Pub Date : 2017-09-01DOI: 10.1109/IDAP.2017.8090295
Yücel Bürhan, Muhammet Baykara, Resul Daş
Social network analysis has become an important issue in recent years because of the development and use of social networks widespread. Almost any kind of data can be easily obtained as valuable information about actors from these data. In this study, general information about the most used social network analysis and visualization tools are given. The author-subject network that analyzed with the help of link estimation methods, the author-writer network showing the links between co-authors is drawn and displayed visually through four different social network analysis and visualization tools.
{"title":"Sosyal ağ analizi ve veri görselleştirme araçlarinin İncelenmesi ve uygulamali karşilaştirilmasi","authors":"Yücel Bürhan, Muhammet Baykara, Resul Daş","doi":"10.1109/IDAP.2017.8090295","DOIUrl":"https://doi.org/10.1109/IDAP.2017.8090295","url":null,"abstract":"Social network analysis has become an important issue in recent years because of the development and use of social networks widespread. Almost any kind of data can be easily obtained as valuable information about actors from these data. In this study, general information about the most used social network analysis and visualization tools are given. The author-subject network that analyzed with the help of link estimation methods, the author-writer network showing the links between co-authors is drawn and displayed visually through four different social network analysis and visualization tools.","PeriodicalId":111721,"journal":{"name":"2017 International Artificial Intelligence and Data Processing Symposium (IDAP)","volume":"16 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":"125191614","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/IDAP.2017.8090308
Şehmus Baday, Hüdayim Başak, Fikret Sönmez
Estimation of surface roughness values, which is an indication of workpiece quality, is important in terms of reducing the cost and duration of machining. In this study, the surface roughness values of the medium carbon steel subjected to the spheronization heat treatment have estimated by artificial neural networks. ANN network model have been created by being chosen feedforward back propagation network model, the adoption of network structure and learning function LEARNGDM, TRAINLM as training algorithm, MSE for assessment of network performance and two hidden layers. The value of each neuron in the network have been transferred another layer by TANSIG, LOGSIG and PURELIN transfer functions. As a result, the artificial neural networks trained and tested have been found to be easy to use for estimating surface roughness values with a high percentage of R = 0.99001 according to MSE performance.
{"title":"Küreselleştirme isil İşlemi uygulanmiş AISI 1050 Çeliğinin yüzey pürüzlülük değerlerinin yapay sinir ağlari ile modellenmesi","authors":"Şehmus Baday, Hüdayim Başak, Fikret Sönmez","doi":"10.1109/IDAP.2017.8090308","DOIUrl":"https://doi.org/10.1109/IDAP.2017.8090308","url":null,"abstract":"Estimation of surface roughness values, which is an indication of workpiece quality, is important in terms of reducing the cost and duration of machining. In this study, the surface roughness values of the medium carbon steel subjected to the spheronization heat treatment have estimated by artificial neural networks. ANN network model have been created by being chosen feedforward back propagation network model, the adoption of network structure and learning function LEARNGDM, TRAINLM as training algorithm, MSE for assessment of network performance and two hidden layers. The value of each neuron in the network have been transferred another layer by TANSIG, LOGSIG and PURELIN transfer functions. As a result, the artificial neural networks trained and tested have been found to be easy to use for estimating surface roughness values with a high percentage of R = 0.99001 according to MSE performance.","PeriodicalId":111721,"journal":{"name":"2017 International Artificial Intelligence and Data Processing Symposium (IDAP)","volume":"25 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":"115048889","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/IDAP.2017.8090220
M. A. Günen, Abdüsselam Kesikoğlu, A. E. Karkinli, E. Beşdok
Kinect sensörler yakin geçmişte oyun konsollari ile birlikte kendini göstersede nesne takip, patern tanima, nesne ebat kontrolü, engel tanima ve iç mekan haritalama gibi bir çok mühendislik alanida kullanilmaktadir. Kinect sensörü içerdiği RBG kamera ve IR kamera ile ayni anda iki farkli kameradan veri alarak nesneye ait farkli özelliklerin kayit edilmesini sağlamaktadir. Bu bildiride RGB-D sensörlerin iç mekan haritalamada sağladiği doğruluk hem görsel hem de istatistiksel olarak sunulmuştur. RGB-D sensörler ile elde edilen nokta bulutu gaussian, median, ortalama ve Diferansiyel Arama Algoritmasi(DSA) tabanli yüzey uydurma filtresi filtrelenmiştir. Filtrelenen verilen yersel lazer tarayici ile elde edilen nokta bulutu ile çakişitirilmiş, çakiştirma sonucundaki standart sapmalar belirlenmiş ve çakiştirmada meydana gelen hata yüzeyleri ise görsel olarak karşilaştirilmiştir. Sonuçlara göre RGB-D sensörlerle elde edilen ortam haritasi her ne kadar zaman maliyeti yüksek olsada çok hassasiyet gerektirmeyen işlerde kullanilabilir olduğunu anlaşilmiştir.
