Pub Date : 2017-12-19DOI: 10.1109/KCIC.2017.8228566
Irene Erlyn Wina Rachmawan, Y. Kiyoki
Tackling Deforestation activity is not an easy task. Many approached on mapping and monitoring the change of forest cover has been actively introduced and yet the deforestation activity is still largely happens. In order to observe the deforestation activity and its natural impact on environment, a new way to serve knowledge is good approach to make more understandable information regarding on how deforestation activity effects on our environment. We proposed semantic spatial-weighted regression to create a system that able to presenting the distribution of deforestation effect on soil degradation based on human language regression. Our system is able to visualize the desire observed are based on the location given by user impression. We use Landsat satellite images as our input data. Our system calculates the band parameters value using semantic orthogonality for producing a new semantic regression model of deforestation area effect to capturing user intention.
{"title":"Semantic spatial weighted regression for realizing spatial correlation of deforestation effect on soil degradation","authors":"Irene Erlyn Wina Rachmawan, Y. Kiyoki","doi":"10.1109/KCIC.2017.8228566","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228566","url":null,"abstract":"Tackling Deforestation activity is not an easy task. Many approached on mapping and monitoring the change of forest cover has been actively introduced and yet the deforestation activity is still largely happens. In order to observe the deforestation activity and its natural impact on environment, a new way to serve knowledge is good approach to make more understandable information regarding on how deforestation activity effects on our environment. We proposed semantic spatial-weighted regression to create a system that able to presenting the distribution of deforestation effect on soil degradation based on human language regression. Our system is able to visualize the desire observed are based on the location given by user impression. We use Landsat satellite images as our input data. Our system calculates the band parameters value using semantic orthogonality for producing a new semantic regression model of deforestation area effect to capturing user intention.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124173476","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 : 2017-12-19DOI: 10.1109/KCIC.2017.8228597
Hanako Fujioka, S. Sasaki, Y. Kiyoki
The most important aim of our study is to realize a multi-database for social sciences and environmental sciences. Our system connects heterogeneous databases about historical phenomena by using common spatiotemporal information and visualize the connected results onto 5D World Map (a set of chronologically ordered global maps). To actualize that, we created a news articles provision system using real-time sensor data as a trigger and determined the effectiveness in the experiment. Here we found that we can get the information about the happening in the same atmospheric condition in the past by exhibiting news articles. These results provide new insight into our understanding of the relationship between real-time situation and past occurrence with news articles.
{"title":"A realtime sensing-data-triggered news article provision system with 5D world map","authors":"Hanako Fujioka, S. Sasaki, Y. Kiyoki","doi":"10.1109/KCIC.2017.8228597","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228597","url":null,"abstract":"The most important aim of our study is to realize a multi-database for social sciences and environmental sciences. Our system connects heterogeneous databases about historical phenomena by using common spatiotemporal information and visualize the connected results onto 5D World Map (a set of chronologically ordered global maps). To actualize that, we created a news articles provision system using real-time sensor data as a trigger and determined the effectiveness in the experiment. Here we found that we can get the information about the happening in the same atmospheric condition in the past by exhibiting news articles. These results provide new insight into our understanding of the relationship between real-time situation and past occurrence with news articles.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131388412","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 : 2017-12-19DOI: 10.1109/KCIC.2017.8228593
S. Amit, Y. Aoki
In recent years, analysis of remote sensing imagery is imperatives in the domain of environmental and climate monitoring primarily for the application of detecting and managing a natural disaster. Satellite imagery or aerial imagery is beneficial because it can widely capture the condition of the surface ground and provides a massive amount of information in a piece of satellite imagery. Since obtaining satellite imagery or aerial imagery is getting more ease in recent years, landslide detection and flood detection is highly in demand. In this paper, we propose automatic natural disaster detection particularly for landslide and flood detection by implementing convolutional neural network (CNN) in extracting the feature of disaster more effectively. CNN is robust to shadow, able to obtain the characteristic of disaster adequately and most importantly able to overcome misdetection or misjudgment by operators, which will affect the effectiveness of disaster relief. The neural network consists of 2 phases: training phase and testing phase. We created training data patches of pre-disaster and post-disaster by clipping and resizing aerial imagery obtained from Google Earth Aerial Imagery. We are currently focusing on two countries which are Japan and Thailand. Training dataset for both landslide and flood consist of 50000 patches. All patches are trained in CNN to extract region where changes occurred or known as disaster region occurred without delay. We obtained accuracy of our system in around 80%–90% of both disaster detections. Based on the promising results, the proposed method may assist in our understanding of the role of deep learning in disaster detection.
