2017 International Artificial Intelligence and Data Processing Symposium (IDAP)最新文献_第4页

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) Pub Date : 2017-09-01 DOI: 10.1109/IDAP.2017.8090213

Serkan Turkeli, B. S. A. Gazioglu, Kenan Kaan Kurt, Hüseyin Tanzer Atay, Yakup Gorur

引用次数: 0

A simple logging system for safe internet use 一个简单的日志系统，安全使用互联网

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) Pub Date : 2017-09-01 DOI: 10.1109/IDAP.2017.8090252

Doygun Demirol, Gurkan Tuna, Resul Das

{"title":"A simple logging system for safe internet use","authors":"Doygun Demirol, Gurkan Tuna, Resul Das","doi":"10.1109/IDAP.2017.8090252","DOIUrl":"https://doi.org/10.1109/IDAP.2017.8090252","url":null,"abstract":"Although the Internet offers numerous advantages, it raises many information security risks, especially against young people and children, who are today amongst the largest user groups of mobile and online technologies all around the world. Therefore, to empower and protect Internet users, it is necessary to develop proper strategies and tools to encapsulate their needs, and identify and prevent all types of the information security risks that may arise during the use of the Internet. In this study, a tracking system to ensure the safe use of the Internet is proposed. Considering the distribution of its potential users, the system has an easy-to-use graphical user interface. By recognizing dangerous web sites and IP addresses, the proposed system blocks access to those sites and this way provides reliable Internet access to its users. During the Internet access, it analyzes accessed IP addresses and port numbers in terms of access type and time and informs the user of the corresponding port numbers which must be closed for safe Internet access. Moreover, by continuously checking the host redirection file of the computer it runs on, it identifies redirections from web addresses to specific IP addresses and this way provides protection against phishing attacks which are becoming one of the most common Internet threats. Although the proposed system is a simple application since it is an open source, freeware application designed for children, it can be improved to consider more sophisticated attack types.","PeriodicalId":111721,"journal":{"name":"2017 International Artificial Intelligence and Data Processing Symposium (IDAP)","volume":"48 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":"128841871","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}

引用次数: 0

Flame detection using HSI color space 火焰检测使用HSI色彩空间

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) Pub Date : 2017-09-01 DOI: 10.1109/IDAP.2017.8090322

Buket Toptaş, D. Hanbay

引用次数: 0

Makine Öğrenmesi sistemi ile görüntü İşleme ve en uygun parametrelerin ayarlanmasi

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) Pub Date : 2017-09-01 DOI: 10.1109/IDAP.2017.8090316

Ebubekir Buber, Ozgur Koray Sahingoz

引用次数: 1

Localization of macular edema region from color retinal images for detection of diabetic retinopathy 视网膜彩色图像定位黄斑水肿区对糖尿病视网膜病变的检测

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) Pub Date : 2017-09-01 DOI: 10.1109/IDAP.2017.8090174

Ümit Budak, A. Şengür, Yaman Akbulut

引用次数: 1

Power delay product optimized hybrid full adder circuits 功率延迟积优化混合全加法器电路

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) Pub Date : 2017-09-01 DOI: 10.1109/IDAP.2017.8090319

M. Rashid, A. Muhtaroğlu

引用次数: 3

Classification rule mining approach based on multiobjective optimization 基于多目标优化的分类规则挖掘方法

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) Pub Date : 2017-09-01 DOI: 10.1109/IDAP.2017.8090264

T. Sağ, H. Kahramanli

引用次数: 2

Validation of fuzzy and possibilistic clustering results 模糊和可能性聚类结果的验证

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) Pub Date : 2017-09-01 DOI: 10.1109/IDAP.2017.8090183

Z. Cebeci, A. T. Kavlak, Figen Yildiz

{"title":"Validation of fuzzy and possibilistic clustering results","authors":"Z. Cebeci, A. T. Kavlak, Figen Yildiz","doi":"10.1109/IDAP.2017.8090183","DOIUrl":"https://doi.org/10.1109/IDAP.2017.8090183","url":null,"abstract":"Unsupervised fuzzy clustering is an important tool for finding the meaningful patterns in data sets. In fuzzy clustering analyses, the performances of clustering algorithms are mostly compared using several internal fuzzy validity indices. However, since the well-known fuzzy indices have originally been proposed for working with membership degrees produced by the traditional Fuzzy c-means Clustering (FCM) algorithm, these indices cannot be used for possibilistic algorithms that produce typicality matrices instead of fuzzy membership matrices. Even more, the variants of FCM and PCM such as Possibilistic Fuzzy C-means (PFCM) and Fuzzy Possibilistic C-means (FPCM) simultaneously result with probabilistic and possibilistic membership degrees. Thus, some kind of validity indices are needed for working with both of these results. For this purpose, a few extended and generalized validity indices has been proposed in recent years. In this paper, the performances of these indices were examined for validating the clustering results from Unsupervised Possibilistic Fuzzy Clustering (UPFC), FCM and PCM algorithms. The findings showed that generalized versions of the fuzzy validity indices based on normalization of typicality degrees can be successfully used to validate the results from PCM, UPFC and the variants of FCM and PCM.","PeriodicalId":111721,"journal":{"name":"2017 International Artificial Intelligence and Data Processing Symposium (IDAP)","volume":"164 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":"122468076","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}

引用次数: 8

SFLA based PI parameter optimization for optimal controlling of a Buck converter's voltage 基于SFLA的PI参数优化用于Buck变换器电压的优化控制

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) Pub Date : 2017-09-01 DOI: 10.1109/IDAP.2017.8090232

E. Kose, Gülçin Mühürcü, Aydin Muhurcu, Buse Sevim

引用次数: 1

A GPU-based convolutional neural network approach for image classification 基于gpu的卷积神经网络图像分类方法

2017 International Artificial Intelligence and Data Processing Symposium (IDAP) Pub Date : 2017-09-01 DOI: 10.1109/IDAP.2017.8090194

Emine Cengil, A. Cinar, Zafer Güler

{"title":"A GPU-based convolutional neural network approach for image classification","authors":"Emine Cengil, A. Cinar, Zafer Güler","doi":"10.1109/IDAP.2017.8090194","DOIUrl":"https://doi.org/10.1109/IDAP.2017.8090194","url":null,"abstract":"Deep learning obtains successful results in solving many machine learning problems. In this study, image classification process is performed by using Convolutional Neural Network (CNN) which is the most used architecture of deep learning. Image classification is used in a lot of basic field like medicine, education and security. Conditions that correct classification has vital importance may be especially in medicine field. Therefore, improved methods are needed in this issue. Although several algorithms for image classification have been developed over the years, they have not been used with the discovery of Convolutional Neural Networks. Convolutional Neural Networks provide better results than existing methods in the literature due to advantages such as processing by extracting hidden features, allowing parallel processing thanks to parallel structure, and real time operation. Furthermore, we use Convolutional Neural Networks in the proposed method. In this study, the image classification process is performed by using like a LeNet network model. The caffe library, which is often used for deep learning, is utilized. Our method is trained and tested with images of cats and dogs taken from the kaggle dataset. 10.000 tagged data is used for training and 5.000 unlabeled data is used for testing. Owing to Convolutional Neural Networks allow parallel processing, GPU technology has been used. In our method is used GPU technology and classification is evaluated with acceptable accuracy rate and speed performance.","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":"122901391","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}

引用次数: 21