{"title":"RGB-D sensörler ile İç mekan haritalamasi","authors":"M. A. Günen, Abdüsselam Kesikoğlu, A. E. Karkinli, E. Beşdok","doi":"10.1109/IDAP.2017.8090220","DOIUrl":"https://doi.org/10.1109/IDAP.2017.8090220","url":null,"abstract":"Kinect sensörler yakin geçmişte oyun konsollari ile birlikte kendini göstersede nesne takip, patern tanima, nesne ebat kontrolü, engel tanima ve iç mekan haritalama gibi bir çok mühendislik alanida kullanilmaktadir. Kinect sensörü içerdiği RBG kamera ve IR kamera ile ayni anda iki farkli kameradan veri alarak nesneye ait farkli özelliklerin kayit edilmesini sağlamaktadir. Bu bildiride RGB-D sensörlerin iç mekan haritalamada sağladiği doğruluk hem görsel hem de istatistiksel olarak sunulmuştur. RGB-D sensörler ile elde edilen nokta bulutu gaussian, median, ortalama ve Diferansiyel Arama Algoritmasi(DSA) tabanli yüzey uydurma filtresi filtrelenmiştir. Filtrelenen verilen yersel lazer tarayici ile elde edilen nokta bulutu ile çakişitirilmiş, çakiştirma sonucundaki standart sapmalar belirlenmiş ve çakiştirmada meydana gelen hata yüzeyleri ise görsel olarak karşilaştirilmiştir. Sonuçlara göre RGB-D sensörlerle elde edilen ortam haritasi her ne kadar zaman maliyeti yüksek olsada çok hassasiyet gerektirmeyen işlerde kullanilabilir olduğunu anlaşilmiştir.","PeriodicalId":111721,"journal":{"name":"2017 International Artificial Intelligence and Data Processing Symposium (IDAP)","volume":"10 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":"115199523","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/IDAP.2017.8090225
Ibrahim Berkan Aydilek, M. A. Nacar, Abdülkadir Gümüşçü, Mehmet Umut Salur
Particle swarm optimization (PSO) is a state-of-the-art algorithm in meta-heuristic optimization study area. It is a swarm based algorithm that mimic fish or bird's behaviors in the nature. Success rate of convergence in an optimization algorithm depends on control balancing between exploration and exploitation. Inertia weight coefficient parameter controls convergence rate of PSO algorithm. In this paper, different inertia weight: constant, random, linear decreasing and global-local best methods are used in CEC 2017 multimodal benchmark functions. Multimodal functions have huge numbers of local optima. Seven multimodal functions are used with 10 and 30 variable dimensions. Obtained result and run time statistics are compared and shown in graphs.