{"title":"Disaster detection from aerial imagery with convolutional neural network","authors":"S. Amit, Y. Aoki","doi":"10.1109/KCIC.2017.8228593","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228593","url":null,"abstract":"In recent years, analysis of remote sensing imagery is imperatives in the domain of environmental and climate monitoring primarily for the application of detecting and managing a natural disaster. Satellite imagery or aerial imagery is beneficial because it can widely capture the condition of the surface ground and provides a massive amount of information in a piece of satellite imagery. Since obtaining satellite imagery or aerial imagery is getting more ease in recent years, landslide detection and flood detection is highly in demand. In this paper, we propose automatic natural disaster detection particularly for landslide and flood detection by implementing convolutional neural network (CNN) in extracting the feature of disaster more effectively. CNN is robust to shadow, able to obtain the characteristic of disaster adequately and most importantly able to overcome misdetection or misjudgment by operators, which will affect the effectiveness of disaster relief. The neural network consists of 2 phases: training phase and testing phase. We created training data patches of pre-disaster and post-disaster by clipping and resizing aerial imagery obtained from Google Earth Aerial Imagery. We are currently focusing on two countries which are Japan and Thailand. Training dataset for both landslide and flood consist of 50000 patches. All patches are trained in CNN to extract region where changes occurred or known as disaster region occurred without delay. We obtained accuracy of our system in around 80%–90% of both disaster detections. Based on the promising results, the proposed method may assist in our understanding of the role of deep learning in disaster detection.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128306594","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 : 2017-12-19DOI: 10.1109/KCIC.2017.8228563
H. Vahidi, Wanglin Yan, B. Klinkenberg
A conceptual model for quality assurance of species occurrence observations in citizen science projects is described below. We adopted the notion of trust as an indicator of VGI quality and define the concept of trustworthiness of a VGI record as a function of three main contexts: consistency with habitat, consistency with neighbors, and the reputation of the volunteer. Using fuzzy control system the quality of an observation is quantified in terms of the level of the trustworthiness of the volunteered species observation. The architecture of the proposed system is briefly described and some results presented. Finally, our paper ends with concluding remarks and some thoughts for future research directions.
{"title":"A fuzzy system for quality assurance of crowdsourced wildlife observation geodata","authors":"H. Vahidi, Wanglin Yan, B. Klinkenberg","doi":"10.1109/KCIC.2017.8228563","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228563","url":null,"abstract":"A conceptual model for quality assurance of species occurrence observations in citizen science projects is described below. We adopted the notion of trust as an indicator of VGI quality and define the concept of trustworthiness of a VGI record as a function of three main contexts: consistency with habitat, consistency with neighbors, and the reputation of the volunteer. Using fuzzy control system the quality of an observation is quantified in terms of the level of the trustworthiness of the volunteered species observation. The architecture of the proposed system is briefly described and some results presented. Finally, our paper ends with concluding remarks and some thoughts for future research directions.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116344812","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 : 2017-12-19DOI: 10.1109/KCIC.2017.8228458
Andrey Ferriyan, A. Thamrin, K. Takeda, J. Murai
In this paper, we present Genetic Algorithm based optimized feature selections for intrusion detection systems. We used one-point crossover for the Genetic Algorithm parameters instead of two-point crossover used by the previous research as it one-point crossover is faster. For evaluations, we used the NSL-KDD Cup 99 data set and we modified the data set by looking into to the recent attacks, hence making the data set more relevant to the current situations. Several classifiers were used on these data sets and we found that Random Forest gave the best results in terms of the classification rate and the training time. The results also showed that our parameters performed better in these two metrics and the classifications using our optimized features on the modified data sets gave mixed results compared to ones with the original features.
