara Alkarisya, G. Hodge
Exposed to a variety of natural hazards, Vanuatu is one of the most disaster-prone countries in the South Pacific. The Government plays a central role in disaster response and has articulated a need for information on disaster-induced displacement in order to target resources. This paper aims to inform preparation and planning by developing a method to predict evacuation destinations before a disaster happens by applying machine learning approaches to mobile network data. In this study, the eruption of Mount Monaro in 2017 is chosen to test the prediction performance of the model in a real disaster scenario. We explored 273 features, extracted from over one-hundred-million anonymized mobile network records, to describe (a) basic phone usage, (b) active user behavior, (c) spatial behavior, (d) regularity, and (e) diversity. Our results show that supervised machine learning methods produce promising results in predicting evacuation destinations.
{"title":"Predicting Evacuation Destinations due to a Natural Hazard using Mobile Network Data","authors":"Muhammad Rizal Khaefi, P.Jutta Prahara, Muhammad Rheza, Dikara Alkarisya, G. Hodge","doi":"10.1109/ICICOS.2018.8621662","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621662","url":null,"abstract":"Exposed to a variety of natural hazards, Vanuatu is one of the most disaster-prone countries in the South Pacific. The Government plays a central role in disaster response and has articulated a need for information on disaster-induced displacement in order to target resources. This paper aims to inform preparation and planning by developing a method to predict evacuation destinations before a disaster happens by applying machine learning approaches to mobile network data. In this study, the eruption of Mount Monaro in 2017 is chosen to test the prediction performance of the model in a real disaster scenario. We explored 273 features, extracted from over one-hundred-million anonymized mobile network records, to describe (a) basic phone usage, (b) active user behavior, (c) spatial behavior, (d) regularity, and (e) diversity. Our results show that supervised machine learning methods produce promising results in predicting evacuation destinations.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126838295","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}
Pub Date : 2018-10-01DOI: 10.1109/ICICOS.2018.8621766
Syamira Merina, A. Bustamam, Gianinna Ardaneswari
Adenoma is a benign type of tumor in the epidermal layer of tissue. Adenoma can turn into malignant cancer which is then called Adenocarcinoma. There is a form of molecular biology data which is developing today, namely microarray gene expression data. Microarray can be used for detection and research in the field of oncology. One method for processing and analyzing microarray gene data is by biclustering. In this study, the writer will be using one method of biclustering, the Binary Inclusion-Maximal algorithm, and implement it on microarray gene expression data. The algorithm will be performed on Colon Adenoma data consisting of 7070 genes with four adenoma cell samples and four normal cell samples. The implementation took less than one second and resulted in 22 biclusters composed of 25 genes.
{"title":"Implementation of The Binary Inclusion-Maximal Biclustering Algorithm on Adenoma Microarray Gene Expression Data","authors":"Syamira Merina, A. Bustamam, Gianinna Ardaneswari","doi":"10.1109/ICICOS.2018.8621766","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621766","url":null,"abstract":"Adenoma is a benign type of tumor in the epidermal layer of tissue. Adenoma can turn into malignant cancer which is then called Adenocarcinoma. There is a form of molecular biology data which is developing today, namely microarray gene expression data. Microarray can be used for detection and research in the field of oncology. One method for processing and analyzing microarray gene data is by biclustering. In this study, the writer will be using one method of biclustering, the Binary Inclusion-Maximal algorithm, and implement it on microarray gene expression data. The algorithm will be performed on Colon Adenoma data consisting of 7070 genes with four adenoma cell samples and four normal cell samples. The implementation took less than one second and resulted in 22 biclusters composed of 25 genes.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129670515","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}
Pub Date : 2018-10-01DOI: 10.1109/ICICOS.2018.8621686
Kabul Kurniawan, F. Ekaputra, Peb Ruswono Aryan
For decades, researchers and practitioners develop various approaches such as Web Service technologies (e.g., UDDI, WSDL, SOAP) to address application integration problems. In particular, Web Service composition methods can solve complex service integrations. However, in highly dynamic environments, these manual service compositions still requires a lot of effort. To address this challenge, researchers have recently introduced semantic web service composition methods. The growing interest in this topic of semantic web service composition has led to an increasing number of approaches, which has not been systematically surveyed so far. Researchers have reported several surveys in the related areas such as web service composition or semantic web service search. However, to the best of our knowledge, none of them provides a survey about these semantic web service compositions in particular. Hence, this review aims to address this issue by identifying existing efforts on semantic web service compositions. The survey focuses on two aspects (i) semantic web service description, as it is an essential aspect for semantic service composition, (ii) semantic web service composition, to identify methods and their implementations on the real world problem.