{"title":"Comparing inertia weights of particle swarm optimization in multimodal functions","authors":"Ibrahim Berkan Aydilek, M. A. Nacar, Abdülkadir Gümüşçü, Mehmet Umut Salur","doi":"10.1109/IDAP.2017.8090225","DOIUrl":"https://doi.org/10.1109/IDAP.2017.8090225","url":null,"abstract":"Particle swarm optimization (PSO) is a state-of-the-art algorithm in meta-heuristic optimization study area. It is a swarm based algorithm that mimic fish or bird's behaviors in the nature. Success rate of convergence in an optimization algorithm depends on control balancing between exploration and exploitation. Inertia weight coefficient parameter controls convergence rate of PSO algorithm. In this paper, different inertia weight: constant, random, linear decreasing and global-local best methods are used in CEC 2017 multimodal benchmark functions. Multimodal functions have huge numbers of local optima. Seven multimodal functions are used with 10 and 30 variable dimensions. Obtained result and run time statistics are compared and shown in graphs.","PeriodicalId":111721,"journal":{"name":"2017 International Artificial Intelligence and Data Processing Symposium (IDAP)","volume":"16 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":"115646339","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/IDAP.2017.8090336
M. Göçken, A. Boru, A. T. Dosdoğru, Mehmet Özçalici
Under today's economic conditions, developing more robust and realistic forecasting methods is needed to make investments more profitable and secure. However, understanding the structure of the stock markets is very difficult because of the dynamic and non-stationary data. In this context, bio-inspired computing approaches including evolutionary computation and swarm intelligence can be used to make more accurate calculations and forecasting results. This paper improved Extreme Learning Machine (ELM) using Genetic Algorithm (GA), Differential Evolution (DE) as a two evolutionary computation methods, and Particle Swarm Optimization (PSO) and Weighted Superposition Attraction (WSA) as a two swarm intelligence methods for stock market forecasting in Turkey. The results of this study show that proposed methods can be successfully used in any real-time stock market forecasting because of the noteworthy improvement in forecasting accuracy.
{"title":"Hybridizing extreme learning machine and bio-inspired computing approaches for improved stock market forecasting","authors":"M. Göçken, A. Boru, A. T. Dosdoğru, Mehmet Özçalici","doi":"10.1109/IDAP.2017.8090336","DOIUrl":"https://doi.org/10.1109/IDAP.2017.8090336","url":null,"abstract":"Under today's economic conditions, developing more robust and realistic forecasting methods is needed to make investments more profitable and secure. However, understanding the structure of the stock markets is very difficult because of the dynamic and non-stationary data. In this context, bio-inspired computing approaches including evolutionary computation and swarm intelligence can be used to make more accurate calculations and forecasting results. This paper improved Extreme Learning Machine (ELM) using Genetic Algorithm (GA), Differential Evolution (DE) as a two evolutionary computation methods, and Particle Swarm Optimization (PSO) and Weighted Superposition Attraction (WSA) as a two swarm intelligence methods for stock market forecasting in Turkey. The results of this study show that proposed methods can be successfully used in any real-time stock market forecasting because of the noteworthy improvement in forecasting accuracy.","PeriodicalId":111721,"journal":{"name":"2017 International Artificial Intelligence and Data Processing Symposium (IDAP)","volume":"65 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":"122476217","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/IDAP.2017.8090269
F. Özkaynak, Mukhlis I. Muhamad
Many services are being moved to digital environments with industry 4.0. however, But the information in the digital environment poses a great risk. Ensuring the security of information on the users of online applications is a necessity. This perspective requires safe signal processing. But it is difficult to guarantee safe signal processing for resource constrained devices. Lightweight cryptology, which has been on the agenda in recent years, has emerged to solve this problem. While designers of lightweight cryptographic algorithms try to reduce the hardware requirements, they ignore software implementation of lightweight protocols. This study presents new design architecture for improving the software implementation of the DES. In the study, two different acceleration techniques will be used to speed up the DES algorithm in software. The results show that the developed code is faster than original DES.