本文提出了一种基于遗传算法的入侵检测系统优化特征选择方法。由于遗传算法参数采用一点交叉而不是以往研究中采用的两点交叉，因为一点交叉速度更快。对于评估，我们使用NSL-KDD Cup 99数据集，并通过查看最近的攻击来修改数据集，从而使数据集与当前情况更相关。在这些数据集上使用了几个分类器，我们发现Random Forest在分类率和训练时间方面给出了最好的结果。结果还表明，我们的参数在这两个指标上表现得更好，并且与使用原始特征的分类相比，使用我们优化的特征在修改后的数据集上的分类结果好坏参半。
{"title":"Feature selection using genetic algorithm to improve classification in network intrusion detection system","authors":"Andrey Ferriyan, A. Thamrin, K. Takeda, J. Murai","doi":"10.1109/KCIC.2017.8228458","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228458","url":null,"abstract":"In this paper, we present Genetic Algorithm based optimized feature selections for intrusion detection systems. We used one-point crossover for the Genetic Algorithm parameters instead of two-point crossover used by the previous research as it one-point crossover is faster. For evaluations, we used the NSL-KDD Cup 99 data set and we modified the data set by looking into to the recent attacks, hence making the data set more relevant to the current situations. Several classifiers were used on these data sets and we found that Random Forest gave the best results in terms of the classification rate and the training time. The results also showed that our parameters performed better in these two metrics and the classifications using our optimized features on the modified data sets gave mixed results compared to ones with the original features.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134362161","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 : 2017-12-19DOI: 10.1109/KCIC.2017.8228571
Jinmika Wijitdechakul, S. Sasaki, Y. Kiyoki, C. Koopipat
This research proposes the multispectral image retrieval method by using spectral feature and semantic computing which is not many studies have focused. The main contributions are to enhance the effectiveness and advantageous of global environmental analysis system and realize semantic associative search and analysis. In this work, we study multispectral image retrieval using spectral feature computed in multispectral semantic-image space. The multispectral semantic-image space is supposing to realize the interpretation of substance (materials) on earth surface which can be provided the analyzed results as human-level interpretation. Our essential approach is utilizing the semantic computing to measure the similarity between multispectral image and the meaningful keywords which according to the user's contexts. Our research results found that this method possible to acquire the spectral feature from the multispectral image and could be used in multispectral image retrieval. In this study, a multispectral image is used as the image query according to user's query contexts. Moreover, the method performance of UAV-based multispectral aerial image retrieval using spectral feature and semantic computing is measured based on the queries with three contexts of multispectral image which is indicated by previous study on agricultural monitoring system and semantic interpretation model.
{"title":"UAV-based multispectral aerial image retrieval using spectral feature and semantic computing","authors":"Jinmika Wijitdechakul, S. Sasaki, Y. Kiyoki, C. Koopipat","doi":"10.1109/KCIC.2017.8228571","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228571","url":null,"abstract":"This research proposes the multispectral image retrieval method by using spectral feature and semantic computing which is not many studies have focused. The main contributions are to enhance the effectiveness and advantageous of global environmental analysis system and realize semantic associative search and analysis. In this work, we study multispectral image retrieval using spectral feature computed in multispectral semantic-image space. The multispectral semantic-image space is supposing to realize the interpretation of substance (materials) on earth surface which can be provided the analyzed results as human-level interpretation. Our essential approach is utilizing the semantic computing to measure the similarity between multispectral image and the meaningful keywords which according to the user's contexts. Our research results found that this method possible to acquire the spectral feature from the multispectral image and could be used in multispectral image retrieval. In this study, a multispectral image is used as the image query according to user's query contexts. Moreover, the method performance of UAV-based multispectral aerial image retrieval using spectral feature and semantic computing is measured based on the queries with three contexts of multispectral image which is indicated by previous study on agricultural monitoring system and semantic interpretation model.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126062490","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 : 2017-09-01DOI: 10.1109/KCIC.2017.8228460
A. Besari, Iwan Kurnianto Wobowo, S. Sukaridhoto, Ricky Setiawan, Muh. Rifqi Rizqullah
Learning about sensor technology and actuator early is important as a step towards knowing and introducing of advanced technologies based on Internet of Things (IoT). The difficulties are how to learn sensor technology and move the actuator with accessing General Purpose Input Output (GPIO) of Raspberry Pi 3 Platforms using programming language syntax which often confusing and difficult to understand. To help people learning IoT by using Raspberry Pi 3 with an interesting Android apps, we believe that this learning module can integrate about the ease and attractiveness of IoT System Editor based on Android apps. This research create a mobile programming apps based on Android which people can build IoT project easily with GUI without program and middleware based on Raspberry Pi to connect between apps and hardware with especially task to manage data communication, data flow, and device driver. Hopefully new developer can develop the IoT application easily by using Android mobile visual programming that combined with Raspberry Pi 3 platform.