{"title":"Semantic Service Description and Compositions: A Systematic Literature Review","authors":"Kabul Kurniawan, F. Ekaputra, Peb Ruswono Aryan","doi":"10.1109/ICICOS.2018.8621686","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621686","url":null,"abstract":"For decades, researchers and practitioners develop various approaches such as Web Service technologies (e.g., UDDI, WSDL, SOAP) to address application integration problems. In particular, Web Service composition methods can solve complex service integrations. However, in highly dynamic environments, these manual service compositions still requires a lot of effort. To address this challenge, researchers have recently introduced semantic web service composition methods. The growing interest in this topic of semantic web service composition has led to an increasing number of approaches, which has not been systematically surveyed so far. Researchers have reported several surveys in the related areas such as web service composition or semantic web service search. However, to the best of our knowledge, none of them provides a survey about these semantic web service compositions in particular. Hence, this review aims to address this issue by identifying existing efforts on semantic web service compositions. The survey focuses on two aspects (i) semantic web service description, as it is an essential aspect for semantic service composition, (ii) semantic web service composition, to identify methods and their implementations on the real world problem.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133280738","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}
Pub Date : 2018-10-01DOI: 10.1109/ICICOS.2018.8621808
B. Aditya, R. Ferdiana, S. Kusumawardani
In digital transformation, modernization IT risk universe plays a major role in planning an effective IT audit program. This paper describes a new requirement in IT audit practices and suggests an IT risk universe framework for the development of IT risk universe toward a more modern (digital transformation setting). This paper concludes with research prospects that can support and intensify research for modernizing IT risk universe in modern IT audit.
{"title":"Requirement and Potential for Modernizing IT Risk Universe in IT Audit Plan","authors":"B. Aditya, R. Ferdiana, S. Kusumawardani","doi":"10.1109/ICICOS.2018.8621808","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621808","url":null,"abstract":"In digital transformation, modernization IT risk universe plays a major role in planning an effective IT audit program. This paper describes a new requirement in IT audit practices and suggests an IT risk universe framework for the development of IT risk universe toward a more modern (digital transformation setting). This paper concludes with research prospects that can support and intensify research for modernizing IT risk universe in modern IT audit.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126701972","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}
Pub Date : 2018-10-01DOI: 10.1109/ICICOS.2018.8621708
Amazona Adorada, Ratih Permatasari, P. W. Wirawan, A. Wibowo, Adi Sujiwo
Cancer is still a major problem for people today because it is one of the biggest causes of death in the world. Based on GLOBOCAN data in 2012., breast cancer accounted for the world's largest cancer mortality rate in women by 14.7% with total deaths amounting to 521., 907 from 3., 548., 190 cases of cancer in the world. The high mortality rate is affected by the absence of sufficient early detection of cancer. MicroRNAs play an essential role in regulating cell division cycles., apoptosis., senescence., migration and cell invasion., and metastasis. The expression of microRNA in breast cancer shows a pattern compared to normal breasts., thus indicating its role as a potential diagnostic marker. However., not all microRNA profiles have a significant role in cancer detection. In this paper., we applied the support vector machine - recursive feature elimination (SVM-RFE) and univariate selection for feature selection of microRNA expression in breast cancer. Several experiments were conducted to select ten features with the highest ranking; therefore., it is expected to obtain a unique feature as a unique feature of breast cancer. Based on experimental results., this study obtained recommended the essential MicroRNA features for cancer analysis and biomarkers.