{"title":"Fast software implementation of des for lightweight platforms","authors":"F. Özkaynak, Mukhlis I. Muhamad","doi":"10.1109/IDAP.2017.8090269","DOIUrl":"https://doi.org/10.1109/IDAP.2017.8090269","url":null,"abstract":"Many services are being moved to digital environments with industry 4.0. however, But the information in the digital environment poses a great risk. Ensuring the security of information on the users of online applications is a necessity. This perspective requires safe signal processing. But it is difficult to guarantee safe signal processing for resource constrained devices. Lightweight cryptology, which has been on the agenda in recent years, has emerged to solve this problem. While designers of lightweight cryptographic algorithms try to reduce the hardware requirements, they ignore software implementation of lightweight protocols. This study presents new design architecture for improving the software implementation of the DES. In the study, two different acceleration techniques will be used to speed up the DES algorithm in software. The results show that the developed code is faster than original DES.","PeriodicalId":111721,"journal":{"name":"2017 International Artificial Intelligence and Data Processing Symposium (IDAP)","volume":"27 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":"122131039","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/IDAP.2017.8090287
Ebubekir Yașar, Şahin Yildirim
Snake-like robots have been the subject of a lot of study in recent years with their ability to adapt and move around, compared to wheeled and legged robots. The snake-like robots perform their walk in the form of S-sinusoidal movement. When the amplitude and joint drive frequency increase in the form of S-sinusoidal movement, the robot is moving faster. It is important for the serpentine robots to move more quickly in order to perform the assigned tasks promptly. In this study, the effect of frequency and environment on robot speed was tried to be determined by ignoring dynamic factors. In the studies, a snake robot which does not have active or passive wheels composed of 12 joints and has a sinus-lifting motion has been used. The robot was run in three different environments (paving stones, grass, carpets) at three different frequency values, and the distance and speed of travel were determined relative to the starting position.
{"title":"Yilansi robotlarda frekans değişiminin hareket hizina etkisi","authors":"Ebubekir Yașar, Şahin Yildirim","doi":"10.1109/IDAP.2017.8090287","DOIUrl":"https://doi.org/10.1109/IDAP.2017.8090287","url":null,"abstract":"Snake-like robots have been the subject of a lot of study in recent years with their ability to adapt and move around, compared to wheeled and legged robots. The snake-like robots perform their walk in the form of S-sinusoidal movement. When the amplitude and joint drive frequency increase in the form of S-sinusoidal movement, the robot is moving faster. It is important for the serpentine robots to move more quickly in order to perform the assigned tasks promptly. In this study, the effect of frequency and environment on robot speed was tried to be determined by ignoring dynamic factors. In the studies, a snake robot which does not have active or passive wheels composed of 12 joints and has a sinus-lifting motion has been used. The robot was run in three different environments (paving stones, grass, carpets) at three different frequency values, and the distance and speed of travel were determined relative to the starting position.","PeriodicalId":111721,"journal":{"name":"2017 International Artificial Intelligence and Data Processing Symposium (IDAP)","volume":"19 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":"116678267","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/IDAP.2017.8090165
F. Ertam, Orhan Yaman
With the internet of objects, the number of devices with internet connection is increasing day by day. This leads to a very high amount of data circulating on the internet. It is one of the most common problems that can be distinguished from normal and abnormal traffic by analyzing in high data amount. In this study, an analysis was carried out by using machine learning approaches to determine whether the data received on the internet is normal or abnormal data. In order to achieve this goal, the KDD Cup 99 data set which is frequently used in literature studies is classified by Naive Bayes (NB), bayes NET (bN), Random Forest (RF), Multilayer Perception (MLP) and Sequential Minimal Optimization (SMO) algorithms. Classifiers are also compared with false rate, precision, recall, and F measure metrics along with accuracy rate values. Classification times of classifiers are also given by comparison.