{"title":"Preliminary design of mobile visual programming apps for Internet of Things applications based on Raspberry Pi 3 platform","authors":"A. Besari, Iwan Kurnianto Wobowo, S. Sukaridhoto, Ricky Setiawan, Muh. Rifqi Rizqullah","doi":"10.1109/KCIC.2017.8228460","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228460","url":null,"abstract":"Learning about sensor technology and actuator early is important as a step towards knowing and introducing of advanced technologies based on Internet of Things (IoT). The difficulties are how to learn sensor technology and move the actuator with accessing General Purpose Input Output (GPIO) of Raspberry Pi 3 Platforms using programming language syntax which often confusing and difficult to understand. To help people learning IoT by using Raspberry Pi 3 with an interesting Android apps, we believe that this learning module can integrate about the ease and attractiveness of IoT System Editor based on Android apps. This research create a mobile programming apps based on Android which people can build IoT project easily with GUI without program and middleware based on Raspberry Pi to connect between apps and hardware with especially task to manage data communication, data flow, and device driver. Hopefully new developer can develop the IoT application easily by using Android mobile visual programming that combined with Raspberry Pi 3 platform.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115986762","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 : 2017-09-01DOI: 10.1109/KCIC.2017.8228567
M. L. Afakh, Anhar Risnumawan, M. Anggraeni, Mohamad Nasyir Tamara, E. S. Ningrum
Aksara jawa is an ancient Javanese character, which has been used since 17th century. The character is mostly written on stones to describe history or naming such as places, wedding, tombstones, etc. This character is however gradually ignored by people. Thus, it is extremely important to preserve this near loss heritage culture. In this paper, as a step toward preserving and converting visual information into text, we develop Aksara Jawa text detection system in scene images employing deep convolutional neural network to localize the occurrence of Aksara Jawa text. This method mainly differs from the existing Aksara Jawa text works that employ manually hand-crafted features and explicitly learn a classifier. The features and classifier of this method are jointly learned from which the back-propagation technique is employed to obtain parameters simultaneously. A text confidence map is then produced followed by bounding boxes formation which is estimated and formed to indicate the occurrence of text lines. Experiments show encouraging result for the benefit of text analysis on Aksara Jawa.
Aksara jawa是一个古老的爪哇文字，自17世纪以来一直使用。这种文字大多写在石头上，用来描述历史或命名，如地点、婚礼、墓碑等。然而这一特点却逐渐被人们所忽视。因此，保护这种濒临消失的文化遗产是极其重要的。在本文中，作为将视觉信息保存和转换为文本的一步，我们在场景图像中开发了Aksara Jawa文本检测系统，该系统采用深度卷积神经网络来定位Aksara Jawa文本的出现。这种方法主要不同于现有的Aksara java文本作品，后者使用手工制作的特征并明确地学习分类器。该方法结合特征和分类器进行学习，并利用反向传播技术同时获取参数。然后生成文本置信度图，然后生成边界框，该边界框是估计和形成的，以指示文本行的出现。实验结果表明，Aksara java的文本分析效果令人鼓舞。
{"title":"Aksara jawa text detection in scene images using convolutional neural network","authors":"M. L. Afakh, Anhar Risnumawan, M. Anggraeni, Mohamad Nasyir Tamara, E. S. Ningrum","doi":"10.1109/KCIC.2017.8228567","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228567","url":null,"abstract":"Aksara jawa is an ancient Javanese character, which has been used since 17th century. The character is mostly written on stones to describe history or naming such as places, wedding, tombstones, etc. This character is however gradually ignored by people. Thus, it is extremely important to preserve this near loss heritage culture. In this paper, as a step toward preserving and converting visual information into text, we develop Aksara Jawa text detection system in scene images employing deep convolutional neural network to localize the occurrence of Aksara Jawa text. This method mainly differs from the existing Aksara Jawa text works that employ manually hand-crafted features and explicitly learn a classifier. The features and classifier of this method are jointly learned from which the back-propagation technique is employed to obtain parameters simultaneously. A text confidence map is then produced followed by bounding boxes formation which is estimated and formed to indicate the occurrence of text lines. Experiments show encouraging result for the benefit of text analysis on Aksara Jawa.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116122670","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 : 2017-09-01DOI: 10.1109/KCIC.2017.8228445
Naufal Suryanto, C. Ikuta, D. Pramadihanto
Particle Swarm Optimization (PSO) is fast and popular algorithm to find the optimum value of non-linear and multi-dimensional function. However, it often easily trapped into local optima because the particles move closer to the best particle quickly. This paper purposes a new algorithm called Multi-Group Particle Swarm Optimization with Random Redistribution (MGRR-PSO) that tried to solve the weakness of standard PSO. MGRR-PSO combines two groups of PSO with opposite acceleration coefficients. In addition, some particles are redistributed when they are trapped in local optima. Experimental studies on 5 benchmark functions with 50-dimensions and 100-dimensions show that the MGRR-PSO can solve the problems that can't be solved by original PSO with better performance.