{"title":"Support Vector Machine - Recursive Feature Elimination (SVM - RFE) for Selection of MicroRNA Expression Features of Breast Cancer","authors":"Amazona Adorada, Ratih Permatasari, P. W. Wirawan, A. Wibowo, Adi Sujiwo","doi":"10.1109/ICICOS.2018.8621708","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621708","url":null,"abstract":"Cancer is still a major problem for people today because it is one of the biggest causes of death in the world. Based on GLOBOCAN data in 2012., breast cancer accounted for the world's largest cancer mortality rate in women by 14.7% with total deaths amounting to 521., 907 from 3., 548., 190 cases of cancer in the world. The high mortality rate is affected by the absence of sufficient early detection of cancer. MicroRNAs play an essential role in regulating cell division cycles., apoptosis., senescence., migration and cell invasion., and metastasis. The expression of microRNA in breast cancer shows a pattern compared to normal breasts., thus indicating its role as a potential diagnostic marker. However., not all microRNA profiles have a significant role in cancer detection. In this paper., we applied the support vector machine - recursive feature elimination (SVM-RFE) and univariate selection for feature selection of microRNA expression in breast cancer. Several experiments were conducted to select ten features with the highest ranking; therefore., it is expected to obtain a unique feature as a unique feature of breast cancer. Based on experimental results., this study obtained recommended the essential MicroRNA features for cancer analysis and biomarkers.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122054891","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}
Pub Date : 2018-10-01DOI: 10.1109/ICICOS.2018.8621821
Agnes Irene Silitonga, E. Nababan, O. S. Sitompul
Images could display visual information more than those of text data. However, when transmitted and acquired through communication channels, those images are always spoiled with noises that will reduce the quality of the image. Noisy image could not provide good quality image for further image processing due to poor quality. In image processing, standard genetic algorithm steps could be used to enhance image quality. The purpose of this research is to deploy uniform crossover of genetic algorithm to reduce noise in order to produce better offsprings. In every noise type, the obtained value of Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) resulted in image noise reduction were calculated and analyzed to see how both values of MSE and PSNR in average will change. For this purpose, we conducted tests with Pc values of 0.2, 0.4, 0.6, and 0.8, each with 100, 200, 300, 400, 500, and 1000 maximum number of generations, respectively. Result shows that uniform crossover obtained the best performance in reducing erlang noise and the worst performance in reducing localvar noise on three categories of images.
{"title":"Reducing Image Noises Using Genetic Algorithm's Uniform Crossover","authors":"Agnes Irene Silitonga, E. Nababan, O. S. Sitompul","doi":"10.1109/ICICOS.2018.8621821","DOIUrl":"https://doi.org/10.1109/ICICOS.2018.8621821","url":null,"abstract":"Images could display visual information more than those of text data. However, when transmitted and acquired through communication channels, those images are always spoiled with noises that will reduce the quality of the image. Noisy image could not provide good quality image for further image processing due to poor quality. In image processing, standard genetic algorithm steps could be used to enhance image quality. The purpose of this research is to deploy uniform crossover of genetic algorithm to reduce noise in order to produce better offsprings. In every noise type, the obtained value of Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) resulted in image noise reduction were calculated and analyzed to see how both values of MSE and PSNR in average will change. For this purpose, we conducted tests with Pc values of 0.2, 0.4, 0.6, and 0.8, each with 100, 200, 300, 400, 500, and 1000 maximum number of generations, respectively. Result shows that uniform crossover obtained the best performance in reducing erlang noise and the worst performance in reducing localvar noise on three categories of images.","PeriodicalId":438473,"journal":{"name":"2018 2nd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116879096","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}
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