随着物联网的发展，联网设备的数量日益增加。这导致大量数据在互联网上流通。通过大数据量的分析，可以区分正常流量和异常流量，这是最常见的问题之一。在本研究中，通过使用机器学习方法进行分析，以确定在互联网上接收的数据是正常数据还是异常数据。为了实现这一目标，文献研究中经常使用的KDD Cup 99数据集通过朴素贝叶斯(NB)、贝叶斯网络(bN)、随机森林(RF)、多层感知(MLP)和顺序最小优化(SMO)算法进行分类。分类器还与错误率、精度、召回率和F度量指标以及准确率值进行比较。通过比较给出了分类器的分类时间。
{"title":"Intrusion detection in computer networks via machine learning algorithms","authors":"F. Ertam, Orhan Yaman","doi":"10.1109/IDAP.2017.8090165","DOIUrl":"https://doi.org/10.1109/IDAP.2017.8090165","url":null,"abstract":"With the internet of objects, the number of devices with internet connection is increasing day by day. This leads to a very high amount of data circulating on the internet. It is one of the most common problems that can be distinguished from normal and abnormal traffic by analyzing in high data amount. In this study, an analysis was carried out by using machine learning approaches to determine whether the data received on the internet is normal or abnormal data. In order to achieve this goal, the KDD Cup 99 data set which is frequently used in literature studies is classified by Naive Bayes (NB), bayes NET (bN), Random Forest (RF), Multilayer Perception (MLP) and Sequential Minimal Optimization (SMO) algorithms. Classifiers are also compared with false rate, precision, recall, and F measure metrics along with accuracy rate values. Classification times of classifiers are also given by comparison.","PeriodicalId":111721,"journal":{"name":"2017 International Artificial Intelligence and Data Processing Symposium (IDAP)","volume":"1 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":"129515904","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/IDAP.2017.8090338
Fatih Ilkbahar, R. Kara
The usage areas of biometric systems are becoming widespread in today's technology. Face recognition systems among biometric systems; Ease of use, reliability, cost, etc., the preference between public institutions, commercial enterprises and researchers is increasing. In this study, it is suggested that students should use face recognition system instead of traditional methods of absenteeism in education and training institutions. It is very important that face recognition systems work quickly with matching people correctly. In this study, the training and recognition times of Eigenfaces, Fisherfaces and Local Binary Pattern algorithms used in face recognition systems are calculated by using Visual C ++ and Python programming languages using ORL dataset.
{"title":"Performance analysis of face recognition algorithms","authors":"Fatih Ilkbahar, R. Kara","doi":"10.1109/IDAP.2017.8090338","DOIUrl":"https://doi.org/10.1109/IDAP.2017.8090338","url":null,"abstract":"The usage areas of biometric systems are becoming widespread in today's technology. Face recognition systems among biometric systems; Ease of use, reliability, cost, etc., the preference between public institutions, commercial enterprises and researchers is increasing. In this study, it is suggested that students should use face recognition system instead of traditional methods of absenteeism in education and training institutions. It is very important that face recognition systems work quickly with matching people correctly. In this study, the training and recognition times of Eigenfaces, Fisherfaces and Local Binary Pattern algorithms used in face recognition systems are calculated by using Visual C ++ and Python programming languages using ORL dataset.","PeriodicalId":111721,"journal":{"name":"2017 International Artificial Intelligence and Data Processing Symposium (IDAP)","volume":"33 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":"129847750","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/IDAP.2017.8090219
Esra Gündogan, Buket Kaya
Social networks we have encountered in different areas and in different forms have a dynamic structure because the relationships they define constantly change. Link prediction is an important and effective solution to understand this dynamic nature of networks and to identify future relations. It estimates of possible future connections between nodes in the network taking advantage of network's current state. In this study, a method for link prediction in the disease-drug network is proposed. Sofar, the most of studies done is usually based on connection prediction in single mode networks. This method has been applied on a bipartite such as disease-drug network, as apart from single mode networks. To compare performance of the proposed method, four of similarity based link prediction methods has been also applied to the network. The results obtained from experiments show that the proposed method has a good percentage of success than the other similarity based link prediction methods.
{"title":"A link prediction approach for drug recommendation in disease-drug bipartite network","authors":"Esra Gündogan, Buket Kaya","doi":"10.1109/IDAP.2017.8090219","DOIUrl":"https://doi.org/10.1109/IDAP.2017.8090219","url":null,"abstract":"Social networks we have encountered in different areas and in different forms have a dynamic structure because the relationships they define constantly change. Link prediction is an important and effective solution to understand this dynamic nature of networks and to identify future relations. It estimates of possible future connections between nodes in the network taking advantage of network's current state. In this study, a method for link prediction in the disease-drug network is proposed. Sofar, the most of studies done is usually based on connection prediction in single mode networks. This method has been applied on a bipartite such as disease-drug network, as apart from single mode networks. To compare performance of the proposed method, four of similarity based link prediction methods has been also applied to the network. The results obtained from experiments show that the proposed method has a good percentage of success than the other similarity based link prediction methods.","PeriodicalId":111721,"journal":{"name":"2017 International Artificial Intelligence and Data Processing Symposium (IDAP)","volume":"130 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":"128596592","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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