{"title":"Multi-group particle swarm optimization with random redistribution","authors":"Naufal Suryanto, C. Ikuta, D. Pramadihanto","doi":"10.1109/KCIC.2017.8228445","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228445","url":null,"abstract":"Particle Swarm Optimization (PSO) is fast and popular algorithm to find the optimum value of non-linear and multi-dimensional function. However, it often easily trapped into local optima because the particles move closer to the best particle quickly. This paper purposes a new algorithm called Multi-Group Particle Swarm Optimization with Random Redistribution (MGRR-PSO) that tried to solve the weakness of standard PSO. MGRR-PSO combines two groups of PSO with opposite acceleration coefficients. In addition, some particles are redistributed when they are trapped in local optima. Experimental studies on 5 benchmark functions with 50-dimensions and 100-dimensions show that the MGRR-PSO can solve the problems that can't be solved by original PSO with better performance.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117023323","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 : 2017-09-01DOI: 10.1109/KCIC.2017.8228574
R. Wulandari, R. Sigit, Setia Wardhana
Lung cancer is a disease that caused by uncontrolled cell growth in lung. Lung cancer is still the first worldwide killer. CT Scan Thorax is a method for early detection of lung cancer patients. However, cancer detection in lung CT-Scan image still done manually. In this paper, the segmentation of lung image is proposed. Cancer segmentation will process the lung CT-Scan as an image input with watershed process to cut off cavity area. The result will be processed by color histogram calculation to obtain mean and standard deviation value. This value is useful for evaluate non-cancer area and produce cancer image. Segmentation process will be followed by measurement of cancer and cavity area. The overall output is percentage between the large of cancer area and cavity area. The experiment represented that this method is able to detect lung cancer automatically. The performance segmentation for assessment errors obtained an average cavity area segmentation 12.75% and cancer area segmentation 31.74%.
{"title":"Automatic lung cancer detection using color histogram calculation","authors":"R. Wulandari, R. Sigit, Setia Wardhana","doi":"10.1109/KCIC.2017.8228574","DOIUrl":"https://doi.org/10.1109/KCIC.2017.8228574","url":null,"abstract":"Lung cancer is a disease that caused by uncontrolled cell growth in lung. Lung cancer is still the first worldwide killer. CT Scan Thorax is a method for early detection of lung cancer patients. However, cancer detection in lung CT-Scan image still done manually. In this paper, the segmentation of lung image is proposed. Cancer segmentation will process the lung CT-Scan as an image input with watershed process to cut off cavity area. The result will be processed by color histogram calculation to obtain mean and standard deviation value. This value is useful for evaluate non-cancer area and produce cancer image. Segmentation process will be followed by measurement of cancer and cavity area. The overall output is percentage between the large of cancer area and cavity area. The experiment represented that this method is able to detect lung cancer automatically. The performance segmentation for assessment errors obtained an average cavity area segmentation 12.75% and cancer area segmentation 31.74%.","PeriodicalId":117148,"journal":{"name":"2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129600586","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：